diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 00000000..b2fcb55b
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,122 @@
+# Use the latest 2.1 version of CircleCI pipeline processing engine, see https://circleci.com/docs/2.0/configuration-reference/
+version: 2 # use CircleCI 2.0
+jobs: # a collection of steps
+  build: # runs not using Workflows must have a `build` job as entry point
+    docker: # run the steps with Docker
+      - image: circleci/node:lts # ...with this image as the primary container; this is where all `steps` will run
+    steps:
+      - checkout
+      - run:
+          name: update-npm
+          command: 'sudo npm install -g npm@latest'
+      - run:
+          name: node version
+          command: node -v
+      - run:
+          name: NPM version
+          command: npm -v
+
+      - restore_cache: # special step to restore the dependency cache
+          # Read about caching dependencies: https://circleci.com/docs/2.0/caching/
+          keys:
+            # Find a cache corresponding to this specific package-lock.json checksum
+            - npm-v1-{{ checksum "package-lock.json" }}
+            # Find the most recently generated cache used from any branch
+            - npm-v1-
+      - run:
+          name: npm install
+          command: npm install
+
+      - save_cache: # special step to save the dependency cache
+          key: npm-v1-{{ checksum "package-lock.json" }}
+          paths:
+            - ./node_modules
+
+      - run: # run tests
+          name: run tests
+          command: npm run ci
+
+      - store_artifacts: # special step to save test results as as artifact
+          # Upload test summary for display in Artifacts: https://circleci.com/docs/2.0/artifacts/
+          path: test-results.xml
+          prefix: tests
+      - store_artifacts: # for display in Artifacts: https://circleci.com/docs/2.0/artifacts/
+          path: coverage
+          prefix: coverage
+      - store_test_results: # for display in Test Summary: https://circleci.com/docs/2.0/collect-test-data/
+          path: test-results.xml
+
+      - run:
+          name: release
+          command: npm run semantic-release || true
+
+  docs:
+    docker:
+      - image: circleci/ruby:2.7.1-buster-node
+    environment:
+      NOKOGIRI_USE_SYSTEM_LIBRARIES: true
+    steps:
+      - add_ssh_keys:
+          fingerprints:
+            - "b5:40:36:ee:3c:52:25:dd:c7:41:f3:10:d1:ce:f9:33"
+      - checkout
+      - run:
+          name: checkout repo and submodules
+          command: git submodule update --init --recursive
+      - run:
+          name: bundler version
+          command: bundle -v
+      - run:
+          name: ruby version
+          command: ruby -v
+      - run:
+          name: node version
+          command: node -v
+      - run:
+          name: NPM version
+          command: npm -v
+
+      - run: sudo apt-get update
+
+      - run:
+          name: graphviz for dot diagrams
+          command: sudo apt-get -qq -y install graphviz
+
+      - run:
+          name: Bundle Install.pre - needed for mathematical -> 'asciidoctor-mathematical' gem
+          command: sudo apt-get -qq -y install cmake bison flex libffi-dev libxml2-dev libgdk-pixbuf2.0-dev libcairo2-dev libpango1.0-dev
+
+      - run:
+          name: Bundle Install.pre - install GraphicsMagick for 'prawn-gmagick' gem
+          command: sudo apt-get -qq -y install graphicsmagick-imagemagick-compat graphicsmagick-libmagick-dev-compat
+
+      # Restore bundle cache
+      - restore_cache:
+          keys:
+            - gemfile-v1-{{ checksum "book/config/Gemfile.lock" }}
+            - gemfile-v1-
+
+      - run:
+          name: Bundle Install
+          command: cd book/config && bundle check --path vendor/bundle || ( bundle install --path vendor/bundle )
+
+      # Store bundle cache
+      - save_cache:
+          key: gemfile-v1-{{ checksum "book/config/Gemfile.lock" }}
+          paths:
+            - book/config/vendor/bundle
+
+      - run:
+          name: generate PDF
+          command: cd book/config && make VERSION="$(npx -c 'echo "$npm_package_version"')"
+
+      - store_artifacts:
+          path: book/dist
+          destination: book
+
+workflows:
+  version: 2
+  build_and_docs:
+    jobs:
+      - build
+      - docs
diff --git a/.editorconfig b/.editorconfig
index b2eb5921..de3acdb2 100644
--- a/.editorconfig
+++ b/.editorconfig
@@ -7,6 +7,7 @@ root = true
 [*]
 end_of_line = lf
 insert_final_newline = true
+trim_trailing_whitespace = true
 
 # Matches multiple files with brace expansion notation
 # Set default charset
diff --git a/.eslintignore b/.eslintignore
new file mode 100644
index 00000000..38bb7054
--- /dev/null
+++ b/.eslintignore
@@ -0,0 +1,6 @@
+/runtimes/**
+src/data-structures/maps/hash-maps/hashing.js
+src/data-structures/maps/hash-maps/*-test.js
+src/data-structures/maps/hash-maps/hash-map-*.js
+src/data-structures/linked-lists/linked-list-*.js
+src/data-structures/custom/lru-cache-*.js
diff --git a/.eslintrc.js b/.eslintrc.js
index 0c7f7af4..404d8807 100644
--- a/.eslintrc.js
+++ b/.eslintrc.js
@@ -1,23 +1,35 @@
 module.exports = {
-  "extends": "airbnb-base",
-  "env": {
-    "jest": true
+  extends: 'airbnb-base',
+  env: {
+    jest: true,
   },
+  plugins: ['jest'],
+  globals: {
+    BigInt: true,
+  },
+
+  // check package.json for files to include
+  // files: ['src/**/*.js', 'book/interview-questions/*.js'],
+
   rules: {
     // https://github.com/airbnb/javascript/issues/1089
 
     // https://stackoverflow.com/a/35637900/684957
     // allow to add properties to arguments
-    "no-param-reassign": [2, { "props": false }],
+    'no-param-reassign': [2, { props: false }],
 
     // https://eslint.org/docs/rules/no-plusplus
     // allows unary operators ++ and -- in the afterthought (final expression) of a for loop.
-    "no-plusplus": [2, { "allowForLoopAfterthoughts": true }],
+    'no-plusplus': [0, { allowForLoopAfterthoughts: true }],
+    'no-continue': [0],
 
     // Allow for..of
-    "no-restricted-syntax": [0, "ForOfStatement"],
+    'no-restricted-syntax': [0, 'ForOfStatement'],
+
+    // jest plugin
+    // 'jest/no-disabled-tests': 'warn',
+    'jest/no-focused-tests': 'error',
+    'jest/no-identical-title': 'warn',
+    'jest/valid-expect': 'warn',
   },
-  globals: {
-    BigInt: true,
-  }
 };
diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 00000000..c685c664
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,5 @@
+# These are supported funding model platforms
+
+github: amejiarosario # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: amejiarosario # Replace with a single Patreon username
+custom: https://adrianmejia.com/support/ # Replace with a single custom sponsorship URL
diff --git a/.gitignore b/.gitignore
index 47f69d6f..9675ebf8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@ coverage
 dist
 _book
 .DS_Store
+.asciidoctor/
 
 # https://raw.githubusercontent.com/mraible/infoq-mini-book/master/.gitignore
 ######################
@@ -71,7 +72,6 @@ local.properties
 # Ruby
 ######################
 /.bundle/
-/Gemfile.lock
 
 ######################
 # Package Files
@@ -86,6 +86,7 @@ local.properties
 ######################
 # Windows image file caches
 Thumbs.db
+*Zone.Identifier
 
 # Folder config file
 Desktop.ini
@@ -135,14 +136,13 @@ Desktop.ini
 # ln -s ~/OneDrive/Authoring/dsaJS/asciidoc/book/fonts .
 # ln -s ~/OneDrive/Authoring/dsaJS/asciidoc/book/images .
 ######################
-Gemfile
-Gemfile.lock
-Makefile
-_conf
-_resources
-extensions
-fonts
-images
+# Gemfile
+# Gemfile.lock
+# Makefile
+# _conf
+# _resources
+# extensions
+# fonts
 
 
 ######################
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 00000000..17750b22
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "book/config"]
+	path = book/config
+	url = git@github.com:amejiarosario/dsa.js-private.git
diff --git a/.node-version b/.node-version
index c4d592e1..66df3b7a 100644
--- a/.node-version
+++ b/.node-version
@@ -1 +1 @@
-10.12.0
+12.16.1
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 00000000..c4dcc94b
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,20 @@
+{
+  "peacock.color": "#f9e64f",
+  "workbench.colorCustomizations": {
+    "activityBar.activeBackground": "#fbed80",
+    "activityBar.activeBorder": "#06b9a5",
+    "activityBar.background": "#fbed80",
+    "activityBar.foreground": "#15202b",
+    "activityBar.inactiveForeground": "#15202b99",
+    "activityBarBadge.background": "#06b9a5",
+    "activityBarBadge.foreground": "#15202b",
+    "statusBar.background": "#f9e64f",
+    "statusBar.foreground": "#15202b",
+    "statusBarItem.hoverBackground": "#f7df1e",
+    "titleBar.activeBackground": "#f9e64f",
+    "titleBar.activeForeground": "#15202b",
+    "titleBar.inactiveBackground": "#f9e64f99",
+    "titleBar.inactiveForeground": "#15202b99"
+  },
+  "peacock.remoteColor": "#f9e64f"
+}
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 00000000..4e021d32
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,453 @@
+## [2.7.6](https://github.com/amejiarosario/dsa.js/compare/2.7.5...2.7.6) (2021-11-30)
+
+
+### Bug Fixes
+
+* **graph:** minor typo in bfs code documentation ([ccfcfe6](https://github.com/amejiarosario/dsa.js/commit/ccfcfe6f039ce3f6e289d368fbdbf67e227c168c)), closes [#110](https://github.com/amejiarosario/dsa.js/issues/110)
+
+## [2.7.5](https://github.com/amejiarosario/dsa.js/compare/2.7.4...2.7.5) (2021-05-24)
+
+
+### Bug Fixes
+
+* **bst:** on duplicates values the same node is returned ([d350da8](https://github.com/amejiarosario/dsa.js/commit/d350da8bd2d7cf5c8f4425e863babf19cbdcbdf4)), closes [#99](https://github.com/amejiarosario/dsa.js/issues/99)
+
+## [2.7.4](https://github.com/amejiarosario/dsa.js/compare/2.7.3...2.7.4) (2021-01-09)
+
+
+### Bug Fixes
+
+* **bst:** some typos on the code ([6b9a4e8](https://github.com/amejiarosario/dsa.js/commit/6b9a4e8c00c2082ed7d083b58f7d462e340725fe))
+
+## [2.7.3](https://github.com/amejiarosario/dsa.js/compare/2.7.2...2.7.3) (2020-12-22)
+
+
+### Bug Fixes
+
+* **book/bst:** better wording ([1d75301](https://github.com/amejiarosario/dsa.js/commit/1d7530199940b926467ad0dcbc3b7dbe76b77820))
+* **book/graph:** add comments for runtimes using hashset implementations ([c7c7947](https://github.com/amejiarosario/dsa.js/commit/c7c7947da33cb61a794e538dff55951318edc341))
+* **book/hashmap:** exercise example and add tests ([c137930](https://github.com/amejiarosario/dsa.js/commit/c137930a8750c0aac95bbbae931886e068371d92))
+
+## [2.7.2](https://github.com/amejiarosario/dsa.js/compare/2.7.1...2.7.2) (2020-12-17)
+
+
+### Bug Fixes
+
+* **book:** Rename fibonacci-dynamic-programming ([ded0a70](https://github.com/amejiarosario/dsa.js/commit/ded0a701815ce4647394e2f5e217c0d88142a92c))
+
+## [2.7.1](https://github.com/amejiarosario/dsa.js/compare/2.7.0...2.7.1) (2020-12-02)
+
+
+### Bug Fixes
+
+* **book:** update company names ([38a4178](https://github.com/amejiarosario/dsa.js/commit/38a4178e69e698a1b3c0d4452f0cfd58aed5290d))
+
+# [2.7.0](https://github.com/amejiarosario/dsa.js/compare/2.6.0...2.7.0) (2020-10-30)
+
+
+### Bug Fixes
+
+* **book/set:** split Set chapter into Hash Set and Tree Set for better ([c1cf57a](https://github.com/amejiarosario/dsa.js/commit/c1cf57a31fc7a698665c82c8fbd2fde7fb825078))
+
+
+### Features
+
+* **book:** show companies asked questions ([b3167f9](https://github.com/amejiarosario/dsa.js/commit/b3167f9c694c5e6719bf1d01804aeec60b41e57f))
+* **book/linkedlist:** add applications ([2b96f00](https://github.com/amejiarosario/dsa.js/commit/2b96f0086632d9ddcbb7e8f76a061a46f90a65a0))
+
+# [2.6.0](https://github.com/amejiarosario/dsa.js/compare/2.5.1...2.6.0) (2020-10-28)
+
+
+### Bug Fixes
+
+* **test:** refactor tests ([571834a](https://github.com/amejiarosario/dsa.js/commit/571834a848d3b4c7d0dd8a94957b73724f3756ac))
+
+
+### Features
+
+* **book:** add chapter numbers ([0f13f90](https://github.com/amejiarosario/dsa.js/commit/0f13f907141d0ad9bb439d131aca6d1d882421ee))
+* **book/linkedlist:** linked lists techniques and common patterns ([8cd126d](https://github.com/amejiarosario/dsa.js/commit/8cd126d71a31473fefdbf0f0a9780cd7b128bcd6))
+
+## [2.5.1](https://github.com/amejiarosario/dsa.js/compare/2.5.0...2.5.1) (2020-10-23)
+
+
+### Bug Fixes
+
+* **linkedlist:** refactored methods and improve book images and ([ba110e2](https://github.com/amejiarosario/dsa.js/commit/ba110e26b2103265bb78809618c7e29c185acd70))
+* **linkedlist:** rename indexOf to getIndex to make it clearer ([fcf3db6](https://github.com/amejiarosario/dsa.js/commit/fcf3db6ade12a7ed88a5b7756945a8b090ac63b9))
+
+# [2.5.0](https://github.com/amejiarosario/dsa.js/compare/2.4.0...2.5.0) (2020-10-22)
+
+
+### Features
+
+* **book/hashmap:** add code examples and patterns ([f7b8d59](https://github.com/amejiarosario/dsa.js/commit/f7b8d59ac8cbddacc34cac6a2f6e0a58419813fd))
+
+# [2.4.0](https://github.com/amejiarosario/dsa.js/compare/2.3.0...2.4.0) (2020-10-19)
+
+
+### Bug Fixes
+
+* **book/array:** improve examples and grammar ([04836cd](https://github.com/amejiarosario/dsa.js/commit/04836cd0cec3afb196d11704a45933e325816c20))
+
+
+### Features
+
+* **book/array:** add array patterns for solving problems ([8c7b7be](https://github.com/amejiarosario/dsa.js/commit/8c7b7be8fe24ec1df66846b05fbc2ef5daeb6c7e))
+* **book/array:** sliding window pattern ([8fa2746](https://github.com/amejiarosario/dsa.js/commit/8fa2746f5c917b0f5a88289d854166210925f9c7))
+
+# [2.3.0](https://github.com/amejiarosario/dsa.js/compare/2.2.1...2.3.0) (2020-10-03)
+
+
+### Features
+
+* **treeMap:** get last entry (highest value) ([249de5d](https://github.com/amejiarosario/dsa.js/commit/249de5d4769549e9f05562bb6dad50d4e0384524))
+
+## [2.2.1](https://github.com/amejiarosario/dsa.js/compare/2.2.0...2.2.1) (2020-10-02)
+
+
+### Bug Fixes
+
+* **book:** add introduction ([4505eb6](https://github.com/amejiarosario/dsa.js/commit/4505eb64a63b282ca9685b3b2cabb657149d9fe7))
+
+# [2.2.0](https://github.com/amejiarosario/dsa.js/compare/2.1.0...2.2.0) (2020-09-29)
+
+
+### Features
+
+* **book/big-o:** add new chapter on how to determine big o from code. ([68c73d4](https://github.com/amejiarosario/dsa.js/commit/68c73d4c3bb5fb1ec637d81b42a2f8dbfc4f0008))
+
+# [2.1.0](https://github.com/amejiarosario/dsa.js/compare/2.0.0...2.1.0) (2020-09-11)
+
+
+### Features
+
+* **book/sorting:** add questions and solutions ([fdb8bd8](https://github.com/amejiarosario/dsa.js/commit/fdb8bd8af644ddeaa07ce62773e9be96fd6b2404))
+
+# [2.0.0](https://github.com/amejiarosario/dsa.js/compare/1.18.0...2.0.0) (2020-09-08)
+
+
+### Features
+
+* **book/pq:** add exercise with pq and dijkstra ([3a2a24f](https://github.com/amejiarosario/dsa.js/commit/3a2a24ffae2af5e5e348c237195f7a39717ae617))
+* **heap:** add error handling for heaps ([827177f](https://github.com/amejiarosario/dsa.js/commit/827177f6023f639db0c2cc267dceb0a27746038a))
+* **pq:** improves docs and usability of priority queues ([edf8808](https://github.com/amejiarosario/dsa.js/commit/edf8808970d57aaf397958d2cab1a6cc2e029d26))
+
+
+### BREAKING CHANGES
+
+* **heap:** size is now an attribute rather than a method. Similar to the built-in Map.size and Set.size
+
+# [1.18.0](https://github.com/amejiarosario/dsa.js/compare/1.17.0...1.18.0) (2020-09-07)
+
+
+### Features
+
+* **book/graph:** add schedule exercise and solution ([6a83cf8](https://github.com/amejiarosario/dsa.js/commit/6a83cf8a5d454b26e3048aa4ca73e44eafca0ed3))
+
+# [1.17.0](https://github.com/amejiarosario/dsa.js/compare/1.16.0...1.17.0) (2020-09-04)
+
+
+### Features
+
+* **book/set:** add questions and solutions ([f40dc63](https://github.com/amejiarosario/dsa.js/commit/f40dc6314a14e1750146a19163b3b70c30f09d70))
+
+# [1.16.0](https://github.com/amejiarosario/dsa.js/compare/1.15.0...1.16.0) (2020-09-03)
+
+
+### Features
+
+* **book/hashmap:** add exercises and solutions ([d18186b](https://github.com/amejiarosario/dsa.js/commit/d18186b61c260e3ae2cc1267cadd16c8cb453e00))
+
+# [1.15.0](https://github.com/amejiarosario/dsa.js/compare/1.14.0...1.15.0) (2020-09-02)
+
+
+### Bug Fixes
+
+* **book/solutions:** fix missing solutions ([464417e](https://github.com/amejiarosario/dsa.js/commit/464417e37ccde00d56d8ebb9c0d0ddc9e510e368))
+
+
+### Features
+
+* **BinaryTreeNode:** add BinaryTreeNode.from ([bcb4c28](https://github.com/amejiarosario/dsa.js/commit/bcb4c28089a96248ab9d623c25e1719e030caedc))
+* **book/binary-tree:** add right side view exercise and solution ([28346a4](https://github.com/amejiarosario/dsa.js/commit/28346a448e7f1be640751098b4a954ac04dd0aad))
+* **book/bt:** add binary tree diameter problem ([068dd9c](https://github.com/amejiarosario/dsa.js/commit/068dd9cc66e5a3050cc44d4cd3a05eb15f8c00d1))
+
+# [1.14.0](https://github.com/amejiarosario/dsa.js/compare/1.13.0...1.14.0) (2020-08-31)
+
+
+### Features
+
+* **book/questions:** add examples ([c1a8f8e](https://github.com/amejiarosario/dsa.js/commit/c1a8f8ec53f6c4c1302001dba6d913c81156fd98))
+* **book/questions:** add where the interview questions have been seen ([1f01baf](https://github.com/amejiarosario/dsa.js/commit/1f01bafdb55dcd82f6b88bdf1252c5aabb39277a))
+* **book/queue:** add recent counter questions and solution ([77d4596](https://github.com/amejiarosario/dsa.js/commit/77d459642ca61f729e698c892928665e76532701))
+* **book/queue:** add solution for queue question ([9a88766](https://github.com/amejiarosario/dsa.js/commit/9a887660bd21e7dbda0d3e87af704b4e41874093))
+* **queue:** add front, back and peek methods ([04aa9db](https://github.com/amejiarosario/dsa.js/commit/04aa9db26659058c09debe985f94845933a2089d))
+
+# [1.13.0](https://github.com/amejiarosario/dsa.js/compare/1.12.0...1.13.0) (2020-08-26)
+
+
+### Features
+
+* **book/exercises:** interview q&a for linked lists and stacks ([3bb86fd](https://github.com/amejiarosario/dsa.js/commit/3bb86fd544f543906b5723aab1f26d5a75f13950))
+
+# [1.12.0](https://github.com/amejiarosario/dsa.js/compare/1.11.0...1.12.0) (2020-08-26)
+
+
+### Features
+
+* **book/exercises:** interview q&a for linked lists and stacks ([#69](https://github.com/amejiarosario/dsa.js/issues/69)) ([ca4bf45](https://github.com/amejiarosario/dsa.js/commit/ca4bf45369ef772f4861e7619d3ea74ddc41cde4))
+
+
+### Reverts
+
+* Revert "(book/exercises): interview q&a for linked lists and stacks (#69)" (#70) ([06bd3f6](https://github.com/amejiarosario/dsa.js/commit/06bd3f6a8542b111e4c9e3b5d41050094b153112)), closes [#69](https://github.com/amejiarosario/dsa.js/issues/69) [#70](https://github.com/amejiarosario/dsa.js/issues/70)
+
+# [1.11.0](https://github.com/amejiarosario/dsa.js/compare/1.10.0...1.11.0) (2020-08-22)
+
+
+### Bug Fixes
+
+* **book:** multiple broken links and bump epub version ([57960e2](https://github.com/amejiarosario/dsa.js/commit/57960e2c4f483e779e8344e07406ce7d67d4d9b8)), closes [#60](https://github.com/amejiarosario/dsa.js/issues/60)
+* **book:** workaround for c++ issue in asciidoc ([dbdef21](https://github.com/amejiarosario/dsa.js/commit/dbdef2141ac1449c649cc76790d7990374e78808))
+
+
+### Features
+
+* **book:** add appendix D with interview question solutions ([ca119f2](https://github.com/amejiarosario/dsa.js/commit/ca119f2b2fe5e4392a0b13278b5a6ba9e907952c))
+* **book:array:** add solutions for interview questions ([b5a00dd](https://github.com/amejiarosario/dsa.js/commit/b5a00dded45a1f4eeab3bbbdd68ccbcbf02419d3))
+* **book/array:** add max subarray questions and solution ([5935b95](https://github.com/amejiarosario/dsa.js/commit/5935b95ea761402b296e5a4fbd6e3643714bcaf9))
+* **book/array:** add stock questions and solution ([6ab8bc4](https://github.com/amejiarosario/dsa.js/commit/6ab8bc4449323b059888edf84d66c76c9e812f4e))
+
+# [1.10.0](https://github.com/amejiarosario/dsa.js/compare/1.9.0...1.10.0) (2020-07-28)
+
+
+### Features
+
+* **book/arrays:** add exercises ([bcaf819](https://github.com/amejiarosario/dsa.js/commit/bcaf81919cb889c1209c6d1e577b037047714c05))
+
+# [1.9.0](https://github.com/amejiarosario/dsa.js/compare/1.8.3...1.9.0) (2020-06-30)
+
+
+### Features
+
+* **heap:** remove by index ([6af937f](https://github.com/amejiarosario/dsa.js/commit/6af937fb380ffcd26558193c1626bdb84b2abe17))
+* **linkedList:** takes iterables in the constructor ([1fa875f](https://github.com/amejiarosario/dsa.js/commit/1fa875fd772eb4a45dd9690bea593b513687caca))
+
+## [1.8.3](https://github.com/amejiarosario/dsa.js/compare/1.8.2...1.8.3) (2020-05-24)
+
+
+### Bug Fixes
+
+* **book/maps:** update space complexity ([0be0176](https://github.com/amejiarosario/dsa.js/commit/0be0176efc121c5608c1a2df25280d6d9c08e6f3))
+
+## [1.8.2](https://github.com/amejiarosario/dsa.js/compare/1.8.1...1.8.2) (2020-05-24)
+
+
+### Bug Fixes
+
+* **book/hashset:** update hashset space complexity ([0319b29](https://github.com/amejiarosario/dsa.js/commit/0319b29e92a630c5f14d5e3208b72fe536b38f43))
+
+## [1.8.1](https://github.com/amejiarosario/dsa.js/compare/1.8.0...1.8.1) (2020-05-24)
+
+
+### Bug Fixes
+
+* **book/maps:** update time/space complexity values ([3c4ef75](https://github.com/amejiarosario/dsa.js/commit/3c4ef7555c400828fb89339184294feb7169b215))
+
+# [1.8.0](https://github.com/amejiarosario/dsa.js/compare/1.7.1...1.8.0) (2020-05-23)
+
+
+### Bug Fixes
+
+* **book/map:** fix typo on maps space complexity ([2f24f57](https://github.com/amejiarosario/dsa.js/commit/2f24f57f989bb97f198bb32f6daa477d6075dc31))
+* **hashmap:** improve methods documentation ([f6b47b5](https://github.com/amejiarosario/dsa.js/commit/f6b47b5a6c53126bebbad72ac92da1a594042232))
+
+
+### Features
+
+* **maps:** implement clear method for hashMap and treeMap ([924c9a7](https://github.com/amejiarosario/dsa.js/commit/924c9a76db91f7df44f7cb38caba3aa9c3f0497f))
+
+## [1.7.1](https://github.com/amejiarosario/dsa.js/compare/1.7.0...1.7.1) (2020-05-20)
+
+
+### Bug Fixes
+
+* **hashmap:** fix TextEncoder reference ([e13ff88](https://github.com/amejiarosario/dsa.js/commit/e13ff88d7f8f9b7f38844befedc001bc1a4b243f))
+
+# [1.7.0](https://github.com/amejiarosario/dsa.js/compare/1.6.0...1.7.0) (2020-05-16)
+
+
+### Features
+
+* **heap:** add min/max/median-heaps ([202ca9f](https://github.com/amejiarosario/dsa.js/commit/202ca9f989ddba433b4f591e27bc094640cbbadf))
+
+# [1.6.0](https://github.com/amejiarosario/dsa.js/compare/1.5.0...1.6.0) (2020-05-08)
+
+# [1.5.0](https://github.com/amejiarosario/dsa.js/compare/1.4.0...1.5.0) (2020-03-30)
+
+
+### Features
+
+* **linkedList:** remove by node ([eac045a](https://github.com/amejiarosario/dsa.js/commit/eac045a6bedd0223a2e8b12225f87c428e6fb66f))
+* **lru-cache:** add new implementations ([3e787c6](https://github.com/amejiarosario/dsa.js/commit/3e787c6f9ba9c094272be5ac05b997ce44a359d3))
+* **trie:** remove method ([16005f3](https://github.com/amejiarosario/dsa.js/commit/16005f3bf39597759918e34b39d27eb25a5755e7))
+
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+
+## [1.3.9]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(book): fix table typos [commit](https://github.com/amejiarosario/dsa.js/commit/bc51a7a0c97aea9dea1afa5f8af22c0bed1382d3)
+
+## [1.3.8]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(book): fix typo, array pop, and BST images [commit](https://github.com/amejiarosario/dsa.js/commit/ac9858348943f9678d116c8076bfa3a3c5362741)
+
+## [1.3.7]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(avl-tree): balance was not working properly [commit](https://github.com/amejiarosario/dsa.js/commit/98e2c037f05caf37731da1dc50dd8867a1804c0e)
+
+## [1.3.6]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- update deps [commit](https://github.com/amejiarosario/dsa.js/commit/d8ce2f5b1a3bfeb861928d6c99d7624cd9ac144a)
+- style: fix eslint issue [commit](https://github.com/amejiarosario/dsa.js/commit/72e3d68e09bb9c7dd3fabf5cbeba1ae5571fc686)
+
+## [1.3.5]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(tree-rotations): prevent losing nodes [commit](https://github.com/amejiarosario/dsa.js/commit/1fa93415a6e14acc24e90443b2e9bdf053c4c983)
+
+## [1.3.4]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(queue): runtime error [commit](https://github.com/amejiarosario/dsa.js/commit/fd3ab480f2bb7cc2b7ce45da4e6a41831eafb7bf)
+- fix(book): remove lorem ipsum [commit](https://github.com/amejiarosario/dsa.js/commit/6a06e3b75915a72ef0dd5ee9d37dcf434c4c8e18)
+- docs(book): improve grammar
+
+## [1.2.3]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(linkedlist): removeFirst's last reference [commit](https://github.com/amejiarosario/dsa.js/commit/d4f9a71f6a22971ad8edcfd982d6b113f627d108)
+
+## [1.2.2]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- Optimize NPM package
+
+## [1.2.1]
+
+### Breaking Changes (major)
+
+### New Features (minor)
+
+### Bug Fixes (patch)
+- fix(linked-list): insert in the middle bug [commit](https://github.com/amejiarosario/dsa.js/commit/f8bd4fd8fa6c8dcb0d26f54a6ed7d80e5443d65b)
+- Various docs fixes.
+
+## [1.2.0] - 2019-04-04
+
+### Breaking changes (major)
+
+### New Features (minor)
+
+- Added Eslint and Travis ci
+
+### Fixes (patch)
+
+- Readme improvements
+- Badges for npm versions and build status
+
+## [1.1.0] - 2019-03-29
+
+### Added
+
+- README.md added because NPM packages doesn't read README.adoc
+- Public API to use the package `dsa.js`
+- This changelog
+
+### Changed
+
+- Updated dependencies (removed lodash since is not needed)
+
+### Removed
+
+-
+
+## [1.0.0] - 2019-03-29
+
+### Added
+
+- Started the project
+- Book released
+- npm package published
+
+### Changed
+
+-
+
+### Removed
+
+-
+
+[Unreleased]: https://github.com/amejiarosario/dsa.js/compare/1.3.9...HEAD
+[1.3.9]: https://github.com/amejiarosario/dsa.js/compare/1.3.8...1.3.9
+[1.3.7]: https://github.com/amejiarosario/dsa.js/compare/1.3.7...1.3.8
+[1.3.6]: https://github.com/amejiarosario/dsa.js/compare/1.3.6...1.3.7
+[1.3.6]: https://github.com/amejiarosario/dsa.js/compare/1.3.5...1.3.6
+[1.3.5]: https://github.com/amejiarosario/dsa.js/compare/1.3.4...1.3.5
+[1.3.4]: https://github.com/amejiarosario/dsa.js/compare/1.2.3...1.3.4
+[1.2.2]: https://github.com/amejiarosario/dsa.js/compare/1.2.2...1.2.3
+[1.2.2]: https://github.com/amejiarosario/dsa.js/compare/1.2.1...1.2.2
+[1.2.1]: https://github.com/amejiarosario/dsa.js/compare/1.2.0...1.2.1
+[1.2.0]: https://github.com/amejiarosario/dsa.js/compare/1.1.0...1.2.0
+[1.1.0]: https://github.com/amejiarosario/dsa.js/compare/1.0.0...1.1.0
+[1.0.0]: https://github.com/amejiarosario/dsa.js/releases/tag/1.0.0
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..43e79136
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,189 @@
+# Contributing
+
+We encourage any form of contribution, whether that will be issues, comments, or pull requests. If you are willing to submit a PR, there are a few things we would appreciate that you do to keep the codebase clean:
+
+* **Write tests (if applicable).** We try as close to 100% code coverage as possible on this repo, so any new code that gets written should have accompanying tests.
+* **Follow the linter.** We use our [ESLint configuration with Airbnb JavaScript Styleguide](https://github.com/airbnb/javascript), and we run `npm run lint` in our Travis builds.
+* **Ask questions if you aren't sure.** If you have any questions while implementing a fix or feature, feel free to create an issue and ask us. We're happy to help!
+
+## <a name="Submit"></a> Submission Guidelines
+
+### <a name="Submit-issue"></a> Submitting an Issue
+
+Before you submit an issue, please search the issue tracker, maybe an issue for your problem already exists, and the discussion might inform you of workarounds readily available.
+
+### <a name="submit-pr"></a> Submitting a Pull Request (PR)
+Before you submit your Pull Request (PR), consider the following guidelines:
+
+1. Search [GitHub](https://github.com/amejiarosario/dsa.js/pulls) for an open or closed PR
+  that relates to your submission. You don't want to duplicate effort.
+1. Be sure that an issue describes the problem you're fixing or documents the design for the feature you'd like to add.
+  Discussing the design upfront helps to ensure that we're ready to accept your work.
+1. Fork the `amejiarosario/dsa.js` repo.
+1. Make your changes in a new git branch:
+
+     ```shell
+     git checkout -b my-fix-branch master
+     ```
+
+1. Create your patch, **including appropriate test cases**.
+1. Run the full test suite, and ensure that all tests pass.
+1. Commit your changes using a descriptive commit message that follows our
+  [commit message conventions](#commit). Adherence to these conventions is necessary because release notes are automatically generated from these messages.
+
+     ```shell
+     git commit -a
+     ```
+
+    Note: the optional commit `-a` command-line option will automatically "add" and "rm" edited files.
+
+1. Push your branch to GitHub:
+
+    ```shell
+    git push origin my-fix-branch
+    ```
+
+1. In GitHub, send a pull request to `dsa.js:master`.
+* If we suggest changes then:
+  * Make the required updates.
+  * Re-run the test suites to ensure tests are still passing.
+  * Rebase your branch and force push to your GitHub repository (this will update your Pull Request):
+
+    ```shell
+    git rebase master -i
+    git push -f
+    ```
+
+That's it! Thank you for your contribution!
+
+#### After your pull request is merged
+
+After your pull request is merged, you can safely delete your branch and pull the changes
+from the main (upstream) repository:
+
+* Delete the remote branch on GitHub either through the GitHub web UI or your local shell as follows:
+
+    ```shell
+    git push origin --delete my-fix-branch
+    ```
+
+* Check out the master branch:
+
+    ```shell
+    git checkout master -f
+    ```
+
+* Delete the local branch:
+
+    ```shell
+    git branch -D my-fix-branch
+    ```
+
+* Update your master with the latest upstream version:
+
+    ```shell
+    git pull --ff upstream master
+    ```
+
+## <a name="commit"></a> Commit Message Guidelines
+
+We have some guidelines on how our git commit messages can be formatted.  These rules lead to more
+readable messages that are easy to follow when looking through the project history.  But also,
+we use the git commit messages to **generate the change log**.
+
+### Commit Message Format
+Each commit message consists of a **header**, a **body** and a **footer**.  The header has a special
+format that includes a **type**, a **scope** and a **subject**:
+
+```
+<type>(<scope>): <subject>
+<BLANK LINE>
+<body>
+<BLANK LINE>
+<footer>
+```
+
+Example of a commit with header, body, and footer:
+
+```
+fix(linked-list): insert in the middle bug
+
+One reference was not updated when inserting an item in the middle of a linked list.
+
+Fixes: #8
+```
+
+The **header** is mandatory, and the **scope** of the header is optional.
+
+Any line of the commit message cannot be longer than 100 characters! This length allows the message to be easier to read on GitHub as well as in various git tools.
+
+The footer should contain a [closing reference to an issue](https://help.github.com/articles/closing-issues-via-commit-messages/), if any.
+
+
+Examples:
+
+```
+feat(heap): add error handling for heaps
+
+BREAKING CHANGE: size is now an attribute rather than a method. Similar to the built-in Map.size and Set.size
+```
+
+```
+fix(book/solutions): fix missing solutions
+```
+
+### Revert
+If the commit reverts a previous commit, it should begin with `revert: `, followed by the reverted commit's header. The body should say: `This reverts commit <hash>.`, where the hash is the SHA of the commit being reverted.
+
+### Type
+Must be one of the following:
+
+* **fix**: A bug fix
+* **feat**: A new feature
+* **chore**: Changes to our CI configuration files and scripts (example scopes: Circle, BrowserStack, SauceLabs)
+
+### Scope
+The scope should be the main directory name. The following is an example of recommended scopes:
+
+* **list**
+* **map**
+* **tree**
+* **graph**
+* **sorting**
+* **book**
+* etc.
+
+### Subject
+The subject contains a succinct description of the change:
+
+* Use the imperative, present tense: "change" not "changed" nor "changes".
+* Don't capitalize the first letter.
+* Don't dot (.) at the end.
+
+### Body
+Just as in the **subject**, use the imperative, present tense: "change" not "changed" nor "changes".
+The body should include the motivation for the change and contrast this with previous behavior.
+
+### Footer
+The footer should contain any information about **BREAKING CHANGES** and is also the place to
+reference GitHub issues that this commit **Closes**.
+
+```
+Closes #234
+```
+
+**Breaking Changes** should start with the word `BREAKING CHANGE:` on the footer with space or two newlines. The rest of the commit message is then used for this.
+
+Examples of breaking changes include:
+
+* removal or redefinition of existing API arguments
+* changing return values
+* removing or modifying existing properties on an options argument
+* adding or removing errors
+* altering expected timing of an event
+* changing the side effects of using a particular API
+
+
+<!-- Examples -->
+<!-- https://github.com/nodejs/node/blob/v12.0.0/COLLABORATOR_GUIDE.md -->
+<!-- https://github.com/nodejs/node/blob/v12.0.0/doc/guides/writing-and-running-benchmarks.md -->
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 00000000..84cdc3eb
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,16 @@
+FROM circleci/ruby:2.5.3-stretch-node
+# FROM circleci/ruby:2.7.1-buster-node
+
+COPY . /app
+
+RUN node -v
+RUN ruby -v
+RUN bundle -v
+
+RUN apt-get update && apt-get install -y \
+  graphviz \
+  graphicsmagick-imagemagick-compat graphicsmagick-libmagick-dev-compat
+
+RUN cd book/config && bundle install
+
+CMD cd book/config && make VERSION="$(npx -c 'echo "$npm_package_version"')"
diff --git a/LICENSE b/LICENSE
index e0f48aea..dcc847b1 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2016 Adrian Mejia
+Copyright (c) 2019 Adrian Mejia
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index fcaf0454..c2e3befa 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,513 @@
-
+![image](https://user-images.githubusercontent.com/418605/59557258-10742880-8fa3-11e9-84fb-4d66a9d89faa.png)
 
 # Data Structures and Algorithms in JavaScript
 
-This repository covers the implementation of the most important algorithms and data structures.
+[![CircleCI](https://circleci.com/gh/amejiarosario/dsa.js-data-structures-algorithms-javascript.svg?style=shield)](https://app.circleci.com/pipelines/github/amejiarosario/dsa.js-data-structures-algorithms-javascript) [![NPM version](https://badge.fury.io/js/dsa.js.svg)](https://badge.fury.io/js/dsa.js) [![chat](https://dsajs-slackin.herokuapp.com/badge.svg)](https://dsajs-slackin.herokuapp.com)
+
+> This is the coding implementations of the [DSA.js book](https://books.adrianmejia.com/dsajs-data-structures-algorithms-javascript/) and the repo for the NPM package.
+
+> In this repository, you can find the implementation of algorithms and data structures in JavaScript.  This material can be used as a reference manual for developers, or you can refresh specific topics before an interview. Also, you can find ideas to solve problems more efficiently.
+
+<!-- (Check out the Time Complexity Cheatsheet) -->
+
+
+<!--
+[![CircleCI](https://circleci.com/gh/amejiarosario/dsa.js-data-structures-and-algorithms-in-javascript.svg?style=svg)](https://circleci.com/gh/amejiarosario/dsa.js-data-structures-and-algorithms-in-javascript)
+[![Code Style Airbnb](https://img.shields.io/badge/code%20style-Airbnb-brightgreen.svg)](https://github.com/airbnb/javascript)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](http://makeapullrequest.com)
+[![ProductHunt](https://img.shields.io/badge/product%20hunt-vote-orange.svg)](https://www.producthunt.com/posts/dsa-js)
+https://bundlephobia.com/result?p=dsa.js
+https://img.shields.io/bundlephobia/min/dsa.js.svg - 16.7kB
+https://img.shields.io/github/repo-size/amejiarosario/dsa.js.svg - 98.1 MB
+-->
+
+![Interactive Data Structures](https://user-images.githubusercontent.com/418605/46118890-ba721180-c1d6-11e8-82bc-6a671428b422.png)
+<!-- [](https://embed.kumu.io/85f1a4de5fb8430a10a1bf9c5118e015) -->
+
+
+## Table of Contents
+
+<!-- START doctoc generated TOC please keep comment here to allow auto update -->
+<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
+
+
+- [Installation](#installation)
+- [Features](#features)
+- [What's Inside](#whats-inside)
+  - [📈 Algorithms Analysis](#-algorithms-analysis)
+  - [🥞 Linear Data Structures](#-linear-data-structures)
+  - [🌲 Non-Linear Data Structures](#-non-linear-data-structures)
+  - [⚒ Algorithms Techniques](#%E2%9A%92-algorithms-techniques)
+- [Book](#book)
+- [FAQ](#faq)
+- [Support](#support)
+- [License](#license)
+
+
+<!-- END doctoc generated TOC please keep comment here to allow auto update -->
+
+
+## Installation
+
+You can clone the repo or install the code from NPM:
+
+```sh
+npm install dsa.js
+```
+
+and then you can import it into your programs or CLI
+
+```js
+const { LinkedList, Queue, Stack } = require('dsa.js');
+```
+
+For a list of all available data structures and algorithms, [see index.js](src/index.js).
+
+
+## Features
+
+Algorithms are an essential toolbox for every programmer.
+
+You will need to mind algorithms runtime when you have to sort data, search for a value in a big dataset, transform data, scale your code to many users, to name a few.
+Algorithms are just the step you follow to solve a problem, while data structures are where you store the data for later manipulation. Both combined create programs.
+
+> Algorithms + Data Structures = Programs.
+
+Most programming languages and libraries indeed provide implementations for basic data structures and algorithms.
+However, to make use of data structures properly, you have to know the tradeoffs to choose the best tool for the job.
+
+This material is going to teach you to:
+
+- 🛠 Apply strategies to tackle algorithm questions. Never to get stuck again. Ace those interviews!
+- ✂️ Construct efficient algorithms. Learn how to break down problems into manageable pieces.
+- 🧠 Improve your problem-solving skills and become a well-rounded developer by understanding fundamental computer science concepts.
+- 🤓 Cover essential topics, such as big O time, data structures, and must-know algorithms. Implement 10+ data structures from scratch.
+
+## What's Inside
+
+All the code and explanations are available on this repo. You can dig through the links and code examples from the ([src folder](src)). However, the inline code examples are not expanded (because of Github's asciidoc limitations), but you can follow the path and see the implementation.
+
+_Note: If you prefer to consume the information more linearly, then the [book format](https://books.adrianmejia.com/dsajs-data-structures-algorithms-javascript/) would be more appropriate for you._
+
+The topics are divided into four main categories, as you can see below:
+
+
+### 📈 [Algorithms Analysis](book/part01-algorithms-analysis.asc)
+
+<!-- - Computer Science nuggets without all the mumbo-jumbo.
+- Learn how to compare algorithms using Big O notation.
+- 8 examples to explain with code how to calculate time complexity. -->
+
+<blockquote>
+
+<details>
+  <summary>
+    Computer Science nuggets without all the mumbo-jumbo. <i>(Click to expand)</i>
+  </summary>
+
+---
+
+### [Computer Science nuggets without all the mumbo-jumbo](book/content/part01/algorithms-analysis.asc)
+
+#### Learn to calculate run time from code examples
+
+![Translating lines of code to an approximate number of operations](book/images/image4.png)
+
+---
+
+</details>
+
+<details>
+  <summary>
+    Learn how to compare algorithms using Big O notation. <i>(Click to expand)</i>
+  </summary>
+
+---
+
+### [Learn how to compare algorithms using Big O notation.](book/content/part01/big-o-examples.asc#finding-if-an-array-is-empty)
+
+#### Comparing algorithms using Big O notation
+
+Let's say you want to find the duplicates on an array.
+Using Big O notation, we can compare different solutions that solve the same problem but has a massive difference in how long it takes to do it.
+
+- [Optimal solution using a map](book/content/part01/big-o-examples.asc#linear-example)
+- [Finding duplicates in an array (naïve approach)](book/content/part01/big-o-examples.asc#quadratic-example)
+
+---
+
+</details>
+
+<details>
+  <summary>
+    8 examples to explain with code how to calculate time complexity. <i>(Click to expand)</i>
+  </summary>
+
+---
+
+[8 examples to explain with code how to calculate time complexity](book/content/part01/big-o-examples.asc#summary)
+
+#### Most common time complexities
+
+[![image](https://user-images.githubusercontent.com/418605/59617342-2a715080-90f4-11e9-9de9-9315fb0133f9.png)](book/content/part01/big-o-examples.asc#summary)
+
+<!-- - Constant time: _O(1)_
+- Logarithmic time: _O(log n)_
+- Linear time: _O(n)_
+- Linearithmic time: _O(n log n)_
+- Quadratic time: _O(n^2^)_
+- Cubic time: _O(n^3^)_
+- Exponential time: _O(2^n^)_
+- Factorial time: _O(n!)_ -->
+
+#### Time complexity graph
+
+![Most common time complexities](book/images/image5.png)
+
+---
+
+</details>
+</blockquote>
+
+### 🥞 [Linear Data Structures](book/part02-linear-data-structures.asc)
+
+<!-- - Understand the ins and outs of the most common data structures.
+- When to use an Array or Linked List. Know the tradeoffs.
+- Build a Stack and a Queue from scratch. -->
+
+<blockquote>
+
+  <details>
+    <summary>
+      Understand the ins and outs of the most common data structures. <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [Understand the ins and outs of the most common data structures](book/part02-linear-data-structures.asc)
+
+
+- [Arrays](book/content/part02/array.asc): Built-in in most languages so not implemented here. [Array Time complexity](book/content/part02/array.asc#array-complexity)
+    <!-- [Post](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Array). -->
+
+- [Linked List](book/content/part02/linked-list.asc): each data node has a link to the next (and
+    previous).
+    [Code](src/data-structures/linked-lists/linked-list.js)
+    |
+    [Linked List Time Complexity](book/content/part02/linked-list.asc#linked-list-complexity-vs-array-complexity)
+    <!-- [Post](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Linked-Lists) -->
+
+- [Queue](book/content/part02/queue.asc): data flows in a "first-in, first-out" (FIFO) manner.
+    [Code](src/data-structures/queues/queue.js)
+    |
+    [Queue Time Complexity](book/content/part02/queue.asc#queue-complexity)
+    <!-- [Post](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Queues) -->
+
+- [Stack](book/content/part02/stack.asc): data flows in a "last-in, first-out" (LIFO) manner.
+    [Code](src/data-structures/stacks/stack.js)
+    |
+    [Stack Time Complexity](book/content/part02/stack.asc#stack-complexity)
+    <!-- [Post](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Stacks) -->
+
+---
+
+  </details>
+  <details>
+    <summary>
+      When to use an Array or Linked List. Know the tradeoffs. <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [When to use an Array or Linked List. Know the tradeoffs](book/content/part02/array-vs-list-vs-queue-vs-stack.asc)
+
+Use Arrays when…
+- You need to access data in random order fast (using an index).
+- Your data is multi-dimensional (e.g., matrix, tensor).
+
+Use Linked Lists when:
+- You will access your data sequentially.
+- You want to save memory and only allocate memory as you need it.
+- You want constant time to remove/add from extremes of the list.
+- when size requirement is unknown - dynamic size advantage
+
+---
+
+  </details>
+  <details>
+    <summary>
+      Build a List, Stack, and a Queue. <i>(Click to expand)</i>
+    </summary>
+
+  ---
+
+  #### [Build a List, Stack and a Queue from scratch](book/part02-linear-data-structures.asc)
+
+  Build any of these data structures from scratch:
+  - [Linked List](src/data-structures/linked-lists/linked-list.js)
+  - [Stack](src/data-structures/stacks/stack.js)
+  - [Queue](src/data-structures/queues/queue.js)
+
+  ---
+
+  </details>
+</blockquote>
+
+### 🌲 [Non-Linear Data Structures](book/part03-graph-data-structures.asc)
+<!-- - Understand one of the most versatile data structure of all: Maps -->
+<!-- - Know the properties of Graphs and Trees. -->
+<!-- - Implement a binary search tree for fast lookups. -->
+
+<blockquote>
+  <details>
+    <summary>
+      Understand one of the most versatile data structure of all: Hash Maps. <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [HashMaps](book/content/part03/map.asc)
+
+Learn how to implement different types of Maps such as:
+- [HashMap](book/content/part02/hash-map.asc)
+- [TreeMap](book/content/part03/tree-map.asc)
+
+Also, [learn the difference between the different Maps implementations](book/content/part03/time-complexity-graph-data-structures.asc):
+
+- `HashMap` is more time-efficient. A `TreeMap` is more space-efficient.
+- `TreeMap` search complexity is *O(log n)*, while an optimized `HashMap` is *O(1)* on average.
+- `HashMap`’s keys are in insertion order (or random depending on the implementation). `TreeMap`’s keys are always sorted.
+- `TreeMap` offers some statistical data for free such as: get minimum, get maximum, median, find ranges of keys. `HashMap` doesn’t.
+- `TreeMap` has a guarantee always an *O(log n)*, while `HashMap`s has an amortized time of *O(1)* but in the rare case of a rehash, it would take an *O(n)*.
+
+---
+
+  </details>
+
+  <details>
+    <summary>
+    Know the properties of Graphs and Trees. <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [Know the properties of Graphs and Trees](book/part03-graph-data-structures.asc)
+
+##### [Graphs](book/content/part03/graph.asc)
+
+Know all the graphs properties with many images and illustrations.
+
+![graph example with USA airports](book/images/image46.png)
+
+ **Graphs**: data **nodes** that can have a connection or **edge** to
+    zero or more adjacent nodes. Unlike trees, nodes can have multiple parents, loops.
+    [Code](src/data-structures/graphs/graph.js)
+    |
+    [Graph Time Complexity](book/content/part03/graph.asc#graph-complexity)
+
+#### [Trees](book/content/part03/tree-intro.asc)
+
+Learn all the different kinds of trees and their properties.
+
+![tree data structure properties](book/images/image31.jpg)
+
+- **Trees**: data nodes has zero or more adjacent nodes a.k.a.
+    children. Each node can only have one parent node otherwise is a
+    graph, not a tree.
+    [Code](src/data-structures/trees)
+    |
+    [Docs](book/content/part03/tree-intro.asc)
+    <!-- [Post](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/) -->
+
+    - **Binary Trees**: same as a tree but only can have two children at
+        most.
+        [Code](src/data-structures/trees)
+        |
+        [Docs](book/content/part03/tree-intro.asc#binary-tree)
+        <!-- [Post](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Trees) -->
+
+    - **Binary Search Trees** (BST): same as a binary tree, but the
+        nodes value keep this order `left < parent < right`.
+        [Code](src/data-structures/trees/binary-search-tree.js)
+        |
+        [BST Time complexity](book/content/part03/binary-search-tree.asc#tree-complexity)
+        <!-- [Post](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Search-Tree-BST) -->
+
+    - **AVL Trees**: Self-balanced BST to maximize lookup time.
+        [Code](src/data-structures/trees/avl-tree.js)
+        |
+        [AVL Tree docs](book/C-AVL-tree.asc)
+        |
+        [Self-balancing & tree rotations docs](book/B-self-balancing-binary-search-trees.asc)
+        <!-- [Post](https://adrianmejia.com/blog/2018/07/16/self-balanced-binary-search-trees-with-avl-tree-data-structure-for-beginners/) -->
+
+    - **Red-Black Trees**: Self-balanced BST looser than AVL to
+        maximize insertion speed.
+        [Code](src/data-structures/trees/red-black-tree.js)
+
+---
+
+  </details>
+
+  <details>
+    <summary>
+      Implement a binary search tree for fast lookups.
+    </summary>
+
+---
+
+#### [Implement a binary search tree for fast lookups](book/content/part03/binary-search-tree.asc)
+
+- Learn how to add/remove/update values in a tree:
+![inserting node in a tree](book/images/image36.png)
+
+- [How to make a tree balanced?](book/B-self-balancing-binary-search-trees.asc)
+
+From unbalanced BST to balanced BST
+```
+1                           2
+  \                       /   \
+   2        =>           1     3
+    \
+     3
+```
+
+
+---
+
+  </details>
+</blockquote>
+
+### ⚒ [Algorithmic Toolbox](book/part04-algorithmic-toolbox.asc)
+
+<!-- - Never get stuck solving a problem with seven simple steps. -->
+<!-- - Master the most popular sorting algorithms (merge sort, quicksort, insertion sort, etc.) -->
+<!-- - Learn different approaches to solve problems such as divide and conquer, dynamic programming, greedy algorithms, and backtracking. -->
+
+<blockquote>
+  <details>
+    <summary>
+      Never get stuck solving a problem with 7 simple steps. <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [Never get stuck solving a problem with 8 simple steps](book/part04-algorithmic-toolbox.asc)
+
+1. Understand the problem
+1. Build a simple example (no edge cases yet)
+1. Brainstorm solutions (greedy algorithm, Divide and Conquer, Backtracking, brute force)
+1. Test your answer on the simple example (mentally)
+1. Optimize the solution
+1. Write code. Yes, now you can code.
+1. Test your written code
+1. Analyse the complexity, both space and time, and make sure to optimize further.
+
+Full details [here](book/part04-algorithmic-toolbox.asc)
+
+---
+
+  </details>
+  <details>
+    <summary>
+      Master the most popular sorting algorithms (merge sort, quicksort, etc.) <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [Master the most popular sorting algorithms](book/content/part04/sorting-algorithms.asc)
+
+We are going to explore three essential sorting algorithms O(n^2), which have low overhead:
+  - Bubble Sort.
+    [Code](src/algorithms/sorting/bubble-sort.js)
+    |
+    [Docs](book/content/part04/bubble-sort.asc)
+
+  - Insertion Sort.
+    [Code](src/algorithms/sorting/insertion-sort.js)
+    |
+    [Docs](book/content/part04/insertion-sort.asc)
+
+  - Selection Sort.
+    [Code](src/algorithms/sorting/selection-sort.js)
+    |
+    [Docs](book/content/part04/selection-sort.asc)
+
+and then discuss efficient sorting algorithms O(n log n) such as:
+  - Merge Sort.
+    [Code](src/algorithms/sorting/merge-sort.js)
+    |
+    [Docs](book/content/part04/merge-sort.asc)
+
+  - Quicksort.
+    [Code](src/algorithms/sorting/quick-sort.js)
+    |
+    [Docs](book/content/part04/quick-sort.asc)
+
+---
+
+  </details>
+  <details>
+    <summary>
+      Learn different approaches to solve problems such as divide and conquer, dynamic programming, greedy algorithms, and backtracking. <i>(Click to expand)</i>
+    </summary>
+
+---
+
+#### [Learn different approaches to solve algorithmic problems](book/part04-algorithmic-toolbox.asc)
+
+We are going to discuss the following techniques for solving algorithms problems:
+- [Greedy Algorithms](book/content/part04/greedy-algorithms.asc): makes greedy choices using heuristics to find the best solution without looking back.
+- [Dynamic Programming](book/content/part04/dynamic-programming.asc): a technique for speeding up recursive algorithms when there are many _overlapping subproblems_. It uses _memoization_ to avoid duplicating work.
+- [Divide and Conquer](book/content/part04/divide-and-conquer.asc): _divide_ problems into smaller pieces, _conquer_ each subproblem, and then _join_ the results.
+- [Backtracking](book/content/part04/backtracking.asc): search _all (or some)_ possible paths. However, it stops, and _go back_ as soon as notice the current solution is not working.
+- _Brute Force_: generate all possible solutions and tries all of them. (Use it as a last resort or as the starting point).
+
+---
+
+  </details>
+</blockquote>
+
+## FAQ
+
+<details>
+    <summary>How would I apply these to my day-to-day work? <i>(Click to expand)</i></summary>
+    <p>
+    As a programmer, we have to solve problems every day. If you want to solve problems well, it's good to know about a broad range of solutions. Often, it's more efficient to learn existing resources than stumble upon the answer yourself. The more tools and practice you have, the better. This book helps you understand the tradeoffs among data structures and reason about algorithms performance.
+    </p>
+</details>
+
+<details>
+    <summary>Why you created this repo/book?</summary>
+    <p>
+        There are not many books about Algorithms in JavaScript. This material fills the gap.
+        Also, it's good practice :)
+    </p>
+</details>
+
+<details>
+    <summary>Is there anyone I can contact if I have questions about something in particular?
+    </summary>
+    <p>
+    Yes, open an issue or ask questions on the [slack channel](https://dsajs-slackin.herokuapp.com).
+    </p>
+</details>
+
+## Book
+
+This project is also available in a [book](https://books.adrianmejia.com/dsajs-data-structures-algorithms-javascript/). You will get a nicely formatted PDF with 180+ pages + ePub and Mobi version.
+
+[![dsa.js book](https://books.adrianmejia.com/dsajs-data-structures-algorithms-javascript/images/dsajs-cover-320h2.png)](https://books.adrianmejia.com/dsajs-data-structures-algorithms-javascript/)
+
+## Support
 
-This goes along with [these posts series](https://adrianmejia.com/tags/tutorial-algorithms/) that explain each implementation in details.
+Reach out to me at one of the following places!
 
-[![Interactive Data Structures](https://user-images.githubusercontent.com/418605/46118890-ba721180-c1d6-11e8-82bc-6a671428b422.png)](https://embed.kumu.io/85f1a4de5fb8430a10a1bf9c5118e015)
+- Twitter at <a href="http://twitter.com/iAmAdrianMejia" target="_blank">`@iAmAdrianMejia`</a>
+- Chat on <a href="https://dsajs-slackin.herokuapp.com" target="_blank">`dsajs.slack.com`</a>
 
-## Data Structures
-We are covering the following data structures.
+## License
 
-### Linear Data Structures
-1. **Arrays**: Built-in in most languages so not implemented here. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/arrays/array.js) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Array).
-2. **Linked Lists**: each data node has a link to the next (and previous). [Code](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/linked-lists) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Linked-Lists).
-3. **Queue**: data flows in a "first-in, first-out" (FIFO) manner. [Code](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/queues) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Queues).
-4. **Stacks**:  data flows in a "last-in, first-out" (LIFO) manner. [Code](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Stacks) | [Details](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/stacks).
+[![License](https://img.shields.io/:license-mit-blue.svg?style=flat-square)](LICENSE)
 
-### Non-Linear Data Structures
-1. **Trees**: data nodes has zero or more adjacent nodes a.k.a. children. Each node can only have one parent node otherwise is a graph not a tree. [Code](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/trees) | [Details](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/)
-	1. **Binary Trees**: same as tree but only can have two children at most. [Details](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Trees)
-	1. **Binary Search Trees** (BST): same as binary tree, but the nodes value keep this order `left < parent < rigth`. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/binary-search-tree.js) | [Details](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Search-Tree-BST)
-	2. **AVL Trees**: Self-balanced BST to maximize look up time. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/avl-tree.js) | [Details](https://adrianmejia.com/blog/2018/07/16/self-balanced-binary-search-trees-with-avl-tree-data-structure-for-beginners/)
-	3. **Red-Black Trees**:  Self-balanced BST more loose than AVL to maximize insertion speed. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/red-black-tree.js) | Details
-2. **Maps**: key-value store.
-	1. **Hash Maps**: implements map using a hash function. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/hash-maps/hashmap.js) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#HashMaps)
-	2. **Tree Maps**: implement map using a self-balanced BST. WIP
-3. **Graphs**: data *nodes* that can have a connection or *edge* to zero or more adjacent nodes. Unlike trees, nodes can have multiple parents, loops. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/graphs/graph.js) | [Details](https://adrianmejia.com/blog/2018/05/14/data-structures-for-beginners-graphs-time-complexity-tutorial/)
+<!-- ![](https://data.uxeranalytics.com/image1.png?uxa=github-dsajs) -->
diff --git a/benchmarks/array-rotation-implementations/01-array-rotation.js b/benchmarks/array-rotation-implementations/01-array-rotation.js
new file mode 100644
index 00000000..907357fd
--- /dev/null
+++ b/benchmarks/array-rotation-implementations/01-array-rotation.js
@@ -0,0 +1,9 @@
+// brute force: O(nd) | O(1)
+function rotLeft(a, d) {
+  for (let i = 0; i < d; i++) { // O(d)
+    a.push(a.shift()); // O(n), shift O(n)
+  }
+  return a;
+}
+
+module.exports = rotLeft;
diff --git a/benchmarks/array-rotation-implementations/01a-array-rotation.js b/benchmarks/array-rotation-implementations/01a-array-rotation.js
new file mode 100644
index 00000000..2053166f
--- /dev/null
+++ b/benchmarks/array-rotation-implementations/01a-array-rotation.js
@@ -0,0 +1,11 @@
+// modulus for rotations: O(n^2) | O(1)
+function rotLeft(a, d) {
+  const len = a.length;
+  const rot = d % len;
+  for (let i = 0; i < rot; i++) { // O(n^2)
+    a.push(a.shift()); // O(n)
+  }
+  return a;
+}
+
+module.exports = rotLeft;
diff --git a/benchmarks/array-rotation-implementations/02-array-rotation.js b/benchmarks/array-rotation-implementations/02-array-rotation.js
new file mode 100644
index 00000000..321d545e
--- /dev/null
+++ b/benchmarks/array-rotation-implementations/02-array-rotation.js
@@ -0,0 +1,12 @@
+// additional space: O(n) | O(n)
+function rotLeft(a, d) {
+  const len = a.length;
+  const rot = d % len;
+  const b = [];
+  for (let i = 0; i < len; i++) { // O(n)
+    b[i] = a[(rot + i) % len]; // O(1)
+  }
+  return b;
+}
+
+module.exports = rotLeft;
diff --git a/benchmarks/array-rotation-implementations/array-rotation.spec.js b/benchmarks/array-rotation-implementations/array-rotation.spec.js
new file mode 100644
index 00000000..2ff3b80c
--- /dev/null
+++ b/benchmarks/array-rotation-implementations/array-rotation.spec.js
@@ -0,0 +1,17 @@
+/*eslint-disable */
+// npx jest benchmarks/two-sum-implementations/two-sum.spec.js --watch --collectCoverage
+const implementations = [
+  { name: 1, fn: require('./01-array-rotation') },
+  { name: '1a', fn: require('./01a-array-rotation') },
+  { name: 2, fn: require('./02-array-rotation') },
+];
+
+implementations.forEach(({name, fn}) => {
+  describe(`Two Sum: ${name}`, () => {
+    it('should work on worst case', () => {
+      const rots = 1000;
+      const array = [1, 2, 3];
+      expect(fn(array, rots)).toEqual([2,3,1]);
+    });
+  });
+});
diff --git a/benchmarks/two-sum-implementations/01-two-sum.js b/benchmarks/two-sum-implementations/01-two-sum.js
new file mode 100644
index 00000000..820df298
--- /dev/null
+++ b/benchmarks/two-sum-implementations/01-two-sum.js
@@ -0,0 +1,13 @@
+// Brute force: O(n^2) | O(1)
+function twoSum(nums, target) {
+  for (let i = 0; i < nums.length - 1; i++) { // O(n^2)
+    for (let j = i + 1; j < nums.length; j++) { // O(n)
+      if (nums[i] + nums[j] === target) {
+        return [i, j];
+      }
+    }
+  }
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/benchmarks/two-sum-implementations/01a-two-sum.js b/benchmarks/two-sum-implementations/01a-two-sum.js
new file mode 100644
index 00000000..47d10818
--- /dev/null
+++ b/benchmarks/two-sum-implementations/01a-two-sum.js
@@ -0,0 +1,13 @@
+// Brute force: O(n^2) | O(1)
+function twoSum(nums, target) {
+  for (let i = 0; i < nums.length - 1; i++) { // O(n^2)
+    const diff = target - nums[i];
+    const offset = i + 1;
+    const idx = nums.slice(offset).findIndex((n) => n === diff); // O(n)
+    const j = offset + idx;
+    if (idx > -1) return [i, j];
+  }
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/benchmarks/two-sum-implementations/02-two-sum.js b/benchmarks/two-sum-implementations/02-two-sum.js
new file mode 100644
index 00000000..602d37d7
--- /dev/null
+++ b/benchmarks/two-sum-implementations/02-two-sum.js
@@ -0,0 +1,20 @@
+// [map w/dups handling] →  O(n^2) | O(n)
+function twoSum(nums, target) {
+  const map = nums.reduce((m, v, i) => { // O(n)
+    const ids = m.get(v) || [];
+    ids.push(i);
+    return m.set(v, ids);
+  }, new Map());
+
+  for (let i = 0; i < nums.length; i++) { // O(n)
+    const diff = target - nums[i];
+    if (map.has(diff)) {
+      const id = map.get(diff).find((j) => j > i);
+      if (id > -1) return [i, id];
+    }
+  }
+
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/benchmarks/two-sum-implementations/02a-two-sum.js b/benchmarks/two-sum-implementations/02a-two-sum.js
new file mode 100644
index 00000000..68b411d5
--- /dev/null
+++ b/benchmarks/two-sum-implementations/02a-two-sum.js
@@ -0,0 +1,24 @@
+// [map w/dups handling] →  O(n^2) | O(n)
+function twoSum(nums, target) {
+  const map = mapify(nums);
+
+  for (let i = 0; i < nums.length; i++) { // O(n)
+    const diff = target - nums[i];
+    if (map.has(diff)) {
+      const id = map.get(diff).find((j) => j > i);
+      if (id > -1) return [i, id];
+    }
+  }
+
+  return [];
+}
+
+function mapify(nums) {
+  return nums.reduce((m, v, i) => { // O(n)
+    const ids = m.get(v) || [];
+    ids.push(i);
+    return m.set(v, ids);
+  }, new Map());
+}
+
+module.exports = twoSum;
diff --git a/benchmarks/two-sum-implementations/03-two-sum.js b/benchmarks/two-sum-implementations/03-two-sum.js
new file mode 100644
index 00000000..8670622f
--- /dev/null
+++ b/benchmarks/two-sum-implementations/03-two-sum.js
@@ -0,0 +1,18 @@
+// With a HashMap: O(n) | O(n), 1-pass
+function twoSum(nums, target) {
+  const map = new Map();
+
+  for (let i = 0; i < nums.length; i++) { // O(n)
+    const complement = target - nums[i];
+
+    if (map.has(complement)) {
+      return [map.get(complement), i];
+    }
+
+    map.set(nums[i], i);
+  }
+
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/benchmarks/two-sum-implementations/04-two-sum.js b/benchmarks/two-sum-implementations/04-two-sum.js
new file mode 100644
index 00000000..45b0f7c9
--- /dev/null
+++ b/benchmarks/two-sum-implementations/04-two-sum.js
@@ -0,0 +1,23 @@
+// sort + two pointers: O(n log n) | O(1)
+function twoSum(nums, target) {
+  nums.sort((a, b) => a - b);
+
+  let lo = 0;
+  let hi = nums.length - 1;
+
+  while (lo < hi) {
+    const sum = nums[lo] + nums[hi];
+    if (sum === target) {
+      return [lo, hi];
+    }
+
+    if (sum > target) {
+      hi--;
+    } else {
+      lo++;
+    }
+  }
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/benchmarks/two-sum-implementations/runner.js b/benchmarks/two-sum-implementations/runner.js
new file mode 100644
index 00000000..2bd280a1
--- /dev/null
+++ b/benchmarks/two-sum-implementations/runner.js
@@ -0,0 +1,16 @@
+const assert = require('assert');
+
+const twoSum = require('./01-two-sum');
+
+function test() {
+  assert.deepEqual(twoSum([1, 2, 3], 4), [0, 2]);
+  assert.deepEqual(twoSum([1, 2, 3], 14), []);
+
+  assert.deepEqual(twoSum([2, 2, 2], 4), [0, 1]);
+  assert.deepEqual(twoSum(Array(1e7).fill(2), 4), [0, 1]); //
+  // assert.deepEqual(twoSum(Array(1e8).fill(2), 4), [0, 1]); // FATAL ERROR: Ineffective mark-compacts near heap limit Allocation failed - JavaScript heap out of memory
+  // assert.deepEqual(twoSum(Array(1e9).fill(2), 4), [0, 1]); // 1e7 - error 137 - OUT OF MEMORY
+  console.log('All tests passed!');
+}
+
+test();
diff --git a/benchmarks/two-sum-implementations/two-sum.spec.js b/benchmarks/two-sum-implementations/two-sum.spec.js
new file mode 100644
index 00000000..c7f5fefb
--- /dev/null
+++ b/benchmarks/two-sum-implementations/two-sum.spec.js
@@ -0,0 +1,23 @@
+/*eslint-disable */
+// npx jest benchmarks/two-sum-implementations/two-sum.spec.js --watch --collectCoverage
+const implementations = [
+  { name: 1, fn: require('./01-two-sum') },
+  { name: '1a', fn: require('./01a-two-sum') },
+  { name: 2, fn: require('./02-two-sum') },
+  { name: '2a', fn: require('./02a-two-sum') },
+  { name: 3, fn: require('./03-two-sum') },
+  { name: 4, fn: require('./04-two-sum') },
+];
+
+implementations.forEach(({name, fn}) => {
+  describe(`Two Sum: ${name}`, () => {
+    xit('should work', () => {
+      expect(fn([1, 2, 3].concat(Array(1e2 - 3).fill(7)), 4)).toEqual([0, 2]);
+    });
+
+    it('should work on worst case', () => {
+      const size = 100;
+      expect(fn([...Array(size).fill(2), 3, 3 * size * 10], 3 * size * 10 + 3)).toEqual([size, size + 1]);
+    });
+  });
+});
diff --git a/book/A-time-complexity-cheatsheet.asc b/book/A-time-complexity-cheatsheet.asc
new file mode 100644
index 00000000..b4089138
--- /dev/null
+++ b/book/A-time-complexity-cheatsheet.asc
@@ -0,0 +1,38 @@
+[appendix]
+== Cheatsheet [[a-time-complexity-cheatsheet]]
+
+This section summerize what we are  going to cover in the rest of this book.
+
+=== Runtimes
+
+include::content/part01/big-o-examples.asc[tag=table]
+
+include::content/part01/algorithms-analysis.asc[tag=table]
+
+=== Linear Data Structures
+
+include::content/part02/array-vs-list-vs-queue-vs-stack.asc[tag=table]
+
+=== Trees and Maps Data Structures
+
+This section covers Binary Search Tree (BST) time complexity (Big O).
+
+include::content/part03/time-complexity-graph-data-structures.asc[tag=table]
+
+include::content/part03/graph.asc[tag=table]
+
+=== Sorting Algorithms
+
+include::content/part04/sorting-algorithms.asc[tag=table]
+
+// // https://algs4.cs.princeton.edu/cheatsheet/
+// // http://bigocheatsheet.com/
+
+// // https://en.wikipedia.org/wiki/Timsort (Tim Peters)
+// // https://bugs.python.org/file4451/timsort.txt
+// // https://www.youtube.com/watch?v=emeME__917E&list=PLMCXHnjXnTntLcLmA5SqhMspm7burHi3m
+
+// // https://en.wikipedia.org/wiki/Sorting_algorithm
+// // http://sorting.at/
+// // https://www.toptal.com/developers/sorting-algorithms
+// // https://www.infopulse.com/blog/timsort-sorting-algorithm/
diff --git a/book/chapters/tree--self-balancing-rotations.adoc b/book/B-self-balancing-binary-search-trees.asc
similarity index 64%
rename from book/chapters/tree--self-balancing-rotations.adoc
rename to book/B-self-balancing-binary-search-trees.asc
index bde52210..d099b8b1 100644
--- a/book/chapters/tree--self-balancing-rotations.adoc
+++ b/book/B-self-balancing-binary-search-trees.asc
@@ -1,11 +1,12 @@
-= Self-balancing Binary Search Trees
+[appendix]
+== Self-balancing Binary Search Trees [[b-self-balancing-binary-search-trees]]
 
-Binary Search Trees (BST) are a great data structure to find elements very fast _O(n log n)_.
-However, when the BST branches has different branch sizes then the performance suffers.
-In the worst case, all nodes can go to one side (e.g. right) and then the search time would be linear.
+Binary Search Trees (BST) are an excellent data structure to find elements very fast _O(log n)_.
+However, when the BST branches have different branch sizes, then the performance suffers.
+In the worst case, all nodes can go to one side (e.g., right) and then the search time would be linear.
 At this point searching element won't be any better on that tree than an array or linked list. Yikes!
 
-Self-balanced trees will automatically balanced the tree when an element is inserted to keep search performace.
+Self-balanced trees will automatically rebalance the tree when an element is inserted to keep search performance.
 We balance a tree by making the height (distance from a node to the root) of any leaf on the tree as similar as possible.
 
 .From unbalanced BST to balanced BST
@@ -26,24 +27,24 @@ As you might notice, we balanced the tree in the example by doing a rotation.
 To be more specific we rotated node `1` to the left to balance the tree.
 Let's examine all the possible rotation we can do to balance a tree.
 
-== Tree Rotations
-
+=== Tree Rotations [[tree-rotations]]
+(((Tree Rotations)))
 We can do single rotations left and right and also we can do double rotations.
 Let's go one by one.
 
-=== Single Right Rotation
+==== Single Right Rotation
 
-Right rotation moves a node on the right as a child of other node.
+Right rotation moves a node on the right as a child of another node.
 
-Take a look at the `@example` in the code below.
-As you can see we have an unbalanced tree `4-3-2-1`.
+Take a look at the examples in the code in the next section.
+As you will see we have an unbalanced tree `4-3-2-1`.
 We want to balance the tree, for that we need to do a right rotation of node 3.
-So, the node 3 is moved as the right child of the previous child.
+So, we move node 3 as the right child of the previous child.
 
 .Single right rotation implementation
 [source, javascript]
 ----
-include::{codedir}/data-structures/trees/tree-rotations.js[tag=rightRotation]
+include::../src/data-structures/trees/tree-rotations.js[tag=rightRotation]
 ----
 
 .In the `rightRotation` we identify 3 nodes:
@@ -54,12 +55,12 @@ include::{codedir}/data-structures/trees/tree-rotations.js[tag=rightRotation]
 The `swapParentChild` as it name says, swap the children.
 For our example, it swaps `node 4`'s left children from `node 3` to `node 2`.
 
-Take a look at the implementation
+Take a look at the implementation.
 
 .Swap Parent and Child Implementation
 [source, javascript]
 ----
-include::{codedir}/data-structures/trees/tree-rotations.js[tag=swapParentChild]
+include::../src/data-structures/trees/tree-rotations.js[tag=swapParentChild]
 ----
 
 After `swapParentChild`, we have the following:
@@ -73,7 +74,7 @@ After `swapParentChild`, we have the following:
 
 Still not quite what we want.
 So, `newParent.setRightAndUpdateParent(node)` will make `node 3` the right child of `node 2`.
-Finally, we remove left child of `node 3` to be `null`.
+Finally, we remove the left child of `node 3` to be `null`.
 
 ----
       4
@@ -88,14 +89,14 @@ Check out the <<Single Right Rotation, rightRotation>> implementation again. It
 This rotation is also known as `RR rotation`.
 
 
-=== Single Left Rotation
+==== Single Left Rotation
 
 Left rotation is similar to the `rightRotation` we explained above.
 
 .Single left rotation implementation
 [source, javascript]
 ----
-include::{codedir}/data-structures/trees/tree-rotations.js[tag=leftRotation]
+include::../src/data-structures/trees/tree-rotations.js[tag=leftRotation]
 ----
 
 As you can see, this function is just the opposite of `rightRotation`. Where ever we used the right now we use the left here and vice versa.
@@ -106,36 +107,36 @@ If you are curious about the `setRightAndUpdateParent` and `setLeftAndUpdatePare
 .Set and update parent implementation
 [source, javascript]
 ----
-include::{codedir}/data-structures/trees/binary-tree-node.js[tag=setAndUpdateParent]
+include::../src/data-structures/trees/binary-tree-node.js[tag=setAndUpdateParent]
 ----
 
-You can also checkout the full
+You can also check out the full
 https://github.com/amejiarosario/dsa.js/blob/adfd8a660bbe0a7068fd7881aff9f51bdb9f92ae/src/data-structures/trees/binary-tree-node.js#L9[binary tree node implementation].
 
-=== Left Right Rotation
+==== Left Right Rotation
 
 This time are we going to do a double rotation.
 
 .Left-Right rotation implementation
 [source, javascript]
 ----
-include::{codedir}/data-structures/trees/tree-rotations.js[tag=leftRightRotation]
+include::../src/data-structures/trees/tree-rotations.js[tag=leftRightRotation]
 ----
 
-As you can see we do a left and then a right rotation. This is also called `LR rotation`
+As you can see we do a left and then a right rotation. This rotation is also known as `LR rotation`
 
-=== Right Left Rotation
+==== Right Left Rotation
 
 Very similar to `leftRightRotation`. The difference is that we rotate right and then left.
 
 .Right-Left rotation implementation
 [source, javascript]
 ----
-include::{codedir}/data-structures/trees/tree-rotations.js[tag=rightLeftRotation]
+include::../src/data-structures/trees/tree-rotations.js[tag=rightLeftRotation]
 ----
 
-This rotation is also refered as `RL rotation`.
+This rotation is also referred to as `RL rotation`.
 
-== Self-balancing trees implementations
+=== Self-balancing trees implementations
 
 So far, we have study how to make tree rotations which are the basis for self-balancing trees. There are different implementations of self-balancing trees such a Red-Black Tree and AVL Tree.
diff --git a/book/C-AVL-tree.asc b/book/C-AVL-tree.asc
new file mode 100644
index 00000000..1a8afe83
--- /dev/null
+++ b/book/C-AVL-tree.asc
@@ -0,0 +1,62 @@
+[appendix]
+== AVL Tree [[c-avl-tree]]
+(((AVL Tree)))
+(((Tree, AVL)))
+AVL Tree is named after their inventors (**A**delson-**V**elsky and **L**andis).
+This self-balancing tree keeps track of subtree sizes to know if a rebalance is needed or not.
+We can compare the size of the left and right subtrees using a balance factor.
+
+[NOTE]
+=====
+
+The *balanced factor* on each node is calculated recursively as follows:
+
+----
+Balance Factor = (left subtree height) - (right subtree height)
+----
+
+=====
+
+The implementation will go in the BST node class.
+We will need two methods to calculate the left and right subtree, and with those, we can get the balance factor.
+
+.Balance Factor methods on the BST node
+[source, javascript]
+----
+include::../src/data-structures/trees/binary-tree-node.js[tag=avl, indent=0]
+----
+
+
+=== Implementing AVL Tree
+
+Implementing an AVL Tree is not too hard since it builds upon what we did in the Binary Search Tree.
+
+.AVL Tree class
+[source, javascript]
+----
+include::../src/data-structures/trees/avl-tree.js[tag=AvlTree]
+----
+
+As you can see, the AVL tree inherits from the BST class.
+The insert and remove operations work the same as in the BST, except that at the end we call `balanceUpstream`.
+This function checks if the tree is symmetrical after every change to the tree. If the tree went out of balance, it would execute the appropriated rotation to fix it.
+
+.Balance Upstream for AVL tree
+[source, javascript]
+----
+include::../src/data-structures/trees/avl-tree.js[tag=balanceUpstream]
+----
+
+This function recursively goes from the modified node to the root checking if each node in between is balanced.
+Now, let's examine how does the balancing works on AVL tree.
+
+.Balance method for AVL tree
+[source, javascript]
+----
+include::../src/data-structures/trees/avl-tree.js[tag=balance]
+----
+
+The first thing we do is to see if one subtree is longer than the other.
+If so, then we check the children balance to determine if need a single or double rotation and in which direction.
+
+You can review <<tree-rotations>> in case you want a refresher.
diff --git a/book/D-interview-questions-solutions.asc b/book/D-interview-questions-solutions.asc
new file mode 100644
index 00000000..1e9a3579
--- /dev/null
+++ b/book/D-interview-questions-solutions.asc
@@ -0,0 +1,943 @@
+<<<
+[appendix]
+[[d-interview-questions-solutions]]
+== Interview Questions Solutions
+(((Interview Questions Solutions)))
+
+=== Solutions for Array Questions
+(((Interview Questions Solutions, Arrays)))
+
+:leveloffset: -1
+
+[#array-q-max-subarray]
+include::content/part02/array.asc[tag=array-q-max-subarray]
+
+The first step is making sure we understand the problem well. Let's do basic examples:
+
+----
+A = [-5, 6, 9, -8]
+B = [-1, 6, -3, 8]
+----
+
+What's the subarray with the maximum sum? For A, it will be `[6, 9]` and for B, it will be `[6, -3, 8]`.
+
+We could generate all possible subarrays, add them up, and then pick the max number.
+
+[source, javascript]
+----
+include::interview-questions/max-subarray.js[tag=maxSubArrayBrute1]
+----
+
+This code is simple to understand; however, not very efficient. The runtime is `O(n^3)`.
+
+Notice we're adding up the numbers from `i` to `j` on each cycle. But, we can optimize this. We can keep a local variable and add the new number to it. That way, we don't have to revisit previous numbers.
+
+[source, javascript]
+----
+include::interview-questions/max-subarray.js[tag=maxSubArrayBrute2]
+----
+
+The runtime is much better: `O(n)`. Can we still do better?
+
+We can use a greedy approach, where do one pass through the array. We only add the numbers if their sum is larger than just taking the current element.
+
+[source, javascript]
+----
+include::interview-questions/max-subarray.js[tag=description]
+include::interview-questions/max-subarray.js[tag=solution]
+----
+
+The runtime is `O(n)` and the space complexity of `O(1)`.
+
+
+
+
+[#array-q-buy-sell-stock]
+include::content/part02/array.asc[tag=array-q-buy-sell-stock]
+
+There are multiple examples that we can simulate: bear markets (when prices are going down), bullish markets (when prices are going up), and zig-zag markets (when prices are going up and down).
+
+[source, javascript]
+----
+// zig-zag market
+maxProfit([5, 10, 5, 10]); // => 5
+// bullish market
+maxProfit([1, 2, 3]); // => 2
+// bearish market
+maxProfit([3, 2, 1]); // => 0
+----
+
+During the bearish markets, the profit will always be 0. Since if you buy, we are only going to lose.
+
+We can do a brute force solution doing all combinations:
+
+[source, javascript]
+----
+include::interview-questions/buy-sell-stock.js[tag=maxProfitBrute1]
+----
+
+The runtime for this solution is `O(n^2)`.
+
+A better solution is to eliminate the 2nd for loop and only do one pass.
+
+Algorithm:
+
+- Do one pass through all the prices
+    ** Keep track of the minimum price seen so far.
+    ** calculate `profit = currentPrice - minPriceSoFar`
+    ** Keep track of the maximun profit seen so far.
+- Return maxProfit.
+
+[source, javascript]
+----
+include::interview-questions/buy-sell-stock.js[tags=description;solution]
+----
+
+The runtime is `O(n)` and the space complexity of `O(1)`.
+
+
+
+:leveloffset: +1
+
+=== Solutions for Linked List Questions
+(((Interview Questions Solutions, Linked Lists)))
+
+:leveloffset: -1
+
+
+
+
+[#linkedlist-q-merge-lists]
+include::content/part02/linked-list.asc[tag=linkedlist-q-merge-lists]
+
+We need to visit each node in both lists and merge them in ascending order. Note: We don't need to copy the values nor create new nodes.
+
+Another case to take into consideration is that lists might have different lengths. So, if one list runs out, we have to keep taking elements from the remaining list.
+
+*Algorithm*:
+
+- Have a pointer for each list
+- While there's a pointer that is not null, visite them
+    ** Compare each list node's value and take the smaller one.
+    ** Advance the pointer of the taken node to the next one.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/merge-lists.js[tags=description;solution]
+----
+
+Notice that we used a "dummy" node or "sentinel node" to have some starting point for the final list.
+
+*Complexity Analysis*:
+
+- Time: `O(m+n)`. Visiting each node from the list 1 and list 2 has a time complexity `O(m + n)`. `m` and `n` represent each list's length.
+- Space: `O(1)`. We reuse the same nodes and only change their `next` pointers. We only create one additional node, "the sentinel node."
+
+
+[#linkedlist-q-linkedlist-same-data]
+include::content/part02/linked-list.asc[tag=linkedlist-q-linkedlist-same-data]
+
+We are given two linked lists that contain string data. We want to know if the concatenated strings from each list are the same.
+
+The tricky part is that the same data can be distributed differently on the linked lists:
+
+----
+L1: he -> ll -> o
+L2: h -> e -> llo
+----
+
+One naive approach could be to go through each list's node and concatenate the strings. Then, we can check if they are equal.
+
+[source, javascript]
+----
+include::interview-questions/linkedlist-same-data.js[tag=hasSameDataBrute1]
+----
+
+Notice that the problem mentions that lists could be huge (millions of nodes). If the first character on each list is different, we are unnecessarily computing millions of nodes, when a straightforward check will do the job.
+
+A better way to solve this problem is iterating over each character on both lists, and when we found a mismatch, we return `false` immediately. If they are the same, we still have to visit all of them.
+
+*Algorithm*:
+
+- Set a pointer to iterate over each node in the lists.
+- For each node, have an index (starting at zero) and compare if both lists have the same data.
+    ** When the index reaches the last character on the current node, we move to the next node.
+    ** If we found that a character from one list doesn't match the other, we return `false`.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/linkedlist-same-data.js[tags=description;solution]
+----
+
+The function `findNextPointerIndex` is a helper to navigate each character on a linked list.
+Notice that we increase the index (`i + 1`) on each iteration.
+If the index overflows, it moves to the next node and reset the index to zero.
+
+
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We go over all the characters on each list
+- Space: `O(1)`. Only using pointers and no auxiliary data structures.
+
+
+
+:leveloffset: +1
+
+=== Solutions for Stack Questions
+(((Interview Questions Solutions, Stack)))
+
+:leveloffset: -1
+
+[#stack-q-valid-parentheses]
+include::content/part02/stack.asc[tag=stack-q-valid-parentheses]
+
+.We need to validate that brackets are correctly opened and closed, following these rules:
+- An opened bracket must be close by the same type.
+- Open brackets mush be closed in the correct order.
+
+We are facing a parsing problem, and usually, stacks are good candidates for them.
+
+*Algorithm*:
+
+- Create a mapping for each opening bracket to its closing counterpart.
+- Iterate through the string
+    ** When we found an opening bracket, insert the corresponding closing bracket into the stack.
+    ** When we found a closing bracket, pop from the stack and make sure it corresponds to the current character.
+- Check the stack is empty. If there's a leftover, it means that something didn't close properly.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/valid-parentheses.js[tag=description]
+include::interview-questions/valid-parentheses.js[tag=solution]
+----
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We iterate over each character of the string.
+- Space: `O(n)`. We use an auxiliary stack.
+
+
+
+[#stack-q-daily-temperatures]
+include::content/part02/stack.asc[tag=stack-q-daily-temperatures]
+
+The first solution that might come to mind it's using two for loops. For each element, we have visit each temperature ahead to find a bigger one.
+
+[source, javascript]
+----
+include::interview-questions/daily-temperatures.js[tag=dailyTemperaturesBrute1]
+----
+
+This solution is an `O(n^2)`. Can we do better? We can!
+
+Here's an idea: start backward, so we know when there's a warmer temperature beforehand. The last element is always 0 (because there are no more temperatures ahead of it). We can place each element's index that we visit on a stack. If the current weather is bigger than the stack top, we remove it until a bigger one remains or the stack is empty. If the stack has a value, we calculate the number of days ahead. Otherwise, it is 0.
+
+*Algorithm*:
+
+- Traverse the daily temperatures backward
+  ** Push each temperature to a stack.
+  ** While the current temperature is larger than the one at the top of the stack, pop it.
+  ** If the stack is empty, then there's no warmer weather ahead, so it's 0.
+  ** If the stack has an element, calculate the index delta.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/daily-temperatures.js[tag=description]
+include::interview-questions/daily-temperatures.js[tag=solution]
+----
+
+The stack contains the indexes rather than the temperatures themselves.
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We visit each element on the array once.
+- Space: `O(1)`. The worst-case scenario is ascending order without duplicates. The stack will hold at most 70 items (100 - 30). If we didn't have the range restriction, then space complexity would be `O(n)`.
+
+
+
+:leveloffset: +1
+
+=== Solutions for Queue Questions
+(((Interview Questions Solutions, Queue)))
+
+:leveloffset: -1
+
+
+[#queue-q-recent-counter]
+include::content/part02/queue.asc[tag=queue-q-recent-counter]
+
+We are asked to keep track of the request's count only within a given time window. A queue is a perfect application for this. We can add any new request to the  Queue. Also, we need to check if the oldest element is outside the time window. If so, we remove it from the queue.
+
+*Algorithm*:
+
+- Enqueue new requests.
+- Take a `peek` at the oldest request in the queue.
+- While `current timestamp - oldest timestamp`, dequeue the oldest.
+- Return the length of the queue.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/recent-counter.js[tags=description;solution]
+----
+
+Notice that we enqueue every request, and then we check all the ones that have "expire" and remove them from the queue.
+
+*Complexity Analysis*:
+
+- Time: `O(n)`, where `n` is the number of requests. One Enqueue/Dequeue operation is O(1). However, we might run into a worst-case where all requests have to be dequeued.
+- Space: `O(W)`, where `W` is the time window. We can have at most W requests in the queue since they are in increasing order without duplicates.
+
+
+[#queue-q-design-snake-game]
+include::content/part02/queue.asc[tag=queue-q-design-snake-game]
+
+This game is perfect to practice working with Queues. There are at least two opportunities to use a Queue. You can enqueue the food location, and also you can keep the snake's body parts on a Queue. We insert a new position into the snake's queue on every move and dequeue the last location to indicate the snake moved. Every time the snake eats food, it grows one more unit. The food gets dequeue, and we place the next food location (if any).
+
+*Algorithm*:
+
+- Based on the snake's head current position, calculate the next location based on the given move `direction`.
+- If the new position is outside the boundaries, it's game over (return -1).
+- If the new location has food, remove that eaten food from its queue and place the next food on the map (if any).
+- If the new position doesn't have food, remove the tail of the snake since it moved.
+- If the snake new position hits itself, game over (return -1). To make this check, we have 2 options:
+    ** Queue: we can visit all the elements on the snake's queue (body) and check if a new position collides. That's `O(n)`
+    ** Set: we can maintain a `set` with all the snake locations, so the check is `O(1)`.
+- Move the snake's head to a new location (enqueue)
+- Return the score (snake's length - 1);
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/design-snake-game.js[tags=description;solution]
+----
+
+As you can see, we opted for using a set to trade speed for memory.
+
+*Complexity Analysis*:
+
+- Time: `O(1)`. Insert/Remove from Queue is constant time. Check for body collisions is `O(1)` when using a set. If instead of a set, you traversed the snake's queue to find a collision, it would be `O(n)`.  Here`n` is the snake's max length, which is the screen size (height x width).
+- Space: `O(n + m)`. `m` is the number of food items, and `n` is the snake's maximum size (height x width).
+
+
+
+:leveloffset: +1
+
+=== Solutions for Binary Tree Questions
+(((Interview Questions Solutions, Binary Tree)))
+
+:leveloffset: -1
+
+[#binary-tree-q-diameter-of-binary-tree]
+include::content/part03/binary-search-tree-traversal.asc[tag=binary-tree-q-diameter-of-binary-tree]
+
+We are asked to find the longest path on a binary tree that might or might not pass through the root node.
+
+We can calculate the height (distance from root to farthest leaf) of a binary tree using this recursive function:
+
+[source, javascript]
+----
+function getHeight(node) {
+  if (!node) return 0;
+  const leftHeight = getHeight(node.left);
+  const rightHeight = getHeight(node.right);
+  return 1 + Math.max(leftHeight, rightHeight);
+}
+----
+
+That function will give us the height from the furthest leaf to the root. However, the problem says that it might or might not go through the root.
+In that case, we can keep track of the maximum distance (`leftHeight + rightHeight`) seen so far.
+
+*Algorithm*:
+
+- Initialize diameter to `0`
+- Recursively find the height of the tree from the root.
+- Keep track of the maximum diameter length seen so far (left + right).
+- Return the diameter.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/diameter-of-binary-tree.js[tags=description;solution]
+----
+
+We are using `Math.max` to keep track of the longest diameter seen.
+
+*Complexity Analysis*:
+
+- Time: `O(n)`, where `n` is each of the tree nodes. We visite each one once.
+- Space: `O(n)`. We use `O(1)` variables, but because we are using the `height` recursive function, we use the implicit call stack, thus `O(n)`.
+
+
+
+
+[#binary-tree-q-binary-tree-right-side-view]
+include::content/part03/binary-search-tree-traversal.asc[tag=binary-tree-q-binary-tree-right-side-view]
+
+The first thing that might come to mind when you have to visit a tree, level by level, is BFS.
+We can visit the tree using a Queue and keep track when a level ends, and the new one starts.
+
+Since during BFS, we dequeue one node and enqueue their two children (left and right), we might have two levels (current and next one). For this problem, we need to know what the last node on the current level is.
+
+.There are several ways to solve this problem by using BFS. Here are some ideas:
+- *1 Queue + Sentinel node*: we can use a special character in the `Queue` like `'*'` or `null` to indicate a level change. So, we would start something like this `const queue = new Queue([root, '*']);`.
+- *2 Queues*: using a "special" character might be seen as hacky, so you can also opt to keep two queues: one for the current level and another for the next level.
+- *1 Queue + size tracking*: we track the Queue's `size` before the children are enqueued. That way, we know where the current level ends.
+
+We are going to implement BFS with "1 Queue + size tracking", since it's arguably the most elegant.
+
+*Algorithm*:
+
+- Enqueue root
+- While the queue has an element
+    ** Check the current size of the queue
+    ** Dequeue only `size` times, and for each dequeued node, enqueue their children.
+    ** Check if the node is the last one in its level and add it to the answer.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/binary-tree-right-side-view.js[tags=description;solution]
+----
+
+This problem is also possible to be solved using DFS. The trick is to start with the right child and add it to the solution when it is the first one on its level.
+
+[source, javascript]
+----
+include::interview-questions/binary-tree-right-side-view.js[tag=dfs]
+----
+
+The complexity of any of the BFS methods or DFS is similar.
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We visit every node, once.
+- Space: `O(n)`. For BFS, the worst-case space is given by the maximum *width*. That is when the binary tree is complete so that the last level would have `(n-1)/2` nodes, thus `O(n)`. For the DFS, the space complexity will be given by the tree's maximum *height*. In the worst-case, the binary tree is skewed to the right so that we will have an implicit call stack of size `n`.
+
+
+
+:leveloffset: +1
+
+=== Solutions for Hash Map Questions
+(((Interview Questions Solutions, Hash Map)))
+
+:leveloffset: -1
+
+[#hashmap-q-two-sum]
+include::content/part02/hash-map.asc[tag=hashmap-q-two-sum]
+// include::content/part02/hash-map.asc[tag=hashmap-q-two-sum]
+
+This simple problem can have many solutions; let's explore some.
+
+_Brute force_
+
+One brute force approach could be doing two for loops. We sum two different numbers and check if they add up to the target. If yes, we return, and if not, we keep increasing the indices until we check every possible pair.
+
+[source, javascript]
+----
+include::interview-questions/two-sum.js[tags=twoSumBrute]
+----
+
+This approach's time complexity is `O(n^2)`, because we visit every number twice in the worst-case. While the space complexity is `O(1)`.
+
+Can we trade space for time? Yes!
+
+_Map_
+
+Based on `nums[i] + nums[j] === target` we can say that `num[j] === target - nums[i]`. We can do one pass and check if we have seen any number equal to `target - nums[i]`. A map is perfect for this job. We could have a HashMap that maps `num` to `index`. Let's see the algorithms to make it work.
+
+
+*Algorithm*:
+
+* Visit every number once
+** Calculate the complement `target - nums[i]`.
+** If the complement exists, return its index and the current index.
+** If not, save the complement and the index number.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/two-sum.js[tags=description;solution]
+----
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We visit every number once.
+- Space: `O(n)`. In the worst-case scenario, we don't find the target, and we ended up with a map with all the numbers from the array.
+
+
+[#hashmap-q-subarray-sum-equals-k]
+include::content/part02/hash-map.asc[tag=hashmap-q-subarray-sum-equals-k]
+// include::content/part02/hash-map.asc[tag=hashmap-q-subarray-sum-equals-k]
+
+This problem has multiple ways to solve it. Let's explore some.
+
+_Brute force_
+
+The most straightforward one is to convert the requirements into code:
+generate all possible subarrays, add them up, and check how many are equal to k.
+
+[source, javascript]
+----
+include::interview-questions/subarray-sum-equals-k.js[tags=subarraySumBrute1]
+----
+
+This solution's time complexity is `O(n^3)` because of the 3 nested loops.
+
+How can we do better? Notice that the last for loop, compute the sum repeatedly just to add one more.
+Let's fix that!
+
+_Cummulative Sum_
+
+For this solution, instead of computing the sum from `i` to `j` all the time. We can calculate a cumulative sum. Every time we see a new number, we add it to the aggregate.
+
+Since we want all possible subarray, We can increase `i` and get sum for each:
+
+[source, javascript]
+----
+array = [1, 2, 3, 0, 1, 4, 0, 5];
+
+// cummulative sum from left to right with i = 0
+sum = [1, 3, 6, 6, 7, 11, 11, 16];
+// cummulative sum from left to right with i = 1
+sum = [2, 5, 5, 6, 10, 10, 15];
+// cummulative sum from left to right with i = 2
+sum = [3, 3, 4, 8, 8, 13];
+// ... and so on ...
+// cummulative sum from left to right with i = 7
+sum = [5];
+----
+
+Here's the code:
+
+[source, javascript]
+----
+include::interview-questions/subarray-sum-equals-k.js[tags=subarraySumBrute1]
+----
+
+The time complexity for this solution is better, `O(n^2)`. Can we still do better?
+
+_Map_
+
+Let's get the intution from our previous cummulative sum:
+
+[source, javascript]
+----
+subarraySum([1, 2, 3, 0, 1, 4, 0, 5], 5); // k = 5
+
+// cummulative sum from left to right is
+sum = [1, 3, 6, 6, 7, 11, 11, 16];
+//           ^  ^
+----
+
+Notice that when the array has a 0, the cumulative sum has a repeated number. If you subtract those numbers, it will give you zero. In the same way, If you take two other ranges and subtract them (`sum[j] - sum[i]`), it will give you the sum of that range `sum(num[i]...num[j])`.
+
+For instance, if we take the index `2` and `0` (with values 6 and 1) and susbtract them we get `6-1=5`. To verify we can add the array values from index 0 to 2, `sum([1, 2, 3]) === 5`.
+
+With that intuition, we can use a Map to keep track of the aggregated sum and the number of times that sum.
+
+*Algorithm*:
+
+* Start sum at 0
+* Visit every number on the array
+** Compute the cumulative sum
+** Check if `sum - k` exits; if so, it means that there's a subarray that adds up to k.
+** Save the sum and the number of times that it has occurred.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/subarray-sum-equals-k.js[tags=description;solution]
+----
+
+You might wonder, what the map is initialized with `[0, 1]`. Consider this test case:
+
+[source, javascript]
+----
+subarraySum([1], 1); // k = 1
+----
+
+The sum is 1, however `sum - k` is `0`. If it doesn't exist on the map, we will get the wrong answer since that number adds up to `k`. We need to add an initial case on the map: `map.set(0, 1)`. If `nums[i] - k = 0`, then that means that `nums[i] = k` and should be part of the solution.
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We visit every number once.
+- Space: `O(n)`. The map size will be the same as the original array.
+
+
+
+:leveloffset: +1
+
+=== Solutions for Set Questions
+(((Interview Questions Solutions, Set)))
+
+:leveloffset: -1
+
+
+[#set-q-most-common-word]
+include::content/part02/hash-set.asc[tag=set-q-most-common-word]
+
+This problem requires multiple steps. We can use a `Set` for quickly looking up banned words. For getting the count of each word, we used a `Map`.
+
+*Algorithm*:
+
+- Convert text to lowercase.
+- Remove any special characters `!?',;.`.
+- Convert the paragraph into words array.
+- Count how many times words occur.
+- Exclude banned words from counts.
+- Return the word (or first one) that is the most repeated.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/most-common-word.js[tags=description;solution]
+----
+
+Here are heavily relying on Regular Expressions:
+
+- `\W+` would match all non-words.
+- `\s+` catches all whitespace.
+
+The line that is mapping words to count seems very busy. Here's another version of the same code a little bit more explicit:
+
+[source, javascript]
+----
+include::interview-questions/most-common-word.js[tags=explicit]
+----
+
+*Complexity Analysis*:
+
+- Time: `O(m + n)`, where `n` is paragraph length and `m` is the number of banned words. If we were NOT using a `Set` for prohibited words, then the runtime would have been `O(mn)`.
+- Space: `O(m + n)`. The extra space complexity is given by the size of the `Map` and `Set`.
+
+
+
+
+
+[#set-q-longest-substring-without-repeating-characters]
+include::content/part02/hash-set.asc[tag=set-q-longest-substring-without-repeating-characters]
+
+One of the most efficient ways to find repeating characters is using a `Map` or `Set`. Use a `Map` when you need to keep track of the count/index (e.g., string -> count) and use a `Set` when you only need to know if there are repeated characters or not.
+
+*Algorithm*:
+
+* Visit each letter.
+** Insert the letter on a Set.
+** Keep track of the maximum size of the Set in `max`.
+** If the letter has been seen before, delete until there's no duplicate.
+* Return max.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/longest-substring-without-repeating-characters.js[tags=description;solution]
+----
+
+We could also have used a Map and keep track of the indexes, but that's not necessary. In this case, the `Set` is all we need.
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We visit each letter once.
+- Space: `O(W)`, where `W` is the max length of non-repeating characters. The maximum size of the Set gives the space complexity. In the worst-case scenario, all letters are unique (`W = n`), so our space complexity would be `O(n)`. In the avg. case where there are one or more duplicates, it uses less space than `n`, because `W < n`.
+
+
+
+
+
+
+
+:leveloffset: +1
+
+=== Solutions for Graph Questions
+(((Interview Questions Solutions, Graph)))
+
+:leveloffset: -1
+
+
+[#graph-q-course-schedule]
+include::content/part03/graph-search.asc[tag=graph-q-course-schedule]
+
+Basically, we have to detect if the graph has a cycle or not.
+There are multiple ways to detect cycles on a graph using BFS and DFS.
+
+One of the most straightforward ways to do it is using DFS one each course (node) and traverse their prerequisites (neighbors). If we start in a node, and then we see that node again, we found a cycle! (maybe)
+
+A critical part of solving this exercise is coming up with good test cases. Let's examine these two:
+
+[graphviz, course-schedule-examples, png]
+....
+digraph G {
+  subgraph cluster_1 {
+    a0 -> a1 -> a2
+    a0 -> a2 [color=gray]
+    label = "Example A"
+  }
+
+  subgraph cluster_2 {
+    b0 -> b1 -> b2 -> b3
+    b3 -> b1 [color=red]
+    label = "Example B";
+  }
+}
+....
+
+Let's say we are using a regular DFS, where we visit the nodes and keep track of visited nodes. If we test the example A, we can get to the course 2 (a2) in two ways. So, we can't blindly assume that "seen" nodes are because of a cycle. To solve this issue, we can keep track of the parent.
+
+For example B, if we start in course 0 (b0), we can find a cycle. However, the cycle does not involve course 0 (parent). When we visit course 1 (b1) and mark it as the parent, we will see that reach to course 1 (b1) again. Then, we found a cycle!
+
+[source, javascript]
+----
+include::interview-questions/course-schedule.js[tags=brute1]
+----
+
+We built the graph on the fly as an adjacency list (Map + Arrays).
+Then we visited each node, checking if there it has cycles. If none has cyles, then we return true.
+
+The cycle check uses DFS. We keep track of seen nodes and also who the parent is. If we get to the parent more than once, we have a cycle like examples A and B.
+
+What's the time complexity?
+
+We visite every node/vertex: `O(|V|)` and then for every node, we visite all it's edges, so we have `O(|V|*|E|)`.
+
+Can we do better?
+
+There's no need to visit nodes more than once. Instead of having a local `seen` variable for each node, we can move it outside the loop. However, it won't be a boolean anymore (seen or not seen). We could see nodes more than once, without being in a cycle (example A). One idea is to have 3 states: `unvisited` (0), `visiting` (1) and `visited` (2). Let's devise the algorithm:
+
+*Algorithm*:
+
+* Build a graph as an adjacency list (map + arrays).
+* Fill in every prerequisite as an edge on the graph.
+* Visit every node and if there's a cycle, return false.
+** When we start visiting a node, we mark it as 1 (visiting)
+** Visit all its adjacent nodes
+** Mark current node as 2 (visited) when we finish visiting neighbors.
+** If we see a node in visiting state more than once, it's a cycle!
+** If we see a node in a visited state, skip it.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/course-schedule.js[tags=description;solution]
+----
+
+In the first line, we initialize the map with the course index and an empty array.
+This time the `seen` array is outside the recursion.
+
+*Complexity Analysis*:
+
+- Time: `O(|V| + |E|)`. We go through each node and edge only once.
+- Space: `O(|V| + |E|)`. The size of the adjacency list.
+
+
+
+
+//
+[#graph-q-critical-connections-in-a-network]
+include::content/part03/graph-search.asc[tag=graph-q-critical-connections-in-a-network]
+
+On idea to find if a path is critical is to remove it. If we visit the graph and see that some nodes are not reachable, then, oops, It was critical!
+
+We can code precisely that. We can remove one link at a time and check if all other nodes are reachable. It's not very efficient, but it's a start.
+
+[source, javascript]
+----
+include::interview-questions/critical-connections-in-a-network.js[tags=criticalConnectionsBrute1]
+----
+
+We are using a function `areAllNodesReachable`, which implements a BFS for visiting the graph, but DFS would have worked too. The runtime is `O(|E| + |V|)`, where `E` is the number of edges and `V` the number of nodes/servers. In `criticalConnectionsBrute1`, We are looping through all `connections` (`E`) to remove one connection at a time and then checking if all servers are still reachable with `areAllNodesReachable`.
+
+The time complexity is `O(|E|^2 * |V|)`. Can we do it on one pass? Sure we can!
+
+*Tarjan's Strongly Connected Components Algorithms*
+
+A connection is critical only if it's not part of the cycle.
+
+In other words, a critical path is like a bridge that connects islands; if you remove it you won't cross from one island to the other.
+
+Connections that are part of the cycle (blue) have redundancy. If you eliminate one, you can still reach other nodes. Check out the examples below.
+
+[graphviz, critical-connections-sol-examples, png]
+....
+graph G {
+  subgraph cluster_0 {
+    a0 -- a1 [color=blue]
+    a1 -- a2 [color=blue]
+    a2 -- a0 [color=blue]
+    a1 -- a3 [color=blue]
+    a3 -- a2 [color=blue]
+    label = "Example A";
+  }
+
+  subgraph cluster_3 {
+    b0 -- b1 [color=blue]
+    b1 -- b2 [color=blue]
+    b2 -- b0 [color=blue]
+    b1 -- b3 [color=red]
+    b3 -- b2 [color=transparent] // removed
+    label = "Example B";
+  }
+
+  subgraph cluster_1 {
+    c0 -- c1 -- c2 -- c3 [color=red]
+    label = "Example C";
+  }
+}
+....
+
+The red connections are critical; if we remove any, some servers won't be reachable.
+
+We can solve this problem in one pass using DFS. But for that, we keep track of the nodes that are part of a loop (strongly connected components). We use the time of visit (or depth in the recursion) each node.
+
+For example C, if we start on `c0`, it belongs to group 0, then we move c1, c2, and c3, increasing the depth counter. Each one will be on its own group since there's no loop.
+
+For example B, we can start at `b0`, and then we move to `b1` and `b2`. However, `b2` circles back to `b0`, which is on group 0. We can update the group of `b1` and `b2` to be 0 since they are all connected in a loop.
+
+For an *undirected graph*, If we found a node on our DFS, that we have previously visited, we found a loop! We can mark all of them with the lowest group number. We know we have a critical path when it's a connection that links two different groups. For example A, they all will belong to group 0, since they are all in a loop. For Example B, we will have `b0`, `b1`, and `b2` on the same group while `b3` will be on a different group.
+
+*Algorithm*:
+
+* Build the graph as an adjacency list (map + array)
+* Run dfs on any node. E.g. `0`.
+** Keep track of the nodes that you have seen using `group` array. But instead of marking them as seen or not. Let's mark it with the `depth`.
+** Visit all the adjacent nodes that are NOT the parent.
+** If we see a node that we have visited yet, do a DFS on it and increase the depth.
+** If the adjacent node has a lower grouping number, update the current node with it.
+** If the adjacent node has a higher grouping number, then we found a critical path.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/critical-connections-in-a-network.js[tags=description;solution]
+----
+
+This algorithm only works with DFS.
+
+*Complexity Analysis*:
+
+- Time: `O(|E| + |V|)`. We visit each node and edge only once.
+- Space: `O(|E| + |V|)`. The graph has all the edges and nodes. Additionally, we use the `group` variable with a size of `|V|`.
+
+
+
+
+
+
+
+//
+
+:leveloffset: +1
+
+=== Solutions for Sorting Questions
+(((Interview Questions Solutions, sorting)))
+
+:leveloffset: -1
+
+
+[#sorting-q-merge-intervals]
+include::content/part04/sorting-algorithms.asc[tag=sorting-q-merge-intervals]
+
+The first thing we need to understand is all the different possibilities for overlaps:
+
+// image::merge-intervals-cases.png[merge intervals cases] // blurry
+// image::intervals-overlap-cases.jpg[merge intervals cases] // too big
+// image::intervals-overlap-cases.svg[merge intervals cases] // errors
+
+// my own image
+image::intervals-overlap-cases-owned.png[merge intervals cases]
+
+One way to solve this problem is sorting by start time. That will eliminate half of the cases! A will always start before B. Only 3 cases apply:
+- No overlap: E.g.,`[[1, 3], [4, 6]]`.
+- Overlap at the end: E.g., `[[1, 3], [2, 4]]`.
+- Eclipse: E.g.,`[[1, 9], [3, 7]]`.
+
+*Algorithm*:
+
+* Sort intervals by start time
+* If the `curr`ent interval's start time is _equal_ or less than the `last` interval's end time, then we have an overlap.
+** Overlaps has two cases: 1) `curr`'s end is larger 2) `last`'s end is larger. For both cases, `Math.max` works.
+* If there's no overlap, we add the interval to the solution.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/merge-intervals.js[tags=description;solution]
+----
+
+For the first interval, it will be added straight to the solution array. For all others, we will make a comparison.
+
+*Complexity Analysis*:
+
+- Time: `O(n log n)`. Standard libraries have a sorting time of `O(n log n)`, then we visit each interval in `O(n)`.
+- Space: `O(n)`. In the worst-case is when there are no overlapping intervals. The size of the solution array would be `n`.
+
+
+
+
+
+
+
+//
+
+[#sorting-q-sort-colors]
+include::content/part04/sorting-algorithms.asc[tag=sorting-q-sort-colors]
+
+We are asked to sort an array with 3 possible values. If we use the standard sorting method `Array.sort`, that will be `O(n log n)`. However, there's a requirement to solve it in linear time and constant space complexity.
+
+The concept of quicksort can help here. We can choose `1` as a pivot and move everything less than 1 to the left and everything more significant than 1 to the right.
+
+*Algorithm*:
+
+* Initialize 3 pointers: `left = 0`, `right = len - 1` and `current = 0`.
+* While the `current` pointer is less than `right`
+** If `current` element is less than pivot 1, swap it to the left and increase the `left` and `current` pointer.
+*** We can safely increase the current pointer
+** If `current` element is bigger than pivot 1, swap it to the right and decrease `right` pointer.
+*** Here, we don't increase the `current` pointer because the number that we swapped with could be another 2 and we might need to keep swapping while decreasing `right`.
+
+*Implementation*:
+
+[source, javascript]
+----
+include::interview-questions/sort-colors.js[tags=description;solution]
+----
+
+We are using the destructive assignment to swap the elements. Here's another version a little bit more compact.
+
+[source, javascript]
+----
+include::interview-questions/sort-colors.js[tags=compact]
+----
+
+*Complexity Analysis*:
+
+- Time: `O(n)`. We only visit each number once.
+- Space: `O(1)`. Operations are in-place. Only O(1) space variables were used.
+
+
+
+
+
+
+
+//
diff --git a/book/book-all.adoc b/book/book-all.adoc
deleted file mode 100644
index 5c872247..00000000
--- a/book/book-all.adoc
+++ /dev/null
@@ -1,155 +0,0 @@
-include::_conf/variables.adoc[]
-
-= {doctitle}
-
-// remove numbering from titles, and sub-titles e.g. 1.1
-:sectnums!:
-
-// Copyright © 2018 Adrian Mejia
-include::chapters/colophon.adoc[]
-
-// Abstract and Dedication MUST have a level-0 heading in EPUB and Kindle
-// but level-1 in PDF and HTML
-ifndef::backend-epub3[:leveloffset: +1]
-include::chapters/dedication.adoc[]
-ifndef::backend-epub3[:leveloffset: -1]
-
-// TODO: pending
-include::chapters/preface.adoc[]
-
-include::chapters/cheatsheet.adoc[]
-
-// add sections to chapters
-:sectnums:
-
-//
-// chapters
-//
-
-= Algorithms Analysis
-
-// TODO: pending
-include::chapters/algorithms-analysis-intro.adoc[]
-
-:leveloffset: +1
-
-include::chapters/algorithms-analysis.adoc[]
-
-include::chapters/big-o-examples.adoc[]
-
-:leveloffset: -1
-
-= Linear Data Structures
-
-include::chapters/linear-data-structures-intro.adoc[]
-
-:leveloffset: +1
-
-include::chapters/array.adoc[]
-
-include::chapters/linked-list.adoc[]
-
-include::chapters/stack.adoc[]
-
-include::chapters/queue.adoc[]
-
-:leveloffset: -1
-
-= Non-Linear Data Structures
-
-include::chapters/non-linear-data-structures-intro.adoc[]
-
-:leveloffset: +1
-
-include::chapters/tree.adoc[]
-
-include::chapters/binary-search-tree.adoc[]
-
-include::chapters/map.adoc[]
-
-include::chapters/set.adoc[]
-
-include::chapters/graph.adoc[]
-
-
-:leveloffset: -1
-
-= Advanced Non-Linear Data Structures
-
-// TODO: pending
-include::chapters/non-linear-data-structures-intro-advanced.adoc[]
-
-:leveloffset: +1
-
-// TODO: pending
-include::chapters/avl-tree.adoc[]
-
-// TODO: pending  (optional)
-// include::chapters/red-black-tree.adoc[]
-
-// TODO: pending
-include::chapters/heap.adoc[]
-
-// TODO: (optional) pending
-// include::chapters/trie.adoc[]
-
-
-:leveloffset: -1
-
-= Algorithms
-
-// TODO: pending
-include::chapters/algorithms-intro.adoc[]
-
-:leveloffset: +1
-
-// TODO: pending
-include::chapters/sorting-intro.adoc[]
-
-//
-// Slow Sorting
-//
-
-include::chapters/insertion-sort.adoc[]
-
-include::chapters/selection-sort.adoc[]
-
-include::chapters/bubble-sort.adoc[]
-
-//
-// Fast Sorting
-//
-
-include::chapters/merge-sort.adoc[]
-
-include::chapters/quick-sort.adoc[]
-
-// TODO: (optional) pending
-// include::chapters/heap-sort.adoc[]
-
-// TODO: (optional) pending
-// include::chapters/tim-sort.adoc[]
-
-//
-// Searching
-//
-
-// TODO: pending
-include::chapters/graph-search.adoc[]
-
-:leveloffset: -1
-
-//
-// end chapters
-//
-
-include::chapters/epigraph.adoc[]
-
-// TODO: (optional) pending
-// include::chapters/appendix.adoc[]
-
-// TODO: (optional) pending
-ifdef::backend-pdf[]
-include::chapters/index.adoc[]
-endif::[]
-
diff --git a/book/book-o.adoc b/book/book-o.adoc
deleted file mode 100644
index d85353a2..00000000
--- a/book/book-o.adoc
+++ /dev/null
@@ -1,194 +0,0 @@
-include::_conf/variables.adoc[]
-
-= {doctitle}
-
-// remove numbering from titles, and sub-titles e.g. 1.1
-:sectnums!:
-
-// Copyright © 2018 Adrian Mejia (g)
-include::chapters/colophon.adoc[]
-
-// Abstract and Dedication MUST have a level-0 heading in EPUB and Kindle
-// but level-1 in PDF and HTML
-ifndef::backend-epub3[:leveloffset: +1]
-include::chapters/dedication.adoc[]
-ifndef::backend-epub3[:leveloffset: -1]
-
-// (g)
-include::chapters/preface.adoc[]
-
-// add sections to chapters
-:sectnums:
-
-
-//-----------------------------------
-// TODO: commment out sample on final
-//-----------------------------------
-
-include::chapters/sample.adoc[]
-
-//-----------------------------------
-// TODO: end remove ------
-//-----------------------------------
-
-//
-// chapters
-//
-
-= Algorithms Analysis
-
-include::chapters/algorithms-analysis-intro.adoc[]
-
-:leveloffset: +1
-
-// (g)
-include::chapters/algorithms-analysis.adoc[]
-
-// (g)
-include::chapters/big-o-examples.adoc[]
-
-:leveloffset: -1
-
-= Linear Data Structures
-
-// (g)
-include::chapters/linear-data-structures-intro.adoc[]
-
-:leveloffset: +1
-
-//  (g)
-include::chapters/array.adoc[]
-
-//  (g)
-include::chapters/linked-list.adoc[]
-
-//  (g)
-include::chapters/stack.adoc[]
-
-// (g)
-include::chapters/queue.adoc[]
-
-// (g)
-include::chapters/linear-data-structures-outro.adoc[]
-
-:leveloffset: -1
-
-
-= Non-Linear Data Structures
-
-// (g)
-include::chapters/non-linear-data-structures-intro.adoc[]
-
-:leveloffset: +1
-
-// (g)
-include::chapters/tree.adoc[]
-
-
-// (g)
-include::chapters/tree--binary-search-tree.adoc[]
-
-include::chapters/tree--search.adoc[]
-
-include::chapters/tree--self-balancing-rotations.adoc[]
-
-:leveloffset: +1
-
-include::chapters/tree--avl.adoc[]
-
-:leveloffset: -1
-
-// (g)
-// include::chapters/map.adoc[]
-include::chapters/map-intro.adoc[]
-
-:leveloffset: +1
-
-// (g)
-include::chapters/map-hashmap.adoc[]
-
-// (g)
-include::chapters/map-treemap.adoc[]
-
-// (g)
-include::chapters/map-hashmap-vs-treemap.adoc[]
-
-:leveloffset: -1
-
-// (g)
-include::chapters/set.adoc[]
-
-// (g)
-include::chapters/graph.adoc[]
-
-// TODO: pending
-include::chapters/graph-search.adoc[]
-
-:leveloffset: -1
-
-= Algorithmic Techniques
-
-// TODO: pending
-include::chapters/algorithms-intro.adoc[]
-
-:leveloffset: +1
-
-//
-// Sorting algorithms
-//
-= Sorting Algorithms
-
-:leveloffset: +1
-
-// TODO: pending
-include::chapters/sorting-intro.adoc[]
-
-// Slow Sorting
-
-include::chapters/insertion-sort.adoc[]
-
-include::chapters/selection-sort.adoc[]
-
-include::chapters/bubble-sort.adoc[]
-
-// Fast Sorting
-
-include::chapters/merge-sort.adoc[]
-
-include::chapters/quick-sort.adoc[]
-
-:leveloffset: -1
-
-
-//
-// Algorithms Techniques
-//
-
-include::chapters/divide-and-conquer.adoc[]
-
-include::chapters/dynamic-programming.adoc[]
-
-include::chapters/greedy-algorithms.adoc[]
-
-include::chapters/backtracking.adoc[]
-
-// --- end algorithms ---
-
-:leveloffset: -1
-
-:sectnums!:
-
-= Appendix
-
-:leveloffset: +1
-
-// TODO: review and complete when the rest is completed
-include::chapters/cheatsheet.adoc[]
-
-:leveloffset: -1
-
-//
-// end chapters
-//
-
-include::chapters/epigraph.adoc[]
diff --git a/book/book.adoc b/book/book.adoc
deleted file mode 100644
index 614fa2ce..00000000
--- a/book/book.adoc
+++ /dev/null
@@ -1,78 +0,0 @@
-= Data Structures and Algorithms Explained in JavaScript
-:book-title: {doctitle}
-:author: Adrian Mejia
-:email:  hello+dsajs@adrianmejia.com
-:revnumber: 1.0.0
-:revdate: {docdate}
-:revyear: 2019
-:revremark: First Edition
-:subject: Algorithms
-:keywords: Algorithms, Data Structures, JavaScript, Coding Interviews, Computer Science, Time Complexity, Linked Lists, Graphs, Binary Search Trees
-:doctype: book
-:media: screen
-:imagesdir: {docdir}/images
-ifeval::["{media}" != "prepress"]
-:front-cover-image: image:cover-a4.png[Front Cover,595,842]
-endif::[]
-:toc:
-:toclevels: 3
-:icons: font
-:lang: en
-:language: javascript
-:experimental:
-:pdf-fontsdir: ./fonts
-:pdf-stylesdir: ./_resources/pdfstyles
-:pdf-style: adrian-screen
-:title-logo-image: image:logo.png[Logo,50,50]
-// custom variables
-:codedir: ../../src
-:datadir: {docdir}/data
-:source-highlighter: pygments
-:pygments-style: xcode
-:stem:
-:plantuml-config: {docdir}/_conf/umlconfig.txt
-// :hide-uri-scheme:
-// :chapter-label: Chapter
-// :appendix-caption: Appendix
-// :chapter-label:
-
-
-ifndef::ebook-format[:leveloffset: 1]
-
-[colophon#colophon%nonfacing]
-include::chapters/colophon.adoc[]
-
-[dedication]
-include::chapters/dedication.adoc[]
-
-// [%nonfacing]
-// include::chapters/acknowledgements.adoc[]
-
-[preface]
-include::chapters/preface.adoc[]
-
-// include::chapters/introduction.adoc[]
-
-// TODO: (optional) include numbers but need to fix the part/chapter labels
-// :sectnums:
-
-include::chapters/chapter1.adoc[]
-
-include::chapters/chapter2.adoc[]
-
-include::chapters/chapter3.adoc[]
-
-include::chapters/chapter4.adoc[]
-
-// :sectnums!:
-
-include::chapters/appendix.adoc[]
-
-// include::chapters/epigraph.adoc[]
-
-// include::chapters/about.adoc[]
-
-ifdef::backend-pdf,backend-docbook[]
-[index]
-= Index
-endif::[]
diff --git a/book/chapters/about.adoc b/book/chapters/about.adoc
deleted file mode 100644
index 73d625c5..00000000
--- a/book/chapters/about.adoc
+++ /dev/null
@@ -1 +0,0 @@
-= About the author
diff --git a/book/chapters/acknowledgements.adoc b/book/chapters/acknowledgements.adoc
deleted file mode 100644
index 37ecc759..00000000
--- a/book/chapters/acknowledgements.adoc
+++ /dev/null
@@ -1 +0,0 @@
-= Acknowledgements
diff --git a/book/chapters/action.adoc b/book/chapters/action.adoc
deleted file mode 100644
index 1e88adb2..00000000
--- a/book/chapters/action.adoc
+++ /dev/null
@@ -1,4 +0,0 @@
-[[action]]
-= Action!
-
-== Additional reading
diff --git a/book/chapters/algorithms-analysis-intro.adoc b/book/chapters/algorithms-analysis-intro.adoc
deleted file mode 100644
index 86c6b682..00000000
--- a/book/chapters/algorithms-analysis-intro.adoc
+++ /dev/null
@@ -1,4 +0,0 @@
-[partintro]
---
-In this section we are going to cover the basics about algorithms analysis. Also, we are going to discuss eight of the most commmon runtimes of algorithms and provide a code example for each one.
---
diff --git a/book/chapters/algorithms-analysis.adoc b/book/chapters/algorithms-analysis.adoc
deleted file mode 100644
index 59f3e440..00000000
--- a/book/chapters/algorithms-analysis.adoc
+++ /dev/null
@@ -1,168 +0,0 @@
-= Fundamentals of Algorithms Analysis
-
-Chances are you are reading this book because you want to write better and faster code.
-How can you do that? Can you time how long it takes to run a program? Of course, you can!
-[big]#⏱#
-However, if you run the same program on a smartwatch, cellphone or desktop computer, it will give you different very times.
-
-image:image3.png[image,width=528,height=137]
-
-Wouldn't it be great if we can compare algorithms regardless of the hardware where we run them?
-That's what *time complexity* is for!
-But why stop with the running time?
-We could also compare the memory "used" by different algorithms, and we called that *space complexity*.
-
-.In this chapter you will learn:
--    What’s the best way to measure your code performance.
--    Learn how to use Big O notation to compare algorithms.
--    How to use algorithms analysis to improve your programs speed.
-
-Before going deeper, into space and time complexity, let's cover the basics real quick.
-
-== What are Algorithms?
-
-Algorithms (as you might know) are steps of how to do some task. When you cook, you follow a recipe (or an algorithm) to prepare a dish. Let's say you want to make a pizza.
-
-.Example of an algorithm
-// [source, js] // undefined functions
-----
-function bakePizza(dough, toppins = ['cheese']) {
-    const heatedOven = heatOvenTo(550);
-    punchDown(dough);
-    rollOut(dough);
-    applyToppings(dough, toppings);
-    const pizza = heatedOven.bakePizza(dough)
-    return pizza;
-}
-----
-
-If you play a game, you are devising strategies (or algorithms) to help you win. Likewise, algorithms in computers are a set of instructions used to solve a problem.
-
-TIP: Algorithms are instructions on how to perform a task.
-
-== Comparing Algorithms
-
-Not all algorithms are created equal. There are “good” and “bad” algorithms. The good ones are fast; the bad ones are slow. Slow algorithms cost more money to run. Inefficient algorithms could make some calculations impossible in our lifespan!
-
-Most algorithms are affected by the size of the input. Let's say you need to arrange numbers in ascending order. Sorting ten digits will naturally take much less time than sorting 2 million of them.
-
-To give you a clearer picture of how different algorithms perform as the input size grows, take a look at the following table.
-
-.Relationship between algorithm input size and time taken to complete
-[cols=",,,,,",options="header",]
-|=============================================================================================
-|Input size -> |10 |100 |10k |100k |1M
-|Finding if a number is odd |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec.
-|Sorting elements in array with merge sort |< 1 sec. |< 1 sec. |< 1 sec. |few sec. |20 sec.
-|Sorting elements in array with Bubble Sort |< 1 sec. |< 1 sec. |2 minutes |3 hours |12 days
-|Finding all subsets of a given set |< 1 sec. |40,170 trillion years |> centillion years |∞ |∞
-|Find all permutations of a string |4 sec. |> vigintillion years |> centillion years |∞ |∞
-|=============================================================================================
-
-indexterm:[Permutation]
-However, if you keep the input size constant, you can notice the difference between an efficient algorithm and a slow one. An excellent sorting algorithm is `mergesort` for instance, and inefficient algorithm for large inputs is `bubble sort` .
-Organizing 1 million elements with merge sort takes 20 seconds while bubble sort takes 12 days, ouch!
-The amazing thing is that both programs are measured on the same hardware with the same data!
-
-After completing this book, you are going to *think differently*.
-You will be able to scale your programs while you are designing them.
-Find bottlenecks of existing software and have an "algorithmic toolbox" to switch algorithms and make them faster without having to upgrade hardware. [big]#💸#
-
-== Increasing your code performance
-
-The first step to improve your code performance is to measure it. As somebody said:
-
-[quote, H. J. Harrington]
-Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.
-
-In this section, we are going to learn the basics of measuring our current code performance and compare it with other algorithms.
-
-=== Calculating Time Complexity
-
-Time complexity, in computer science, is a function that describes the number of operations a program will execute given the size of the input `n`.
-
-How do get a function that gives us the number of operations that will be executed? Well, we count line by line and mind code inside loops. Let's do an example to explain this point. For instance, we have a function to find the minimum value on an array called `getMin`.
-
-.Translating lines of code to an approximate number of operations
-image:image4.png[Operations per line]
-
-Assuming that each line of code is an operation, we get the following:
-
-_3n + 3_
-
-`n` =  input size.
-
-That means that if give an array of 3 elements e.g. `getMin([3, 2, 9])`, then it will execute around _3(3)+3 = 12_ operations. Of course, this is not for every case. For instance, Line 12 is only executed if the condition on line 11 is met. As you might learn in the next section, we want to get the big picture and get rid of smaller terms to compare algorithms easier.
-
-== Space Complexity
-
-Space complexity is similar to time complexity. However, instead of the count of operations executed, it will account for the amount of memory used additionally to the input.
-
-For calculating the *space complexity* we keep track of the “variables” and memory used. In the `getMin` example, we just create a single variable called `min`. So, the space complexity is 1. On other algorithms, If we have to use an auxiliary array, then the space complexity would be `n`.
-
-=== Simplifying Complexity with Asymptotic Analysis
-
-When we are comparing algorithms, we don't want to have complex expressions. What would you prefer comparing two algorithms like "3n^2^ + 7n" vs. "1000 n + 2000" or compare them as "n^2^ vs. n"? Well, that when the asymptotic analysis comes to the rescue.
-
-Asymptotic analysis is the of functions when their inputs approach infinity.
-
-In the previous example, we analyzed `getMin` with an array of size 3, what happen size is 10 or 10k or a million?
-
-.Operations performed by an algorithm with a time complexity of 3n+3
-[cols=",,",options="header",]
-|===========================
-|n (size) |Operations |total
-|10 |3(10) + 3 |33
-|10k |3(10k)+3 |30,003
-|1M |3(1M)+3 |3,000,003
-|===========================
-
-As the input size `n` grows bigger and bigger then the expression _3n + 3_ could be represented as _3n_ without loosing too much or even _n_. Dropping terms might look like a stretch at first, but you will see that what matters the most is the higher order terms of the function rather than lesser terms and constants. There’s a notation called *Big O*, where O refers to the *order of the function*.
-
-If you have a program which runs time is like
-
-_7n^3^ + 3n^2^ + 5_
-
-You can safely say that its run time is _n^3^_. The other terms will become less and less significant as the input grows bigger.
-
-=== What is Big O Notation anyways?
-
-Big O notation, only cares about the “biggest” terms in the time/space complexity. So, it combines what we learn about time and space complexity, asymptotic analysis and adds a worst-case scenario.
-
-.All algorithms have three scenarios:
-* Best-case scenario: the most favorable input arrange where the program will take the least amount of operations to complete. E.g., array already sorted is beneficial for some sorting algorithms.
-* Average-case scenario: this is the most common case. E.g., array items in random order for a sorting algorithm.
-* Worst-case scenario: the inputs are arranged in such a way that causes the program to take the longest to complete. E.g., array items in reversed order for some sorting algorithm will take the longest to run.
-
-To sum up:
-
-TIP: Big O only cares about the highest order of the run time function and the worst-case scenario.
-
-WARNING: Don't drop terms that multiplying other terms. _O(n log n)_ is not equivalent to _O(n)_. However, _O(n + log n)_ is.
-
-There are many common notations like polynomial, _O(n^2^)_ like we saw in the `getMin` example; constant O(1) and many more that we are going to explore in the next chapter.
-
-Again, time complexity is not a direct measure of how long a program takes to execute but rather how many operations it performs in given the input size. Nevertheless, there’s a relationship between time complexity and clock time as we can see in the following table.
-
-.How long an algorithm takes to run based on their time complexity and input size
-[cols=",,,,,,",options="header",]
-|===============================================================
-|Input Size |O(1) |O(n) |O(n log n) |O(n^2^) |O(2^n^) |O(n!)
-|1 |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec.
-|10 |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |4 seconds
-|10k |< 1 sec. |< 1 sec. |< 1 sec. |2 minutes |∞ |∞
-|100k |< 1 sec. |< 1 sec. |1 second |3 hours |∞ |∞
-|1M |< 1 sec. |1 second |20 seconds |12 days |∞ |∞
-|===============================================================
-
-This just an illustration since in different hardware the times will be slightly different.
-
-NOTE: These times are under the assumption of running on 1 GHz CPU and that it can execute on average one instruction in 1 nanosecond (usually takes more time). Also, bear in mind that each line might be translated into dozens of CPU instructions depending on the programming language. Regardless, bad algorithms would perform poorly even on a supercomputer.
-
-== Summary
-
-In this chapter, we learned how you could measure your algorithm performance using time complexity. Rather than timing how long your program take to run you can approximate the number of operations it will perform based on the input size.
-
-We learned about time and space complexity and how they can be translated to Big O notation. Big O refers to the *order* of the function.
-
-In the next section, we are going to provide examples of each of the most common time complexities!
diff --git a/book/chapters/algorithms-intro.adoc b/book/chapters/algorithms-intro.adoc
deleted file mode 100644
index 7c8d0acb..00000000
--- a/book/chapters/algorithms-intro.adoc
+++ /dev/null
@@ -1,16 +0,0 @@
-In this part of the book, we are going to cover algorithms in more details. We will provide algorithmic tools for classic problems that will help you improve your problem-solving skills.
-
-
-IMPORTANT: There's not a single approach to solve all problems but knowing well-known techniques can help you build your own faster.
-
-We are going to start with <<Sorting Algorithms>>
-//  and searching algorithms,
-then you are going to learn some techniques for solving problems.
-
-
-.We are going to discuss the following techniques for solving algorithms problems:
-- <<Greedy Algorithms>>: makes greedy choices using heuristics to find the best solution without looking back.
-- <<Dynamic Programming>>: technique for solving problems with _overlapping subproblems_. It uses _memoization_ to avoid duplicated work.
-- <<Divide and Conquer>>: _divide_ problems into smaller pieces, _conquer_ each subproblem and then _join_ the results.
-- <<Backtracking>>: search _all (or some)_ possible paths. However it stops and _go back_ as soon as some contraint is broken.
-- _Brute Force_: generate all possible solutions. (Use it as the last resort or as starting point to optimize it with other techniques).
diff --git a/book/chapters/appendix.adoc b/book/chapters/appendix.adoc
deleted file mode 100644
index 50074282..00000000
--- a/book/chapters/appendix.adoc
+++ /dev/null
@@ -1,3 +0,0 @@
-// Appendix A:
-include::cheatsheet.adoc[]
-
diff --git a/book/chapters/array.adoc b/book/chapters/array.adoc
deleted file mode 100644
index 68fe1cd7..00000000
--- a/book/chapters/array.adoc
+++ /dev/null
@@ -1,187 +0,0 @@
-= Array
-
-Arrays are one of the most used data structures. You probably have used it a lot but are you aware of the runtimes of  `splice`, `shift`  and other operations? In this chapter, we are going deeper into the most common operations and their runtimes.
-
-== Array  Basics
-
-An array is a collection of things (strings, characters, numbers, objects, etc.). They can be many or zero. Strings are a collection of Unicode characters and most of the array concepts apply to them.
-
-.Fixed vs. Dynamic Size Arrays
-****
-Some programming languages have fixed size arrays like Java and C++. Fixed size arrays might be a hassle when your collection gets full, and you have to create a new one with a bigger size. For that, those programming languages also have built-in dynamic arrays: we have `vector` in C++ and `ArrayList` in Java. Dynamic programming languages like JavaScript, Ruby, Python use dynamic arrays by default.
-****
-
-Arrays look like this:
-
-.Array representation: each value is accessed through an index.
-image:image16.png[image,width=388,height=110]
-
-Arrays are a sequential collection of elements that can be accessed randomly using an index. Let’s take a look into the different operations that we can do with arrays.
-
-== Insertion
-
-Arrays are built-in into most languages. Inserting element in an array is you can either:
-
-.Inserting elements into an array
-[source, javascript]
-----
-// (1) Add elements at the creation time:
-const array = [2, 5, 1, 9, 6, 7];
-
-// (2) initialize the array (empty) and values later
-const array0 = [];
-array0[2] = 1;
-----
-
-Using the index, you can replace whatever value you want.
-
-=== Inserting at the beginning of the array
-
-What if you want to insert a new element at the beginning of the array? You would have to push every item to the right.
-
-.Insert to head
-[source, javascript]
-----
-array.unshift(0); //=> [0, 2, 5, 1, 9, 6, 7]
-----
-
-As you can see, `2` was the index 0, now was pushed to index 1, and everything else was moved one place. `unshift` takes *O(n)* since it affects all the elements in the array.
-
-.JavaScript built-in `array.unshift`
-****
-The `unshift()` method adds one or more elements to the beginning of an array and returns the new length of the array. Runtime: O(n).
-****
-
-=== Inserting at the middle of the array
-
-Inserting a new element in the middle involves moving part of the array but not all of the items.
-
-.Inserting element in the middle
-[source, javascript]
-----
-array.splice(1, 0, 111); // <1>
-----
-<1> at the position 1, delete 0 elements and insert 111. The array would be `[2, 111, 5, 1, 9, 6, 7]`
-
-The Big O for this operation would be *O(n)* since in worst case it would move most of the elements to the right.
-
-.JavaScript built-in `array.splice`
-****
-The `splice()` method changes the contents of an array by removing existing elements and/or adding new elements. Runtime: O(n).
-****
-
-=== Inserting at the end of the array
-
-We can push new values to the end of the array like this:
-
-.Insert to tail
-[source, javascript]
-----
-const array = [2, 5, 1, 9, 6, 7];
-array.push(4); // <1>
-----
-<1> The `4` element would be pushed to the end `[2, 5, 1, 9, 6, 7, 4]`.
-
-Adding to the tail of the array doesn’t change other indexes. E.g., element 2 is still at index 0. So, this is a constant time operation *O(1)*.
-
-.JavaScript built-in `array.push`
-****
-The `push()` method adds one or more elements to the end of an array and returns the new length of the array. Runtime: O(1).
-****
-
-== Searching by value and index
-
-Searching by index is very easy using the `[]` operator:
-
-.Search by index
-[source, javascript]
-----
-const array = [2, 5, 1, 9, 6, 7];
-array[4]; //↪️ 6
-----
-
-Searching by index takes a constant time, *O(1)*, to retrieve values out of the array. If we want to get fancier we can create a function:
-
-// image:image17.png[image,width=528,height=293]
-
-.Search by index
-[source, javascript]
-----
-include::{codedir}/data-structures/arrays/array.js[tag=searchByIndex]
-----
-
-Finding out if an element is in the array or not is a different story.
-
-// image:image18.png[image,width=528,height=338]
-
-.Search by value
-[source, javascript]
-----
-include::{codedir}/data-structures/arrays/array.js[tag=searchByValue]
-----
-
-We would have to loop through the whole array (worst case) or until we find it: *O(n)*.
-
-== Deletion
-
-Deleting (similar to insertion) there are three possible scenarios, removing at the beginning, middle or end.
-
-=== Deleting element from the beginning
-
-Deleting from the beginning can be done using the `splice` function and also the `shift`. Let’s use the `shift` since it’s simpler.
-
-.Deleting from the beginning of the array.
-[source, javascript]
-----
-array.shift(); //=> [5, 1, 9, 6, 7]
-----
-
-As expected, this will make every index to change, so this takes *O(n)*.
-
-.JavaScript built-in array.shift
-****
-The `shift()` method removes the first element from an array and returns that removed element. This method changes the length of the array. Runtime: O(n).
-****
-
-
-=== Deleting element from the middle
-
-We can use the splice operator for this.
-
-.Deleting from the middle
-[source, javascript]
-----
-array.splice(2, 1); // delete 1 element at position 2
-// => array: [2, 5, 9, 6, 7]
-----
-
-Deleting from the middle might cause most the elements of the array to move back one position to fill in for the eliminated item. Thus, runtime: O(n).
-
-=== Deleting element from the end
-
-Removing the last element is very straightforward:
-
-.Deleting last element from the array
-[source, javascript]
-----
-array.pop(); // => array: [2, 5, 1, 9, 6]
-----
-
-No element other element has been shifted, so it’s an _O(1)_ runtime.
-
-.JavaScript built-in `array.pop`
-****
-The `pop()` method removes the last element from an array and returns that element. This method changes the length of the array. Runtime: O(1).
-****
-
-== Array Complexity
-
-To sum up, the time complexity on an array is:
-
-.Time complexity for the array operations
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| Array ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
-|===
-indexterm:[Runtime, Linear]
diff --git a/book/chapters/avl-tree.adoc b/book/chapters/avl-tree.adoc
deleted file mode 100644
index 569c30df..00000000
--- a/book/chapters/avl-tree.adoc
+++ /dev/null
@@ -1,5 +0,0 @@
-= AVL Tree
-
-The AVL tree builds on top of a <<Binary Search Tree>> and it keeps it balanced on insertions. It prevents a BST worst case scenario when the tree is totally unbalanced to one side (similar to linked list), then it takes O(n) to find an element instead of O(log n).
-
-
diff --git a/book/chapters/big-o-examples.adoc b/book/chapters/big-o-examples.adoc
deleted file mode 100644
index 9c416ada..00000000
--- a/book/chapters/big-o-examples.adoc
+++ /dev/null
@@ -1,280 +0,0 @@
-= Big O examples
-// = Eight Running Times You Should Know
-
-There are many kinds of algorithms. Most of them fall into one of the eight of the time complexities that we are going to explore in this chapter.
-
-.Most common time complexities
-- Constant time: _O(1)_
-- Logarithmic time: _O(log n)_
-- Linear time: _O(n)_
-- Linearithmic time: _O(n log n)_
-- Quadratic time: _O(n^2^)_
-- Cubic time: _O(n^3^)_
-- Exponential time: _O(2^n^)_
-- Factorial time: _O(n!)_
-
-We a going to provide examples for each one of them.
-
-Before we dive in, here’s a plot with all of them.
-
-.CPU operations vs. Algorithm runtime as the input size grows
-image:image5.png[CPU time needed vs. Algorithm runtime as the input size increases]
-
-The above chart shows how the running time of an algorithm is related to the amount of work the CPU has to perform. As you can see O(1) and O(log n) are very scalable. However, O(n^2^) and worst can make your computer run for years [big]#😵# on large datasets. We are going to give some examples so you can identify each one.
-
-== Constant
-
-Represented as *O(1)*, it means that regardless of the input size the number of operations executed is always the same. Let’s see an example.
-
-[#constant-example]
-=== Finding if an array is empty
-
-Let's implement a function that finds out if an array is empty or not.
-
-//.is-empty.js
-//image:image6.png[image,width=528,height=401]
-
-[source, javascript]
-----
-include::{codedir}/runtimes/01-is-empty.js[tag=isEmpty]
-----
-
-Another more real life example is adding an element to the begining of a <<Linked List>>. You can check out the implementation <<linked-list-inserting-beginning, here>>.
-
-As you can see, in both examples (array and linked list) if the input is a collection of 10 elements or 10M it would take the same amount of time to execute. You can't get any more performance than this!
-
-== Logarithmic
-
-Represented in Big O notation as *O(log n)*, when an algorithm has this running time it means that as the size of the input grows the number of operations grows very slowly. Logarithmic algorithms are very scalable. One example is the *binary search*.
-indexterm:[Runtime, Logarithmic]
-
-[#logarithmic-example]
-=== Searching on a sorted array
-
-The binary search only works for sorted lists. It starts searching for an element on the middle of the array and then it moves to the right or left depending if the value you are looking for is bigger or smaller.
-
-// image:image7.png[image,width=528,height=437]
-
-[source, javascript]
-----
-include::{codedir}/runtimes/02-binary-search.js[tag=binarySearchRecursive]
-----
-
-This binary search implementation is a recursive algorithm, which means that the function `binarySearch` calls itself multiple times until the solution is found. The binary search split the array in half every time.
-
-Finding the runtime of recursive algorithms is not very obvious sometimes. It requires some tools like recursion trees or the https://adrianmejia.com/blog/2018/04/24/analysis-of-recursive-algorithms/[Master Theorem]. The `binarySearch` divides the input in half each time. As a rule of thumb, when you have an algorithm that divides the data in half on each call you are most likely in front of a logarithmic runtime: _O(log n)_.
-
-== Linear
-
-Linear algorithms are one of the most common runtimes. It’s represented as *O(n)*. Usually, an algorithm has a linear running time when it iterates over all the elements in the input.
-
-[#linear-example]
-=== Finding duplicates in an array using a map
-
-Let’s say that we want to find duplicate elements in an array. What’s the first implementation that comes to mind? Check out this implementation:
-
-// image:image8.png[image,width=528,height=383]
-
-[source, javascript]
-----
-include::{codedir}/runtimes/03-has-duplicates.js[tag=hasDuplicates]
-----
-
-.`hasDuplicates` has multiple scenarios:
-* *Best-case scenario*: first two elements are duplicates. It only has to visit two elements.
-* *Worst-case scenario*: no duplicated or duplicated are the last two. In either case, it has to visit every item on the array.
-* *Average-case scenario*: duplicates are somewhere in the middle of the collection. Only, half of the array will be visited.
-
-As we learned before, the big O cares about the worst-case scenario, where we would have to visit every element on the array. So, we have an *O(n)* runtime.
-
-Space complexity is also *O(n)* since we are using an auxiliary data structure.  We have a map that in the worst case (no duplicates) it will hold every word.
-
-== Linearithmic
-
-An algorithm with a linearithmic runtime is represented as _O(n log n)_. This one is important because it is the best runtime for sorting! Let’s see the merge-sort.
-
-[#linearithmic-example]
-=== Sorting elements in an array
-
-The ((Merge Sort)), like its name indicates, has two functions merge and sort. Let’s start with the sort function:
-
-// image:image9.png[image,width=528,height=383]
-
-.Sort part of the mergeSort
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/merge-sort.js[tag=splitSort]
-----
-<1> If the array only has two elements we can sort them manually.
-<2> We divide the array into two halves.
-<3> Merge the two parts recursively with the `merge` function explained below
-
-// image:image10.png[image,width=528,height=380]
-
-.Merge part of the mergeSort
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/merge-sort.js[tag=merge]
-----
-
-The merge function combines two sorted arrays in ascending order. Let’s say that we want to sort the array `[9, 2, 5, 1, 7, 6]`. In the following illustration, you can see what each function does.
-
-.Mergesort visualization. Shows the split, sort and merge steps
-image:image11.png[Mergesort visualization,width=500,height=600]
-
-How do we obtain the running time of the merge sort algorithm? The mergesort divides the array in half each time in the split phase, _log n_, and the merge function join each splits, _n_. The total work we have *O(n log n)*. There more formal ways to reach to this runtime like using the https://adrianmejia.com/blog/2018/04/24/analysis-of-recursive-algorithms/[Master Method] and https://www.cs.cornell.edu/courses/cs3110/2012sp/lectures/lec20-master/lec20.html[recursion trees].
-
-== Quadratic
-
-indexterm:[Runtime, Quadratic]
-Running times that are quadratic, O(n^2^), are the ones to watch out for. They usually don’t scale well when they have a large amount of data to process.
-
-Usually, they have double-nested loops that where each one visits all or most elements in the input. One example of this is a naïve implementation to find duplicate words on an array.
-
-[#quadratic-example]
-=== Finding duplicates in an array (naïve approach)
-
-If you remember we have solved this problem more efficiently on the <<Linear, Linear>> section. We solved this problem before using an _O(n)_, let’s solve it this time with an _O(n^2^)_:
-
-// image:image12.png[image,width=527,height=389]
-
-.Naïve implementation of has duplicates function
-[source, javascript]
-----
-include::{codedir}/runtimes/05-has-duplicates-naive.js[tag=hasDuplicates]
-----
-
-As you can see, we have two nested loops causing the running time to be quadratic. How much different is a linear vs. quadratic algorithm?
-
-Let’s say you want to find a duplicated middle name in a phone directory book of a city of ~1 million people. If you use this quadratic solution you would have to wait for ~12 days to get an answer [big]#🐢#; while if you use the <<Linear, linear solution>> you will get the answer in seconds! [big]#🚀#
-
-== Cubic
-
-Cubic *O(n^3^)* and higher polynomial functions usually involve many nested loops. As an example of a cubic algorithm is a multi-variable equation solver (using brute force):
-
-[#cubic-example]
-=== Solving a multi-variable equation
-
-Let’s say we want to find the solution for this multi-variable equation:
-
-_3x + 9y + 8z = 79_
-
-A naïve approach to solve this will be the following program:
-
-//image:image13.png[image,width=528,height=448]
-
-.Naïve implementation of multi-variable equation solver
-[source, javascript]
-----
-include::{codedir}/runtimes/06-multi-variable-equation-solver.js[tag=findXYZ]
-----
-
-WARNING: This just an example, there are better ways to solve multi-variable equations.
-
-As you can see three nested loops usually translates to O(n^3^). If you have a four variable equation and four nested loops it would be O(n^4^) and so on when we have a runtime in the form of _O(n^c^)_, where _c > 1_, we can refer as a *polynomial runtime*.
-
-== Exponential
-
-Exponential runtimes, O(2^n^), means that every time the input grows by one the number of operations doubles. Exponential programs are only usable for a tiny number of elements (<100) otherwise it might not finish on your lifetime.  [big]#💀#
-
-Let’s do an example.
-
-[#exponential-example]
-=== Finding subsets of a set
-
-Finding all distinct subsets of a given set can be implemented as follows:
-
-// image:image14.png[image,width=528,height=401]
-
-.Subsets in a Set
-[source, javascript]
-----
-include::{codedir}/runtimes/07-sub-sets.js[tag=snippet]
-----
-<1> Base case is empty element.
-<2> For each element from the input append it to the results array.
-<3> The new results array will be what it was before + the duplicated with the appended element.
-
-//.The way this algorithm generates all subsets is:
-//1.  The base case is an empty element (line 13). E.g. ['']
-//2.  For each element from the input append it to the results array (line 16)
-//3.  The new results array will be what it was before + the duplicated with the appended element (line 17)
-
-Every time the input grows by one the resulting array doubles. That’s why it has an *O(2^n^)*.
-
-== Factorial
-
-Factorial runtime, O(n!), is not scalable at all. Even with input sizes of ~10 elements, it will take a couple of seconds to compute. It’s that slow! [big]*🍯🐝*
-
-.Factorial
-****
-A factorial is the multiplication of all the numbers less than itself down to 1.
-
-.For instance:
-- 3! = 3 x 2 x 1 = 6
-- 5! = 5 x 4 x 3 x 2 x 1 = 120
-- 10! = 3,628,800
-- 11! = 39,916,800
-****
-
-[#factorial-example]
-=== Getting all permutations of a word
-
-One classic example of an _O(n!)_ algorithm is finding all the different words that can be formed with a given set of letters.
-indexterm:[Permutation]
-
-.Word's permutations
-// image:image15.png[image,width=528,height=377]
-[source, javascript]
-----
-include::{codedir}/runtimes/08-permutations.js[tag=snippet]
-----
-
-As you can see in the `getPermutations` function, the resulting array is the factorial of the word length.
-
-Factorial start very slow and then it quickly becomes uncontrollable. A word size of just 11 characters would take a couple of hours in most computers!
-[big]*🤯*
-
-== Summary
-
-We went through 8 of the most common time complexities and provided examples for each of them. Hopefully, this will give you a toolbox to analyze algorithms.
-
-.Most common algorithmic running times and their examples
-[cols="2,2,5",options="header"]
-|===
-|Big O Notation
-|Name
-|Example(s)
-
-|O(1)
-|<<Constant>>
-|<<constant-example>>
-
-|O(log n)
-|<<Logarithmic>>
-|<<logarithmic-example>>
-
-|O(n)
-|<<Linear>>
-|<<linear-example>>
-
-|O(n log n)
-|<<Linearithmic>>
-|<<linearithmic-example>>
-
-|O(n^2^)
-|<<Quadratic>>
-|<<quadratic-example>>
-
-|O(n^3^)
-|<<Cubic>>
-|<<cubic-example>>
-
-|O(2^n^)
-|<<Exponential>>
-|<<exponential-example>>
-
-|O(n!)
-|<<Factorial>>
-|<<factorial-example>>
-|===
diff --git a/book/chapters/bubble-sort.adoc b/book/chapters/bubble-sort.adoc
deleted file mode 100644
index d7453bf2..00000000
--- a/book/chapters/bubble-sort.adoc
+++ /dev/null
@@ -1,76 +0,0 @@
-= Bubble Sort
-
-((Bubble sort)) is a simple sorting algorithm that "bubbles up" the biggest values to the right side of the array.
-It's also call _((sinking sort))_ because the largest values sinks to the right side of the array.
-This algorithm is adaptive which means that if the array is already sorted it will take only _O(n)_ to "sort".
-However, if the array is quite out of order it will require _O(n^2^)_ to sort.
-
-== Bubble Sort Implementation
-
-Bubble sort can be implemented in any programming language. Let's implement it first and explain it.
-
-.Bubble Sort implementation in JavaScript
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/bubble-sort.js[tag=sort, indent=0]
-----
-<1> Convert any kind of iterable (array, sets, etc.) into an array or if is already and array it clones it so the input is not modified.
-<2> Starting from 0 compare current and next element
-<3> If they are out of order, swap the pair
-<4> Repeat pair comparison `n - 1` times, until the largest element is on the right side.
-<5> (optimization) If there was no swap means that the array is already sorted and no more work is needed. This make this sorting adaptive, if everything is sorted it will be only one pass: _O(n)_.
-<6> Each step moves the largest element from `0` to `n - i` to the right side. So, we need to do this `n - 1` to sort the array in case most elements needs to be swapped.
-
-
-The swap function is implemented as follows.
-
-.Swap function
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/sorting-common.js[tag=swap, indent=0]
-----
-
-It uses JavaScript ES6 destructing arrays.
-
-.JavaScript Array destructuring
-****
-*Assignment separate from declaration*
-
-A variable can be assign to its values using the destructing syntax.
-
-[source, javascript]
-----
-let a, b;
-
-[a, b] = [1, 2];
-console.log(a); //↪️ 1
-console.log(b); //️↪️ 2
-----
-
-*Swapping variables*
-
-Two variables values can be swapped in one destructuring expression.
-
-[source, javascript]
-----
-[a, b] = [b, a];
-console.log(a); //↪️ 2
-console.log(b); //️↪️ 1
-----
-
-Without destructuring assignment, swapping two values requires a temporary variable.
-****
-
-
-Bubble sort has a <<Quadratic>> running time, as you might infer from the nested for-loop.
-
-== Bubble Sort Properties
-
-- <<Stable>>: [big]#✅# Yes
-- <<In-place>>: [big]#✅# Yes
-- <<Online>>: [big]#✅# Yes
-- <<Adaptive>>: [big]#✅# Yes, _O(n)_ when already sorted
-- Time Complexity: [big]#⛔️# <<Quadratic>> _O(n^2^)_
-- Space Complexity: [big]#✅# <<Constant>> _O(1)_
-
-indexterm:[Runtime, Quadratic]
diff --git a/book/chapters/chapter1.adoc b/book/chapters/chapter1.adoc
deleted file mode 100644
index b55a7c98..00000000
--- a/book/chapters/chapter1.adoc
+++ /dev/null
@@ -1,16 +0,0 @@
-[[chapter-1]]
-= Algorithms Analysis
-
-include::algorithms-analysis-intro.adoc[]
-
-:leveloffset: +1
-
-// (g)
-include::algorithms-analysis.adoc[]
-
-// (g)
-include::big-o-examples.adoc[]
-
-:leveloffset: -1
-
-
diff --git a/book/chapters/chapter2.adoc b/book/chapters/chapter2.adoc
deleted file mode 100644
index fb9674e2..00000000
--- a/book/chapters/chapter2.adoc
+++ /dev/null
@@ -1,25 +0,0 @@
-[[chapter-2]]
-= Linear Data Structures
-
-// (g)
-include::linear-data-structures-intro.adoc[]
-
-:leveloffset: +1
-
-//  (g)
-include::array.adoc[]
-
-//  (g)
-include::linked-list.adoc[]
-
-//  (g)
-include::stack.adoc[]
-
-// (g)
-include::queue.adoc[]
-
-// (g)
-include::linear-data-structures-outro.adoc[]
-
-:leveloffset: -1
-
diff --git a/book/chapters/chapter3.adoc b/book/chapters/chapter3.adoc
deleted file mode 100644
index 2e63d7c6..00000000
--- a/book/chapters/chapter3.adoc
+++ /dev/null
@@ -1,57 +0,0 @@
-[[chapter-3]]
-= Non-Linear Data Structures
-
-// (g)
-include::non-linear-data-structures-intro.adoc[]
-
-:leveloffset: +1
-
-// (g)
-include::tree.adoc[]
-
-
-// (g)
-include::tree--binary-search-tree.adoc[]
-
-include::tree--search.adoc[]
-
-include::tree--self-balancing-rotations.adoc[]
-
-:leveloffset: +1
-
-include::tree--avl.adoc[]
-
-:leveloffset: -1
-
-// (g)
-// include::map.adoc[]
-include::map-intro.adoc[]
-
-:leveloffset: +1
-
-// (g)
-include::map-hashmap.adoc[]
-
-// (g)
-include::map-treemap.adoc[]
-
-// (g)
-include::map-hashmap-vs-treemap.adoc[]
-
-:leveloffset: -1
-
-// (g)
-include::set.adoc[]
-
-// (g)
-include::graph.adoc[]
-
-// TODO: pending
-include::graph-search.adoc[]
-
-// Graph summary
-= Summary
-
-In this section, we learned about Graphs applications, properties and how we can implement them. We mention that you can represent a graph as a matrix or as a list of adjacencies. We went for implementing the later since it's more space efficient. We cover the basic graph operations like adding and removing nodes and edges.  In the algorithms section, we are going to cover searching values in the graph.
-
-:leveloffset: -1
diff --git a/book/chapters/chapter4.adoc b/book/chapters/chapter4.adoc
deleted file mode 100644
index 70cdfbb1..00000000
--- a/book/chapters/chapter4.adoc
+++ /dev/null
@@ -1,50 +0,0 @@
-[[chapter-4]]
-= Algorithmic Techniques
-
-// TODO: pending
-include::algorithms-intro.adoc[]
-
-:leveloffset: +1
-
-include::sorting-intro.adoc[]
-
-:leveloffset: +1
-
-include::sorting-properties.adoc[]
-
-
-// Slow Sorting
-
-include::bubble-sort.adoc[]
-
-include::insertion-sort.adoc[]
-
-include::selection-sort.adoc[]
-
-
-// include::insertion-selection-bubble-sort.adoc[]
-
-// Fast Sorting
-
-include::merge-sort.adoc[]
-
-include::quick-sort.adoc[]
-
-:leveloffset: -1
-
-
-//
-// Algorithms Techniques
-//
-
-include::divide-and-conquer.adoc[]
-
-include::dynamic-programming.adoc[]
-
-include::greedy-algorithms.adoc[]
-
-include::backtracking.adoc[]
-
-// --- end algorithms ---
-
-:leveloffset: -1
diff --git a/book/chapters/cheatsheet.adoc b/book/chapters/cheatsheet.adoc
deleted file mode 100644
index b9b920ea..00000000
--- a/book/chapters/cheatsheet.adoc
+++ /dev/null
@@ -1,124 +0,0 @@
-[appendix]
-= Time Complexity Cheatsheet
-
-This section summerize what we are  going to cover in the rest of this book.
-
-== Runtimes
-
-.Most common algorithmic running times and their examples
-[cols="2,2,5",options="header"]
-|===
-|Big O Notation
-|Name
-|Example(s)
-
-|O(1)
-|<<Constant>>
-|#<<constant-example>>, #<<linked-list-inserting-beginning>>
-
-|O(log n)
-|<<Logarithmic>>
-|<<logarithmic-example>>
-
-|O(n)
-|<<Linear>>
-|<<linear-example>>
-
-|O(n log n)
-|<<Linearithmic>>
-|<<linearithmic-example>>
-
-|O(n^2^)
-|<<Quadratic>>
-|<<quadratic-example>>
-
-|O(n^3^)
-|<<Cubic>>
-|<<cubic-example>>
-
-|O(2^n^)
-|<<Exponential>>
-|<<exponential-example>>
-
-|O(n!)
-|<<Factorial>>
-|<<factorial-example>>
-|===
-
-.How long an algorithm takes to run based on their time complexity and input size
-[cols=",,,,,,",options="header",]
-|===============================================================
-|Input Size |O(1) |O(n) |O(n log n) |O(n^2^) |O(2^n^) |O(n!)
-|1 |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec.
-|10 |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |4 seconds
-|10k |< 1 sec. |< 1 sec. |< 1 sec. |2 minutes |∞ |∞
-|100k |< 1 sec. |< 1 sec. |1 second |3 hours |∞ |∞
-|1M |< 1 sec. |1 second |20 seconds |12 days |∞ |∞
-|===============================================================
-
-== Linear Data Structures
-
-.Time and Space Complexity of Linear Data Structures (Array, LinkedList, Stack & Queues)
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| <<Array>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
-| <<Singly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
-| <<Doubly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
-| <<Stack>> ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-| Queue (w/array) ^|- ^|- ^|- ^|- ^|*O(n)* ^|- ^|- ^|O(1) ^|O(n)
-| <<Queue>> (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-|===
-
-== Trees and Maps Data Structures
-
-This section covers Binary Search Tree (BST) time complexity (Big O).
-
-.Time and Space Complexity for Non-Linear Data Structures
-|===
-.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
-^|_Index/Key_ ^|_Value_
-| BST (**un**balanced) ^|- ^|O(n) ^|O(n) ^|O(n) ^|O(n)
-| BST (balanced) ^|- ^|O(log n) ^|O(log n) ^|O(log n) ^|O(n)
-| Hash Map (naïve) ^|O(n) ^|O(n) ^|O(n) ^|O(n) ^|O(n)
-| Hash Map (optimized) ^|O(1)* ^|O(n) ^|O(1)* ^|O(1)* ^|O(1)*
-| Tree Map (Red-Black Tree) ^|O(log n) ^|O(n) ^|O(log n) ^|O(log n) ^|O(log n)
-| HashSet ^|- ^|O(n) ^|O(1)* ^|O(1)* ^|O(1)*
-| TreeSet ^|- ^|O(n) ^|O(log n) ^|O(log n) ^|O(log n)
-|===
-{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
-
-
-.Time complexity for a Graph data structure
-|===
-.2+.^s| Data Structure 2+^s| Vertices 2+^s| Edges .2+^.^s| Space Complexity
-^|_Add_ ^|_Remove_ ^|_Add_ ^|_Remove_
-| Graph (adj. matrix) ^| O(\|V\|^2^) ^| O(\|V\|^2^) ^|O(1) ^|O(1) ^|O(\|V\|^2^)
-| Graph (adj. list w/array) ^| O(1) ^| O(\|V\| + \|E\|)) ^|O(1) ^|O(\|V\| + \|E\|) ^|O(\|V\| + \|E\|)
-| Graph (adj. list w/HashSet) ^| O(1) ^| O(\|V\|)) ^|O(1) ^|O(\|V\|) ^|O(\|V\| + \|E\|)
-|===
-
-== Sorting Algorithms
-
-.Sorting algorithms comparison
-|===
-| Algorithms     | Runtime    | Space       | Stable | In-place | Online | Adaptive | Comments
-| Insertion sort | O(n^2^)    | O(1)        | Yes    | Yes      | Yes    | Yes      |
-| Selection sort | O(n^2^)    | O(1)        | Yes    | Yes      | Yes    | Yes      |
-| Bubble sort    | O(n^2^)    | O(1)        | Yes    | Yes      | Yes    | Yes      |
-| Merge sort     | O(n log n) | O(n)        | Yes    | No       | No     | No       |
-| Quicksort     | O(n log n) | O(log n)    | Yes    | No       | No     | No       |
-// | Tim sort       | O(n log n) | O(log n)    | Yes    | No       | No     | Yes      | Hybrid of merge and insertion sort
-|===
-
-// https://algs4.cs.princeton.edu/cheatsheet/
-// http://bigocheatsheet.com/
-
-// https://en.wikipedia.org/wiki/Timsort (Tim Peters)
-// https://bugs.python.org/file4451/timsort.txt
-// https://www.youtube.com/watch?v=emeME__917E&list=PLMCXHnjXnTntLcLmA5SqhMspm7burHi3m
-
-// https://en.wikipedia.org/wiki/Sorting_algorithm
-// http://sorting.at/
-// https://www.toptal.com/developers/sorting-algorithms
-// https://www.infopulse.com/blog/timsort-sorting-algorithm/
diff --git a/book/chapters/dedication.adoc b/book/chapters/dedication.adoc
deleted file mode 100644
index e9c9590b..00000000
--- a/book/chapters/dedication.adoc
+++ /dev/null
@@ -1,5 +0,0 @@
-[dedication]
-= Dedication
-
-To my wife Nathalie that supported me in my long hours of writing and my baby girl Abigail.
-
diff --git a/book/chapters/divide-and-conquer--fibonacci.adoc b/book/chapters/divide-and-conquer--fibonacci.adoc
deleted file mode 100644
index 6649ff09..00000000
--- a/book/chapters/divide-and-conquer--fibonacci.adoc
+++ /dev/null
@@ -1,66 +0,0 @@
-= Recursive Fibonacci Numers
-
-To illustrate how we can solve a problem using divide and conquer, let's write a program to find the n-th fibonacci number.
-indexterm:[Fibonacci]
-
-.Fibonacci Numbers
-****
-Fibancci sequence is a serie of numbers that starts with `0, 1`, the next values are calculated as the sum of the previous two. So, we have:
-
-`0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, ...`
-****
-
-We can get the n-th fibonacci number with the following recursive program:
-
-.Recursive Fibonacci implemenation
-[source, javascript]
-----
-include::{codedir}/algorithms/fibonacci-recursive.js[tag=snippet,indent=0]
-----
-
-.Let's see how this fit divide and conquer:
-- *Divide*: `n` is divided in two subproblems `f(n-1)` and `f(n-2)`.
-- *Conquer*: solve each subproblem independently
-- *Combine*: sum the subproblem results to get the final solution.
-
-The implementation above does the job, but what's the runtime?
-
-For that, let's take a look at the job performed calculating the `fib(5)` number. Since `fib(5) = fib(4) + fib(3)`, we need to find the answer for `fib(4)` and `fib(3)`. We do that recursively until we reach the base cases of `fib(1)` and `fib(0)`. If we represent the calls in a tree, we would have the following:
-
-// http://bit.ly/2UmwzZV
-[graphviz, Recursive fibonacci call tree, png]
-....
-graph G {
-    "fib(5)" -- { "fib(4)", "fib(3)" }
-    "fib(4)" -- { "fib(3)*", "fib(2)" }
-    "fib(3)" -- { "fib(2)*", "fib(1)" }
-    "fib(2)" -- { "fib(1)*", "fib(0)" }
-    "fib(2)*" -- { "fib(1)***", "fib(0)*" }
-    "fib(3)*" -- { "fib(2)**", "fib(1)**" }
-    "fib(2)**" -- { "fib(1)****", "fib(0)**" }
-
-    // red colors
-    "fib(0)*" [color="#FF5252"];
-    "fib(0)**" [color="#FF5252"];
-    "fib(1)*" [color="#FF5252"];
-    "fib(1)**" [color="#FF5252"];
-    "fib(1)***" [color="#FF5252"];
-    "fib(1)****" [color="#FF5252"];
-    "fib(2)*" [color="#FF5252"];
-    "fib(2)**" [color="#FF5252"];
-    "fib(3)*" [color="#FF5252"];
-}
-....
-
-In the diagram, we see the two recursive calls needed to compute each number. So if we follow the _O(branches^depth^)_ we get O(2^n^). [big]#🐢#
-
-NOTE: Fibonacci is not a perfect binary tree since some nodes only have one children instead of two. The exact runtime for recursive Fibonacci is _O(1.6^n^)_ (still exponential time complexity).
-
-An exponential time complexity is pretty bad. Can we do better?
-
-In the call tree you can notice that every element in red and with asterisks `*` it's called more than once. We are repeating calculations too many times!
-
-[quote, Dynamic Programming]
-Those who cannot rememeber the past are condemned to repated it.
-
-For these cases when subproblems repeat themselves, we can optimize them using dynamic programming.
diff --git a/book/chapters/divide-and-conquer--intro.adoc b/book/chapters/divide-and-conquer--intro.adoc
deleted file mode 100644
index 343292ac..00000000
--- a/book/chapters/divide-and-conquer--intro.adoc
+++ /dev/null
@@ -1,16 +0,0 @@
-Divide and conquer is an strategy for solving algorithmic problems.
-It splits the input into manageble parts recursively and finally join solved pieces to form the end result.
-indexterm:[Divide and Conquer]
-
-We have already done some divide and conquer algorithms. This list will refresh you the memory.
-
-.Examples of divide and conquer algorithms:
-- <<Merge Sort>>: *divides* the input into pairs, sort them and them *join* all the pieces in ascending order.
-- <<Quicksort>>: *splits* the data by a random number called "pivot", then move everything smaller than the pivot to the left and anything bigger to the right. Repeat the process on the left and right side. Note: since this works in place doesn't need a join part.
-- <<logarithmic-example, Binary Search>>: find a value in a sorted collection by *spliting* the data in half until it finds the value.
-- <<Getting all permutations of a word, Permutations>>: *Take out* the first element from the input and solve permutation for the reminder of the data recursively, then *join* results and append the elements that were take out.
-
-.In general, the divide and conquer algorithms have the following receipe:
-1. *Divide* data into subproblems.
-2. *Conquer* each subproblem.
-3. *Combine* solutions.
diff --git a/book/chapters/divide-and-conquer.adoc b/book/chapters/divide-and-conquer.adoc
deleted file mode 100644
index 5ee393f9..00000000
--- a/book/chapters/divide-and-conquer.adoc
+++ /dev/null
@@ -1,9 +0,0 @@
-= Divide and Conquer
-
-include::divide-and-conquer--intro.adoc[]
-
-:leveloffset: +1
-
-include::divide-and-conquer--fibonacci.adoc[]
-
-:leveloffset: -1
diff --git a/book/chapters/dynamic-programming--fibonacci.adoc b/book/chapters/dynamic-programming--fibonacci.adoc
deleted file mode 100644
index 26066210..00000000
--- a/book/chapters/dynamic-programming--fibonacci.adoc
+++ /dev/null
@@ -1,29 +0,0 @@
-= Fibonacci Sequence with Dynamic Programming
-
-Let's solve the same Fibonacci problem but this time with dynamic programming.
-
-When we have recursive functions doing duplicated work is the perfect place for a dynamic programming optimization. We can save (or cache) the results of previous operations and speed up future computations.
-indexterm:[Fibonacci]
-
-.Recursive Fibonacci Implemenation using Dynamic Programming
-[source, javascript]
-----
-include::{codedir}/algorithms/fibanacci-dynamic-programming.js[tag=snippet,indent=0]
-----
-
-This implementation checks if we already calculated the value in the past, if not it will save it for later use.
-
-[graphviz, Recursive Fibonacci call tree with dp, svg]
-....
-graph G {
-    "fib(5)" -- { "fib(4)" }
-    "fib(4)" -- { "fib(3)" }
-    "fib(3)" -- { "fib(2)" }
-    "fib(2)" -- { "fib(1)", "fib(0)" }
-}
-....
-
-This looks pretty linear now. It's runtime _O(n)_!
-indexterm:[Runtime, Linear]
-
-TIP: Saving previous results for later is a technique called "memoization" and is very common to optimize recursive algorithms with exponential time complexity.
diff --git a/book/chapters/dynamic-programming--intro.adoc b/book/chapters/dynamic-programming--intro.adoc
deleted file mode 100644
index 115cb03c..00000000
--- a/book/chapters/dynamic-programming--intro.adoc
+++ /dev/null
@@ -1,23 +0,0 @@
-Dynamic programming (dp) is a way to solve algorithmic problems with *overlapping subproblems*. Algorithms using dp find the base case and building a solution from the ground-up. They also _keep track_ of previous answers to avoid re-computing the same operations.
-
-// https://twitter.com/amejiarosario/status/1103050924933726208
-// https://www.quora.com/How-should-I-explain-dynamic-programming-to-a-4-year-old/answer/Jonathan-Paulson
-// https://medium.com/@codingfreak/top-50-dynamic-programming-practice-problems-4208fed71aa3
-// https://www.slideshare.net/balamoorthy39/greedy-algorithm-knapsack-problem
-
-.How to explain dynamic programming to kids? 👶
-****
-
-$$*$$*_Write down 1+1+1+1+1+1+1+1+1+1_*$$*$$
-
---{sp} What's that equal to?
-
---{sp} $$*$$*_Kid counting one by one_*$$*$$ Ten!
-
---{sp} Add another "+1". What's the total now?
-
---{sp} $$*$$*_Quickly_*$$*$$  Eleven!
-
---{sp} Why you get the result so quickly? Ah, you got it faster by adding one to the memorized previous result. So Dynamic Programming is a fancy way of saying: "remembering past results to save time later"
-****
-
diff --git a/book/chapters/dynamic-programming--knapsack-problem.adoc b/book/chapters/dynamic-programming--knapsack-problem.adoc
deleted file mode 100644
index daf2eaa5..00000000
--- a/book/chapters/dynamic-programming--knapsack-problem.adoc
+++ /dev/null
@@ -1,47 +0,0 @@
-= Knapsack Problem
-
-The knapsack (backpack [big]#🎒#) problem is the following:
-
-> A thief breaks into a museum with a backpack that can carry certain weight.
-What items shoud he pick to maximize his loot?
-
-Take a look at the following example to understand better the problem.
-
-.Knapsack Problem Examples
-[source, javascript]
-----
-
-// Input:
-const museumGoods = [
-  { value: 1, weight: 1},
-  { value: 4, weight: 3 },
-  { value: 5, weight: 4 },
-  { value: 7, weight: 5 },
-]
-
-const maxBackpackWeight = 7;
-
-// Solution:
-const backpack = solveKnapsackProblem(museumGoods, maxBackpackWeight);
-
-// Output:
-expect(backpack.items).to.equal([
-  { value: 4, weight: 3 },
-  { value: 5, weight: 4 }
-])
-
-expect(backpack.weight).toBeLessThanOrEqual(7);
-expect(backpack.value).toBe(9);
-----
-
-How can we solve this problem? You cannot take them all since total weight is 13 and we only can carry 7. You should not take only one, since that would not be the maximum loot and you would
-
-One idea would be sort the items by weight and take the items if they do not exceed the max weight.
-In that case, the result would be:
-
-----
-  { value: 7, weight: 5 },
-  { value: 1, weight: 1},
-----
-
-As you can see, this solution is not optimal. The value total value is `8` and the weight just `6`.
diff --git a/book/chapters/dynamic-programming.adoc b/book/chapters/dynamic-programming.adoc
deleted file mode 100644
index 56780119..00000000
--- a/book/chapters/dynamic-programming.adoc
+++ /dev/null
@@ -1,11 +0,0 @@
-= Dynamic Programming
-
-include::dynamic-programming--intro.adoc[]
-
-:leveloffset: +1
-
-include::dynamic-programming--fibonacci.adoc[]
-
-// include::chapters/dynamic-programming--knapsack-problem.adoc[]
-
-:leveloffset: -1
diff --git a/book/chapters/epigraph.adoc b/book/chapters/epigraph.adoc
deleted file mode 100644
index 4eecf051..00000000
--- a/book/chapters/epigraph.adoc
+++ /dev/null
@@ -1,4 +0,0 @@
-[epigraph]
-= Epigraph
-
-Thanks for reading this book!
diff --git a/book/chapters/graph-search.adoc b/book/chapters/graph-search.adoc
deleted file mode 100644
index ce126fc8..00000000
--- a/book/chapters/graph-search.adoc
+++ /dev/null
@@ -1,66 +0,0 @@
-= Graph Search
-
-Graph search allows you to visit all the elements connected given a starting node. There are two ways to navigate the graph, one is using Depth-First Search (DFS) and the other one is Breadth-First Search (BFS). Let's see the difference.
-
-== Depth-First Search for Graphs
-
-With Depth-First Search (DFS) we go deep before going wide.
-
-// TODO: add arrows to show DFS and create another one for BFS
-
-[graphviz, dfs-graph, png]
-....
-digraph G {
-
-     node [fillcolor="#F8E71C" style=filled shape=circle] 0;
-     node [fillcolor="#F5A623"] 1;
-     node [fillcolor="#B8E986"] 2;
-     node [fillcolor="#BD10E0"] 3;
-     node [fillcolor="#50E3C2"] 4;
-     node [fillcolor="#4A90E2"] 5;
-    //  node [fillcolor="#FF5252"] 6;
-
-        0 -> 5
-        0 -> 4
-        0 -> 1
-        1 -> 4
-        1 -> 3
-        2 -> 1
-        3 -> 4
-        3 -> 2
-
-    label="DFS"
-
-    { rank=same; 3, 1 }
-    { rank=same; 0, 4 }
-
-}
-....
-
-== Breadth-First Search for Graphs
-
-With Breadth-First Search (BFS) we go wide before going deep.
-
-// TODO: BFS traversal
-
-== Depth-First Search vs Breadth-First Search in a Graph
-
-DFS and BFS can implementation can be almost identical the difference is the underlying data structured. In our implementation, we have a generic `graphSearch` where we pass the first element to start the search the data structure that we can to use:
-
-.DFS and BFS implemenation
-[source, javascript]
-----
-include::{codedir}/data-structures/graphs/graph.js[tag=graphSearch,indent=0]
-----
-
-Using an <<Stack>> (LIFO) for DFS will make use keep visiting the last node children, while having a <<Queue>> (FIFO) will allows to visit adjacent nodes first and "queue" their children for later visting.
-
-TIP: you can also implement the DFS as a recursive function, similar to what we did in the <<Depth-First Search for Binary Tree, DFS for trees>>.
-
-You might wonder what's the difference between search algorithms in a tree and a graph? Check out the next section.
-
-== DFS/BFS on Tree vs Graph
-
-The difference between searching a tree and a graph is that the tree has always an starting point (root node). However, in a graph you can start searching anywhere. There's no root in graph.
-
-NOTE: Every tree is a graph but not every graph is a tree.
diff --git a/book/chapters/greedy-algorithms--intro.adoc b/book/chapters/greedy-algorithms--intro.adoc
deleted file mode 100644
index fe21fdf0..00000000
--- a/book/chapters/greedy-algorithms--intro.adoc
+++ /dev/null
@@ -1,43 +0,0 @@
-Greedy algorithms are designed to find solution by going one step at time and using heuristics to determine the best choice.
-They are quick but not always lead to most optimum results since it might not take into consideration all the options to give a solution.
-
-A good example of a greedy algorithms that doesn't work well is finding the largest sum on a tree.
-
-[graphviz, Find largest sum, svg]
-....
-graph G {
-  5 -- 3 [color="#B8E986", penwidth=2]
-  5 -- 7 [color="#FF5252", penwidth=2]
-  3 -- 87 [color="#B8E986", penwidth=2]
-  3 -- 1
-  7 -- 2
-  7 -- 4 [color="#FF5252", penwidth=2]
-
-  label="Optimal vs. Greedy path"
-}
-....
-
-The greedy algorithm will start at the root and say "Which number is bigger 3 or 7?" Then go with 7 and later 4. As you can see in the diagram, the largest sum would be the path `7 - 3 - 87`. A greedy algorithm never go back on it's options. This makes it different from dynamic programming which exhaustive and it's gurantee to find the best option.
-
-Greedy algorithms are well suited when a local optimal solution is also a global optimal solution.
-
-[TIP]
-====
-Greedy algorithms makes the choice that looks best in the moment based on a heuristic such as smallest, largest, best ratio, and so on.
-This algorithm only give one shot at finding the solution and never goes back to consider other options.
-====
-
-Don't get the wrong idea some greedy algorithms works very well if they are designed correctly.
-
-.Some examples greedy algorithms that works well:
-- <<Selection Sort>>: we select the best (minimum value) remove it from the input and then select the next minimum until everything is processed.
-- <<Merge Sort>>: the "merge" uses a greedy algorithm, where it combine two sorted arrays by looking at their current values and choosing the best (minimum) at every time.
-indexterm:[Merge Sort]
-
-
-.In general, we can follow these steps to design Greedy Algorithms:
-1. Take a sample from the input data (usually in a data structure like array/list, tree, graph).
-2. Greedy choice: use a heuristic function that will choose the best candidate. E.g., Largest/smallest number, best ratio, etc.
-3. Reduce the processed input and pepeat step #1 and #2 until all data is gone.
-4. Return solution.
-5. Check correctness with different examples and edge cases.
diff --git a/book/chapters/greedy-algorithms--knapsack-problem.adoc b/book/chapters/greedy-algorithms--knapsack-problem.adoc
deleted file mode 100644
index 51e16cef..00000000
--- a/book/chapters/greedy-algorithms--knapsack-problem.adoc
+++ /dev/null
@@ -1,54 +0,0 @@
-= Fractional Knapsack Problem
-
-We are going to use the "Fractional Knapsack Problem" to learn how to design greedy algorithms. The problem is the following:
-
-> You are going to steal legumes (rice, beans, chickpeas, lentils) and you only brought a knapsack. What proportion of items can you choose to to get the highest loot without exceeding the maximum weight of the bag?
-
-Let's say we have the following items available.
-
-.Knpasack Input
-[source, javascript]
-----
-const items = [
-  { value: 1, weight: 1},
-  { value: 4, weight: 3 },
-  { value: 5, weight: 4 },
-  { value: 7, weight: 5 },
-];
-
-const maxWeight = 7;
-----
-
-So, we have 4 items that we can take. We can't take them all because the total weight is `13` and the maximum we can carry is `7`. We can't just take the first one because with value `1` because obviosly is not the best profit.
-
-How would you solve this problem?
-
-
-First, we have to define what parameters are we going to use to make our *greedy choice*. This some ideas:
-
-- We can take items with the *largest* value in hopes to get maximize profit. Based on that we can make take the last item and first having a total weight of 7 and total value of 8.
-
-- Also, we could take items *smallest* weight so we can fit as much as possible. Let's analyze both options. So we can take the first 2 items for a total value of 5 and total weight of 4. This is worst! [big]#👎#
-
-- One last idea, we can take items based on the *best* value/weight ratio and take fractions of an item to fill up the knapsack to maximum weight. In that case, we can take the last item and 2/3 of the 2nd item. We get a total value of `9.67` and total weight of `7`.
-
-.Items value/weight ratio
-----
-  { value: 1, weight: 1 }, // 1/1  = 1
-  { value: 4, weight: 3 }, // 4/3 = 1.33 ✅
-  { value: 5, weight: 4 }, // 5/4 = 1.25
-  { value: 7, weight: 5 }, // 7/5 = 1.4 ✅
-----
-
-Let's implement this algorithm!
-
-
-.Factional Knapsack Problem Implementation
-[source, javascript]
-----
-include::{codedir}/algorithms/knapsack-fractional.js[tag=snippet,indent=0]
-----
-
-What's the runtime of this algorithm?
-
-We have to sort the array based on value/weight ratio. Sorting runtime is O(n log n). The rest is linear operations, so we the answer is _O(n log n)_ for our greedy algorithm.
diff --git a/book/chapters/greedy-algorithms.adoc b/book/chapters/greedy-algorithms.adoc
deleted file mode 100644
index 057b4a21..00000000
--- a/book/chapters/greedy-algorithms.adoc
+++ /dev/null
@@ -1,9 +0,0 @@
-= Greedy Algorithms
-
-include::greedy-algorithms--intro.adoc[]
-
-:leveloffset: +1
-
-include::greedy-algorithms--knapsack-problem.adoc[]
-
-:leveloffset: -1
diff --git a/book/chapters/heap-sort.adoc b/book/chapters/heap-sort.adoc
deleted file mode 100644
index 421cab79..00000000
--- a/book/chapters/heap-sort.adoc
+++ /dev/null
@@ -1,7 +0,0 @@
-= Heap Sort
-
-Voluptate consequat magna laborum consectetur fugiat deserunt. Id sit est ullamco magna sint laborum proident. Exercitation cupidatat exercitation excepteur ex pariatur qui qui sint amet consectetur laborum ex mollit dolore.
-
-Et do sunt do labore culpa est eu ut fugiat eiusmod ea excepteur. Irure commodo adipisicing in aute aliquip laborum laboris reprehenderit incididunt in sunt. Cupidatat veniam est culpa ex eu aute voluptate tempor aliqua ullamco sunt et consectetur. Eu laboris mollit culpa consequat. Sunt mollit quis dolor nostrud. In duis mollit do adipisicing veniam do deserunt exercitation Lorem deserunt aliquip. Ea esse reprehenderit incididunt eu deserunt sit nulla sint non eiusmod nisi eu et irure.
-
-Ad commodo anim nulla occaecat non. Aute fugiat laborum ut mollit exercitation aute proident reprehenderit culpa consectetur. Cillum officia laborum proident labore sunt est eiusmod proident. Lorem nostrud ea qui tempor culpa ullamco ipsum. Dolore nulla minim qui incididunt qui sint consectetur quis tempor esse minim. Do id consequat commodo sit officia aliqua officia reprehenderit eiusmod elit do amet.
diff --git a/book/chapters/heap.adoc b/book/chapters/heap.adoc
deleted file mode 100644
index 104af153..00000000
--- a/book/chapters/heap.adoc
+++ /dev/null
@@ -1,3 +0,0 @@
-= Heap
-
-Sit nostrud Lorem nulla ipsum occaecat enim eiusmod adipisicing velit et cupidatat laboris incididunt. Sunt ex eiusmod amet nulla quis. Officia elit non sunt esse sint. Non enim do laborum adipisicing officia et aliquip cillum ut nisi ipsum. Minim duis minim velit amet laborum aliquip pariatur irure deserunt ex.
diff --git a/book/chapters/index.adoc b/book/chapters/index.adoc
deleted file mode 100644
index 85e81b85..00000000
--- a/book/chapters/index.adoc
+++ /dev/null
@@ -1,2 +0,0 @@
-[index]
-= Index
diff --git a/book/chapters/insertion-selection-bubble-sort.adoc b/book/chapters/insertion-selection-bubble-sort.adoc
deleted file mode 100644
index 4b0ea8e2..00000000
--- a/book/chapters/insertion-selection-bubble-sort.adoc
+++ /dev/null
@@ -1,3 +0,0 @@
-= Insertion vs Selection vs Bubble Sort
-
-All these three algorithms
diff --git a/book/chapters/insertion-sort.adoc b/book/chapters/insertion-sort.adoc
deleted file mode 100644
index 4606c26a..00000000
--- a/book/chapters/insertion-sort.adoc
+++ /dev/null
@@ -1,39 +0,0 @@
-= Insertion Sort
-
-((Insertion sort)) is a simple sorting algorithm. It is one of the most natural way of sorting.  If you are given some cards that's probably how you are going to sort them.
-
-// Good illustration on of sorting a deck of cards:  https://www.khanacademy.org/computing/computer-science/algorithms/insertion-sort/a/insertion-sort
-
-== Insertion Sort Implementation
-
-.Insertion sort
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/insertion-sort.js[tag=sort, indent=0]
-----
-<1> Convert to array or clone the array.
-<2> Start with the 2nd element. Everything on the left is considered sorted.
-<3> Compare current element (2nd) to the previous one. If is bigger swap them, if not move to the next one.
-<4> We check every element on the left side and swap any of them that are out of order
-
-
-// .Swap function
-// [source, javascript]
-// ----
-// include::{codedir}/algorithms/insertion-sort.js[tag=swap, indent=0]
-// ----
-
-// .This is how it works:
-// . Visit 2nd. If the current element is smaller than the previous one move to the place it should be.
-// . Visit next element and do the same thing. The left side is always sorted and the right is not (yet).
-// . Repeat for the rest of the array.
-
-== Insertion Sort Properties
-
-- <<Stable>>: [big]#✅# Yes
-- <<In-place>>: [big]#✅# Yes
-- <<Online>>: [big]#✅# Yes
-- <<Adaptive>>: [big]#✅# Yes
-- Time Complexity: [big]#⛔️# <<Quadratic>> _O(n^2^)_
-- Space Complexity: [big]#✅# <<Constant>> _O(1)_
-indexterm:[Runtime, Quadratic]
diff --git a/book/chapters/introduction.adoc b/book/chapters/introduction.adoc
deleted file mode 100644
index eb9f7947..00000000
--- a/book/chapters/introduction.adoc
+++ /dev/null
@@ -1 +0,0 @@
-= Introduction
diff --git a/book/chapters/linear-data-structures-intro.adoc b/book/chapters/linear-data-structures-intro.adoc
deleted file mode 100644
index 8687088d..00000000
--- a/book/chapters/linear-data-structures-intro.adoc
+++ /dev/null
@@ -1,22 +0,0 @@
-[partintro]
---
-Data Structures comes in many flavors. There’s no one to rule them all. There are tradeoffs for each one of them. Even thought in your day-to-day, you might not need to re-implementing them, knowing how they work internally would help you choose the right tool for the job. We are going to explore the most common data structures time and space complexity.
-
-.In this part we are going to learn about the following linear data structures:
--	Array
--	Linked List
--	Stack
--	Queue
-
-Later, in the next part we are going to explore non-linear data structures like Graphs and Trees.
-
-ifdef::backend-html5[]
-If you want to have a general overview of each one, take a look at the following interactive diagram:
-+++
-<iframe
-  src="https://embed.kumu.io/85f1a4de5fb8430a10a1bf9c5118e015"
-  width="940" height="600" frameborder="0"></iframe>
-+++
-endif::[]
-
---
diff --git a/book/chapters/linear-data-structures-outro.adoc b/book/chapters/linear-data-structures-outro.adoc
deleted file mode 100644
index 55de385b..00000000
--- a/book/chapters/linear-data-structures-outro.adoc
+++ /dev/null
@@ -1,35 +0,0 @@
-
-= Array vs. Linked List & Queue vs. Stack
-
-In this chapter, we explored the most used linear data structures such as Arrays, Linked Lists, Stacks and Queues. We implemented them and discussed the runtime of their operations.
-
-To sum up,
-
-.Use Arrays when…
-* You need to access data in random order fast (using an index).
-* Your data is multi-dimensional (e.g., matrix, tensor).
-
-.Use Linked Lists when:
-* You will access your data sequentially.
-* You want to save memory and only allocate memory as you need it.
-* You want constant time to remove/add from extremes of the list.
-
-.Use a Queue when:
-* You need to access your data in a first-come, first served basis (FIFO).
-* You need to implement a <<Breadth-First Search for Binary Tree, Breadth-First Search>>
-
-.Use a Stack when:
-* You need to access your data as last-in, first-out (LIFO).
-* You need to implement a <<Depth-First Search for Binary Tree, Depth-First Search>>
-
-.Time Complexity of Linear Data Structures (Array, LinkedList, Stack & Queues)
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| <<Array>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
-| <<Singly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
-| <<Doubly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
-| <<Stack>> ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-| Queue (w/array) ^|- ^|- ^|- ^|- ^|*O(n)* ^|- ^|- ^|O(1) ^|O(n)
-| <<Queue>> (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-|===
diff --git a/book/chapters/linked-list.adoc b/book/chapters/linked-list.adoc
deleted file mode 100644
index 3f71823a..00000000
--- a/book/chapters/linked-list.adoc
+++ /dev/null
@@ -1,269 +0,0 @@
-= Linked List
-
-A list (or Linked List) is a linear data structure where each node is linked to another one.
-
-Linked Lists can be:
-- Singly: every item has a pointer to the next node
-- Doubly: every node has a reference to the next and previous object
-- Circular: the last element points to the first one.
-We are going to explore the first two in the next sections.
-
-== Singly Linked List
-
-Each element or node is *linked* to the next one by a reference. When a node only has the reference to the next element, it's called *singly linked list*:
-
-.Singly Linked List Representation: each node has a reference (blue arrow) to the next one.
-image:image19.png[image,width=498,height=97]
-
-
-Usually, a Linked List is referenced by the first element in called *head* (or *root* node). For instance, if you want to get the `cat` element from the example above, then the only way to get there is using the next field on the head node. You would get `art` first, then use the next field recursively until you eventually get the `cat` element.
-
-== Doubly Linked List
-
-When each node has a reference to the next item and also the previous one, then we have a *doubly linked list*.
-
-.Doubly Linked List: each node has a reference to the next and previous element.
-image:image20.png[image,width=528,height=74]
-
-If we implement the code for the `Node` elements, it would be something like this:
-
-// image:image21.png[image,width=528,height=285]
-
-.Linked List Node
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/node.js[tag=snippet]
-----
-
-== Linked List vs. Array
-
-Arrays allow you to access data anywhere in the collection using an index. However, Linked List visits nodes in sequential order. In the worst case scenario, it takes _O(n)_ to get an element from a Linked List. You might be wondering: Isn’t always an array more efficient with O(1) access time? It depends.
-
-We also have to understand the space complexity to see the trade-offs between arrays and linked lists. An array pre-allocates contiguous blocks of memory. When the array is getting full, it has to copy all the elements over to a new space usually 2x bigger.  It takes _O(n)_ to copy all the items over. On the other hand, LinkedList’s nodes only reserve precisely the amount of memory it needs. They don’t have to be next to each other, nor large chunks of memory have to be booked beforehand like arrays. Linked List is more on a "grow as you go" basis.
-
-Another difference is that adding/deleting at the beginning on an array takes O(n), however, in the linked list is a constant operation O(1) as we will implement later.
-
-A drawback of a linked list is that if you want to insert/delete an element at the end of the list, you would have to navigate the whole collection to find the last one O(n). However, this can be solved by keeping track of the last element in the list. We are going to implement that!
-
-== Implementing a Linked List
-
-We are going to implement a doubly linked list. First, let's start with the constructor.
-
-// image:image22.png[image,width=528,height=251]
-
-.Linked List's constructor
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=constructor]
-
-  // ... methods go here ...
-}
-----
-
-In our constructor, we keep a reference of the first (and last node for performance reasons).
-
-== Searching by value
-
-Finding an element by value there’s no other way than iterating through the whole list.
-
-.Linked List's searching by values
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=searchByValue, indent=0]
-----
-
-If we find the element, we will return the index otherwise `undefined`. The runtime for locating an item by value is _O(n)_.
-
-For finding elements by value or position we are using the following helper function:
-.Find elements using a callback
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=find, indent=0]
-----
-<1> We initialize two variables `current` to the first node and `position` to keep track of the index.
-<2> While `current` node is not null we keep going.
-<3> On each loop we move to the next node and increment the index.
-<4> We invoke the callback passing the current position and node. If the callback returns something, then we stop and return that value.
-<5> Return whatever result we got from the callback. E.g., we can return the index or the node itself or any other calculation.
-
-We are going to use this `find` method again to implement searching by index.
-
-== Searching by index
-
-Searching by index is very similar, we iterate through the list until we find the element that matches the position.
-
-.Linked List's searching by index (position)
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=searchByIndex, indent=0]
-----
-
-If there’s no match, we return `undefined` then. The runtime is _O(n)_. As you might notice the search by index and by position methods looks pretty similar. If you want to take a look at the whole implementation https://github.com/amejiarosario/dsa.js/blob/7694c20d13f6c53457ee24fbdfd3c0ac57139ff4/src/data-structures/linked-lists/linked-list.js#L8[click here].
-
-== Insertion
-
-Similar to the array, with a linked list you can add elements at the beginning, end or anywhere in the middle of the list. So, let's implement each case.
-
-=== Inserting elements at the beginning of the list
-
-We are going to use the `Node` class to create a new element and stick it at the beginning of the list as shown below.
-
-.Insert at the beginning by linking the new node with the current first node.
-image:image23.png[image,width=498,height=217]
-
-
-To insert at the beginning, we create a new node with the next reference to the current first node. Then we make first the new node. In code, it would look something like this:
-
-[#linked-list-inserting-beginning]
-
-.Add item to the beginning of a Linked List
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=addFirst, indent=0]
-----
-
-As you can see, we create a new node and make it the first one.
-
-
-=== Inserting element at the end of the list
-
-Appending an element at the end of the list can be done very effectively if we have a pointer to the `last` item in the list. Otherwise, you would have to iterate through the whole list.
-
-.Add element to the end of the linked list
-image:image24.png[image,width=498,height=208]
-
-
-In code:
-
-.Linked List's add to the end of the list implementation
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=addLast, indent=0]
-----
-
-If there’s no element in the list yet, the first and last node would be the same. If there’s something, then, we go to the `last` item and add the reference `next` to the new node. That’s it! We got a constant time for inserting at the beginning and the end of the list: *O(1)*.
-
-
-=== Inserting element at the middle of the list
-
-For inserting an element at the middle of the list, you would need to specify the position (index) in the collection. Then, you create the new node and update the references to it.
-
-Let’s do an example, with a doubly linked list. We want to insert the `new` node in the 2^nd^ position.
-
-.Inserting node in the middle of a doubly linked list.
-image:image25.png[image,width=528,height=358]
-
-Let’s work in the code to do this:
-
-.Linked List's add to the middle of the list
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=addMiddle, indent=0]
-----
-<1> If the new item goes to position 0, then we reuse the `addFirst` method, and we are done!
-<2> However, If we are adding to the last position, then we reuse the `addLast` method, and done!
-<3> Adding `newNode` to the middle: First, create the `new` node only if the position exists. Take a look at <<Searching by index>> to see `get` implementation.
-<4> Set newNode `previous` reference.
-<5> Set newNode `next` link.
-<6> No other node in the list is pointing to `newNode`, so we have to make the prior element point to `newNode`.
-<7> Make the next element point to `newNode`.
-
-
-Take notice that we reused, `addFirst` and `addLast` methods. For all the other cases the insertion is in the middle. We use `current.previous.next` and `current.next.previous` to update the surrounding elements and make them point to the new node. Inserting on the middle takes *O(n)* because we have to iterate through the list using the `get` method.
-
-== Deletion
-
-Deleting is an interesting one. We don’t delete an element; we remove all references to that node. Let’s go case by case to explore what happens.
-
-=== Deleting element from the head
-
-Deleting the first element (or head) is a matter of removing all references to it.
-
-.Deleting an element from the head of the list
-image:image26.png[image,width=528,height=74]
-
-For instance, to remove the head (“art”) node, we change the variable `first` to point to the second node “dog”. We also remove the variable `previous` from the "dog" node, so it doesn't point to the “art” node. The garbage collector will get rid of the “art” node when it seems nothing is using it anymore.
-
-In code, it looks like this:
-
-.Linked List's remove from the beginning of the list
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=removeFirst, indent=0]
-----
-
-As you can see, when we want to remove the first node we make the 2nd element the first one.
-
-=== Deleting element from the tail
-
-Removing the last element from the list would require to iterate from the head until we find the last one, that’s O(n). But, If we have a reference to the last element, which we do, We can do it in _O(1)_ instead!
-
-.Removing last element from the list using the last reference.
-image:image27.png[image,width=528,height=221]
-
-
-For instance, if we want to remove the last node “cat”. We use the last pointer to avoid iterating through the whole list. We check `last.previous` to get the “dog” node and make it the new `last` and remove its next reference to “cat”. Since nothing is pointing to “cat” then is out of the list and eventually is deleted from memory by the garbage collector.
-
-Let’s code this up like this:
-
-.Linked List's remove from the end of the list
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=removeLast, indent=0]
-----
-
-
-The code is very similar to `removeFirst`, but instead of first we update `last` reference, and instead of nullifying `previous` we nullify its `next` reference.
-
-=== Deleting element from the middle
-
-To remove a node from the middle, we make the surrounding nodes to bypass the one we want to delete.
-
-.Remove the middle node
-image:image28.png[image,width=528,height=259]
-
-
-In the illustration, we are removing the middle node “dog” by making art’s `next` variable to point to cat and cat’s `previous` to be “art” totally bypassing “dog”.
-
-Let’s implement it:
-
-.Linked List's remove from the middle of the list
-[source, javascript]
-----
-include::{codedir}/data-structures/linked-lists/linked-list.js[tag=removeByPosition, indent=0]
-----
-
-Notice that we are using the `get` method to get the node at the current position. That method loops through the list until it found the node at the specified location. This iteration has a runtime of _O(n)_.
-
-== Linked List Complexity vs. Array Complexity
-
-So far, we have seen two liner data structures with different use cases. Here’s a summary:
-
-
-.Big O cheat sheet for Linked List and Array
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| Array ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
-| Linked List (singly) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
-| Linked List (doubly) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
-|===
-
-indexterm:[Runtime, Linear]
-If you compare the singly linked list vs. doubly linked list, you will notice that the main difference is deleting elements from the end. For a singly list is *O(n)*, while for a doubly list is *O(1)*.
-
-Comparing an array with a doubly linked list, both have different use cases:
-
-Use arrays when:
-
-* You want to access *random* elements by numeric key or index in constant time O(1).
-* You need two-dimensional and multi-dimensional arrays.
-
-Use a doubly linked list when:
-
-* You want to access elements in a *sequential* manner only like <<Stack>> or <<Queue>>.
-
-* You want to insert elements at the start and end of the list. The linked list has O(1) while array has O(n).
-* You want to save some memory when dealing with possibly large data sets. Arrays pre-allocate a large chunk of contiguous memory on initialization. Lists are more “grow as you go”.
-
-For the next two linear data structures <<Stack>> and <<Queue>>, we are going to use a doubly linked list to implement them. We could use an array as well, but since inserting/deleting from the start perform better on linked-list, we are going use that.
diff --git a/book/chapters/map-hashmap-vs-treemap.adoc b/book/chapters/map-hashmap-vs-treemap.adoc
deleted file mode 100644
index 5a6ce3bc..00000000
--- a/book/chapters/map-hashmap-vs-treemap.adoc
+++ /dev/null
@@ -1,29 +0,0 @@
-= HashMap vs TreeMap
-
-.A map can be implemented using hash functions or binary search tree:
-- *HashMap*: it’s a map implementation using an *array* and *hash function*. The job of the hash function is to convert the key into an index that contains the matching data. Optimized HashMap can have an average runtime of *O(1)*.
-- *TreeMap*: it’s a map implementation that uses a self-balanced Binary Search Tree (red-black tree). The BST nodes store the key, and the value and nodes are sorted by key guaranteeing an *O(log n)* look up.
-
-
-.When to use a TreeMap vs. HashMap?
-* `HashMap` is more time-efficient. A `TreeMap` is more space-efficient.
-* `TreeMap` search complexity is *O(log n)*, while an optimized `HashMap` is *O(1)* on average. 
-* `HashMap`’s keys are in insertion order (or random in some implementations). `TreeMap`’s keys are always sorted.
-* `TreeMap` offers some statistical data for free such as: get minimum, get maximum, median, find ranges of keys. `HashMap` doesn’t.
-* `TreeMap` has a guarantee always an *O(log n)*, while `HashMap`s has an amortized time of *O(1)* but in the rare case of a rehash, it would take an *O(n)*.
-
-== TreeMap Time complexity vs HashMap
-
-As we discussed so far, there are trade-off between the implementations
-
-.Time complexity for different Maps implementations
-|===
-.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
-^|_Index/Key_ ^|_Value_
-| Hash Map (naïve) ^|O(n) ^|O(n) ^|O(n) ^|O(n) ^|O(n)
-| Hash Map (optimized) ^|O(1)* ^|O(n) ^|O(1)* ^|O(1)* ^|O(1)*
-| Tree Map (Red-Black Tree) ^|O(log n) ^|O(n) ^|O(log n) ^|O(log n) ^|O(log n)
-|===
-{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
-
-indexterm:[Runtime, Logarithmic]
diff --git a/book/chapters/map-intro.adoc b/book/chapters/map-intro.adoc
deleted file mode 100644
index 628071a3..00000000
--- a/book/chapters/map-intro.adoc
+++ /dev/null
@@ -1,20 +0,0 @@
-= Map
-
-A map is a data structure to store pairs of data: *key* and *value*. In an array, you can only store values. The array’s key is always the position index. However, in a *Map* the key can be whatever you want.
-
-IMPORTANT: Map is a data structure that _maps_ *keys* to *values*.
-
-Many languages have maps already built-in. JavaScript/Node has `Map`:
-
-.JavaScript Built-in Map Usage
-[source, javascript]
-----
-include::{codedir}/data-structures/maps/map.js[tag=snippet, indent=0]
-----
-
-In short, you set `key`/`value` pair and then you can get the `value` using the `key`.
-
-The attractive part of Maps is that they are very performant usually *O(1)* or *O(log n)* depending on the implementation. We can implement the maps using two different techniques:
-
-* *HashMap*: it’s a map implementation using an *array* and a *hash function*. The job of the hash function is to convert the `key` into an index that maps to the `value`. Optimized HashMap can have an average runtime of *O(1)*.
-* *TreeMap*: it’s a map implementation that uses a self-balanced Binary Search Tree (like <<AVL Tree>>). The BST nodes store the key, and the value and nodes are sorted by key guaranteeing an *O(log n)* look up.
diff --git a/book/chapters/map-treemap.adoc b/book/chapters/map-treemap.adoc
deleted file mode 100644
index a830d237..00000000
--- a/book/chapters/map-treemap.adoc
+++ /dev/null
@@ -1,88 +0,0 @@
-= TreeMap
-
-A TreeMap is a Map implementation using Binary Search Trees.
-
-Implementing a Map with a tree, TreeMap, has a couple of advantages over a HashMap:
-
-* Keys are always sorted.
-* Statistical data can be easily obtained like the median, highest, lowest key.
-* Collisions are not a concern so in the worst case is still *O(log n)*.
-* Trees are more space efficient and don’t need to allocate memory beforehand (e.g. `HashMap`’s initial capacity) nor you have to rehash when is getting full.
-
-indexterm:[Runtime, Logarithmic]
-Ok, now that you know the advantages, let’s implement it!
-For a full comparison read the <<HashMap vs TreeMap>> section.
-
-Let’s get started with the essential functions. They have the same interface as the `HashMap` (but the implementation is different).
-
-.TreeMap class overview
-[source, javascript]
-----
-class TreeMap {
-  constructor(){}
-  set(key, value) {}
-  get(key) {}
-  has(key) {}
-  delete(key) {}
-}
-----
-
-== Inserting values into a TreeMap
-
-For inserting a value on a TreeMap, we first need to inialize the tree:
-
-.TreeMap constructor
-[source, javascript]
-----
-include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=constructor, indent=0]
-----
-
-The tree can be an instance of any Binary Search Tree that we implemented so far. However, for better performance, it should be a self-balanced tree like a https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/trees/red-black-tree.js#L20[Red-Black Tree] or https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/trees/avl-tree.js#L64[AVL Tree].
-
-Let's implement the method to add values to the tree.
-
-.TreeMap `add` method and `size` attribute
-[source, javascript]
-----
-include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=set, indent=0]
-----
-
-Adding values is very easy (once we have the underlying tree implementation).
-
-== Getting values out of a TreeMap
-
-When We search by key in a tree map, it takes *O(log n)*. This is the implementation:
-
-.TreeMap `get` and `has` method
-[source, javascript]
-----
-include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=get, indent=0]
-----
-
-One side effect of storing keys in a tree is that they don't come up in insertion order. Instead, they ordered by value.
-
-.TreeMap iterators
-[source, javascript]
-----
-include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=iterators, indent=0]
-----
-<1> We implemented the default iterator using the in-order traversal. That's useful for getting the keys sorted.
-
-.JavaScript Iterators and Generators
-****
-Generators are useful for producing values that can you can iterate in a `for...of` loop. Generators use the `function*` syntax which expects to have a `yield` with a value.
-****
-
-== Deleting values from a TreeMap
-
-Removing elements from TreeMap is simple.
-
-.TreeMap `delete` method
-[source, javascript]
-----
-include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=delete, indent=0]
-----
-
-The BST implementation does all the heavy lifting.
-
-That’s it! To see the full file in context, click here: https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/maps/tree-maps/tree-map.js#L22[here]
diff --git a/book/chapters/merge-sort.adoc b/book/chapters/merge-sort.adoc
deleted file mode 100644
index 27157d95..00000000
--- a/book/chapters/merge-sort.adoc
+++ /dev/null
@@ -1,64 +0,0 @@
-= Merge Sort
-
-((Merge Sort)) is an efficient sorting algorithm that uses <<Divide and Conquer, divide and conquer>> paradigm to accomplish its task faster. It uses auxiliary memory in the process of sorting.
-
-indexterm:[Divide and Conquer]
-Merge sort algorithm splits the array in halves until 2 or less elements are left. It sorts these two elements and then merge back all halves until the whole array is sorted.
-
-image:image11.png[Mergesort visualization,width=500,height=600]
-
-== Merge Sort Implementation
-
-Merge sort implementation is as follows
-
-.Merge Sort Algorithm
-. It moves one element at a time (from left to right). Everything on the left of the current element is already sorted, while everything to the right is not.
-. Start with the first element and make it the current element.
-. Compare elements to right of the current element.
-. Merge up big values to the right of the array.
-.. Swap elements if the previous element is bigger than the previous one.
-. Move the current pointer to the next element and repeat for the rest of the array
-
-
-Let's convert these words into code!
-
-.Merge Sort implementation in JavaScript (mergeSort)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/merge-sort.js[tag=sort, indent=0]
-----
-<1> Convert any kind of iterable (array, sets, etc.) into an array
-
-As you can see this function is just a wrapper to transform things to array. The heavy lifting is done in `splitSort` as you can see below.
-
-.Merge Sort implementation in JavaScript (splitSort)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/merge-sort.js[tag=splitSort, indent=0]
-----
-<1> Sort two elements manually.
-<2> Recursively divide the array in half until two or less elements are left.
-<3> Merge back the sorted halves in ascending order.
-
-Let's now take a look at the merge function:
-
-.Merge Sort implementation in JavaScript (merge)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/merge-sort.js[tag=merge, indent=0]
-----
-<1> We need to keep track of 3 arrays indices (mergedArray, a1 and a2).
-<2> If `array1` has the lowest current value, we insert it into the merged array if not we then insert `array2`.
-<3> End result is array1 and array2 combined in ascending order (sorted).
-
-Merge sort has a _O(n log n)_  running time. For more details about the how to extract the runtime go to <<Linearithmic>>.
-
-== Merge Sort Properties
-
-- <<Stable>>: [big]#✅# Yes
-- <<In-place>>: [big]#️❌# No, it requires auxiliary memory O(n).
-- <<Online>>: [big]#️❌# No, new elements will require to sort the whole array.
-- <<Adaptive>>: [big]#️❌# No, mostly sorted array takes the same time O(n log n).
-- Recursive: Yes
-- Time Complexity: [big]#✅# <<Linearithmic>> _O(n log n)_
-- Space Complexity: [big]#⚠️# <<Linear>> _O(n)_, use auxiliary memory
diff --git a/book/chapters/non-linear-data-structures-intro-advanced.adoc b/book/chapters/non-linear-data-structures-intro-advanced.adoc
deleted file mode 100644
index 33917a8d..00000000
--- a/book/chapters/non-linear-data-structures-intro-advanced.adoc
+++ /dev/null
@@ -1,8 +0,0 @@
-[partintro]
---
-Nulla aute exercitation adipisicing exercitation sunt nostrud sunt Lorem amet aute proident sit deserunt. Ex ullamco velit eu quis aliqua aliquip quis ullamco esse eiusmod. Enim dolor ex do adipisicing ullamco ipsum. Ad anim magna pariatur enim enim excepteur est cupidatat qui tempor cillum.
-
-Minim elit Lorem commodo labore amet culpa amet. Culpa ut fugiat est velit. Consectetur deserunt occaecat do cupidatat adipisicing sunt ullamco ut anim. Ut anim aute culpa minim nisi qui. Incididunt mollit quis veniam reprehenderit ad tempor voluptate aliquip ut ut.
-
-Magna consectetur anim laborum excepteur laborum. Ea magna tempor officia in elit nisi tempor proident aute. Tempor quis tempor sit culpa aliquip.
---
diff --git a/book/chapters/non-linear-data-structures-intro.adoc b/book/chapters/non-linear-data-structures-intro.adoc
deleted file mode 100644
index 99fdc409..00000000
--- a/book/chapters/non-linear-data-structures-intro.adoc
+++ /dev/null
@@ -1,12 +0,0 @@
-[partintro]
---
-Non-Linear data structures are everywhere whether you realize it or not. They are used in databases, Web (HTML DOM tree), search algorithms, finding the best route to get home and so on. We are going to learn the basic concepts and when to choose one over the other.
-
-.In this chapter we are going to learn:
-- Exciting <<Graph>> data structure applications
-- Searching efficiently with a <<Tree>> data structures.
-- One of the most versatile data structure of all <<HashMap>>.
-- Keeping dups out with a <<Set>>.
-
-By the end of this section, you will know the data structures trade-offs and when to use one over the other.
---
diff --git a/book/chapters/preface.adoc b/book/chapters/preface.adoc
deleted file mode 100644
index 37daf2e9..00000000
--- a/book/chapters/preface.adoc
+++ /dev/null
@@ -1,82 +0,0 @@
-[preface]
-= Preface
-
-== What is in this book?
-
-_{book-title}_ is a book that can be read from cover to cover, where each section builds on top of the previous one. Also, it can be used as a reference manual where developers can refresh certain topics before an interview or looking for ideas to solve a problem optimally. (Cheat sheet summarizing all time complexities and implementations can be found in the <<Time Complexity Cheatsheet, appendix>>)
-
-This publication is designed to be concise, intending to serve software developers looking to get a firm conceptual understanding of data structures in a quick yet in-depth fashion. After reading this book, the reader should have a fundamental understanding of algorithms, including when and where to apply it, what are the trade-offs of using one data structure over the other. The reader will then be able to make intelligent decisions about algorithms and data structures in their projects require.
-
-== Who this book is for
-
-This book is for software developers familiar with JavaScript looking to improve their problem-solving skills or preparing for a job interview.
-
-_NOTE: The concepts in this book can be applied to any programming language. However, instead of doing examples on pseudo-code we are going to use JavaScript to implement the examples._
-
-== What you need for this book
-
-You will need Node.js. The book code was tested against Node.js v10.12, but newer versions should also work.
-
-== Conventions
-
-We use a number of typographical conventions within this book that distinguish between different kinds of information.
-
-Code in the text, including commands, variables, file names, CSS class names, and property names are shown as follows:
-
-[example]
-The `edgeDirection` parameter allow us to use one class for both undirected and directed graphs.
-
-A block of code is set out as follows. It may be colored, depending on the format in which you're reading this book.
-
-[source, javascript]
-----
-function* dummyIdMaker() {
-  yield 0;
-  yield 1;
-  yield 2;
-}
-
-const generator = dummyIdMaker()
-
-// getting values
-console.log(generator.next()); // ↪️ {value: 0, done: false}
-----
-
-When we want to draw your attention to certain lines of code, those lines are annotated using numbers accompanied by brief descriptions.
-
-.Quicksort implementation in JavaScript (QuickSort)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/quick-sort.js[tag=quickSort, indent=0]
-----
-<1> Partition: picks a pivot and find the index where the pivot will be when the array is sorted.
-<2> Do the partition of the sub-array at the left of the pivot.
-<3> Do the partition of the sub-array at the right of the pivot.
-<4> Only do the partition when there's something to divide.
-
-
-The following admonitions are used to highlight content
-
-IMPORTANT: Reword essential concepts. Good for memorizing, tweeting and sharing.
-
-TIP: Tips are shown using callouts like this.
-
-WARNING: Warnings are shown using callouts like this.
-
-NOTE: This is a side note
-
-.Sidebar
-****
-Additional information about a certain topic may be displayed in a sidebar like this one.
-****
-
-Finally, this text shows what a quote looks like:
-
-[quote, H. J. Harrington]
-Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t manage it, you can’t improve it.
-
-== Reader feedback
-
-Your feedback is very welcome and valuable. Let us know what your thoughts about this book — what you like or ideas to make it better.
-
-To send us feedback, e-mail us at hello+dsajs@adrianmejia.com, send a tweet to @amejiarosario, or post a question on Stack Overflow using the `algorithms`, `javascript` tags.
diff --git a/book/chapters/queue.adoc b/book/chapters/queue.adoc
deleted file mode 100644
index 1e6579b3..00000000
--- a/book/chapters/queue.adoc
+++ /dev/null
@@ -1,107 +0,0 @@
-= Queue
-
-A queue is a linear data structure where the data flows in a *First-In-First-Out* (FIFO) manner.
-
-.Queue data structure is like a line of people: the First-in, is the First-out
-image:image30.png[image,width=528,height=171]
-
-A queue is like a line of people at the bank, the person that arrived first is the first to go out as well.
-
-Similar to the stack, we only have two operations (insert and remove). In a Queue, we add elements to the back of the list and remove it from the front.
-
-We could use an array or a linked list to implement a Queue. However, it is recommended only to use a linked list. Why? An array has a linear runtime _O(n)_ to remove an element from the start while a linked list has constant time _O(1)_.
-
-.Queue's constructor
-[source, javascript]
-----
-include::{codedir}/data-structures/queues/queue.js[tag=constructor]
-  // ... methods goes here ...
-}
-----
-
-We initialize the Queue creating a linked list. Now, let’s add the `enqueue` and `dequeue` methods.
-
-== Insertion
-
-For inserting elements on queue, also know as *enqueue*, we add items to the back of the list using `addLast`:
-
-.Queue's enqueue
-[source, javascript]
-----
-include::{codedir}/data-structures/queues/queue.js[tag=enqueue, indent=0]
-----
-
-As discussed, this operation has a constant runtime.
-
-== Deletion
-
-For removing elements from a queue, also know as *dequeue*, we remove elements from the front of the list using `removeFirst`:
-
-.Queue's dequeue
-[source, javascript]
-----
-include::{codedir}/data-structures/queues/queue.js[tag=dequeue, indent=0]
-----
-
-As discussed, this operation has a constant runtime.
-
-== Implementation usage
-
-We can use our Queue class like follows:
-
-.Queue usage example
-[source, javascript]
-----
-include::{codedir}/data-structures/queues/queue.js[tag=snippet, indent=0]
-----
-
-You can see that the items are dequeue in the same order they were added, FIFO (first-in, first out).
-
-== Queue Complexity
-
-As an experiment, we can see in the following table that if we had implemented the Queue using an array, its enqueue time would be _O(n)_ instead of _O(1)_. Check it out:
-
-
-.Time complexity for queue operations
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| Queue (w/array) ^|- ^|- ^|- ^|- ^|*O(n)* ^|- ^|- ^|O(1) ^|O(n)
-| Queue (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-|===
-indexterm:[Runtime, Linear]
-
-= Summary
-
-In this chapter, we explored the most used linear data structures such as Arrays, Linked Lists, Stacks and Queues. We implemented them and discussed the runtime of their operations.
-
-To sum up,
-
-.Use Arrays when…
-* You need to access data in random order fast (using an index).
-* Your data is multi-dimensional (e.g., matrix, tensor).
-
-.Use Linked Lists when:
-* You will access your data sequentially.
-* You want to save memory and only allocate memory as you need it.
-* You want constant time to remove/add from extremes of the list.
-
-.Use a Queue when:
-* You need to access your data in a first-come, first served basis (FIFO).
-* You need to implement a <<Breadth-First Search for Binary Tree, Breadth-First Search>>
-
-.Use a Stack when:
-* You need to access your data as last-in, first-out (LIFO).
-* You need to implement a <<Depth-First Search for Binary Tree, Depth-First Search>>
-
-.Time Complexity of Linear Data Structures (Array, LinkedList, Stack & Queues)
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| <<Array>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
-| <<Singly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
-| <<Doubly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
-| <<Stack>> ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-| Queue (w/array) ^|- ^|- ^|- ^|- ^|*O(n)* ^|- ^|- ^|O(1) ^|O(n)
-| <<Queue>> (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-|===
diff --git a/book/chapters/quick-sort.adoc b/book/chapters/quick-sort.adoc
deleted file mode 100644
index 73abcd5f..00000000
--- a/book/chapters/quick-sort.adoc
+++ /dev/null
@@ -1,61 +0,0 @@
-= Quicksort
-
-Quicksort is an efficient recursive sorting algorithm that uses <<Divide and Conquer, divide and conquer>> paradigm to sort faster. It can be implemented in-place so it doesn't require additonal memory.
-
-indexterm:[Divide and Conquer]
-In practice quicksort outperforms efficient sorting algorithms like <<Merge Sort>>. And, of course, It also outperforms simple sorting algorithms like <<Selection Sort>>, <<Insertion Sort>> and <<Bubble Sort>>.
-
-Quicksort basically picks a "pivot" element (preferably random) and move all the elements that are smaller than the pivot to the right and the ones that are bigger to the left. It does this recursively until all the array is sorted.
-
-== Quicksort Implementation
-
-Quicksort implementation uses the divide-and-conquer in the following way:
-
-.Quicksort Algorithm
-. Pick a "pivot" element (at random)
-. Move everything that is lower than the pivot to the left and everything that is bigger than the pivot to the right.
-. Recursively repeat step 1 and 2, the sub-arrays on the left and on the right WITHOUT including the pivot.
-
-Let's convert these words into code!
-
-.Quicksort implementation in JavaScript (QuickSort)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/quick-sort.js[tag=quickSort, indent=0]
-----
-<1> Partition: picks a pivot and find the index where the pivot will be when the array is sorted.
-<2> Do the partition of the sub-array at the left of the pivot.
-<3> Do the partition of the sub-array at the right of the pivot.
-<4> Only do the partition when there's something to divide.
-
-The real heavy-lifting is don in the partion function. Let's implement that:
-
-.Quicksort implementation in JavaScript (partition)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/Quick-sort.js[tag=partition, indent=0]
-----
-<1> Make the rightmost element as the pivot.
-<2> This is the place holder for the final pivot index. We start in low and as we move all the lower elements to the left we will get the final place where the pivot should be.
-<3> Move one element at a time comparing it to the pivot value.
-<4> If the current element value is less than the pivot, then increment pivot index (pivot should be place after all the lower values). We also swap the value before incrementing because current element that is lower than the pivot to be at its left side.
-
-Merge sort has a _O(n log n)_ running time. For more details about the how to extract the runtime go to <<Linearithmic>>.
-
-== Quicksort Properties
-
-- <<Stable>>: [big]#✅# Yes
-- <<In-place>>: [big]#✅# Yes
-- <<Adaptive>>: [big]#️❌# No, mostly sorted array takes the same time O(n log n).
-- <<Online>>: [big]#️❌# No, the pivot element can be choose at random.
-- Recursive: Yes
-- Time Complexity: [big]#✅# <<Linearithmic>> _O(n log n)_
-- Space Complexity: [big]#⚠️# <<Linear>> _O(n)_
-indexterm:[Space Complexity, Linear]
-
-// Resources:
-// https://www.khanacademy.org/computing/computer-science/algorithms/quick-sort/a/linear-time-partitioning
-// https://www.khanacademy.org/computing/computer-science/algorithms/quick-sort/a/overview-of-quicksort
-// https://algs4.cs.princeton.edu/23quicksort/
-// https://twitter.com/mathias/status/1036626116654637057?lang=en
-// https://www.toptal.com/developers/sorting-algorithms/quick-sort
diff --git a/book/chapters/red-black-tree.adoc b/book/chapters/red-black-tree.adoc
deleted file mode 100644
index 40c414b2..00000000
--- a/book/chapters/red-black-tree.adoc
+++ /dev/null
@@ -1,3 +0,0 @@
-= Red-Black Tree
-
-Nisi ex aliqua minim commodo cupidatat proident sint fugiat commodo irure. Duis quis ullamco ut veniam pariatur cillum voluptate irure. Irure aliqua elit cupidatat exercitation eiusmod et duis mollit proident reprehenderit ad. Aute aliquip cillum nostrud irure quis. Sint reprehenderit voluptate adipisicing amet ut aliquip. Eiusmod laborum nisi eu irure est consectetur ut ex sit cupidatat non.
diff --git a/book/chapters/sample.adoc b/book/chapters/sample.adoc
deleted file mode 100644
index 142de0af..00000000
--- a/book/chapters/sample.adoc
+++ /dev/null
@@ -1,359 +0,0 @@
-[[chapter-3]]
-= Sample Section
-
-
-:leveloffset: +1
-
-// ------------------
-
-
-= Sample Title 1
-
-((Provident)) architecto soluta `commodi` odit accusamus non molestias necessitatibus, culpa possimus repudiandae, ex sit officiis, sint hic doloribus harum vero quisquam aspernatur.
-
-Voluptate minim `laborum` velit irure labore commodo officia cillum qui. Id excepteur aliqua eiusmod voluptate dolor laboris. Ex laboris deserunt laboris consectetur adipisicing mollit consequat reprehenderit amet eiusmod. Anim magna ut culpa aliquip ea commodo proident exercitation duis nulla. Nulla sunt officia aliquip est sit incididunt sint ad fugiat. Qui fugiat elit magna id duis culpa aute. Officia magna et id esse qui nisi sint eu nostrud ex exercitation anim sunt magna.
-
-Ullamco eu veniam nisi `aliqua`. Lorem nulla nostrud Lorem quis ex. Minim magna irure ex et. Cillum est incididunt consequat deserunt do enim adipisicing elit esse nostrud nisi exercitation. Excepteur fugiat et veniam sit. Ipsum in sit qui exercitation ut in Lorem in esse excepteur aliqua voluptate proident duis.
-
-== Sample Title 2
-
-Optio ab voluptate impedit, iusto explicabo tempore? Ipsam eaque accusamus mollitia accusantium quod aperiam. Sit cum tempora quod! Placeat assumenda adipisci eius?
-
-Minim pariatur in aute nulla non ea commodo velit enim ad nostrud culpa. Occaecat laborum aute nostrud excepteur aliquip eiusmod irure est nisi ut. Deserunt mollit qui pariatur irure voluptate labore adipisicing. Lorem exercitation pariatur cupidatat minim do sit ea cillum. Esse duis cillum veniam Lorem cillum do nulla. Sint esse ((dolor)) veniam dolor non cillum id voluptate. Nulla irure incididunt cillum in incididunt dolore sit amet pariatur.
-
-[#random-ref]
-Sint nisi do deserunt elit. Laboris qui eiusmod ut et dolore laboris dolore tempor consectetur aliqua aliqua quis culpa. Duis deserunt ea reprehenderit duis commodo consequat et ea tempor culpa laborum anim. Tempor pariatur aute labore consectetur. Ut laboris amet aliquip in fugiat laborum ex.
-
-Occaecat eu ex incididunt proident consectetur proident eiusmod fugiat veniam dolor voluptate adipisicing. Eiusmod ullamco pariatur adipisicing fugiat ipsum cillum ullamco. Reprehenderit eu do tempor et amet. Ex ea sint non nisi enim tempor consequat labore.
-
-=== Sample Title 3
-
-Quisquam, vero facere voluptatem impedit optio fuga accusamus non dignissimos, exercitationem culpa error debitis, molestiae corporis? Repudiandae eum dolor quae nemo reiciendis.
-
-Labore consequat deserunt ea sint incididunt duis laborum sit et ex adipisicing tempor. Labore nulla fugiat officia est irure nisi. Amet sint excepteur quis deserunt commodo mollit amet aute est qui aute laboris cillum. Tempor magna dolore velit reprehenderit dolore. Excepteur fugiat ullamco tempor ea labore magna ea in deserunt nulla. Consectetur eiusmod ea sit sint nulla officia consectetur. Elit proident qui voluptate nulla occaecat consectetur ex ullamco.
-
-Velit Lorem deserunt pariatur ex tempor. Exercitation incididunt nostrud elit pariatur incididunt labore dolor in incididunt nisi. Aliquip est nulla esse id aliqua mollit sit sunt tempor et excepteur. Adipisicing sunt tempor non consectetur reprehenderit excepteur sint laboris.
-
-==== Sample Title 4
-
-Sint reprehenderit sit veniam eu adipisicing nisi. Fugiat tempor cillum pariatur Lorem eu cupidatat. Reprehenderit proident deserunt ipsum cupidatat fugiat enim proident officia. Consequat ad laboris aute qui labore ullamco nisi proident occaecat cupidatat eiusmod. Ullamco irure eiusmod velit aliqua enim anim est incididunt irure laborum ex non enim. Do exercitation nostrud duis sint minim nulla dolor veniam.
-
-Exercitation cupidatat ipsum velit consequat exercitation Lorem et anim labore minim cupidatat duis commodo. Deserunt amet aliquip sunt laborum officia. Reprehenderit id excepteur consectetur tempor eiusmod eu. Lorem pariatur ut ipsum irure Lorem irure nulla fugiat in ad officia. Sunt esse in sit aliquip.
-
-Laboris commodo labore anim ea. Nostrud culpa Lorem enim labore esse qui enim incididunt sunt eiusmod cupidatat veniam enim irure. Culpa velit duis duis esse amet adipisicing fugiat dolore do minim exercitation. Nostrud magna id nostrud nostrud minim cupidatat. Sunt amet qui amet deserunt commodo Lorem.
-
-===== Sample title 5
-
-Irure nisi laboris amet do sit Lorem do. Aliqua esse ex in dolore nulla. Aute deserunt nostrud eiusmod fugiat aliquip proident ad eiusmod incididunt est in nisi deserunt Lorem.
-
-====== Sample title 6
-
-Et nisi fugiat in culpa id voluptate incididunt anim commodo. Irure non dolor velit irure non incididunt nisi laborum minim. Elit duis consectetur aliqua laborum tempor et nulla. Nulla dolore magna dolor occaecat velit magna sint nulla. Proident veniam officia in nisi cillum ut deserunt consequat mollit laborum.
-
-<<random-ref, this is a random reference>>
-
-======= Sample title 7
-
-Ullamco ipsum consequat consequat in pariatur ad tempor nisi minim deserunt sunt ex. Minim enim irure quis aliqua anim nisi amet aliquip labore excepteur nisi amet commodo mollit. Est dolor eu commodo Lorem cillum. Elit deserunt cillum excepteur proident aliquip sunt incididunt. Deserunt voluptate enim incididunt incididunt pariatur.
-
-
-== Code
-
-.Subsets in a Set
-[source, javascript]
-----
-include::{codedir}/runtimes/07-sub-sets.js[tag=snippet]
-----
-<1> Base case is empty element.
-<2> For each element from the input append it to the results array.
-<3> The new results array will be what it was before + the duplicated with the appended element.
-
-//.The way this algorithm generates all subsets is:
-//1.  The base case is an empty element (line 13). E.g. ['']
-//2.  For each element from the input append it to the results array (line 16)
-//3.  The new results array will be what it was before + the duplicated with the appended element (line 17)
-
-== Side notes
-
-.JavaScript built-in `BigInt`
-****
-BigInt allows to operate beyond the maximum safe limit of integers (Number.MAX_SAFE_INTEGER => 9,007,199,254,740,991). BigInt uses the suffix n, e.g. 1n + 3n === 4n.
-****
-
-== ((Emojis))
-
-Money flying:
-[big]#💸#
-
-Clockwatch:
-[big]*⏱*
-
-Honey and Bee:
-[big]*🍯🐝*
-
-Skull:
-[big]#💀#
-
-Scream:
-[big]#😱#
-
-Sunglasses and thumbs up
-[big]#😎👍#
-
-Turtle
-[big]#🐢#
-
-Rocket:
-[big]#🚀#
-
-Mindblown:
-[big]*🤯*
-
-THumbsdown
-[big]#👎#
-
-- Space Complexity: [big]#⚠️# <<Linear>> _O(n)_
-- <<Adaptive>>: [big]#✅# Yes
-- Time Complexity: [big]#⛔️# <<Quadratic>> _O(n^2^)_
-
-.How to explain dynamic programming to kids? 👶
-----
-Lorem ipsum dolor sit amet consectetur adipisicing elit. Dignissimos commodi qui maxime fugiat at eveniet blanditiis adipisci ex autem, numquam quidem iusto! Ex illum cupiditate ratione vero, molestiae beatae laboriosam? test [big]*🤯*
-----
-
-== Images
-
-.CPU operations vs. Algorithm runtime as the input size grows
-image:image5.png[CPU time needed vs. Algorithm runtime as the input size increases]
-
-== Quotes
-
-Lorem, ipsum ((dolor)) sit amet consectetur adipisicing elit. Perspiciatis doloremque fuga nobis tempora saepe sed iste quod quia blanditiis dolorem alias, accusantium quas nihil ullam assumenda nostrum similique ad itaque?
-
-[quote, H. J. Harrington]
-Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t manage it, you can’t improve it.
-
-== Tables
-
-.Time and Space Complexity of Linear Data Structures (Array, LinkedList, Stack & Queues)
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| <<Array>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
-| <<Singly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
-| <<Doubly Linked List>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
-| <<Stack>> ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-| Queue (w/array) ^|- ^|- ^|- ^|- ^|*O(n)* ^|- ^|- ^|O(1) ^|O(n)
-| <<Queue>> (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-|===
-{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
-
-.Time complexity for a Graph data structure
-|===
-.2+.^s| Data Structure 2+^s| Vertices 2+^s| Edges .2+^.^s| Space Complexity
-^|_Add_ ^|_Remove_ ^|_Add_ ^|_Remove_
-| Graph (adj. matrix) ^| O(\|V\|^2^) ^| O(\|V\|^2^) ^|O(1) ^|O(1) ^|O(\|V\|^2^)
-| Graph (adj. list w/array) ^| O(1) ^| O(\|V\| + \|E\|)) ^|O(1) ^|O(\|V\| + \|E\|) ^|O(\|V\| + \|E\|)
-| Graph (adj. list w/HashSet) ^| O(1) ^| O(\|V\|)) ^|O(1) ^|O(\|V\|) ^|O(\|V\| + \|E\|)
-|===
-
-
-.Most common algorithmic running times and their examples
-[cols="2,2,5",options="header"]
-|===
-|Big O Notation
-|Name
-|Example(s)
-
-|O(1)
-|<<Constant>>
-|#<<constant-example>>, #<<linked-list-inserting-beginning>>
-
-|O(log n)
-|<<Logarithmic>>
-|<<logarithmic-example>>
-
-|O(n)
-|<<Linear>>
-|<<linear-example>>
-
-|O(n log n)
-|<<Linearithmic>>
-|<<linearithmic-example>>
-
-|O(n^2^)
-|<<Quadratic>>
-|<<quadratic-example>>
-
-|O(n^3^)
-|<<Cubic>>
-|<<cubic-example>>
-
-|O(2^n^)
-|<<Exponential>>
-|<<exponential-example>>
-
-|O(n!)
-|<<Factorial>>
-|<<factorial-example>>
-|===
-
-== Admonitions
-
-The following admonitions are used to highlight content
-
-IMPORTANT: Reword essential concepts. Good for memorizing, tweeting and sharing.
-
-.Side Note with Title
-[NOTE]
-====
-Side notes. E.g. provide language specific: BigInt, charCodeAt...
-[source,javascript]
-----
-function a(test) {
-  return `${test}`;
-}
-----
-====
-
-=== Legend
-
-==== Note
-
-NOTE: Lorem ipsum ((dolor)) sit amet, consectetur adipisicing elit. Odio laudantium et consequuntur, eveniet numquam voluptatibus molestias nostrum reprehenderit blanditiis enim asperiores consequatur dolore tempore laboriosam! At aliquam mollitia aspernatur magnam.
-
-==== Tip
-
-TIP: Lorem ipsum dolor sit amet, consectetur adipisicing elit. Id nesciunt quaerat sint provident beatae. Assumenda necessitatibus ea non illum ipsa eveniet! Odio, blanditiis debitis harum porro autem ut fugiat deserunt.
-
-==== Important
-
-IMPORTANT: Lorem ipsum dolor sit amet consectetur adipisicing elit. Sequi maiores aperiam quasi error facilis ducimus quis vero architecto soluta! Fuga alias aspernatur voluptate voluptas veniam maxime eligendi nemo neque excepturi.
-
-
-==== Caution
-
-CAUTION: Lorem ipsum dolor sit amet consectetur adipisicing elit. Sed deserunt error alias quod, modi ex, repudiandae voluptatem atque saepe, vero eius vel numquam aperiam neque incidunt eum nobis earum nostrum.
-
-==== Warning
-
-WARNING: Lorem ipsum dolor sit amet, consectetur adipisicing elit. Eaque quam delectus consequatur omnis nostrum, minus consectetur animi quod adipisci, architecto similique quasi voluptatem voluptas repudiandae minima. Fuga incidunt maiores magnam!
-
-== HTML-only content (iframe)
-
-Later, in the 2nd part we are going to explore non-linear data structures like Graphs and Trees.
-ifdef::backend-html5[]
-If you want to have a general overview of each one, take a look at the following interactive diagram:
-+++
-<iframe
-  src="https://embed.kumu.io/85f1a4de5fb8430a10a1bf9c5118e015"
-  width="940" height="600" frameborder="0"></iframe>
-+++
-endif::[]
-
-
-== Graphviz Diagrams
-
-=== Graphviz with png
-
-[graphviz, dfs-graph, png]
-....
-digraph G {
-
-     node [fillcolor="#F8E71C" style=filled shape=circle] 0;
-     node [fillcolor="#F5A623"] 1;
-     node [fillcolor="#B8E986"] 2;
-     node [fillcolor="#BD10E0"] 3;
-     node [fillcolor="#50E3C2"] 4;
-     node [fillcolor="#4A90E2"] 5;
-    //  node [fillcolor="#FF5252"] 6;
-
-        0 -> 5
-        0 -> 4
-        0 -> 1
-        1 -> 4
-        1 -> 3
-        2 -> 1
-        3 -> 4
-        3 -> 2
-
-    label="DFS"
-
-    { rank=same; 3, 1 }
-    { rank=same; 0, 4 }
-
-}
-....
-
-=== Graphviz with svg
-indexterm:[Fibonacci]
-
-[graphviz, Recursive Fibonacci call tree with dp (sample), svg]
-....
-graph G {
-    "fib(5)" -- { "fib(4)" }
-    "fib(4)" -- { "fib(3)" }
-    "fib(3)" -- { "fib(2)" }
-    "fib(2)" -- { "fib(1)", "fib(0)" }
-}
-....
-
-[graphviz, Words permutations (sample), svg]
-....
-digraph g {
-    node [shape = record,height=.1];
-
-    art[label = "<f0> A|<f1> R|<f2> T"];
-    art1[label = "<f0> A|<f1> R|<f2> T"];
-    art2[label = "<f0> A|<f1> R|<f2> T", color="red"];
-    atr[label = "<f0> A|<f1> T|<f2> R", color="red"];
-    rat[label = "<f0> R|<f1> A|<f2> T"];
-    rat1[label = "<f0> R|<f1> A|<f2> T", color="red"];
-    rta[label = "<f0> R|<f1> T|<f2> A", color="red"];
-    tra[label = "<f0> T|<f1> R|<f2> A"];
-    tra1[label = "<f0> T|<f1> R|<f2> A", color="red"];
-    tar[label = "<f0> T|<f1> A|<f2> R", color="red"];
-
-    "art":f0 -> "art1":f0 [ label = "1. swap A/A"];
-    "art1":f0 -> "art2":f0 [ label = "2. swap R/R"];
-    "art2":f2 -> "art1":f1 [ label = "3", color="grey"];
-    "art1":f2 -> "atr":f0 [ label = "4. swap R/T"];
-    "atr":f2 -> "art1":f2  [ label = "5", color="grey"];
-    "art1":f1 -> "art":f0  [ label = "6", color="grey"];
-
-    "art":f1 -> "rat":f0 [ label = "7. swap A/R"];
-    "rat":f0 -> "rat1":f0 [ label = "8. swap A/A"];
-    "rat1":f2 -> "rat":f1 [ label = "9", color="grey"];
-    "rat":f2 -> "rta":f0 [ label = "10. swap A/T"];
-
-    "art":f2 -> "tra":f0 [ label = "swap A/T"];
-    "tra":f0 -> "tra1":f0 [ label = "swap R/R"];
-    "tra":f2 -> "tar":f0 [ label = "swap R/A"];
-
-}
-....
-
-
-
-// ------------------
-
-:leveloffset: -1
-
-
-[[chapter-2]]
-= Section 2
-
-== Another sample
-
-[.lead]
-Id excepteur sit duis ut ad nulla commodo elit consectetur est enim. Consectetur duis fugiat eu aute id cupidatat ullamco. Ad duis non consectetur aliqua nulla nostrud.
-
-Quis aute et consectetur ad pariatur ea commodo amet cupidatat velit ullamco. Cupidatat enim enim non est reprehenderit exercitation ut excepteur amet consectetur. Mollit quis ipsum eu adipisicing ipsum magna.
-
-Cupidatat sit aute et minim do. Non est anim fugiat proident ex enim voluptate non et consectetur. Culpa proident deserunt ipsum irure reprehenderit voluptate irure tempor tempor proident. Quis ipsum eiusmod tempor et reprehenderit non adipisicing. Consectetur dolore ullamco adipisicing ad adipisicing aliqua eu. Eu excepteur ipsum magna aliqua cillum in sint sint id exercitation est. Enim occaecat ut aute elit.
-
-Eiusmod excepteur eiusmod ((dolor)) magna nulla pariatur exercitation ex. Laborum sunt quis dolor qui do magna. Non velit reprehenderit minim duis est dolor commodo irure fugiat. Velit culpa laboris mollit esse minim sit id incididunt minim duis excepteur eiusmod ullamco. Pariatur pariatur ut laboris aute velit id nulla reprehenderit.
-
-Officia sint sit Lorem eu officia sint ad et et do. Deserunt nulla exercitation aute nulla enim laboris aliquip nulla. Commodo laboris amet enim veniam cupidatat culpa culpa duis deserunt esse ipsum. Deserunt nisi qui ullamco deserunt pariatur nisi velit tempor fugiat et pariatur do deserunt cillum. Dolore officia occaecat eiusmod occaecat reprehenderit cillum laboris irure irure ad tempor fugiat amet Lorem. Qui dolor elit ipsum consectetur commodo amet mollit eiusmod ex officia. Sit enim sunt nisi consectetur sint est id.
-
-Eu sint labore esse labore amet magna commodo exercitation labore. Esse aliquip dolor consequat exercitation ut amet labore esse velit. Et veniam ullamco aute laboris nulla exercitation qui aliqua eu. Ea cupidatat cupidatat non quis irure amet in minim laboris mollit. Enim eu pariatur et magna voluptate.
diff --git a/book/chapters/selection-sort.adoc b/book/chapters/selection-sort.adoc
deleted file mode 100644
index 4297bddc..00000000
--- a/book/chapters/selection-sort.adoc
+++ /dev/null
@@ -1,41 +0,0 @@
-= Selection Sort
-
-The ((selection sort)) is a simple sorting algorithm. As its name indicates, it chooses the lowest element from the list and move it where it should be.
-
-.Selection sort algorithm
-. Start with the element in position 0.
-. Find minimum element in the rest of array. If a new minimun is found swap them.
-. Repeat with the element in postion 1 and so until the last one.
-
-image:selection-sort.gif[]
-
-== Selection sort implementation
-For implementing the selection sort we need 2 indexes.
-
-.Selection sort
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/selection-sort.js[tag=sort, indent=0]
-----
-<1> Converts any collection to an array or clone existing array
-<2> Visit all elements in the array starting from 1st element (index 0)
-<3> Everything to the left side is considered sorted in it's final position. So, select `left` as the minimun value
-<4> Compare the `selection` to every element to the right
-<5> If it finds a value *smaller* than the selection, then update the `selection`.
-<6> Put the next smallest item to it's final position
-
-TIP: Selection sort minimize the number of swaps. It only does a swap per cycle while insertion sort and bubble sort swap inmediately.
-
-One index is for the position in question (selection/left) and another one for finding the minimun in the rest of the array (right).
-
-== Selection Sort Properties
-
-- <<In-place>>: [big]#✅# Yes
-- <<Stable>>: [big]#️️❌# No
-- <<Adaptive>>: [big]#️️❌# No
-- <<Online>>: [big]#️️❌# No
-- Time Complexity: [big]#⛔️# <<Quadratic>> _O(n^2^)_
-- Space Complexity: [big]#✅# <<Constant>> _O(1)_
-
-CAUTION: Selection sort is the worst compared <<Bubble Sort>> and <<Insertion Sort>>. The only advantage of selection sort is that it minimizes the number of swaps. In case, that swapping is expensive then it could make sense to use this one over the others.
-indexterm:[Runtime, Quadratic]
diff --git a/book/chapters/set.adoc b/book/chapters/set.adoc
deleted file mode 100644
index 8f2f3498..00000000
--- a/book/chapters/set.adoc
+++ /dev/null
@@ -1,220 +0,0 @@
-= Set
-
-A set is a data structure where duplicated entries are not allowed. Set is like an array with unique values.
-
-NOTE: JavaScript has already a built-in Set data structure.
-
-Take a look at the following
-example:
-
-.Set usage example (using JavaScript built-in Set)
-[source, javascript]
-----
-const set = new Set();
-
-set.add(1); //↪️ Set [ 1 ]
-set.add(1); //↪️ Set [ 1 ]
-set.add(2); //↪️ Set [ 1, 2 ]
-set.add(3); //↪️ Set [ 1, 2, 3 ]
-set.has(1); //↪️ true
-set.delete(1); //↪️ removes 1 from the set
-set.has(1);    //↪️ false, 1 has been removed
-set.size; //↪️ 2, we just removed one value
-console.log(set); //↪️ Set(2) {2, 3}
-----
-
-As you can see, even if we insert the same value multiple times, it only gets added once.
-
-Can you think in a way how to implement it?
-
-TIP: A hint... it should perform all operations in *O(1)** or at most *O(log n)*
-
-If we use a `map`, we can accomplish this. However, maps use a key/value pair. If we only use the keys, we can avoid duplicates. Since in a `map` you can only have one key at a time.
-
-As you might remember from the <<Map>> chapter, there are two ways of implementing a `map` and both can be used to create a `set`.  Let's explore the difference between the two implementations are.
-
-== HashSet vs TreeSet
-
-We can implement a `map` using a *balanced BST* and using a *hash function*. If we use them to implement a `Set`, then we would have a `HashSet` and `TreeSet` respectively.
-
-* `TreeSet`, would return the values sorted in ascending order.
-* `HashSet`, would return the values in insertion order.
-* Operations on a `HashSet` would take on average O(1) and in the worst case (rehash is due), it would take O(n).
-* Operation on a `TreeSet` is always O(log n).
-
-Let’s implement both!
-
-== Implementing a TreeSet
-
-We are to use a balanced BST (Red-Black Tree) to implement TreeSet.
-
-.TreeSet's constructor method and size attribute
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/tree-set.js[tag=constructor]
-}
-----
-<1> Converts an array or any iterable data structure to a set.
-
-A common use case for Sets is to remove duplicated values from an array. We can do that by passing them in the constructor as follows:
-
-.Removing duplicates from an Array using a Set
-[source, javascript]
-----
-set = new TreeSet([1, 2, 3, 2, 1]);
-expect(set.size).toBe(3);
-expect(Array.from(set.keys())).toEqual([1, 2, 3]);
-----
-
-Ok, now let’s implement the add method.
-
-=== Adding elements to a TreeSet
-
-For adding values to the set, we `Tree.add` method.
-
-.TreeSet's constructor method and size attribute
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/tree-set.js[tag=add,indent=0]
-----
-
-Our <<Inserting new elements in a BST, BST implementation>> can hold duplicated values. It has a multiplicity tally to keep track of duplicates. However, we don’t dupe in a set. For that, we check if the value is already in the tree.
-Don’t worry about adding extra lookups. The
-`Tree.has` is also very performant *O(log n)*.
-
-=== Searching for values in a TreeSet
-
-Again, we rely on the Tree implementation to do the heavy lifting:
-
-.TreeSet's `has` method
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/tree-set.js[tag=has, indent=0]
-----
-
-=== Deleting elements from a TreeSet
-
-We delete the elements from the TreeSet using the remove method of the BST.
-
-.TreeSet's `delete` method
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/tree-set.js[tag=delete, indent=0]
-----
-
-Voilà! That’s it!
-
-=== Converting TreeSet to Array
-
-A common use case for a Set is to convert it to an array or use in an iterator (for loops, forEach, …). Let’s provide the method for that:
-
-.TreeSet's iterator
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/tree-set.js[tag=iterator, indent=0]
-----
-
-We are using the `inOrderTraversal` method of the BST to go each key in an
-ascending order.
-
-.JavaScript Built-in `Symbol` iterator
-****
-The `Symbol.iterator` built-in symbol specifies the default iterator for
-an object. Used by `for...of`, `Array.from` and others.
-****
-
-Now we can convert from set to array and vice versa easily. For
-instance:
-
-.TreeSet's iterator
-[source, javascript]
-----
-const array = [1, 1, 2, 3, 5];
-
-// array to set
-const set = new TreeSet(array);
-
-// set to array
-Array.from(set); //↪️ (4) [1, 2, 3, 5]
-----
-
-No more duplicates in our array!
-
-Check out our https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/sets/tree-set.js#L12[GitHub repo for the full TreeSet implementation].
-
-Let’s now, implement a `HashSet`.
-
-== Implementing a HashSet
-
-The *HashSet* is the set implementation using a HashMap as its underlying data structure.
-
-The HashSet interface will be the same as the built-in `Set` or our previously implemented `TreeSet`.
-
-.HashSet's constructor method and size attribute
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/hash-set.js[tag=constructor]
-}
-----
-
-This constructor is useful for converting an array to set and initializing the `HashMap`.
-
-=== Inserting values to a HashSet
-
-To insert items in a HashSet we use the `set` method of the `HashMap`:
-
-.HashSet's `add` method
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/hash-set.js[tag=add, indent=0]
-}
-----
-
-`HashMap` stores key/value pairs, but for this, we only need the key, and we ignore the value.
-
-=== Finding values in a HashSet
-
-We use the method `has` to check if a value is on the `Set` or not.
-
-.HashSet's `has` method
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/hash-set.js[tag=has, indent=0]
-----
-
-Internally, the `HashMap` will convert the key into an array index using a hash function. If there’s something in the array index bucket, it will return
-true, and if it’s empty, it will be false.
-
-=== Deleting values from a HashSet
-
-For deleting a value from a hashSet we use the HashMap’s delete method:
-
-.HashSet's `delete` method
-[source, javascript]
-----
-include::{codedir}/data-structures/sets/hash-set.js[tag=delete, indent=0]
-----
-
-This method has an average runtime of *O(1)*.
-
-== HashSet vs HashMap Time Complexity
-
-We can say that `HashMap` in on average more performant O(1) vs. O(log n). However, if a
-rehash happens, it will take *O(n)* instead of *O(1)*. A `TreeSet` is always *O(log n)*.
-
-.Time complexity HashSet vs TreeSet
-|===
-.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
-^|_Index/Key_ ^|_Value_
-| HashSet ^|- ^|O(n) ^|O(1)* ^|O(1)* ^|O(1)*
-| TreeSet ^|- ^|O(n) ^|O(log n) ^|O(log n) ^|O(log n)
-|===
-{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
-
-indexterm:[Runtime, Linear]
-To recap, HashSet and TreeSet will keep data without duplicates. The
-difference besides runtime is that:
-
-.TreeSet vs HashSet
-* HashSet keeps data in insertion order
-* TreeSet keeps data sorted in ascending order.
diff --git a/book/chapters/sorting-intro.adoc b/book/chapters/sorting-intro.adoc
deleted file mode 100644
index 050650ee..00000000
--- a/book/chapters/sorting-intro.adoc
+++ /dev/null
@@ -1,15 +0,0 @@
-= Sorting Algorithms
-
-Sorting is one of the most common solutions when we want to extract some insights about a collection of data.
-We can sort to get the maximum or minimun value and many algorithmic problems involves sorting data first.
-
-.We are going to explore 3 basic sorting algorithms _O(n^2^)_ which have low overhead:
-- <<Bubble Sort>>
-- <<Selection Sort>>
-- <<Insertion Sort>>
-
-.Efficient sorting algorithms _O(n log n)_:
-- <<Merge Sort>>
-- <<Quicksort>>
-
-Before we dive into the most well-known sorting algorithms, let's discuess about the sorting properties.
diff --git a/book/chapters/sorting-properties.adoc b/book/chapters/sorting-properties.adoc
deleted file mode 100644
index e0fe0fa3..00000000
--- a/book/chapters/sorting-properties.adoc
+++ /dev/null
@@ -1,71 +0,0 @@
-= Sorting Properties
-
-Sorting implementations with the same time complexity might manipulate the data differently. We want to understand these differences so we can be aware of the side-effects it will have on data or extra resources. For instance, some solutions will need auxiliary memory to store temporary data while sorting while other can do it in place.
-
-== Stable
-
-An ((stable sorting)) algorithms keeps the relative order of items with the same comparison criteria.
-
-This specially useful when you want to sort on multiple phases.
-
-.Let's say you have the following data:
-[source, javascript]
-----
-const users = [
-  { name: 'Bob', age: 32 },
-  { name: 'Alice', age: 30 },
-  { name: 'Don', age: 30 },
-  { name: 'Charly', age: 32 },
-];
-----
-
-.If you sort by `name` you would have:
-[source, javascript]
-----
-[
-  { name: 'Alice', age: 30 },
-  { name: 'Bob', age: 32 },
-  { name: 'Charly', age: 32 },
-  { name: 'Don', age: 30 },
-];
-----
-
-Then, here comes the important part, if you sort by `age` you might get two different results.
-
-.If the sorting algorithm is *stable*, it should keep the items with the same age ordered by `name`:
-[source, javascript]
-----
-[
-  { name: 'Alice', age: 30 },
-  { name: 'Don', age: 30 },
-  { name: 'Bob', age: 32 },
-  { name: 'Charly', age: 32 },
-];
-----
-
-.However, if the sorting is *not stable*, then you will lose the relative order of the items and get something like this:
-[source, javascript]
-----
-[
-  { name: 'Don', age: 30 },
-  { name: 'Alice', age: 30 },
-  { name: 'Charly', age: 32 },
-  { name: 'Bob', age: 32 },
-];
-----
-
-Both results are correctly sorted by `age`, however, having a stable sorting is better if you want to keep the relative possition of keys with the same value.
-
-== In-place
-
-An ((in-place sorting)) algorithm would a _space complexity_ of O(1). In other words, it does not use any other auxiliary memory because it moves the items in the collection itself.
-This is specially useful for memory constraint enviroments like robotics or embedded systems in appliances.
-
-== Online
-
-It can sort a list as it receives it.
-((Online sorting)) algorithms doesn't have to re-sort the whole collection for every new item added.
-
-== Adaptive
-
-Algorithms with ((adaptive sorting)) run faster, close to _O(n)_, on an already sorted (or partially sorted) collection.
diff --git a/book/chapters/stack.adoc b/book/chapters/stack.adoc
deleted file mode 100644
index f869d999..00000000
--- a/book/chapters/stack.adoc
+++ /dev/null
@@ -1,75 +0,0 @@
-= Stack
-
-The stack is a data structure that restricts the way you add and remove data. It only allows you to insert and retrieve in a *Last-In-First-Out* (LIFO) fashion.
-
-An analogy is to think the stack is a rod and the data are discs. You can only take out the last one you put in.
-
-.Stack data structure is like a stack of disks: the last element in is the first element out
-image:image29.png[image,width=240,height=238]
-
-// #Change image from https://www.khanacademy.org/computing/computer-science/algorithms/towers-of-hanoi/a/towers-of-hanoi[Khan Academy]#
-
-As you can see in the image above, If you insert the disks in the order `5`, `4`, `3`, `2`, `1`. Then you can remove them on `1`, `2`, `3`, `4`, `5`.
-
-The stack inserts items to the end of the collection and also removes from the end. Both, an array and linked list would do it in constant time. However, since we don’t need the Array’s random access, a linked list makes more sense.
-
-.Stack's constructor
-[source, javascript]
-----
-include::{codedir}/data-structures/stacks/stack.js[tag=constructor]
-  // ... methods goes here ...
-}
-----
-
-As you can see in the stack constructor, we are using a  linked list as the underlying data structure.
-
-Let's now develop the insert and remove operations in a stack.
-
-== Insertion
-
-We can insert into a stack using the linked list’s `addLast` method.
-
-.Stack's add
-[source, javascript]
-----
-include::{codedir}/data-structures/stacks/stack.js[tag=add, indent=0]
-----
-
-We are returning `this`, in case we want to chain multiple add commands.
-
-== Deletion
-
-Deleting is straightforward as well.
-
-.Stack's remove
-[source, javascript]
-----
-include::{codedir}/data-structures/stacks/stack.js[tag=remove, indent=0]
-----
-
-This time we used the linked list’s `removeLast` method. That’s all we need for a stack implementation. Check out the full implementation https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/stacks/stack.js#L6[here].
-
-== Implementation Usage
-
-We can use our stack implementation as follows:
-
-.Stack usage example
-[source, javascript]
-----
-include::{codedir}/data-structures/stacks/stack.js[tag=snippet, indent=0]
-----
-
-As you can see if we add new items they will be the first to go out to honor LIFO.
-
-== Stack Complexity
-
-Implementing the stack with an array and linked list would lead to the same time complexity:
-
-.Time complexity for the stack operations
-|===
-.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space Complexity
-^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
-| Stack ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
-|===
-
-It's not very common to search for values on a stack (other Data Structures are better suited for this). Stacks especially useful for implementing <<Depth-First Search for Binary Tree, Depth-First Search>>.
diff --git a/book/chapters/tim-sort.adoc b/book/chapters/tim-sort.adoc
deleted file mode 100644
index e37061db..00000000
--- a/book/chapters/tim-sort.adoc
+++ /dev/null
@@ -1,3 +0,0 @@
-= Tim Sort
-
-Ea duis sunt consequat sit mollit elit proident sint. Deserunt et fugiat duis pariatur deserunt officia mollit elit consectetur pariatur nisi. Sit non enim quis ut quis. Consequat commodo consequat veniam labore excepteur incididunt amet in eiusmod enim laborum veniam incididunt. Adipisicing fugiat mollit tempor sint ipsum eiusmod excepteur est.
diff --git a/book/chapters/timsort.adoc b/book/chapters/timsort.adoc
deleted file mode 100644
index 8cf6d3e3..00000000
--- a/book/chapters/timsort.adoc
+++ /dev/null
@@ -1,58 +0,0 @@
-= Tim Sort
-
-tim sort is an efficient recursive sorting algorithm that uses "divide and conquer" paradigm to sort faster. It can be implemented in-place so it doesn't require additonal memory.
-indexterm:[Divide and Conquer]
-
-In practice timsort outperforms efficient sorting algorithms like <<Merge Sort>>. And, of course, It also outperforms simple sorting algorithms like <<Selection Sort>>, <<Insertion Sort>> and <<Bubble Sort>>.
-
-tim sort basically picks a "pivot" element (preferably random) and move all the elements that are smaller than the pivot to the right and the ones that are bigger to the left. It does this recursively until all the array is sorted.
-
-== tim Sort Implementation
-
-tim sort implementation uses the divide-and-conquer in the following way:
-
-.tim Sort Algorithm
-. Pick a "pivot" element (at random)
-. Move everything that is lower than the pivot to the left and everything that is bigger than the pivot to the right.
-. Recursively repeat step 1 and 2, the sub-arrays on the left and on the right WITHOUT including the pivot.
-
-Let's convert these words into code!
-
-.tim Sort implementation in JavaScript (timSort)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/tim-sort.js[tag=timSort, indent=0]
-----
-<1> Partition: picks a pivot and find the index where the pivot will be when the array is sorted.
-<2> Do the partition of the sub-array at the left of the pivot.
-<3> Do the partition of the sub-array at the right of the pivot.
-<4> Only do the partition when there's something to divide.
-
-The real heavy-lifting is don in the partion function. Let's implement that:
-
-.tim Sort implementation in JavaScript (partition)
-[source, javascript]
-----
-include::{codedir}/algorithms/sorting/tim-sort.js[tag=partition, indent=0]
-----
-<1> Make the rightmost element as the pivot.
-<2> This is the place holder for the final pivot index. We start in low and as we move all the lower elements to the left we will get the final place where the pivot should be.
-<3> Move one element at a time comparing it to the pivot value.
-<4> If the current element value is less than the pivot, then increment pivot index (pivot should be place after all the lower values). We also swap the value before incrementing because current element that is lower than the pivot to be at its left side.
-
-Merge sort has a _O(n log n)_ running time. For more details about the how to extract the runtime go to <<Linearithmic>>.
-
-== tim Sort Properties
-
-- Time Complexity: [big]#✅# <<Linearithmic>> _O(n log n)_
-- Space Complexity: [big]#⚠️# <<Linear>> _O(n)_
-- <<Stable>>: [big]#✅# Yes
-- <<In-place>>: [big]#✅# Yes
-- <<Adaptive>>: [big]#️️️️️️️⛔️️️️️# No, mostly sorted array takes the same time O(n log n).
-- <<Online>>: [big]#️️️️️️️⛔️️️️️# No, the pivot element can be choose at random.
-- Recursive: Yes
-
-
-// Resources:
-// https://twitter.com/mathias/status/1036626116654637057?lang=en
-// http://cr.openjdk.java.net/~martin/webrevs/openjdk7/timsort/raw_files/new/src/share/classes/java/util/TimSort.java
diff --git a/book/chapters/tree--avl.adoc b/book/chapters/tree--avl.adoc
deleted file mode 100644
index b84c17f7..00000000
--- a/book/chapters/tree--avl.adoc
+++ /dev/null
@@ -1,60 +0,0 @@
-= AVL Tree
-
-AVL Tree is named after their inventors (**A**delson-**V**elsky and **L**andis).
-This self-balancing tree keep track of subtree sizes to know if a rebalance is needed or not.
-We can compare the size of the left and right subtrees using a balance factor.
-
-[NOTE]
-====
-
-The *balanced factor* on each node is calculated recurviely as follows:
-
-----
-Balance Factor = (left subtree height) - (right subtree height)
-----
-
-====
-
-The implementation will got into the BST node.
-We will need two methods to calculate the left and right subtree, and with those we can get the balance factor.
-
-.Balance Factor methods on the BST node
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/binary-tree-node.js[tag=avl, indent=0]
-----
-
-
-== Implementing AVL Tree
-
-Implementing an AVL Tree is not too hard, since it builds upon what we did in the Binary Search Tree.
-
-.AVL Tree class
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/avl-tree.js[tag=AvlTree]
-----
-
-As you can see, AVL tree inherits from the BST class.
-The insert and remove operations works the same as in the BST, except that at the end we call `balanceUptream`.
-This function makes balance the tree after every change if is needed. Let's see how it's implemented.
-
-.Balance Upstream for AVL tree
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/avl-tree.js[tag=balanceUptream]
-----
-
-This function recurively goes from the modified node to the root checking if each node in between is balanced.
-Now, let's examine how does the balancing works on AVL tree.
-
-.Balance method for AVL tree
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/avl-tree.js[tag=balance]
-----
-
-The first thing we do is to see if one subtree is longer than the other.
-If so, then we check the children balance to determine if need a single or double rotation and in which direction.
-
-You can review <<Tree Rotations>> in case you want a refresher.
diff --git a/book/chapters/tree--binary-tree-traversal.adoc b/book/chapters/tree--binary-tree-traversal.adoc
deleted file mode 100644
index 9b8d7ce6..00000000
--- a/book/chapters/tree--binary-tree-traversal.adoc
+++ /dev/null
@@ -1,86 +0,0 @@
-= Binary Tree Traversal
-
-As mentioned before, there are different ways to visit all the nodes or search for a value in a binary tree. On this section we are going to focus on depth-first tree traversal. The implementations are recursive since it's more elegant and concise. Let's explore them.
-
-== In Order Traversal
-
-If you tree happens to be a binary search tree (BST), then could use "in order" traversal to get the values sorted in ascending order. To accomplish this, you have to visit the nodes in a `left-root-right` order.
-
-If we have the following tree:
-----
-         10
-       /    \
-      5      30
-    /       /  \
-   4       15   40
- /
-3
-----
-
-In-order traverval will return `3, 4, 5, 10, 15, 30, 40`.
-
-Check out the implementation:
-
-.In-order traversal implementation
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/binary-search-tree.js[tag=inOrderTraversal, indent=0]
-----
-
-This function goes recursively to the leftmost element and then yield that node, then we go to the right child (if any) and repeat the process. This will get us the values ordered.
-
-== Pre Order Traversal
-
-Pre-order traveral visits nodes in this order `root-left-right` recursively.
-
-.Usage of pre-order traversal:
-- Create a copy of the tree.
-- Get prefix expression on of an expression tree used in the https://en.wikipedia.org/wiki/Polish_notation[polish notation].
-
-
-.Pre-order traversal implementation
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/binary-search-tree.js[tag=preOrderTraversal, indent=0]
-----
-
-If we have the following tree:
-----
-         10
-       /    \
-      5      30
-    /       /  \
-   4       15   40
- /
-3
-----
-
-Pre-order traverval will return `10, 5, 4, 3, 30, 15, 40`.
-
-== Post-order Traversal
-
-Post-order traveral goes to each node in this order `left-right-root` recursively.
-
-.Usages of the post-order tree traveral
-- Traversal is used to delete the tree because you visit the children before removing the parent.
-- Get the postfix expression of an expression tree used in the http://en.wikipedia.org/wiki/Reverse_Polish_notation[reverse polish notation].
-
-.Post-order traversal implementation
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/binary-search-tree.js[tag=postOrderTraversal, indent=0]
-----
-
-
-If we have the following tree:
-----
-         10
-       /    \
-      5      30
-    /       /  \
-   4       15   40
- /
-3
-----
-
-Post-order traverval will return `3, 4, 5, 15, 40, 30, 10`.
diff --git a/book/chapters/tree--search.adoc b/book/chapters/tree--search.adoc
deleted file mode 100644
index daedd965..00000000
--- a/book/chapters/tree--search.adoc
+++ /dev/null
@@ -1,121 +0,0 @@
-= Tree Search & Traversal
-
-So far we covered, how to insert/delete/search values in a binary search tree (BST).
-However, not all binary trees are BST, so there are other ways to look for values or visit all nodes in a certain order.
-
-If we have the following tree:
-----
-         10
-       /    \
-      5      30
-    /       /  \
-   4       15   40
- /
-3
-----
-
-Depending on what traversal methods we used we will have a different visiting order.
-
-.Tree traversal methods
-- Breadth-first traversal (a.k.a level order traversal): `10, 5, 30, 4, 15, 40, 3`
-- Depth-first traversal
-** In-order (left-root-right): `3, 4, 5, 10, 15, 30, 40`
-** Pre-order (root-left-right): `10, 5, 4, 3, 30, 15, 40`
-** Post-order (left-right-root): `3, 4, 5, 15, 40, 30, 10`
-
-Why do we care? Well, there are certain problems that you solve more optimally using one or another traversal method. For instance to get the size of a subtree, finding maximums/minimums, and so on.
-
-Let's cover Breadth-first search (BFS) and Depth-first search (DFS).
-
-[Breadth First Search]
-== Breadth-First Search for Binary Tree
-
-Breadth-first search goeas wide (breadth) before going deep. Hence, the name. In other words, it goes level by level. It visits all the inmediate nodes or children and then move on to the children's children.
-Let's how can we implement it!
-
-.Breath-First Search (BFS) Implementation
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/binary-search-tree.js[tag=bfs,indent=0]
-----
-
-As you see, the BFS uses a <<Queue>> data structure. We enqueue all the children of the current node and then dequeue them as we visit them.
-
-Note the asterisk (`*`) in front of the function means that this function is a generator that yield values.
-
-.JavaScript Generators
-****
-
-JavaScript generators were added as part of ES6, they allow process possibly expensive operations one by one. You can convert any function into a generator by adding the asterisk in front and `yield`ing a value.
-
-Then you can use `next()` to get the value and also `done` to know if it's the last value. Here are some examples:
-
-[source, javascript]
-----
-function* dummyIdMaker() {
-  yield 0;
-  yield 1;
-  yield 2;
-}
-
-const generator = dummyIdMaker()
-
-// getting values
-console.log(generator.next()); // ↪️ {value: 0, done: false}
-console.log(generator.next()); // ↪️ {value: 1, done: false}
-console.log(generator.next()); // ↪️ {value: 2, done: false}
-console.log(generator.next()); // ↪️ {value: undefined, done: true}
-
-// iterating generator values with for..of loops
-for(const n of dummyIdMaker()) {
-  console.log(n);
-}
-
-// converting a generator to an array
-console.log(Array.from(dummyIdMaker())); // [0, 1, 2]
-----
-
-****
-
-
-== Depth-First Search for Binary Tree
-
-Depth-First search goes deep before going wide. It means, that starting for the root it goes as deep as it can until it found a leaf node (node without children), then it visits all the remaing nodes that were in the path.
-
-.Depth-First Search (DFS) Implementation with a Stack
-[source, javascript]
-----
-include::{codedir}/data-structures/trees/binary-search-tree.js[tag=dfs,indent=0]
-----
-
-This is a iterative implementation of a DFS using an <<Stack>>.
-It's almost identical to the BFS but instead of using a <<Queue>> we usa a Stack.
-We can also implement it as recursive functions are we are going to see in the <<Binary Tree Traversal>> section.
-
-== Depth-First Search vs. Breadth-First Search
-
-We can see visually the difference on how the DFS and BFS search for nodes:
-
-.Depth-First Search vs. Breadth-First Search
-image:depth-first-search-dfs-breadth-first-search-bfs.jpg[]
-
-As you can see the DFS in two iterations is already at one of the farthest node from the root while BFS search nearby nodes first.
-
-.Use DFS when:
-- The node you are looking for is likely to be *far* from the root.
-
-.Use BFS when:
-- The node you are looking for is *nearby* the root.
-
-:leveloffset: +1
-
-include::tree--binary-tree-traversal.adoc[]
-
-:leveloffset: -1
-
-
-
-
-
-
-
diff --git a/book/chapters/trie.adoc b/book/chapters/trie.adoc
deleted file mode 100644
index 6c310e76..00000000
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+++ /dev/null
@@ -1,3 +0,0 @@
-= Trie
-
-Aute ad cupidatat cillum enim deserunt. Reprehenderit eiusmod non do eiusmod duis culpa ipsum consequat tempor magna elit. Fugiat est eu proident incididunt adipisicing. Enim tempor aute ad quis officia enim enim pariatur do commodo labore sunt minim.
diff --git a/book/config b/book/config
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+Subproject commit 95b431fb37af4b23a7ce17c183da7313f1d1acb4
diff --git a/book/chapters/colophon.adoc b/book/content/colophon.asc
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rename to book/content/colophon.asc
index 5be4908a..6387ef12 100644
--- a/book/chapters/colophon.adoc
+++ b/book/content/colophon.asc
@@ -1,5 +1,5 @@
 [colophon#colophon%nonfacing]
-= {doctitle}
+== Data Structures & Algorithms in JavaScript
 
 Copyright © {docyear} Adrian Mejia
 
@@ -9,6 +9,7 @@ For online information and ordering this and other books, please visit https://a
 
 No part of this publication may be produced, store in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher.
 
-While every precaution has been taking in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or damages resulting from the use of the information contained herein.
+While every precaution has been taking in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or damages resulting from using the information contained herein.
 
-{revremark}, {revdate}.
+// {revremark}, {revdate}.
+Version {revnumber}, {revdate}.
diff --git a/book/content/cover.png b/book/content/cover.png
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diff --git a/book/content/dedication.asc b/book/content/dedication.asc
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+[dedication]
+== Dedication
+
+_To my wife Nathalie, who supported me in my long hours of writing, and my baby girl Abigail._
diff --git a/book/content/introduction.asc b/book/content/introduction.asc
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+[preface]
+== Introduction
+
+You are about to become a better programmer and grasp the fundamentals of Algorithms and Data Structures.
+Let's take a moment to explain how we are going to do that.
+
+This book is divided into four main parts:
+
+In *Part 1*, we will cover the framework to compare and analyze algorithms: Big O notation. When you have multiple solutions to a problem, this framework comes in handy to know which solution will scale better.
+
+In *Part 2*, we will go over linear data structures and trade-offs about using one over another.
+After reading this part, you will know how to trade space for speed using Maps, when to use a linked list over an array, or what problems can be solved using a stack over a queue.
+
+*Part 3* is about graphs and trees and its algorithms.
+Here you'll learn how to translate real-world problems into graphs and different algorithms to solve them.
+
+*Part 4* will cover tools and techniques to solve algorithmic problems. This section is for those who want to get better at recognizing patterns and improving problem-solving skills. We cover sorting algorithms and standard practices like dynamic programming, greedy algorithms, divide and conquer, and more.
+
+Finally, in *Appendix A*, we summarize all the topics covered in this book in a cheatsheet. *Appendix B and C* covers self-balancing binary search tree algorithms. *Appendix D* cover the solutions to the problems presented at the end of each chapter.
+
+Let's get started.
diff --git a/book/content/part01/algorithms-analysis.asc b/book/content/part01/algorithms-analysis.asc
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+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Fundamentals of Algorithms Analysis
+
+You are probably reading this book because you want to write better and faster code.
+How can you do that? Can you time how long it takes to run a program? Of course, you can!
+[big]#⏱#
+However, if you run the same program on a computer, cellphone, or even a smartwatch, it will take different times.
+
+image::image3.png[image,width=528,height=137]
+
+Wouldn't it be great if we can compare algorithms regardless of the hardware where we run them?
+That's what *time complexity* is for!
+But, why stop with the running time?
+We could also compare the memory "used" by different algorithms, and we call that *space complexity*.
+
+.In this chapter, you will learn:
+- What’s the best way to measure your code's performance regardless of what hardware you use.
+-    Learn how to use Big O notation to compare algorithms.
+-    How to use algorithms analysis to improve your programs speed.
+
+Before going deeper into space and time complexity, let's cover the basics real quick.
+
+==== What are Algorithms?
+
+Algorithms (as you might know) are steps of how to do some tasks. When you cook, you follow a recipe (or an algorithm) to prepare a dish. Let's say you want to make a pizza.
+
+.Example of an algorithm to make pizza
+[source, javascript]
+----
+import { rollOut, applyToppings, Oven } from '../pizza-utils';
+
+function makePizza(dough, toppings = ['cheese']) {
+    const oven = new Oven(450);
+    const rolledDough = rollOut(dough);
+    const rawPizza = applyToppings(rolledDough, toppings);
+    const pizzaPromise = oven.bake(rawPizza, { minutes: 20 });
+    return pizzaPromise;
+}
+----
+
+If you play a game, you'll devise strategies (or algorithms) to win. Likewise, algorithms in computers are a set of instructions used to solve a problem.
+
+TIP: Algorithms are the steps on how to perform a task.
+
+==== Comparing Algorithms
+(((Comparing Algorithms)))
+Not all algorithms are created equal. There are “good” and “bad” algorithms. The good ones are fast; the bad ones are slow. Slow algorithms cost more money to run. Inefficient algorithms could make some calculations impossible in our lifespan!
+
+Let's say you want to compute the shortest path from Boston to San Francisco. Slow algorithms can take hours or crash before finishing. On the other hand, a "good" algorithm might compute in a few seconds.
+
+Usually, algorithms time grows as the size of the input increases. For instance, calculating the shortest distance from your house to the local supermarket will take less time than other destination thousands of miles away.
+
+Another example is sorting an array. A good sorting algorithm is <<part04-algorithmic-toolbox#merge-sort>>, and an inefficient algorithm for large inputs is <<part04-algorithmic-toolbox#selection-sort>>.
+Organizing 1 million elements with merge sort could take 20 seconds, for instance, while selection sort takes 12 days, ouch! The fantastic thing is that both programs solve the same problem with comparable data and hardware; yet, there's a big difference in time! Bad algorithms would perform poorly, even on a supercomputer.
+
+To give you a clearer picture of how different algorithms perform as the input size grows, look at the following problems and how their relative execution time changes as the input size increases.
+(((Tables, Intro, Algorithms input size vs Time)))
+
+.Relationship between algorithm input size and time taken to complete
+[cols=",,,,,",options="header",]
+|=============================================================================================
+|Input size -> |10 |100 |10k |100k |1M
+|Finding if a number is odd |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec.
+|Sorting array with merge sort |< 1 sec. |< 1 sec. |< 1 sec. |few sec. |20 sec.
+|Sorting array with Selection Sort |< 1 sec. |< 1 sec. |2 minutes |3 hours |12 days
+|Finding all subsets |< 1 sec. |40,170 trillion years |> centillion years |∞ |∞
+|Finding string permutations |4 sec. |> vigintillion years |> centillion years |∞ |∞
+|=============================================================================================
+
+As you can see in the table, most algorithms on the table are affected by the input size. But not all and not at the same rate. Finding out if a number is odd will take the same if it is 1 or 1 million. We say then that the growth rate is constant. Others grow very fast. Finding all the permutations on a string of length 10 takes a few seconds, while if the string has a size of 100, it won't even finish!
+
+After completing this book, you are going to _think algorithmically_. You will be able to tell the growth rate of your programs and scale them. You'll find bottlenecks of existing software and have an <<part04-algorithmic-toolbox#algorithms-toolbox>>.
+
+<<<
+==== Increasing your code performance
+
+The first step to improve your code performance is to learn how to measure it. As somebody said:
+
+(((quotes)))
+[quote, H. J. Harrington]
+Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.
+
+This section will learn the basics of measuring our current code performance and compare it with other algorithms.
+
+===== Calculating Time Complexity
+(((Time complexity)))
+In computer science, time complexity describes the number of operations a program will execute given the size of the input `n`.
+
+How do you get a function that gives you the rough number of operations that the CPU will execute?
+
+One idea is to analyze your code line by line and mind code inside loops. Let's do an example to explain this point. For instance, we have a function to find the minimum value on an array called `getMin`.
+
+.Translating lines of code to an approximate number of operations
+image::image4.png[Operations per line]
+
+Assuming that each line of code is an operation, we get the following:
+
+_3n + 3_
+
+`n` =  input size.
+
+That means that if you have an array of 3 elements, e.g. `getMin([3, 2, 9])`, then it will execute around _3(3)+3 = 12_ operations. Of course, this is not for every case. For instance, Line 12 is only executed if the condition on line 11 is met. As you might learn in the next section, we want to get the big picture and get rid of smaller terms to compare algorithms easier.
+
+==== Space Complexity
+(((Space Complexity)))
+Space complexity is similar to time complexity. However, instead of the count of operations executed, it will account for the amount of memory used additionally to the input.
+
+For calculating the *space complexity*, we keep track of the “variables” and memory used. In the `getMin` example, we create a variable called `min`, which only holds one value at a time. So, the space complexity is `1`. On other algorithms, If we have to use an auxiliary array that holds the same number of elements as the input, then the space complexity would be `n`.
+
+===== Simplifying Complexity with Asymptotic Analysis
+(((Asymptotic Analysis)))
+When we compare algorithms, we about the growth rate when the input gets huge (towards infinity). Then you have a function like `20*n^3 + 100`. If `n` is one million. The term `+ 100` makes a tiny contribution to the result (less than 0.000001%). Here is when the asymptotic analysis comes to the rescue.
+
+TIP: Asymptotic analysis describes the behavior of functions as their inputs approach to infinity.
+
+In the previous example, we analyzed `getMin` with an array of size 3; what happens if the size is 10, 10k, or 10 million?
+(((Tables, Intro, Operations of 3n+3)))
+
+.Operations performed by an algorithm with a time complexity of `3n + 3`
+[cols=",,",options="header",]
+|===========================
+|n (size) |Operations |total
+|10 |3(10) + 3 |33
+|10k |3(10k)+3 |30,003
+|1M |3(1M)+3 |3,000,003
+|===========================
+
+As the input size `n` grows bigger and bigger, then the expression _3n + 3_ is closer and closer to _3n_. Dropping terms might look like a stretch at first, but you will see that what matters the most is the higher-order terms of the function rather than lesser terms and constants.
+
+===== What is Big O Notation?
+(((Big O)))
+There’s a notation called *Big O*, where `O` refers to the *order of a function* in the worst-case scenario.
+
+TIP: Big O = Big Order (rate of growth) of a function.
+
+If you have a program that has a runtime of:
+
+_7n^3^ + 3n^2^ + 5_
+
+You can express it in Big O notation as _O(n^3^)_. The other terms (_3n^2^ + 5_) will become less significant as the input grows bigger.
+
+Big O notation only cares about the “biggest” terms in the time/space complexity. It combines what we learn about time and space complexity, asymptotic analysis and adds a worst-case scenario.
+
+.All algorithms have three scenarios:
+* Best-case scenario: the most favorable input arrangement where the program will take the least amount of operations to complete. E.g., a sorted array is beneficial for some sorting algorithms.
+* Average-case scenario: this is the most common case. E.g., array items in random order for a sorting algorithm.
+* Worst-case scenario: the inputs are arranged in such a way that causes the program to take the longest to complete. E.g., array items in reversed order for some sorting algorithm will take the longest to run.
+
+To sum up:
+
+TIP: Big O only cares about the run time function's highest order in the worst-case scenario.
+
+WARNING: Don't drop terms that are multiplying other terms. _O(n log n)_ is not equivalent to _O(n)_. However, _O(n + log n)_ is.
+
+There are many common notations like polynomial, _O(n^2^)_ as we saw in the `getMin` example, constant _O(1)_, and many more that we are going to explore in the next chapter.
+
+Again,
+
+TIP: the time complexity is not a direct measure of how long a program takes to execute, but rather how many operations it performs given the input size.
+
+Nevertheless, there’s a relationship between time complexity and clock time, as shown in the following table.
+(((Tables, Intro, Input size vs. clock time by Big O)))
+
+// tag::table[]
+.How long an algorithm takes to run based on their time complexity and input size
+[cols=",,,,,,",options="header",]
+|===============================================================
+|Input Size |O(1) |O(n) |O(n log n) |O(n^2^) |O(2^n^) |O(n!)
+|1 |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec.
+|10 |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |< 1 sec. |4 seconds
+|10k |< 1 sec. |< 1 sec. |< 1 sec. |2 minutes |∞ |∞
+|100k |< 1 sec. |< 1 sec. |1 second |3 hours |∞ |∞
+|1M |< 1 sec. |1 second |20 seconds |12 days |∞ |∞
+|===============================================================
+// end::table[]
+
+
+
+NOTE: This is just an illustration since, in different hardware, the times will be distinct. These times are under the assumption of running on 1 GHz CPU, and it can execute on average one instruction in 1 nanosecond (usually takes more time). Also, keep in mind that each line might be translated into dozens of CPU instructions depending on the programming language.
+
+==== Summary
+
+In this chapter, we learned how you could measure your algorithm performance using time complexity. Rather than timing how long your program takes to run, you can approximate the number of operations it will perform based on the input size.
+
+We learned about time and space complexity and how they can be translated to Big O notation. Big O refers to the *order* of the function.
+
+In the next section, we will go deeper into how to analyze time complexity and provide examples!
diff --git a/book/content/part01/big-o-examples.asc b/book/content/part01/big-o-examples.asc
new file mode 100644
index 00000000..c7755736
--- /dev/null
+++ b/book/content/part01/big-o-examples.asc
@@ -0,0 +1,308 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Big O examples
+
+There are many kinds of algorithms. Most of them fall into one of the eight-time complexities that we will explore in this chapter.
+
+.Eight Running Time Complexities You Should Know
+- Constant time: _O(1)_
+- Logarithmic time: _O(log n)_
+- Linear time: _O(n)_
+- Linearithmic time: _O(n log n)_
+- Quadratic time: _O(n^2^)_
+- Cubic time: _O(n^3^)_
+- Exponential time: _O(2^n^)_
+- Factorial time: _O(n!)_
+
+We are going to provide examples for each one of them.
+
+Before we dive in, here’s a plot with all of them.
+
+.CPU operations vs. Algorithm runtime as the input size grows
+// image::image5.png[CPU time needed vs. Algorithm runtime as the input size increases]
+image::time-complexity-manual.png[{half-size}]
+
+The above chart shows how the algorithm's running time is related to the CPU's work. As you can see, O(1) and O(log n) is very scalable. However, O(n^2^) and worst can convert your CPU into a furnace 🔥 for massive inputs.
+
+[[constant]]
+==== Constant
+(((Constant)))
+(((Runtime, Constant)))
+Represented as *O(1)*, it means that regardless of the input size, the number of operations executed is always the same. Let’s see an example:
+
+[#constant-example]
+===== Finding if an array is empty
+
+Let's implement a function that finds out if an array is empty or not.
+
+//.is-empty.js
+//image:image6.png[image,width=528,height=401]
+
+[source, javascript]
+----
+include::{codedir}/runtimes/01-is-empty.js[tag=isEmpty]
+----
+
+Another more real life example is adding an element to the begining of a <<part02-linear-data-structures#linked-list>>. You can check out the implementation <<part02-linear-data-structures#linked-list-inserting-beginning, here>>.
+
+As you can see in both examples (array and linked list), if the input is a collection of 10 elements or 10M, it would take the same amount of time to execute. You can't get any more performant than this!
+
+[[logarithmic]]
+==== Logarithmic
+(((Logarithmic)))
+(((Runtime, Logarithmic)))
+Represented in Big O notation as *O(log n)*, when an algorithm has this running time, it means that as the input size grows, the number of operations grows very slowly. Logarithmic algorithms are very scalable. One example is the *binary search*.
+indexterm:[Runtime, Logarithmic]
+
+[[logarithmic-example]]
+===== Searching on a sorted array
+
+The binary search only works for sorted lists. It starts searching for an element in the middle of the array, and then it moves to the right or left depending on if the value you are looking for is bigger or smaller.
+
+// image:image7.png[image,width=528,height=437]
+
+[source, javascript]
+----
+include::{codedir}/runtimes/02-binary-search.js[tag=binarySearchRecursive]
+----
+
+This binary search implementation is a recursive algorithm, which means that the function `binarySearchRecursive` calls itself multiple times until the program finds a solution. The binary search splits the array in half every time.
+
+Finding the runtime of recursive algorithms is not very obvious sometimes. It requires some approaches like recursion trees or the https://adrianmejia.com/blog/2018/04/24/analysis-of-recursive-algorithms/[Master Theorem].
+
+Since the `binarySearch` divides the input in half each time. As a rule of thumb, when you have an algorithm that divides the data in half on each call, you are most likely in front of a logarithmic runtime: _O(log n)_.
+
+[[linear]]
+==== Linear
+(((Linear)))
+(((Runtime, Linear)))
+Linear algorithms are one of the most common runtimes. Their Big O representation is *O(n)*. Usually, an algorithm has a linear running time when it visits every input element a fixed number of times.
+
+[[linear-example]]
+===== Finding duplicates in an array using a map
+
+Let’s say that we want to find duplicate elements in an array. What’s the first implementation that comes to mind? Check out this implementation:
+
+// image:image8.png[image,width=528,height=383]
+
+[source, javascript]
+----
+include::{codedir}/runtimes/03-has-duplicates.js[tag=hasDuplicates]
+----
+
+.`hasDuplicates` has multiple scenarios:
+* *Best-case scenario*: the first two elements are duplicates. It only has to visit two elements and return.
+* *Worst-case scenario*: no duplicates or duplicates are the last two. In either case, it has to visit every item in the array.
+* *Average-case scenario*: duplicates are somewhere in the middle of the collection.
+
+As we learned before, the big O cares about the worst-case scenario, where we would have to visit every element on the array. So, we have an *O(n)* runtime.
+
+Space complexity is also *O(n)* since we are using an auxiliary data structure.  We have a map that, in the worst case (no duplicates), it will hold every word.
+
+[[linearithmic]]
+==== Linearithmic
+(((Linearithmic)))
+(((Runtime, Linearithmic)))
+You can represent linearithmic algorithms as _O(n log n)_. This one is important because it is the best runtime for sorting! Let’s see the merge-sort.
+
+[[linearithmic-example]]
+===== Sorting elements in an array
+
+The ((Merge Sort)), like its name indicates, has two functions merge and sort. Let’s start with the sort function:
+
+// image:image9.png[image,width=528,height=383]
+
+.Sort part of the mergeSort
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/merge-sort.js[tag=splitSort]
+----
+<1> If the array only has two elements, we can sort them manually.
+<2> We divide the array into two halves.
+<3> Merge the two parts recursively with the `merge` function explained below
+
+// image:image10.png[image,width=528,height=380]
+
+.Merge part of the mergeSort
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/merge-sort.js[tag=merge]
+----
+
+The merge function combines two sorted arrays in ascending order. Let’s say that we want to sort the array `[9, 2, 5, 1, 7, 6]`. In the following illustration, you can see what each function does.
+
+.Mergesort visualization. Shows the split, sort and merge steps
+image::image11.png[Mergesort visualization,width=500,height=600]
+
+How do we obtain the running time of the merge sort algorithm? The merge-sort divides the array in half each time in the split phase, _log n_, and the merge function join each splits, _n_. The total work is *O(n log n)*. There are more formal ways to reach this runtime, like using the https://adrianmejia.com/blog/2018/04/24/analysis-of-recursive-algorithms/[Master Method] and https://www.cs.cornell.edu/courses/cs3110/2012sp/lectures/lec20-master/lec20.html[recursion trees].
+
+[[quadratic]]
+==== Quadratic
+(((Quadratic)))
+(((Runtime, Quadratic)))
+Quadratic running times, O(n^2^), are the ones to watch out for. They usually don’t scale well when they have a large amount of data to process.
+
+Usually, they have double-nested loops, where each one visits all or most elements in the input. One example of this is a naïve implementation to find duplicate words on an array.
+
+[[quadratic-example]]
+===== Finding duplicates in an array (naïve approach)
+
+If you remember, we have solved this problem more efficiently in the <<part01-algorithms-analysis#linear, Linear>> section. We solved this problem before using an _O(n)_, let’s solve it this time with an _O(n^2^)_:
+
+// image:image12.png[image,width=527,height=389]
+
+.Naïve implementation of hasDuplicates function
+[source, javascript]
+----
+include::{codedir}/runtimes/05-has-duplicates-naive.js[tag=hasDuplicates]
+----
+
+As you can see, we have two nested loops causing the running time to be quadratic. How much difference is there between a linear vs. quadratic algorithm?
+
+Let’s say you want to find a duplicated middle name in a phone directory book of a city of ~1 million people. If you use this quadratic solution, you would have to wait for ~12 days to get an answer [big]#🐢#; while if you use the <<part01-algorithms-analysis#linear, linear solution>>, you will get the answer in seconds! [big]#🚀#
+
+[[cubic]]
+==== Cubic
+(((Cubic)))
+(((Runtime, Cubic)))
+Cubic *O(n^3^)* and higher polynomial functions usually involve many nested loops. An example of a cubic algorithm is a multi-variable equation solver (using brute force) or finding three elements on an array that add up to a given number.
+
+[[cubic-example]]
+===== 3 Sum
+
+Let's say you want to find 3 items in an array that add up to a target number. One brute force solution would be to visit every possible combination of 3 elements and add them to see if they are equal to the target.
+
+[source, javascript]
+----
+function threeSum(nums, target = 0) {
+  const ans = [];
+
+  for(let i = 0; i < nums.length -2; i++)
+    for(let j = i + 1; j < nums.length - 1; j++)
+      for(let k = j + 1; k < nums.length; k++)
+        if (nums[i] + nums[j] + nums[k] === target)
+          ans.push([nums[i], nums[j], nums[k]]);
+
+  return ans;
+}
+----
+
+As you can see, three nested loops usually translate to O(n^3^). If we had four nested loops (4sum), it would be O(n^4^) and so on. A runtime in the form of _O(n^c^)_, where _c > 1_, we refer to this as a *polynomial runtime*.
+
+NOTE: You can improve the runtime of 3sum from _O(n^3^)_ to _O(n^2^)_, if we sort items first and then use one loop and two pointers to find the solutions.
+
+[[exponential]]
+==== Exponential
+(((Exponential)))
+(((Runtime, Exponential)))
+Exponential runtimes, _O(2^n^)_, means that every time the input grows by one, the number of operations doubles. Exponential programs are only usable for a tiny number of elements (<100); otherwise, it might not finish in your lifetime.  [big]#💀#
+
+Let’s do an example.
+
+[[exponential-example]]
+===== Finding subsets of a set
+
+Finding all distinct subsets of a given set can be implemented as follows:
+
+// image:image14.png[image,width=528,height=401]
+
+.Subsets in a Set
+[source, javascript]
+----
+include::{codedir}/runtimes/07-sub-sets.js[tag=snippet]
+----
+<1> Base case is empty element.
+<2> For each element from the input, append it to the results array.
+<3> The new results array will be what it was before + the duplicated with the appended element.
+
+//.The way this algorithm generates all subsets is:
+//1.  The base case is an empty element (line 13). E.g. ['']
+//2.  For each element from the input, append it to the results array (line 16)
+//3.  The new results array will be what it was before + the duplicated with the appended element (line 17)
+
+Every time the input grows by one, the resulting array doubles. That’s why it has an *O(2^n^)*.
+
+[[factorial]]
+==== Factorial
+(((Factorial)))
+(((Runtime, Factorial)))
+The factorial runtime, `O(n!)`, is not scalable at all. Even with input sizes of ~10 elements, it will take a couple of seconds to compute. It’s that slow! [big]*🍯🐝*
+
+.Factorial
+****
+A factorial is the multiplication of all the numbers less than itself down to 1.
+
+.For instance:
+- 3! = 3 x 2 x 1 = 6
+- 5! = 5 x 4 x 3 x 2 x 1 = 120
+- 10! = 3,628,800
+- 11! = 39,916,800
+****
+
+[[factorial-example]]
+===== Getting all permutations of a word
+(((Permutations)))
+(((Words permutations)))
+One classic example of an _O(n!)_ algorithm is finding all the different words formed with a given set of letters.
+
+.Word's permutations
+// image:image15.png[image,width=528,height=377]
+[source, javascript]
+----
+include::{codedir}/runtimes/08-permutations.js[tag=snippet]
+----
+
+As you can see in the `getPermutations` function, the resulting array is the factorial of the word length.
+
+Factorial starts very slow and quickly becomes unmanageable. A word size of just 11 characters would take a couple of hours in most computers!
+[big]*🤯*
+
+==== Summary
+
+We went through 8 of the most common time complexities and provided examples for each of them. Hopefully, this will give you a toolbox to analyze algorithms.
+(((Tables, Intro, Common time complexities and examples)))
+
+// tag::table[]
+.Most common algorithmic running times and their examples
+[cols="2,2,5",options="header"]
+|===
+|Big O Notation
+|Name
+| example (s)
+
+|O(1)
+|<<part01-algorithms-analysis#constant>>
+|<<part01-algorithms-analysis#constant-example>>
+
+|O(log n)
+|<<part01-algorithms-analysis#logarithmic>>
+|<<part01-algorithms-analysis#logarithmic-example>>
+
+|O(n)
+|<<part01-algorithms-analysis#linear>>
+|<<part01-algorithms-analysis#linear-example>>
+
+|O(n log n)
+|<<part01-algorithms-analysis#linearithmic>>
+|<<part01-algorithms-analysis#linearithmic-example>>
+
+|O(n^2^)
+|<<part01-algorithms-analysis#quadratic>>
+|<<part01-algorithms-analysis#quadratic-example>>
+
+|O(n^3^)
+|<<part01-algorithms-analysis#cubic>>
+|<<part01-algorithms-analysis#cubic-example>>
+
+|O(2^n^)
+|<<part01-algorithms-analysis#exponential>>
+|<<part01-algorithms-analysis#exponential-example>>
+
+|O(n!)
+|<<part01-algorithms-analysis#factorial>>
+|<<part01-algorithms-analysis#factorial-example>>
+|===
+// end::table[]
diff --git a/book/content/part01/how-to-big-o.asc b/book/content/part01/how-to-big-o.asc
new file mode 100644
index 00000000..26a3358e
--- /dev/null
+++ b/book/content/part01/how-to-big-o.asc
@@ -0,0 +1,222 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== How to determine time complexity from code?
+
+In general, you can determine the time complexity by analyzing the program's statements.
+However, you have to be mindful of how are the statements arranged. Suppose they are inside a loop or have function calls or even recursion. All these factors affect the runtime of your code. Let's see how to deal with these cases.
+
+*Sequential Statements*
+
+If we have statements with basic operations like conditionals, assignments, reading a variable.
+We can assume they take constant time.
+
+----
+statement 1;
+statement 2;
+...
+statement n;
+----
+
+If we calculate the total time complexity, it would be something like this:
+
+----
+total = time(statement 1) + time(statement 2) + ... time (statement n)
+----
+
+Let's use `T(n)` as the total time in function of the input size `n`, and `t` as the time complexity taken by a statement or group of statements.
+
+----
+T(n) = t(statement 1) + t(statement 2) + ... + t(statement n);
+----
+
+If each statement executes a basic operation, we can say it takes constant time `O(1)`. As long as you have a fixed number of operations, it will be constant time, even if we have 1 or 100 of these statements.
+
+WARNING: be careful with function calls. You will have to go to the implementation and check their run time. More on that later.
+
+*Conditional Statements*
+
+Very rarely, you have a code without any conditional statement.
+How do you calculate the time complexity? Remember that we care about the worst-case with Big O so that we will take the maximum possible runtime.
+
+----
+if (isValid) {
+  statement 1;
+  statement 2;
+} else {
+  statement 3;
+}
+----
+
+Since, we are after the worst-case we take the whichever is larger of the two possibilities:
+
+----
+T(n) = Math.max([t(statement 1) + t(statement 2)], [time(statement 3)])
+----
+
+*Loop Statements*
+
+Another prevalent scenario is loops like for-loops or while-loops.
+For any loop, we find out the runtime of the block inside them and multiply it by the number of times the program will repeat the loop.
+
+----
+for (let i = 0; i < array.length; i++) {
+  statement 1;
+  statement 2;
+}
+----
+
+For this example, the loop is executed `array.length`, assuming `n` is length of the array we get the following:
+
+----
+T(n) = n * [ t(statement 1) + t(statement 2) ]
+----
+
+All loops that grow proportionally to the input size have a linear time complexity `O(n)`. If you loop through only half of the array, that's still `O(n)`. Remember that we drop the constants so `1/2 n => O(n)`.
+
+However, if a constant number bounds the loop, let's say 4 (or even 400). Then, the runtime is constant `O(4) -> O(1)`. See the following example.
+
+----
+for (let i = 0; i < 4; i++) {
+  statement 1;
+  statement 2;
+}
+----
+
+That code is `O(1)` because it no longer depends on the input size.
+
+*Nested loops statements*
+
+Sometimes you might need to visit all the elements on a 2D array (grid/table). For such cases, you might find yourself with two nested loops.
+
+----
+for (let i = 0; i < n; i++) {
+  statement 1;
+
+  for (let j = 0; j < m; j++) {
+    statement 2;
+    statement 3;
+  }
+}
+----
+
+For this case you would have something like this:
+
+----
+T(n) = n * [t(statement 1) + m * t(statement 2...3)]
+----
+
+Assuming the statements from 1 to 3 are `O(1)`, we would have a runtime of `O(n * m)`.
+If instead of `m`, you had to iterate on `n` again, then it would be `O(n^2)`. Another typical case is having a function inside a loop. Let's see how to deal with that next.
+
+[[big-o-function-statement]]
+*Function call statements*
+
+When you calculate your programs' time complexity and invoke a function, you need to be aware of its runtime. If you created the function, that might be a simple inspection of the implementation. However, you might infer it from the language/library documentation if you use a 3rd party function.
+
+Let's say you have the following program:
+
+----
+for (let i = 0; i < n; i++) {
+  fn1();
+  for (let j = 0; j < n; j++) {
+    fn2();
+    for (let k = 0; k < n; k++) {
+      fn3();
+    }
+  }
+}
+----
+
+.Depending on the runtime of fn1, fn2, and fn3, you would have different runtimes.
+- If they all are constant `O(1)`, then the final runtime would be `O(n^3)`.
+- However, if only `fn1` and `fn2` are constant and `fn3` has a runtime of `O(n^2)`, this program will have a runtime of `O(n^5)`. Another way to look at it is, if `fn3` has two nested and you replace the invocation with the actual implementation, you would have five nested loops.
+
+In general, you will have something like this:
+
+----
+T(n) = n * [ t(fn1()) + n * [ t(fn2()) + n * [ t(fn3()) ] ] ]
+----
+
+*Recursive Functions Statements*
+
+Analyzing the runtime of recursive functions might get a little tricky. There are different ways to do it. One intuitive way is to explore the recursion tree.
+
+Let's say that we have the following program:
+
+----
+function fn(n) {
+  if (n < 0) return 0;
+  if (n < 2) return n;
+
+  return fn(n - 1) + fn(n - 2);
+}
+----
+
+You can represent each function invocation as a bubble (or node).
+
+.Let's do some examples:
+- When you n = 2, you have 3 function calls. First `fn(2)` which in turn calls `fn(1)` and `fn(0)`.
+- For `n = 3`, you have 5 function calls. First `fn(3)`, which in turn calls `fn(2)` and `fn(1)` and so on.
+- For `n = 4`, you have 9 function calls. First `fn(4)`, which in turn calls `fn(3)` and `fn(2)` and so on.
+
+Since it's a binary tree, we can sense that every time `n` increases by one, we would have to perform at most the double of operations.
+
+Here's the graphical representation of the 3 examples:
+
+[graphviz, big-o-recursive-example, 'png']
+----
+graph G {
+    subgraph cluster_2 {
+      label = "fn(2)"
+      "fn(2)-" -- { "fn(1)-", "fn(0)-" }
+
+      "fn(0)-" [label="fn(0)"];
+      "fn(1)-" [label="fn(1)", color=red];
+      "fn(2)-" [label="fn(2)", color=red];
+    }
+
+    subgraph cluster_1 {
+      label = "fn(3)"
+      "fn(3)1" -- { "fn(2)1", "fn(1)1" }
+      "fn(2)1" -- { "fn(1)3", "fn(0)1" }
+
+      "fn(0)1" [label="fn(0)"];
+      "fn(1)3" [label="fn(1)", color=red];
+      "fn(1)1" [label="fn(1)"];
+      "fn(2)1" [label="fn(2)", color=red];
+      "fn(3)1" [label="fn(3)", color=red];
+    }
+
+    subgraph cluster_0 {
+      label = "fn(4)"
+      "fn(4)" -- { "fn(3)*", "fn(2)" }
+      "fn(2)" -- { "fn(1)*", "fn(0)" }
+      "fn(3)*" -- { "fn(2)**", "fn(1)**" }
+      "fn(2)**" -- { "fn(1)****", "fn(0)**" }
+
+      "fn(0)**" [label="fn(0)"];
+      "fn(1)*" [label="fn(1)"];
+      "fn(1)**" [label="fn(1)"];
+      "fn(1)****" [label="fn(1)", color=red];
+      "fn(2)**" [label="fn(2)", color=red];
+      "fn(3)*" [label="fn(3)", color=red];
+      "fn(4)" [label="fn(4)", color=red];
+    }
+}
+----
+
+If you take a look at the generated tree calls, the leftmost nodes go down in descending order: `fn(4)`, `fn(3)`, `fn(2)`, `fn(1)`, which means that the height of the tree (or the number of levels) on the tree will be `n`.
+
+The total number of calls in a complete binary tree is `2^n - 1`. As you can see in `fn(4)`, the tree is not complete. The last level will only have two nodes, `fn(1)` and `fn(0)`, while a full tree would have eight nodes. But still, we can say the runtime would be exponential `O(2^n)`. It won't get any worst because `2^n` is the upper bound.
+
+==== Summary
+
+.In this chapter, we learned how to calculate the time complexity of our code when we have the following elements:
+- Basic operations like assignments, bit, and math operators.
+- Loops and nested loops
+- Function invocations and recursions.
+
+In the next section, we are going to the most common time complexities and real code examples.
diff --git a/book/content/part02/array-vs-list-vs-queue-vs-stack.asc b/book/content/part02/array-vs-list-vs-queue-vs-stack.asc
new file mode 100644
index 00000000..1c88b696
--- /dev/null
+++ b/book/content/part02/array-vs-list-vs-queue-vs-stack.asc
@@ -0,0 +1,41 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Array vs. Linked List & Queue vs. Stack
+
+In this part of the book, we explored the most used linear data structures such as Arrays, Linked Lists, Stacks, and Queues. We implemented them and discussed the runtime of their operations.
+
+.Use Arrays when…
+* You need to access data in random order fast (using an index).
+* Your data is multi-dimensional (e.g., matrix, tensor).
+
+.Use Linked Lists when:
+* You will access your data sequentially.
+* You want to save memory and only allocate memory as you need it.
+* You want constant time to remove/add from extremes of the list.
+
+.Use a Queue when:
+* You need to access your data on a first-come, first-served basis (FIFO).
+* You need to implement a <<part03-graph-data-structures#bfs-tree, Breadth-First Search>>
+
+.Use a Stack when:
+* You need to access your data as last-in, first-out (LIFO).
+* You need to implement a <<part03-graph-data-structures#dfs-tree, Depth-First Search>>
+(((Tables, Linear DS, Array/Lists/Stack/Queue complexities)))
+
+[[linear-data-structures-table]]
+// tag::table[]
+.Time/Space Complexity of Linear Data Structures (Array, LinkedList, Stack & Queues)
+|===
+.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space
+^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
+| <<array-chap>> ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
+| <<part02-linear-data-structures#singly-linked-list>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|*O(n)* ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
+| <<part02-linear-data-structures#doubly-linked-list>> ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|*O(1)* ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
+| <<part02-linear-data-structures#stack>> ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
+| Queue (w/array) ^|- ^|- ^|- ^|- ^|O(1) ^|*O(n)* ^|- ^|- ^|O(n)
+| <<part02-linear-data-structures#queue>> (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|*O(1)* ^|- ^|- ^|O(n)
+|===
+// end::table[]
diff --git a/book/content/part02/array.asc b/book/content/part02/array.asc
new file mode 100644
index 00000000..25c029ee
--- /dev/null
+++ b/book/content/part02/array.asc
@@ -0,0 +1,563 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Array))) (((Data Structures, Linear, Array)))
+[[array]]
+[[array-chap]]
+=== Array
+
+Arrays are one of the most used data structures. You probably have used it a lot already. But, are you aware of the runtimes of `push`, `splice`, `shift`, `indexOf`, and other operations? In this chapter, we are going deeper into the most common operations and their runtimes.
+
+==== Array  Basics
+
+An array is a collection of things (strings, characters, numbers, objects, etc.). They can be many or zero.
+
+TIP: Strings are a collection of characters. Most of the array methods apply to strings as well.
+
+Arrays look like this:
+
+.Array representation: You can access each value in constant time through its index.
+image::image16.png[image,width=388,height=110]
+
+===== Read and Update
+
+Arrays are a contiguous collection of elements that can be accessed randomly using an index. This access by index operation takes `O(1)` time. Let’s take a look at the different functions that we can do with arrays.
+
+.Reading elements from an array and string
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 6, 7];
+const string = "hello";
+console.log(array[2]); // 1
+console.log(string[1]); // "e"
+----
+
+As you can see, you can access the string's characters using the same operator as arrays.
+
+You can update arrays in the same way, using the `[]` operator. However, you can't modify strings. They are immutable!
+
+.Reading elements from an array and string
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 6, 7];
+const string = "hello";
+array[2] = 117;
+console.log(array[2]); // 117
+string[1] = "z"; // doesn't change the string.
+console.log(string[1]); // "e"
+----
+
+WARNING: When you try to modify and string, you won't get an error or anything. It just gets ignored! Your only option is to create a new string with the adjusted value.
+
+===== Insertion
+
+Insertions on an array have different times complexities.
+O(1): constant time (on average) to append a value at the end of the array.
+O(n): linear time to insert a value at the beginning or middle.
+
+====== Inserting at the beginning of the array
+
+What if you want to insert a new element at the beginning of the array? You would have to push every item to the right. We can use the following method:
+
+.Syntax
+[source, javascript]
+----
+const newArrLength = arr.unshift(element1[, ...[, elementN]]);
+----
+
+Here's an example:
+
+.Insert to head
+[source, javascript]
+----
+const array = [2, 5, 1];
+array.unshift(0); // ↪️ 4
+console.log(array); // [ 0, 2, 5, 1 ]
+array.unshift(-2, -1); // ↪️ 6
+console.log(array); // [ -2, -1, 0, 2, 5, 1 ]
+----
+
+As you can see, `2` was at index 0, now was pushed to index 1, and everything else is on a different index. `unshift` takes *O(n)* since it affects **all** the elements of the array.
+
+.JavaScript built-in `array.unshift`
+****
+The `unshift()` method adds one or more elements to the beginning of an array and returns its new length.
+
+Runtime: `O(n)`.
+****
+
+====== Inserting at the middle of the array
+
+Inserting a new element in the middle involves moving part of the array but not all of the items. We can use `splice` for that:
+
+.Syntax
+[source, javascript]
+----
+const arrDeletedItems = arr.splice(start[, deleteCount[, item1[, item2[, ...]]]]);
+----
+
+Based on the parameters it takes, you can see that we can add and delete items. Here's an example of inserting in the middle.
+
+.Inserting element in the middle
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 6, 7];
+array.splice(1, 0, 111); // ↪️ [] <1>
+// array: [2, 111, 5, 1, 9, 6, 7]
+----
+<1> at position `1`, delete `0` elements and insert `111`.
+
+The Big O for this operation would be *O(n)* since, in the worst case, it would move most of the elements to the right.
+
+.JavaScript built-in `array.splice`
+****
+The `splice()` method changes an array's contents by removing existing elements or adding new items.  Splice returns an array containing the deleted items.
+
+Runtime: O(n).
+****
+
+====== Inserting at the end of the array
+
+For inserting items at the end of the array, we can use: push.
+
+.Syntax
+[source, javascript]
+----
+const newArrLength = arr.push([element1[, ...[, elementN]]]);
+----
+
+We can push new values to the end of the array like this:
+
+.Insert to tail
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 6, 7];
+array.push(4); // ↪️ 7 <1>
+// array: [2, 5, 1, 9, 6, 7, 4]
+----
+<1> The `4` element would be pushed to the end of the array. Notice that `push` returns the new length of the array.
+
+Adding to the tail of the array doesn’t change other indexes. E.g., element 2 is still at index 0. So, this is a constant time operation *O(1)*.
+
+.JavaScript built-in `array.push`
+****
+The `push()` method adds one or more elements to the end of an array and returns its new length.
+
+Runtime: O(1).
+****
+
+[[array-search-by-value]]
+===== Searching by value and index
+
+As we saw before, searching by the index is very easy using the `[]` operator:
+
+.Search by index
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 6, 7];
+array[4]; // ↪️ 6
+----
+
+Searching by index takes constant time - *O(1)* - to retrieve values out of the array.
+
+Searching by value can be done using `indexOf`.
+
+.Syntax
+[source, javascript]
+----
+const index = arr.indexOf(searchElement[, fromIndex]);
+----
+
+If the value is there, we will get the index, otherwise `-1`.
+
+.Search by value
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 6, 7];
+console.log(array.indexOf(9)); // ↪️ 3
+console.log(array.indexOf(90)); // ↪️ -1
+----
+
+Internally, `indexOf` has to loop through the whole array (worst case) or until we find the first occurrence. Time complexity is *O(n)*.
+
+===== Deletion
+
+There are three possible deletion scenarios (similar to insertion): removing at the beginning, middle, or end.
+
+====== Deleting element from the beginning
+
+Deleting from the beginning can be done using the `splice` function and the `shift`. For simplicity, we will use the latter.
+
+.Syntax
+[source, javascript]
+----
+const removedElement = arr.shift();
+let arrDeletedItems = arr.splice(start[, deleteCount[, item1[, item2[, ...]]]]);
+----
+
+.Deleting from the beginning of the array.
+[source, javascript]
+----
+const array = [2, 111, 5, 1, 9, 6, 7];
+// Deleting from the beginning of the array.
+array.shift(); // ↪️ 2
+array.shift(); // ↪️ 111
+console.log(array); // [5, 1, 9, 6, 7]
+array.splice(0, 1); // ↪️ [ 5 ]
+console.log(array); // [ 1, 9, 6, 7 ]
+----
+
+As expected, this will change every index on the array, so this takes linear time: *O(n)*.
+
+.JavaScript built-in array.shift
+****
+The `shift()` method shift all elements to the left. In turn, it removes the first element from an array and returns that removed element. This method changes the length of the array.
+
+Runtime: O(n).
+****
+
+====== Deleting element from the middle
+
+We can use the `splice` method for deleting an item from the middle of an array.
+
+You can delete multiple items at once:
+
+.Deleting from the middle
+[source, javascript]
+----
+const array = [0, 1, 2, 3, 4];
+// Deleting from the middle
+array.splice(2, 3); // ↪️ [ 2, 3, 4 ] <1>
+console.log(array); // [0, 1]
+----
+<1> delete 3 elements starting on position 2
+
+Deleting from the middle might cause most of the array elements to move up one position to fill in for the eliminated item. Thus, runtime: O(n).
+
+====== Deleting element from the end
+
+Removing the last element is very straightforward using pop:
+
+.Syntax
+[source, javascript]
+----
+const removedItem = arr.pop();
+----
+
+Here's an example:
+
+.Deleting last element from the array
+[source, javascript]
+----
+const array = [2, 5, 1, 9, 111];
+array.pop();  // ↪️111
+// array: [2, 5, 1, 9]
+----
+
+While deleting the last element, no other item was touched, so that’s an _O(1)_ runtime.
+
+.JavaScript built-in `array.pop`
+****
+The `pop()` method removes the last element from an array and returns that element. This method changes the length of the array.
+
+Runtime: O(1).
+****
+
+===== Array Complexity
+
+To sum up, the time complexity of an array is:
+(((Tables, Linear DS, Array Complexities)))
+
+// tag::table[]
+.Time/Space complexity for the array operations
+|===
+.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space
+^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
+| Array ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
+|===
+(((Linear)))
+(((Runtime, Linear)))
+(((Constant)))
+(((Runtime, Constant)))
+(((Tables, Linear DS, JavaScript Array buit-in operations Complexities)))
+
+.Array Operations time complexity
+|===
+| Operation | Time Complexity | Usage
+| `push` ^| O(1) | Insert element on the right side.
+| `pop` ^| O(1) | Remove the rightmost element.
+| `[]` ^| O(1) | Search for element by index.
+| `indexOf` ^| O(n) | Search for element by value.
+| `unshift` ^| O(n) | Insert element on the left side.
+| `shift` ^| O(n) | Remove leftmost element.
+| `splice` ^| O(n) | Insert and remove from anywhere.
+| `slice` ^| O(n) | Returns a shallow copy of the array.
+|===
+//end::table
+
+==== Array Patterns for Solving Interview Questions
+
+Many programming problems involve manipulating arrays. Here are some patterns that can help you improve your problem-solving skills.
+
+===== Two Pointers Pattern (((Patterns, Two Pointers)))
+
+Usually, we use one pointer to navigate each element in an array. However, there are times when having two pointers (left/right, low/high) comes in handy. Let's do some examples.
+
+*AR-A*) _Given a sorted `array` of integers, find two numbers that add up to a `target` and return their values._
+
+.Function Signature
+[source, javascript]
+----
+/**
+ * Find two numbers that add up target.
+ * @param arr - The array of integers
+ * @param target - The target
+ * @returns {number[]} - array with the values that add up to target.
+ */
+function twoSum(arr, target) {
+  // give it a try on your own ...
+}
+----
+
+
+.Examples
+[source, javascript]
+----
+twoSum([ -5, -3, 1, 10 ], 7); // [-3, 10] // (10 - 3 = 7)
+twoSum([ -5, -3, -1, 1, 2 ], 30); // [] // no 2 numbers add up to 30
+twoSum([ -3, -2, -1, 1, 1,  3,  4], -4); // [-3, -1] // (-3 -1 = -4)
+----
+
+**Solutions:**
+
+One naive solution would be to use two pointers in a nested loop:
+
+.Solution 1: Brute force
+[source, javascript]
+----
+function twoSum(arr, target) {
+  for (let i = 0; i < arr.length - 1; i++)
+    for (let j = i + 1; j < arr.length; j++)
+      if (arr[i] + arr[j] === target) return [arr[i], arr[j]];
+  return [];
+}
+----
+
+The runtime of this solution would be `O(n^2)`. Because of the nested loops. Can we do better? We are not using the fact that the array is SORTED!
+
+We can use two pointers: one pointer starting from the left side and the other from the right side.
+
+Depending on whether the sum is bigger or smaller than the target, we move right or left. If the sum is equal to the target, we return the current left and right pointer's values.
+
+.Solution 2: Two Pointers
+[source, javascript]
+----
+function twoSum(arr, target) {
+  let left = 0, right = arr.length -1;
+  while (left < right) {
+    const sum = arr[left] + arr[right];
+    if (sum === target) return [arr[left], arr[right]];
+    else if (sum > target) right--;
+    else left++;
+  }
+  return [];
+}
+----
+
+These two pointers have a runtime of `O(n)`.
+
+WARNING: This technique only works for sorted arrays. If the array was not sorted, you would have to sort it first or choose another approach.
+
+===== Sliding Window Pattern (((Patterns, Sliding Window))) [[sliding-window-array]]
+
+The sliding window pattern is similar to the two pointers. The difference is that the distance between the left and right pointer is always the same. Also, the numbers don't need to be sorted. Let's do an example!
+
+*AR-B*) _Find the max sum of an array of integers, only taking `k` items from the right and left side sequentially._
+**Constraints**: `k` won't exceed the number of elements in the array: `1 <= k <= n`.
+
+.Function Signature
+[source, javascript]
+----
+/**
+ * Find the max sum of an array of integers,
+ * only taking `k` items from the right and left side.
+ *
+ * @param {number[]} arr - The array of integers
+ * @param {number} k - The number of elements to sum up.
+ * @returns {number}
+ */
+function maxSum(arr, k) {
+  // Give it a try
+};
+----
+
+.Examples
+[source, javascript]
+----
+maxSum([1,2,3], 3); // 6 // (1 + 2 + 3 = 6)
+maxSum([1,1,1,1,200,1], 3); // 202 // (1 + 200 + 1 = 202)
+maxSum([3, 10, 12, 4, 7, 2, 100, 1], 3); // 104 // (3 + 1 + 100 = 104)
+maxSum([1,200,1], 1); // 6 // (1 + 2 + 3 = 6)
+----
+
+There are multiple ways to solve this problem. Before applying the sliding window, let's consider this other algorithm:
+
+*Backtracking algorithm*
+
+Let's take `[3, 10, 12, 4, 7, 2, 100, 1], k = 3` as an example.
+
+- We have two initial choices: going left with `3` or right with `1`.
+- We can take the first element from the left side `3`; from there, we can keep going left with `10` or right `1`.
+- If we go right with `1` on the right side, next, we have two options from the right side `100` or `10`.
+- If we go with `100`, then we compute the sum `3 + 1 + 100 = 104`.
+- Repeat with other combinations and keep track of the max sum.
+
+How many combinations can we form? `2^k`, since in the worst-case `k` is equal to `n`, then we have a runtime of `O(2^n)`!
+
+// image::max-sum-backtracking.png[max sum with backtracking]
+
+We can also visualize all the options as follows. If you add up the numbers from top to bottom, you get the result for all combinations:
+
+[graphviz, max-sum-sliding-window-red, png]
+....
+graph G {
+    0 -- 3
+    0 -- 1
+
+    3 -- 10
+    3 -- a1
+
+    10 -- 12
+    10 -- b1
+
+    a1 -- a10
+    a1 -- 100
+
+    1 -- a3
+    1 -- a100
+
+    a3 -- b10
+    a3 -- b100
+
+    a100 -- b3
+    a100 -- 2
+
+    1, a1, b1 [label = 1 color = red]
+    10, a10, b10 [label = 10 color = red]
+    3, a3, b3 [label = 3 color = red]
+    100, a100, b100 [label = 100 color = red]
+
+    12 -- res1 [color = gray]
+    b1 -- res2 [color = gray]
+    a10 -- res3 [color = gray]
+    100 -- res4 [color = gray]
+    b10 -- res5 [color = gray]
+    b100 -- res6 [color = gray]
+    b3 -- res7 [color = gray]
+    2 -- res8 [color = gray]
+
+    res1 [label = "Sum: 25", shape=plaintext, fontcolor=gray]
+    res2 [label = "Sum: 14", shape=plaintext, fontcolor=gray]
+    res3 [label = "Sum: 14", shape=plaintext, fontcolor=gray]
+    res4 [label = "Sum: 104", shape=plaintext, fontcolor=gray]
+    res5 [label = "Sum: 14", shape=plaintext, fontcolor=gray]
+    res6 [label = "Sum: 104", shape=plaintext, fontcolor=gray]
+    res7 [label = "Sum: 104", shape=plaintext, fontcolor=gray]
+    res8 [label = "Sum: 103", shape=plaintext, fontcolor=gray]
+}
+....
+
+
+Notice that many middle branches (in red color) have the same numbers, but in a different order, so their sums oscillate between 104 and 14. That's why this algorithm is not very optimal for this problem.
+
+*Sliding window algorithm*
+
+Another approach is using sliding windows. Since the sum always has `k` elements, we can compute the cumulative sum for the k first elements from the left. Then, we slide the "window" to the right and remove one from the left until we cover all the right items. In the end, we would have all the possible combinations without duplicated work.
+
+Check out the following illustration:
+
+image::max-sum-sliding-window.png[sliding window for arrays]
+
+Here's the implementation:
+
+.Solution: using sliding window pointers
+[source, javascript]
+----
+function maxSum(arr, k) {
+  let left = k - 1;
+  let right = arr.length -1;
+  let sum = 0;
+  for (let i = 0; i < k; i++) sum += arr[i];
+  let max = sum;
+
+  for (let i = 0; i < k; i++) {
+    sum += arr[right--] - arr[left--];
+    max = Math.max(max, sum);
+  }
+
+  return max;
+};
+----
+
+The difference between the two pointers pattern and the sliding windows, it's that we move both pointers at the same time to keep the length of the window the same.
+
+The runtime for this code is: `k`. As we move the window k times. Since `k <= n`, the final runtime is `O(n)`.
+
+==== Practice Questions
+(((Interview Questions, Arrays)))
+
+// tag::array-q-max-subarray[]
+===== Max Subarray
+
+*AR-1*) _Given an array of integers, find the maximum sum of consecutive elements (subarray)._
+
+Examples:
+
+[source, javascript]
+----
+maxSubArray([1, -3, 10, -5]); // 10 (taking only 10)
+maxSubArray([-3, 4,-1, 2, 1, -5]); // 6 (sum [4,-1, 2, 1])
+maxSubArray([-2, 1, -3, 4, -1, 3, 1]); // 7 (sum [4,-1, 3, 1])
+----
+
+_Common in interviews at: FAANG, Microsoft_
+// end::array-q-max-subarray[]
+
+[source, javascript]
+----
+include::../../interview-questions/max-subarray.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<array-q-max-subarray>>_
+
+// tag::array-q-buy-sell-stock[]
+===== Best Time to Buy and Sell a Stock
+
+*AR-2*) _You have an array of integers. Each value represents the closing value of the stock on that day. You have only one chance to buy and then sell. What's the maximum profit you can obtain? (Note: you have to buy first and then sell)_
+
+Examples:
+
+[source, javascript]
+----
+maxProfit([1, 2, 3]) // 2 (buying at 1 and selling at 3)
+maxProfit([3, 2, 1]) // 2 (no buys)
+maxProfit([5, 10, 5, 10]) // 5 (buying at 5 and selling at 10)
+----
+
+_Common in interviews at: Amazon, Facebook, Bloomberg_
+// end::array-q-buy-sell-stock[]
+
+[source, javascript]
+----
+include::../../interview-questions/buy-sell-stock.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<array-q-buy-sell-stock>>_
+
+// https://leetcode.com/problemset/algorithms/?topicSlugs=array
diff --git a/book/content/part02/hash-map.asc b/book/content/part02/hash-map.asc
new file mode 100644
index 00000000..8f239cba
--- /dev/null
+++ b/book/content/part02/hash-map.asc
@@ -0,0 +1,681 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Map))) (((HashMap))) (((HashTable))) (((Data Structures, Linear, HashMap)))
+[[hashmap-chap]]
+=== Map
+
+A Map is a data structure where a `key` is mapped to a `value`. It's used for a fast lookup of values based on the given key. Only one key can map to a value (no key duplicates are possible).
+
+NOTE: Map has many terms depending on the programming language. Here are some other names: Hash Map, Hash Table, Associative Array, Unordered Map, Dictionary.
+
+==== Map Applications
+
+Maps are one of the most popular data structures because of their fast lookup time.
+
+.Holding key/value pairs have many applications like:
+- *Caching*: keys are URLs, and values are website content.
+- *Indexing*: keys are words, and values are the list of documents where they are found.
+- *Spell checking*: keys are English words.
+- *Networks*: the key is an IP address/port number, while the value is the corresponding application.
+
+There are many other use cases. We will explore some techniques that we can use to speed up your code with it. But first, let's get the fundamentals out of the way.
+
+==== Map vs Array
+
+A map shares some similarities with an array. In an array, the key/index is always a number, while the value in a Map can be anything!
+
+Both an Array and Map are very fast for getting values by key in constant time O(1) on average.
+
+A Map uses an array internally. It translates the key into an array's index using a hash function. That's why it is also called "Hash Map" or "Hash Table".
+
+==== Map vs Objects
+
+JavaScript has two ways to use Maps: one uses objects (`{}`), and the other is using the built-in `Map`.
+
+[[hashmap-examples]]
+.Using Objects as a HashMap.
+[source, javascript]
+----
+const objMap = {};
+// mapping values to keys
+objMap['str'] = 'foo'; // string as key
+objMap[1] = 'bar'; // number as key
+objMap[{}] = 'test1'; // object as key (not recommended)
+const obj1 = {};
+objMap[obj1] = 'test2'; // object as key (not recommended)
+
+// searching values by key
+console.log(objMap[1]); //↪️ bar
+console.log(objMap['str']); //↪️ foo
+console.log(objMap[{}]); //↪️ test2 👀
+console.log(objMap[obj1]); //↪️ test2 👀
+
+console.log(objMap); // {1: "bar", str: "foo", [object Object]: "test"}
+----
+
+Notice that the `objMap[{}]` and `objMap[obj1]` return the same value! They both were converted to `[object Object]` as a key.
+
+Let's now use the built-in Map
+
+.JavaScript Built-in Map Usage
+[source, javascript]
+----
+const myMap = new Map();
+// mapping values to keys
+myMap.set('str', 'foo'); // string as key
+myMap.set(1, 'bar'); // number as key
+myMap.set({}, 'test1'); // object as key
+const obj1 = {};
+myMap.set(obj1, 'test2');
+
+// searching values by key
+console.log(myMap.get(1)); //↪️ bar
+console.log(myMap.get('str')); //↪️ foo
+console.log(myMap.get({})); //↪️ undefined 👀
+console.log(myMap.get(obj1)); //↪️ test2
+
+console.log(myMap);
+// Map(4) {"str" => "foo", 1 => "bar", {…} => "test1", {…} => "test2"}
+----
+
+As you can see, `Map` handled other objects as a key much better.
+
+Objects are one of the oldest data structures in JavaScript. Maps were introduced as part of the ES2015 enhancements to solve the shortcomings of using Object as a Hashmap. Having two methods can be confusing. We are going to make it clear when to use one or the other.
+
+.Map vs. Object main differences:
+- *Object*'s keys should be strings, numbers, or symbols. *Map*'s keys can be anything! Strings, numbers, symbols, arrays, objects, and even other maps!
+- *Objects* are not guaranteed to be in insertion order. *Maps* guarantee insertion order.
+- When using *Objects* as HashMaps, they might be polluted with other keys defined at the prototype chain. You need to use `hasOwnProperty` or `Object.keys` to avoid these issues. *Maps* doesn't get polluted by the prototype chain.
+- *Maps* has been optimized for cases of frequent additions and removals. *Objects* are not optimized.
+
+When do you use an Object over a Map then? When you need to use JSON since it doesn't support Maps yet.
+
+You can convert from Map to Object and vice-versa:
+
+```js
+const objMap = Object.fromEntries(myMap.entries());   // map -> obj
+const map = new Map(objMap.entries());                // obj -> map
+```
+
+For completeness, here are some of the most basic operations side-by-side.
+
+.Object vs Map Side-by-Side
+[source, javascript]
+----
+//
+// Initialization
+//
+const obj1 = {};                            // Empty
+const obj2 = { adrian: 33, nathalie: 32 };  // w/values
+
+const map1 = new Map();                                   // Empty
+const map2 = new Map([['adrian', 33], ['nathalie', 32]]); // w/values
+
+//
+// Access
+//
+assert.equal(obj1.adrian, undefined);
+assert.equal(obj2['adrian'], 33); // also "obj2.adrian"
+
+assert.equal(map1.get('adrian'), undefined);
+assert.equal(map2.get('adrian'), 33);
+
+//
+// Check if the key exists
+//
+assert.equal(obj1.adrian !== undefined, false);
+assert.equal(obj2['adrian'] !== undefined, true);
+
+assert.equal(map1.has('adrian'), false);
+assert.equal(map2.has('adrian'), true);
+
+//
+// Adding new elements
+//
+obj2['Abi'] = 2;
+obj2['Dudu'] = 2;
+
+map2.set('Abi', 2).set('Dudu', 2);
+
+//
+// Deleting
+//
+delete obj2.Dudu;
+
+map2.delete('Dudu');
+
+//
+// Iterating key/value pairs with for loops
+//
+for (var k in obj2){
+   console.log(`key: ${k}, value: ${obj2[k]}`);
+}
+
+for (const [k, v] of map2){
+  console.log(`key: ${k}, value: ${v}`);
+}
+
+//
+// Iterating key/value pairs with
+//
+Object.keys(obj2)
+  .forEach(k => console.log(`key: ${k}, value: ${obj2[k]}`));
+
+map2
+  .forEach((v, k) => console.log(`key: ${k}, value: ${v}`));
+
+//
+// Getting the size
+//
+assert.equal(Object.keys(obj2).length, 3);
+assert.equal(map2.size, 3);
+
+//
+// Representation
+//
+console.log(obj2);
+// { adrian: 33, nathalie: 32, Abi: 2 }
+console.log(map2);
+// Map { 'adrian' => 33, 'nathalie' => 32, 'Abi' => 2 }
+----
+
+From this point on, we will use built-in Maps (and not objects).
+
+==== Key by Reference vs. by Value
+
+There's a catch when you use objects/arrays/classes as keys on a `Map`. JavaScript will match the key only if it has the same reference in memory.
+
+Look at the following example:
+
+.Array as a Map's key
+[source, javascript]
+----
+const map = new Map();
+
+map.set([1, 2, 3], 'value');
+console.log(map.get([1, 2, 3])); // undefined 👀
+----
+
+Trying to access a Map's value using a complex type is a common gotcha. If you want array as a key to work, you need to hold on to a reference, like the following example.
+
+.Array reference as a Map's key
+[source, javascript]
+----
+const map = new Map();
+const arr = [1, 2, 3];
+
+map.set(arr, 'value');
+console.log(map.get(arr)); // 'value'
+----
+
+The same applies to any key that is not a number, string, or symbol.
+
+===== Map Inner Workings
+
+.There are two popular ways to implement Maps, key/value pair data structures:
+- Array + Hash Function: Hash Map
+- Balanced Binary Search Tree: TreeMap.
+
+In this chapter, we will focus on the Hash Map implementation, which is the one that JavaScript has built-in. In the next parts, we will cover TreeMap.
+
+A map uses an array to store the values and a hash function that translate the key into an array index behind the scenes.
+
+Let's say we have the following key/value pairs.
+
+[source, javascript]
+----
+const map = new Map();
+
+map.set('cat', 2);
+map.set('dog', 1);
+map.set('rat', 7);
+map.set('art', 8);
+----
+
+.There are multiple algorithms for hashing keys. One of them is using modulo division:
+1. Convert the key into a number (a.k.a hash code or pre-hashing).
+2. Convert the number from step 1 into an array index using modulo. (`hashCode % arrayLength`).
+
+.Internal HashMap representation
+image::image41.png[image,width=528,height=299]
+
+No hash function is perfect, so it will map two different keys to the same value for some cases. That's what we called a *collision*. When that happens, we chain the results on the same bucket. If we have too many collisions, it could degrade the lookup time from `O(1)` to `O(n)`.
+
+The Map doubles the size of its internal array to minimize collisions when it reaches a certain threshold. This restructuring is called a *rehash*. This *rehash* operation takes `O(n)`, since we have to visit every old key/value pair and remap it to the new internal array. Rehash doesn't happen very often, so statistically speaking, Maps can insert/read/search in constant time `O(1)`.
+
+NOTE: collisions and rehashes are handled automatically. But it's still good to know the trade-offs. We will go into more detail when we compare it with TreeMaps.
+
+
+===== HashMap time complexity
+
+Hash Map is optimal for searching values by key in constant time *O(1)*. However, searching by value is not any better than an array since we have to visit every value *O(n)*.
+
+(((Tables, Non-Linear DS, HashMap complexities)))
+
+// tag::table[]
+.Time complexity for a Hash Map
+|===
+.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
+^|_Index/Key_ ^|_Value_
+| Hash Map ^|O(1) ^|O(n) ^|O(1)* ^|O(1) ^|O(n)
+|===
+{empty}* = Amortized run time. E.g. rehashing might affect run time.
+// end::table[]
+
+indexterm:[Runtime, Linear]
+As you can notice, we have amortized times since it will take O(n) while it resizes in the unfortunate case of a rehash. After that, it will be *O(1)*.
+
+
+==== HashMap Patterns for Solving Interview Questions
+
+HashMaps are one of the most versatile data structures. You can speed up many programs by using them correctly. In this section, we are going to explore some common patterns.
+
+===== Smart Caching (((Patterns, Caching)))
+One everyday use case for key/value data structures is caching. If you are working on a trendy website, you can save scale better if you cache the results instead of hitting the database and other expensive services every time. That way, you can server many more users requesting the same data.
+
+A common issue with cache you want to expire data you don't often use to make room for hot data. This next exercise is going to help you do that.
+
+*HM-A*) _Design a Least Recently Used (LRU) cache. This cache has a limit on the number of items it can store. Once the limit it's reached, it will discard the least recently used item. Design a class that takes a limit value, and the methods put and get, to insert and get data._
+
+.Signature
+[source, javascript]
+----
+/**
+ * Least Recently Used (LRU) cache.
+ * Key/Value storage with fixed max number of items.
+ * Least recently used items are discarded once the limit is reached.
+ * Reading and updating the values mark the items as recently used.
+ */
+class LRUCache {
+  /**
+   * @param {number} capacity - The max number of items on the cache
+   */
+  constructor(capacity) {
+
+  }
+
+  /**
+   * Get the value associated with the key. Mark keys as recently used.
+   * @param {number} key
+   * @returns {number} value or if not found -1
+   */
+  get(key: number): number {
+
+  }
+
+  /**
+   * Upsert key/value pair. Updates mark keys are recently used.
+   * @param {number} key
+   * @param {number} value
+   * @returns {void}
+   */
+  put(key, value) {
+
+  }
+}
+----
+
+.Examples:
+[source, javascript]
+----
+const c = new LRUCache(2); // capacity: 2
+c.put(2, 1); // Cache is [2:1]
+c.put(1, 1); // Cache is [2:1, 1:1]
+c.put(2, 3); // Cache is [1:1, 2:3]
+c.put(4, 1); // Removed 1. Cache is [2:3, 4:1]
+c.get(1); // Returns -1 (key 1 not found)
+c.get(2); // Returns 3. Cache is [4:1, 2:3]
+c.put(5, 5); // Removed key 4. Cache is [2:3, 5:5]
+c.get(4); // Returns -1 (key 4 not found)
+----
+
+TIP: Try it on your own before reading the solution on the next page!
+
+<<<
+*Solution*
+
+The LRU cache behavior is almost identical to the Map.
+
+.The differences are:
+- LRU cache has a limited size, while Map grows until you run out of memory.
+- LRU cache removes the least used items once the limit is reached.
+
+We can extend the Map functionality. Also, the Map implementation on JavaScript already keeps the items by insertion order. Every time we read or update a value, we can remove it from where it was and add it back. That way, the oldest (least used) it's the first element on the Map.
+
+.Solution: extending Map
+[source, javascript]
+----
+class LRUCache extends Map {
+  constructor(capacity) {
+    super();
+    this.capacity = capacity;
+  }
+
+  get(key) {
+    if (!super.has(key)) return -1;
+    const value = super.get(key);
+    this.put(key, value); // re-insert at the top (most recent).
+    return value;
+  }
+
+  put(key, value) {
+    if (super.has(key)) super.delete(key);
+    super.set(key, value);
+    if (super.size > this.capacity) {
+      const oldestKey = super.keys().next().value;
+      super.delete(oldestKey);
+    }
+  }
+}
+----
+
+Notice that we call `put` within `get`. This is to rotate the keys to the top (most recent place).
+
+.Complexity Analysis
+- Time Complexity: `O(1)`. All operations read, write, update, and delete takes `O(1)`.
+- Space complexity: `O(k)`. In this case, k, is the capacity of the cache. Even if n has 1 million items, we would only hold to the k most recent ones.
+
+
+
+===== Trading Speed for Space (((Patterns, Space for Speed)))
+
+Maps have a `O(1)` runtime for lookups and `O(n)` space complexity. It can improve the speed of programs in exchange for using a little bit more of memory. Let's do an example.
+
+Let's say you are working on a webcrawler, and for each site, you want to find out the most common words used. How would you do it?
+
+*HM-B*) _Given a text, return the most common words in descending order. You should sanitize the input by removing punctuation `!?',;.` and converting all letters to lowercase. Return the most common words in descending order._
+
+.Signature
+[source, javascript]
+----
+/**
+ * Given text and banned words,
+ * return the most common words in descending order.
+ * @param {string} text - The text to parse.
+ * @param {number} n - The number of results.
+ * @return {string[]}
+ */
+function mostCommonWords(text, n = 1) {
+  // you code goes here
+}
+----
+
+.Examples:
+[source, javascript]
+----
+mostCommonWords(
+  'The map, maps keys to values; Keys can be anything.',
+  1); // ['keys']
+mostCommonWords(
+  'Look at it! What is it? It does look like my code from 1 year ago',
+  2); // ['it', 'look']
+mostCommonWords(
+  'a; a,b, a\'s c A!; b,B,    c.',
+  4); // ['a', 'b', 'c', 's']
+----
+
+
+TIP: Try it on your own before reading the solution on the next page!
+
+<<<
+*Solutions*
+
+.This is a problem that has multiple steps:
+1. Split the text into lowercased words and remove whitespaces and punctuation.
+2. Count the frequency of words.
+3. Sort words by frequency and return the top n words.
+
+.Possible implementations for each of the steps:
+1. We can use regex (regular expressions) and split on non-words `\W+`. The runtime of this will be `O(n)`.
+2. Let's discuss this later.
+3. We have an array of the word -> frequency pairs. We can sort by the frequency using the built-in sort function and return the subarray with the top n results. The time complexity would be `O(n log n)`.
+
+For step 2, we can do it in multiple ways. A brute force solution is using 2 for loops to count the number of times each word appear:
+
+.Solution 1: Brute Force
+[source, javascript]
+----
+include::../../interview-questions/most-common-words-ii.js[tags=brute]
+----
+
+Notice that we null out the counted words. That's to avoid counting the phrase more than once.
+
+.Complexity Analysis:
+- Time complexity: `O(n^2)`. We have three steps and each one has its time complexity: O(n) + O(n^2) + O(n log n). Remember that with Big O notation, we only care about the term with the highest order: `n^2`.
+- Space complexity: `O(n)`. We use multiple O(n) auxiliary spaces for these variables: `words`, `entries`, and the solution is also n space.
+
+
+Another alternative is to use a Map to count.
+
+.Solution 2: Map counter
+[source, javascript]
+----
+include::../../interview-questions/most-common-words-ii.js[tags=map]
+----
+
+With this solution, we iterate over the words only once. We first get the current count and add one. If the word didn't exist, we would default to a count of 0. Steps 1 and 3 are almost identical to solution #1.
+
+.Complexity Analysis
+- Time Complexity: `O(n log n)`. We have 3 steps: O(n) + O(n) + O(n log n). The most significant term is `n log n`.
+- Space complexity: `O(n)`. We used the same O(n) auxiliary space as solution #1 for `words`, `entries`, and the solution. Additionally, we added one more O(n) space for the Map.
+
+
+===== Sliding Window (((Patterns, Sliding Window)))
+
+We saw <<sliding-window-array, sliding windows with arrays>> before. We are now going to use them with strings. The idea is very similar, we still use the two pointers, and the solution is the "window" between the pointers. We can increase or decrease the window as long as it keeps the constraints of the problem. Let's do an example for better understanding.
+
+
+*HM-C*) _Return the length of the longest substring without repeating characters._
+
+.Signature
+[source, javascript]
+----
+/**
+ * Return the length of the longest substring without repeating characters.
+ * @param {string} s
+ * @return {number}
+ */
+function longestSubstring(s) {
+  // your code goes here!
+};
+----
+
+.Examples
+[source, javascript]
+----
+longestSubstring('abcdaefg'); // 7 ('bcdaefg')
+longestSubstring('abbaa'); // 2 ('ab')
+longestSubstring('abbadvdf') // 4 ('badv')
+----
+
+
+TIP: Try it on your own before reading the solution on the next page!
+
+<<<
+*Solutions*
+
+We are going to solve this problem by using a sliding window approach. We have two pointers called `lo` and `hi`. They start both at zero, and we increase the window as long as they don't have duplicates. If we found a duplicate, we reopen a new window past the duplicated value.
+
+Take a look at this illustration doing an example for the string `abbadvdf`:
+
+image:sliding-window-map.png[sliding window for abbadvdf]
+
+As you can see, we calculate the length of the string on each iteration and keep track of the maximum value.
+
+What would this look like in code? Let's try a couple of solutions. Let's go first with the brute force and then how we can improve it.
+
+We can have two pointers, `lo` and `hi`, to define a window. We can use two for-loops for that. Later, within `lo` to `hi` window, we want to know if there's a duplicate value. A simple and naive approach is to use another two for-loops to check for duplicates (4 nested for-loop)! We need labeled breaks to skip updating the max if there's a duplicate.
+
+WARNING: The following code can hurt your eyes. Don't try this in production; for better solutions, keep reading.
+
+.Solution 1: Super Brute Force
+[source, javascript]
+----
+/**
+ * Return the length of the longest substring without repeating characters.
+ * @param {string} s
+ * @return {number}
+ */
+function longestSubstring(s) {
+  let max = 0;
+
+  for (let lo = 0; lo < s.length; lo++) {
+    repeatedFound:
+    for (let hi = lo; hi < s.length; hi++) {
+      // check if it's unique withing [lo,hi] range
+      for (let i = lo; i < hi; i++) {
+        for (let j = lo + 1; j <= hi; j++) {
+          if (i !== j && s[i] === s[j]) break repeatedFound;
+        }
+      }
+      // all are unique between [lo,hi] range
+      max = Math.max(max, hi - lo + 1);
+    }
+  }
+
+  return max;
+};
+----
+
+This function gets the job done. But how efficient is it?
+
+.Complexity Analysis
+- Time Complexity: `O(n^4)`. In the worst-case, when the string has all unique characters, we have n^4!
+- Space complexity: `O(1)`. The only variable we are using is integers.
+
+Solution 1 has a horrible runtime, but the space complexity is constant. Can we trade space for a better speed performance? Absolutely!
+
+Instead of having two loops for finding duplicates, we can do one pass and use a Map to detect duplicates.
+
+.Solution 2: Brute force with Map
+[source, javascript]
+----
+/**
+ * Return the length of the longest substring without repeating characters.
+ * @param {string} s
+ * @return {number}
+ */
+function longestSubstring(s) {
+  let max = 0;
+
+  for (let lo = 0; lo < s.length; lo++) {
+    repeatedFound:
+    for (let hi = lo; hi < s.length; hi++) {
+      // check if it's unique withing [lo,hi] range
+      const map = new Map();
+      for (let i = lo; i <= hi; i++) {
+        if (map.has(s[i])) break repeatedFound;
+        map.set(s[i], true);
+      }
+      // all are unique between [lo,hi] range
+      max = Math.max(max, hi - lo + 1);
+    }
+  }
+
+  return max;
+}
+----
+
+We are using the Map to detect duplicates, where the characters are the keys.
+
+.Complexity Analysis
+- Time Complexity: `O(n^3)`. We have three nested loops that, in the worst-case, each will visit `n` items.
+- Space complexity: `O(n)`. We have a map that might grow as big as size `n`.
+
+One optimization that we can do the solution 2 is to store the index where we last saw a character. We can map each character to its index. That way, when we find a duplicate, we can update the `lo` pointer with it, shrinking the window.
+
+.Solution 3: Optimized Sliding Window
+[source, javascript]
+----
+/**
+ * Return the length of the longest substring without repeating characters.
+ * @param {string} s
+ * @return {number}
+ */
+function longestSubstring(s) {
+  const map = new Map();
+  let max = 0;
+
+  for (let hi = 0, lo = 0; hi < s.length; hi++) {
+    if (map.has(s[hi])) lo = Math.max(lo, map.get(s[hi]) + 1);
+    map.set(s[hi], hi);
+    max = Math.max(max, hi - lo + 1);
+  }
+
+  return max;
+};
+----
+
+This solution has the least amount of code, and it's also the most efficient!
+
+Something that might look unnecessary is the `Math.max` when updating the `lo` pointer. You can remove it and try running it with the string "abba", what would happen?
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. We do one pass and visit each character once.
+- Space complexity: `O(n)`. We store everything on the Map so that the max size would be `n`.
+
+<<<
+==== Practice Questions (((Interview Questions, Hash Map)))
+
+// tag::hashmap-q-two-sum[]
+===== Fit two movies in a flight
+
+*HM-1*) _You are working in an entertainment recommendation system for an airline. Given a flight duration (target) and an array of movies length, you need to recommend two movies that fit exactly the length of the flight. Return an array with the indices of the two numbers that add up to the target. No duplicates are allowed. If it's not possible to return empty `[]`._
+
+// end::hashmap-q-two-sum[]
+
+_Common in interviews at: FAANG._
+
+Examples:
+
+[source, javascript]
+----
+twoSum([113, 248, 80, 200, 91, 201, 68], 316); // [1, 6] (248 + 68 = 316)
+twoSum([150, 100, 200], 300); // [2, 3] (100 + 200 = 300)
+twoSum([150, 100, 200], 150); // [] (No two numbers add up to 150)
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/two-sum.js[tags=description;placeholder]
+----
+
+
+_Solution: <<hashmap-q-two-sum>>_
+
+
+// tag::hashmap-q-subarray-sum-equals-k[]
+===== Subarray Sum that Equals K
+
+*HM-2*) _Given an array of integers, find all the possible subarrays to add up to k. Return the count._
+
+// end::hashmap-q-subarray-sum-equals-k[]
+
+_Common in interviews at: FAANG_
+
+Examples:
+
+[source, javascript]
+----
+subarraySum([1], 1); // 1 (1 equals to 1 :)
+subarraySum([1, 1, 1], 1); // 3 ([1], [1], [1] equals 1)
+subarraySum([1, -1, 1], 0); // 2 (sum([1, -1]), sum([-1, 1]) equals 0)
+subaraySum([1, 2, 3, 0, 1, 4, 0, 5], 5) // 8
+// All of these 8 sub arrays add up to 5:
+// [2, 30], [2,3,0], [0,1,4], [0,1,4,0], [1,4], [1,4,0], [0,5], [5]
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/subarray-sum-equals-k.js[tags=description;placeholder]
+----
+
+
+_Solution: <<hashmap-q-subarray-sum-equals-k>>_
diff --git a/book/content/part02/hash-set.asc b/book/content/part02/hash-set.asc
new file mode 100644
index 00000000..766db939
--- /dev/null
+++ b/book/content/part02/hash-set.asc
@@ -0,0 +1,195 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Set))) (((Data Structures, Non-Linear, Set)))
+[[hash-set-chap]]
+=== Set
+Set is a data structure that allows you to store unique values. If you try to add the same value multiple times, the Set will only add it once and ignore all other requests. Also, you can check very quickly if a value exists or not. Searching by value on arrays takes `O(n)`. However, searching by value on a Set takes `O(1)` on average.
+
+A Set can be implemented in different ways. One way it's using a <<hashmap-chap, Hash Map>>, and other is using a <<tree-map-chap, Tree Map>>. JavaScript has a built-in Hash Set, so that' the one we are going to focus on.
+
+TIP: We will go more in details with <<tree-map-chap, Tree Map>> after we cover the <<binary-search-tree-chap>>.
+
+
+==== Set vs Array
+
+An array allows you to search a value by index in constant time `O(1)`; however, if you don't know the index, searching a value would take you linear time `O(n)`. A Set has doesn't allow you to search value by index, but you can search by value in constant time. The `Set.add` and `Set.has` method both are `O(1)` in average.
+
+Take a look at the following examples:
+
+.Set usage example (using JavaScript built-in Set)
+[source, javascript]
+----
+const set = new Set();
+
+set.add(1); //↪️ Set [ 1 ]
+set.add(1); //↪️ Set [ 1 ]
+set.add(2); //↪️ Set [ 1, 2 ]
+set.add(3); //↪️ Set [ 1, 2, 3 ]
+set.has(1); //↪️ true
+set.delete(1); //↪️ removes 1 from the set
+set.has(1);    //↪️ false, 1 has been removed
+set.size; //↪️ 2, we just removed one value
+console.log(set); //↪️ Set(2) {2, 3}
+----
+
+As you can see, even if we insert the same value multiple times, it only gets added once.
+
+Like a <<hashmap-examples, map>>, you can also insert objects, arrays, maps, and even other sets. However, be careful because anything that is not a number, string, or symbol would be matched by reference. Let's do some examples.
+
+.Using a Set with objects
+[source, javascript]
+----
+const set = new Set();
+
+// matching by value
+set.add({a: 1, b: 2});
+set.has({a: 1, b: 2}); // ↪️ false
+set.add({a: 1, b: 2}); // not ignored
+
+// matching by reference
+const a = {a: 1, b: 2};
+set.add(a);
+set.has(a); // ↪️ true
+set.add(a); // this requests will be ignore.
+
+// Set has 3 arrays with the same value, but since they all have different memory address it's allowed.
+console.log(set); // Set { {a: 1, b: 2}, {a: 1, b: 2}, {a: 1, b: 2} }
+----
+
+As you can see, you can't find an object using a new object (e.g. `{a: 1, b: 2}`); you need the reference to find it.
+If you need to match by value, you would need to convert it to a string using `JSON.stringify`.
+
+.Workaround to find objects by value.
+[source, javascript]
+----
+const set = new Set();
+
+set.add(JSON.stringify({a: 1, b: 2}));
+set.add(JSON.stringify({a: 1, b: 2})); // ignored
+
+set.has(JSON.stringify({a: 1, b: 2})); // ↪️ true
+
+// Only one object, since strings are matched by value and not by reference.
+console.log(set); // Set { '{"a":1,"b":2}' }
+----
+
+
+==== Removing duplicates from an array.
+
+One typical case for a Set is to eliminate duplicates from an array.
+
+.Removing duplicates from an array
+[source, javascript]
+----
+const arr = [1, 2, 2, 1, 3, 2];
+
+// convert array to set
+const set = new Set(arr);
+// convert set to array
+const uniqueValues = Array.from(set);
+// check array
+console.log(uniqueValues); // [ 1, 2, 3 ]
+----
+
+You can also do it all in one line.
+
+.One-liner to remove duplicates from array.
+[source, javascript]
+----
+const arr = [1, 2, 2, 1, 3, 2];
+console.log([...new Set(arr)]); // [ 1, 2, 3 ]
+----
+
+==== Time Complexity of a Hash Set
+
+All operations on Hash Set are constant time on average: `O(1)`. Like the Hash Map, there are cases when the the Set is getting full, and it would do a rehash taking `O(n)` for that one insertion.
+
+// tag::table[]
+.Time complexity HashSet
+|===
+.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
+^|_Index/Key_ ^|_Value_
+| Hash Set ^|O(1) ^|- ^|O(1)* ^|O(1) ^|O(n)
+|===
+{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
+// end::table[]
+
+
+==== Practice Questions
+(((Interview Questions, Set)))
+
+// tag::set-q-most-common-word[]
+===== Most common word
+
+*ST-1*) _Given a text and a list of banned words.
+Find the most common word that is not on the banned list.
+You might need to sanitize the text and strip out punctuation `?!,'.`_
+// end::set-q-most-common-word[]
+
+_Common in interviews at: Amazon._
+
+Examples:
+
+[source, javascript]
+----
+mostCommonWord(
+  `How much wood, would a Woodchuck chuck,
+  if a woodchuck could chuck?`,
+  ['a'],
+); // woodchuck or chuck (both show up twice)
+
+mostCommonWord(
+`It's a blue ball and its shade... Very BLUE!`,
+['and']); // blue (it show up twice, "it" and "its" once)
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/most-common-word.js[tags=description;placeholder]
+----
+
+
+_Solution: <<set-q-most-common-word>>_
+
+
+
+
+
+
+
+
+
+
+
+// tag::set-q-longest-substring-without-repeating-characters[]
+===== Longest Without Repeating
+
+*ST-2*) _Find the length of the longest substring without repeating characters._
+
+// end::set-q-longest-substring-without-repeating-characters[]
+
+_Common in interviews at: Amazon, Facebook, Bloomberg._
+
+Examples:
+
+[source, javascript]
+----
+lenLongestSubstring('aaaaa'); // 1 ('a')
+lenLongestSubstring('abccdefg'); // 5 ('cdefg')
+lenLongestSubstring('abc'); // 3 ('abc')
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/longest-substring-without-repeating-characters.js[tags=description;placeholder]
+----
+
+
+_Solution: <<set-q-longest-substring-without-repeating-characters>>_
diff --git a/book/content/part02/linked-list.asc b/book/content/part02/linked-list.asc
new file mode 100644
index 00000000..234ca937
--- /dev/null
+++ b/book/content/part02/linked-list.asc
@@ -0,0 +1,625 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Linked List)))
+(((List)))
+(((Data Structures, Linear, Linked List)))
+[[linked-list]]
+=== Linked List
+
+A list (or Linked List) is a linear data structure where each object has a pointer to the next one creating a chain. You can also have a back reference to the previous node.
+
+image::dllx4-compact.png[]
+
+The data doesn't have to be a number. It can be anything that you need (e.g., images, songs, menu items).
+
+.Some features powered by linked lists:
+- _Image viewer_ – The previous and next images are linked in an image viewer so that the user can navigate them.
+- _Previous and next page in web browser_ – We can access the previous and next URL searched in a web browser by pressing the back and next button since they are linked.
+- _Music Player_ - Queue of songs in a music player connects them so you can move to the next song or previous one.
+
+.Other Applications:
+- Build <<Stack>> and <<Queue>> data structures, which are useful for Graph Traversal and other things.
+- Linked Lists are used on <<hashmap-chap>> to handle collisions.
+- Linked Lists can be used when representing a <<graph>> as an adjacency list.
+- Operate arbitrary big numbers (think hundreds of digits). Each digit is a node of a linked list.
+- Manipulation of polynomials by storing constants in the node of a linked list.
+- Representing sparse matrices (an array representation will waste a lot of memory when most of the cells are empty). The linked list will represent only the non-zero values saving significant space.
+
+Hopefully, this will get you excited about learning Linked Lists since it's the base of many interesting applications. Let's learn more about the different types of linked lists.
+
+==== Types of Linked List
+
+.Linked Lists can be:
+- *Singly*: every item has a pointer to the next.
+- *Doubly*: every item has a reference to the next and the previous.
+- *Circular*: the last element points to the first one, forming an infinite loop.
+
+JavaScript doesn't have a built-in List. However, it's straightforward to create.
+
+[[node-class]]
+.Linked List Node Implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/node.js[tag=snippet]
+----
+
+Let's go one by one!
+
+[[singly-linked-list]]
+==== Singly Linked List
+
+In a singly linked list, each element or node is *connected* to the next one by a reference.
+
+Usually, a Linked List is referenced by the first element called *head* (or *root* node). Let's say that we have a list of strings with the following values: `"art" -> "dog" -> "cat"`. It would look something like the following image.
+
+.Singly Linked List Representation: each node has a reference (blue arrow) to the next one.
+image::sllx4.png[]
+
+If you want to get the `cat` element from the example above, then the only way to get there is by using the `next` field on the head node. You would get `art` first, then use the next field recursively until you eventually get the `cat` element.
+
+==== Circular Linked Lists
+
+Circular linked lists happen when the last node points to any node on the list, creating a loop. In the following illustration, you can see two circular linked lists.
+
+image:cll.png[Circular linked lists examples]
+
+One circular linked list happens when the last element points to the first element. Another kind of circular linked list is when the last node points to any node in the middle. There are some efficient algorithms to detect when the list has a loop or not. More on that later in this chapter.
+
+[[doubly-linked-list]]
+==== Doubly Linked List
+
+Doubly Linked List has two references to the `next` and `previous` node.
+
+.Doubly Linked List: each node has a reference to the next and previous element.
+image::dll.png[]
+
+With a doubly-linked list, you can move not only forward but also backward. If you keep a pointer to the `last` element (`cat`), you can step back recursively.
+
+Finding an item on the linked list takes O(n) time. Because in the worst-case, you will have to iterate over the whole list.
+
+
+==== Implementing a Linked List
+
+We are going to implement a doubly linked list. First, let's start with the constructor.
+
+TIP: if you want to implement a singly linked list instead, it's the same in most parts, but without the setting the `previous` pointers.
+
+The only must-have field on the constructor is the `first` or head reference.  If you want to insert data to the back of the list in constant time, then the `last` pointer is needed. Everything else is complimentary.
+
+.Linked List's constructor
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=constructor]
+
+  // ... methods go here ...
+}
+----
+
+The iterable parameter is a nice to have. That will allow you to convert an array of items into a linked list. E.g. `const list = new LinkedList([1, 2, 3]);`
+
+==== Searching by value or index
+
+There’s no other way to find an element by value than iterating through the list. So, the runtime is `O(n)`.
+
+There are two prominent use cases for search: find an element by value, or find them by their index/position.
+
+We can use a for-loop to keep track of the index and the current node simultaneously. Whichever fulfill first, we return that one.
+
+[[linkedlist.findBy]]
+.Linked List's searching by values or index
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=find, indent=0]
+----
+<1> We initialize two variables `current` to the first node and `position` to 0 to keep track of the ordinal number.
+<2> While the `current` node is not null, we keep going.
+<3> On each loop, we move to the next node and increment the index.
+<4> We check if the index is the one provided or if the node has the expected value.
+<5> Returns the index and the current node if found.
+
+==== Insertion
+
+You can add elements at the beginning, end, or anywhere in the middle of the list in a linked list. So, let's implement each case.
+
+[[linked-list-inserting-beginning]]
+===== Inserting elements at the beginning of the list
+
+We will use the <<node-class, Node class>> to create a new element and stick it at the beginning of the list, as shown below.
+
+.Insert at the beginning by linking the new node with the current first node.
+image::dll-add-first.png[]
+
+
+To insert at the beginning, we create a new node with the next reference to the current first node. Then we update the pointer `first` to the new node. In code, it would look something like this:
+
+.Add item to the beginning of a Linked List
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=addFirst, indent=0]
+----
+<1> It might be confusing seen `this.first.previous`. It means that we are updating the `previous` pointer of the `art` node to point to `new`.
+
+
+===== Inserting element at the end of the list
+
+Appending an element at the end of the list can be done very effectively if we have a pointer to the `last` item. Otherwise, you would have to iterate through the whole list.
+
+.Add element to the end of the linked list
+image::dll-add-last.png[]
+
+.Linked List's add to the end of the list implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=addLast, indent=0]
+----
+
+If there’s no element in the list yet, the first and last node would be the same. If there’s something, we go to the `last` item and add the reference `next` to the new node. That’s it! We got a constant time for inserting at the beginning and the end of the list: *O(1)*.
+
+
+===== Inserting element at the middle of the list
+
+For inserting an element in the middle of the list, you would need to specify the position (index) in the list. Then, you create the new node and update the references around it.
+
+.There are four references to update:
+. New node's `next`.
+. New node's `previous`.
+. New node's previous `next`.
+. New node's next `previous`.
+
+
+Let’s do an example with the following doubly linked list:
+
+----
+art <-> dog <-> cat
+----
+
+We want to insert the `new` node in the 2^nd^ position (index 1). For that, we first create the "new" node and update the references around it.
+
+[[dll-insert-middle-img]]
+.Inserting node in the middle.
+image::dll-insert-middle.png[]
+
+Take a look into the implementation of `LinkedList.add`:
+
+.Linked List's add to the middle of the list
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=addMiddle, indent=0]
+----
+<1> If the new item goes to position 0, then we reuse the `addFirst` method, and we are done!
+<2> However, if we add to the last position, we reuse the `addLast` method and done!
+<3> Adding `newNode` to the middle: First, create the `new` node only if it exists. Take a look at <<linkedlist.findBy>> to see `findBy` implementation again.
+<4> Set newNode `previous` reference.
+<5> Set newNode `next` link.
+<6> So far, no other node in the list points to `newNode`, so we the `art` node's next point to `new` (refer to the <<dll-insert-middle-img, illustration>>).
+<7> Make `dog` node's previous point to `new`.
+
+Take notice that we reused `addFirst` and `addLast` methods. For all the other cases, the insertion is in the middle. We use `current.previous.next` and `current.next` to update the surrounding elements and point to the new node. Inserting in the middle takes *O(n)* because we have to iterate through the list using the `findBy` method.
+
+==== Deletion
+
+Deleting is an interesting one. We don’t delete an element; we remove all references to that node. The garbage collector will remove it when no one points to it. Let’s go case by case to explore what happens.
+
+===== Deleting element from the head
+
+Deleting the first element (or head) is a matter of removing all references to it.
+
+.Deleting an element from the head of the list
+image::dll-remove-first.png[]
+
+For instance, to remove the head (“art”) node, we change the variable `first` to point to the second node, “dog”. We also remove the variable `previous` from the "dog" node, so it doesn't reference the “art” node anymore. The garbage collector will get rid of the “art” node when it sees nothing is using it anymore.
+
+.Linked List's remove from the beginning of the list
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=removeFirst, indent=0]
+----
+.Check for edge cases:
+- List is already empty.
+- Removing the last node.
+
+As you can see, when we want to remove the first node, we make the 2nd element (`head.next`) the first one.
+
+===== Deleting element from the tail
+
+Removing the last element from the list would require iterate from the head until we find the last one: `O(n)`. But, since we referenced the last element, we can do it in _O(1)_ instead!
+
+.Removing the last element from the list.
+image::dll-remove-last.png[]
+
+
+For instance, if we want to remove the last node “cat”. We use the last pointer to avoid iterating through the whole list. We check `last.previous` to get the “dog” node and make it the new `last` and remove its next reference to “cat.” Since nothing is pointing to “cat” it is out of the list and eventually is deleted from memory by the garbage collector.
+
+.Linked List's remove from the end of the list
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=removeLast, indent=0]
+----
+
+
+The code is very similar to `removeFirst`, but instead of first, we update `last` reference, and instead of nullifying `previous`, we null out the `next` pointer.
+
+===== Deleting element from the middle
+
+To remove a node from the middle, we make the surrounding nodes bypass the one we want to delete.
+
+.Remove the middle node
+image::dll-remove-middle.png[]
+
+In the illustration, we are removing the middle node “dog” by making art’s `next` variable to point to cat and cat’s `previous` to be “art,” totally bypassing “dog.”
+
+Let’s implement it:
+
+.Linked List's remove from the middle of the list
+[source, javascript]
+----
+include::{codedir}/data-structures/linked-lists/linked-list.js[tag=removeByPosition, indent=0]
+----
+
+Notice that we are using the `get` method to get the node at the current position. That method loops through the list until it found the node at the specified location. This iteration has a runtime of _O(n)_.
+
+(((Tables, Linear DS, Array/Lists complexities)))
+
+==== Linked List vs. Array
+
+Arrays give you instant access to data anywhere in the collection using an index. However, Linked List visits nodes in sequential order. In the worst-case scenario, it takes _O(n)_ to get an element from a Linked List. You might be wondering: Isn’t an array always more efficient with _O(1)_ access time? It depends.
+
+We also have to understand the space complexity to see the trade-offs between arrays and linked lists. An array pre-allocates contiguous blocks of memory. If the array fillup, it has to create a larger array (usually 2x) and copy all the elements when it is getting full.  That takes _O(n)_ to copy all the items over. On the other hand, LinkedList’s nodes only reserve precisely the amount of memory they need. They don’t have to be next to each other in RAM, nor are large chunks of memory is booked beforehand like arrays. Linked List is more on a "grow as you go" basis. *Linked list wins on memory usage over an array.*
+
+Another difference is that adding/deleting at the beginning of an array takes `O(n)`; however, the linked list is a constant operation `O(1)` as we will implement later. *Linked List has better runtime than an array for inserting items at the beginning.*
+
+// tag::table[]
+.Big O cheat sheet for Linked List and Array
+|===
+.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space
+^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
+| Array ^|O(1) ^|O(n) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n)
+| Linked List (singly) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|*O(n)* ^|O(n)
+| Linked List (doubly) ^|O(n) ^|O(n) ^|O(1) ^|O(n) ^|O(1) ^|O(1) ^|O(n) ^|*O(1)* ^|O(n)
+|===
+// end::table[]
+(((Linear)))
+(((Runtime, Linear)))
+
+If you compare the singly linked list vs. doubly linked list, you will notice that the main difference is inserting elements to and deleting elements from the end. For a singly linked list, it's *O(n)*, while a doubly-linked list is *O(1)*.
+
+Comparing an array with a doubly-linked list, both have different use cases:
+
+.Use arrays when:
+- You want to access *random* elements by numeric key or index in constant time O(1).
+- You need two-dimensional and multi-dimensional arrays.
+
+.Use a doubly linked list when:
+- You want to access elements in a *sequential* manner only like <<part02-linear-data-structures#stack>> or <<part02-linear-data-structures#queue>>.
+- You want to insert elements at the start and end of the list. The linked list has O(1) while the array has O(n).
+- You want to save some memory when dealing with possibly large data sets. Arrays pre-allocate a large chunk of contiguous memory on initialization. Lists are more “grow as you go.”
+
+For the next two linear data structures <<part02-linear-data-structures#stack>> and <<part02-linear-data-structures#queue>>, we are going to use a doubly-linked list to implement them. We could use an array as well, but since inserting/deleting from the start performs better with linked-lists, we will use that.
+
+==== Linked List patterns for Interview Questions
+
+Most linked list problems are solved using 1 to 3 pointers. Sometimes we move them in tandem or individually.
+
+.Examples of problems that can be solved using multiple pointers:
+- Detecting if the linked list is circular (has a loop).
+- Finding the middle node of a linked list in 1-pass without any auxiliary data structure.
+- Reversing the linked list in 1-pass without any auxiliary data structure. e.g. `1->2->3` to `3->2->1`.
+
+Let's do some examples!
+
+===== Fast/Slow Pointers
+
+One standard algorithm to detect loops in a linked list is fast/slow runner pointers (a.k.a The Tortoise 🐢 and the Hare🐇 or Floyd’s Algorithm). The slow pointer moves one node per iteration, while the fast pointer moves two nodes every time. You can see an example code below:
+
+.Fast/Slow pointers
+[source, javascript]
+----
+let fast = head, slow = head;
+while (fast && fast.next) {
+  slow = slow.next; // slow moves 1 by 1.
+  fast = fast.next.next; // slow moves 2 by 2.
+}
+----
+
+If the list has a loop, then at some point, both pointers will point to the same node. Take a look at the following image; take notice that both points to `node I` on the 8th iteration.
+
+image:cll-fast-slow-pointers.png[fast/slow pointer in a circular linked list]
+
+.You can detect the intersection point (`node D` on the example) by using this algorithm:
+- When `fast` and `slow` are the same, then create a (3rd) new pointer from the start.
+- Keep moving the 3rd pointer and the `slow` simultaneously one by one.
+- Where slow and 3rd pointer meets, that's the beginning of the loop or intersection (e.g., `node D`).
+
+Fast/slow pointer has essential properties, even if the list doesn't have a loop!
+
+If you don't have a loop, then fast and slow will never meet. However, by the time the `fast` pointer reaches the end, the `slow` pointer would be precisely in the middle!
+
+image:sll-fast-slow-pointers.png[fast/slow pointer in a singly linked list]
+
+This technique is useful for getting the middle element of a singly list in one pass without using any auxiliary data structure (like array or map).
+
+
+*LL-A*) _Find out if a linked list has a cycle and, if so, return the intersection node (where the cycle begins)._
+
+.Signature
+[source, javascript]
+----
+/**
+ * Find the node where the cycle begins or null.
+ * @param {Node} head
+ * @returns {Node|null}
+ */
+function findCycleStart(head) {
+
+};
+----
+
+.Examples
+[source, javascript]
+----
+findCycleStart(1 -> 2 -> 3); // null // no loops
+findCycleStart(1 -> 2 -> 3 -> *1); // 1 // node 3 loops back to 1
+findCycleStart(1 -> 2 -> 3 -> *2); // 2 // node 3 loops back to 2
+----
+
+*Solution*
+
+One solution is to find a loop using a HashMap (`Map`) or HashSet (`Set`) to track the visited nodes. If we found a node that is already on `Set`, then that's where the loop starts.
+
+.Solution 1: Map/Set for detecting loop
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-find-cycle-start.js[tag=brute]
+----
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. We might visit all nodes on the list (e.g., no loops).
+- Space complexity: `O(n)`. In the worst-case (no loop), we store all the nodes on the Set.
+
+Can we improve anything here? We can solve this problem without using any auxiliary data structure using the fast/slow pointer.
+
+.Solution 2: Fast/Slow pointer
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-find-cycle-start.js[tag=fn]
+----
+
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. In the worst case (no loop), we visit every node.
+- Space complexity: `O(1)`. We didn't use any auxiliary data structure.
+
+
+===== Multiple Pointers
+
+
+*LL-B*) _Determine if a singly linked list is a palindrome. A palindrome is a sequence that reads the same backward as forward._
+
+.Signature
+[source, javascript]
+----
+/**
+include::{codedir}/data-structures/linked-lists/node.js[tag=singly, indent=2]
+ */
+
+/**
+ * Determine if a list is a palindrome
+ * @param {Node} head
+ * @returns {boolean}
+ */
+function isPalindrome(head) {
+  // you code goes here!
+}
+----
+
+.Examples
+[source, javascript]
+----
+const toList = (arr) => new LinkedList(arr).first;
+isPalindrome(toList([1, 2, 3])); // false
+isPalindrome(toList([1, 2, 3, 2, 1])); // true
+isPalindrome(toList([1, 1, 2, 1])); // false
+isPalindrome(toList([1, 2, 2, 1])); // true
+----
+
+*Solution*
+
+To solve this problem, we have to check if the first and last node has the same value. Then we check if the second node and second last are the same, and so on. If we found any that's not equal; then it's not a palindrome. We can use two pointers, one at the start and the other at the end, and move them until they meet in the middle.
+
+The issue is that with a singly linked list, we can't move backward! We could either convert it into a doubly-linked list (with the last pointer) or copy the nodes into an array. Let's do the latter as a first approach.
+
+.Solution 1: List to array
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-is-palindrome.js[tag=fn2]
+----
+<1> Copy each one of the nodes' value into an array.
+<2> Given two indices (`lo` and `hi`), one with the lowest index (0) and the other with the highest index (length - 1). Move both of them towards the center. If any values are not the same, then it's not a palindrome.
+
+What's the time complexity?
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. We do two passes, one on the for-loop and the other in the array.
+- Space complexity: `O(n)`. We are using auxiliary storage with the array O(n).
+
+That's not bad, but can we do it without using any auxiliary data structure, O(1) space?
+
+.Here's another algorithm to solve this problem in O(1) space:
+- Find the middle node of the list (using fast/slow pointers).
+- Reverse the list from the middle to the end.
+- Have two new pointers, one at the beginning of the list and the other at the head of the reversed list.
+- If all nodes have the same value, then we have a palindrome. Otherwise, we don't.
+
+.Solution 2: Reverse half of the list
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-is-palindrome.js[tag=fn]
+----
+
+This solution is a little longer, but it's more space-efficient since it doesn't use any auxiliary data structure to hold the nodes.
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. We visit every node once.
+- Space complexity: `O(1)`. We didn't use any auxiliary data structure. We changed data in-place.
+
+
+===== Multi-level Linked Lists
+
+It's good to know that linked lists might have other connections besides the `next` and `previous` pointers. They might also reference different lists forming a multi-leveled linked list.
+
+image:mll-3-levels.png[multi-level linked list]
+
+Let's explore the following example:
+
+*LL-C*) _Flatten a multi-level to a single level. You will be operating a doubly-linked list that, besides the pointers `next` and `previous`, also has a `child` pointer. Return the head of the flattened list._
+
+.Signature
+[source, javascript]
+----
+/**
+ * Flatten a multi-level list
+ * @param {Node} head
+ * @return {Node}
+ */
+function flatten(head) {
+
+}
+----
+
+.Examples
+[source, javascript]
+----
+class Node {
+  value = null;
+  next = null;
+  prev = null;
+  child = null;
+  constructor(value) { this.value = value; }
+}
+
+const ll = (nums) => Array.from(new LinkedList(nums, Node));
+const l1 = ll([1, 2, 3, 4, 5, 6]);
+const l2 = ll([10, 12, 14, 16]);
+const l3 = ll([21, 23]);
+const l4 = ll([36, 37]);
+l1[2].child = l2;
+l2[1].child = l3;
+l2[2].child = l4;
+const head = l1[0];
+
+// Head:
+//
+// 1--- 2--- 3--- 4--- 5--- 6
+//      |
+//     10---12---14---16
+//           |    |
+//           |   36---37
+//           |
+//           21--23
+
+flatten(head); // 1->2->10->12->21->23->14->36->37->16->3->4->5->6
+----
+
+Our job is to flatten a multi-level LinkedList. So far, we know how to navigate a list using the `next` pointer. If we found another list on the `child` pointer, we can flatten it out by moving the child's chain to the `next` pointer. However, if we are not careful, we will lose whatever nodes were on `next`. One idea is to store that in an array and bring them back at a later time.
+
+.Algorithm summary:
+* Starting from the `head`, visit all nodes using the `next` pointer.
+  ** If any node has a `child` pointer, move it to the `next`.
+  ** Save `next` on the array (stack) for later use.
+  ** When we don't have more nodes on `next`, pop from the array (stack).
+
+.Solution 1: Array/Stack approach
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-flatten-multilevel.js[tag=fn2]
+----
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. We visit every node only once.
+- Space complexity: `O(n)`. The stack array might hold almost all nodes.
+
+
+This approach works well. However, we can do better in terms of space complexity. Instead of holding the data on an auxiliary array, we can append it to the end of the child's list.
+
+.Algorithm summary:
+* Starting from the `head`, visit all nodes using the `next` pointer.
+  ** If node `curr` has a `child`.
+    *** Follow the child's chain to the end.
+    *** Then connect the child's tail to `curr.next`. By doing this, we merged the child's chain with the main thread.
+    *** Move the child's chain to `curr.next`.
+
+.Solution 2: In-place approach
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-flatten-multilevel.js[tag=fn]
+----
+
+.Complexity Analysis
+- Time Complexity: `O(n)`. In the worst-case, we will visit most nodes twice `2n` -> `O(n)`.
+- Space complexity: `O(1)`. No auxiliary structure was used to hold the lists.
+
+
+// ===== Intersections of linked lists
+
+// WIP
+// // https://leetcode.com/problems/intersection-of-two-linked-lists/
+
+
+<<<
+==== Practice Questions
+(((Interview Questions, Linked Lists)))
+
+// tag::linkedlist-q-merge-lists[]
+===== Merge Linked Lists into One
+
+*LL-1*) _Merge two sorted lists into one (and keep them sorted)_
+
+Examples:
+
+----
+mergeTwoLists(2->3->4, 1->2); // 1->2->2->3->4
+mergeTwoLists(2->3->4,null); // 2->3->4
+----
+
+_Common in interviews at: FAANG, Adobe, Microsoft_
+// end::linkedlist-q-merge-lists[]
+
+[source, javascript]
+----
+include::../../interview-questions/merge-lists.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<linkedlist-q-merge-lists>>_
+
+
+
+
+// tag::linkedlist-q-linkedlist-same-data[]
+===== Check if two strings lists are the same
+
+*LL-2*) _Given two linked lists with strings, check if the data is equivalent._
+
+Examples:
+
+----
+hasSameData(he->ll->o, hel->lo); // true
+hasSameData(hello, hel->lo); // true
+hasSameData(he->ll->o, h->i); // false
+----
+
+_Common in interviews at: Facebook_
+// end::linkedlist-q-linkedlist-same-data[]
+
+[source, javascript]
+----
+include::../../interview-questions/linkedlist-same-data.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<linkedlist-q-linkedlist-same-data>>_
diff --git a/book/content/part02/queue.asc b/book/content/part02/queue.asc
new file mode 100644
index 00000000..609b720a
--- /dev/null
+++ b/book/content/part02/queue.asc
@@ -0,0 +1,146 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[queue]]
+=== Queue
+(((Queue)))
+(((Data Structures, Linear, Queue)))
+(((First-In First-out)))
+(((FIFO)))
+A queue is a linear data structure where the data flows in a *First-In-First-Out* (FIFO) manner.
+
+.Queue data structure is like a line of people: the First-in, is the First-out
+image::image30.png[image,width=528,height=171]
+
+A queue is like a line of people at the bank; the person who arrived first is the first to go out.
+
+Similar to the stack, we only have two operations (insert and remove). In a Queue, we add elements to the back of the list and remove it from the front.
+
+We could use an array or a linked list to implement a Queue. However, it is recommended only to use a linked list. Why? An array has a linear runtime _O(n)_ to remove an element from the start, while a linked list has constant time _O(1)_.
+
+.Queue's constructor
+[source, javascript]
+----
+include::{codedir}/data-structures/queues/queue.js[tag=constructor]
+  // ... methods go here ...
+}
+----
+
+We initialize the Queue creating a linked list. Now, let’s add the `enqueue` and `dequeue` methods.
+
+==== Insertion
+(((Enqueue)))
+For inserting elements into a queue, also know as *enqueue*, we add items to the back of the list using `addLast`:
+
+.Queue's enqueue
+[source, javascript]
+----
+include::{codedir}/data-structures/queues/queue.js[tag=enqueue, indent=0]
+----
+
+As discussed, this operation has a constant runtime.
+
+==== Deletion
+(((Dequeue)))
+For removing elements from a queue, also known as *dequeue*, we remove elements from the front of the list using `removeFirst`:
+
+.Queue's dequeue
+[source, javascript]
+----
+include::{codedir}/data-structures/queues/queue.js[tag=dequeue, indent=0]
+----
+
+As discussed, this operation has a constant runtime.
+
+==== Implementation usage
+
+We can use our Queue class as follows:
+
+.Queue usage example
+[source, javascript]
+----
+include::{codedir}/data-structures/queues/queue.js[tag=snippet, indent=0]
+----
+
+You can see that the items are dequeued in the same order they were added, FIFO (first-in, first-out).
+
+==== Queue Complexity
+
+As an experiment, we can see in the following table that if we had implemented the Queue using an array, its enqueue time would be _O(n)_ instead of _O(1)_. Check it out:
+(((Tables, Linear DS, Queue complexities)))
+
+// tag::table[]
+.Time/Space complexity for queue operations
+|===
+.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space
+^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
+| Queue (w/array) ^|- ^|- ^|- ^|- ^|O(1) ^|*O(n)* ^|- ^|- ^|O(n)
+| Queue (w/list) ^|- ^|- ^|- ^|- ^|O(1) ^|O(1) ^|- ^|- ^|O(n)
+|===
+// end::table[]
+indexterm:[Runtime, Linear]
+
+
+==== Practice Questions
+(((Interview Questions, Queue)))
+
+
+// tag::queue-q-recent-counter[]
+===== Recent Counter
+
+*QU-1*) _Design a class that counts the most recent requests within a time window._
+
+Example:
+
+[source, javascript]
+----
+const counter = new RecentCounter(10); // The time window is 10 ms.
+counter.request(1000); // 1 (first request, it always counts)
+counter.request(3000); // 1 (last requests was 2000 ms ago, > 10ms, so doesn't count)
+counter.request(3100); // 1 (last requests was 100 ms ago, > 10ms, so doesn't count)
+counter.request(3105); // 2 (last requests was 5 ms ago, <= 10ms, so it counts)
+----
+
+_Common in interviews at: FAANG, Bloomberg, Yandex_
+// end::queue-q-recent-counter[]
+
+
+[source, javascript]
+----
+include::../../interview-questions/recent-counter.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<queue-q-recent-counter>>_
+
+
+// tag::queue-q-design-snake-game[]
+===== Design Snake Game
+
+*QU-2*) _Design the `move` function for the snake game. The move function returns an integer representing the current score. If the snake goes out of the given height and width or hit itself, the game is over and return `-1`._
+
+Example:
+
+[source, javascript]
+----
+const snakeGame = new SnakeGame(4, 2, [[1, 2], [0, 1]]);
+expect(snakeGame.move('R')).toEqual(0); //  0
+expect(snakeGame.move('D')).toEqual(0); //  0
+expect(snakeGame.move('R')).toEqual(1); //  1 (ate food1)
+expect(snakeGame.move('U')).toEqual(1); //  1
+expect(snakeGame.move('L')).toEqual(2); //  2 (ate food2)
+expect(snakeGame.move('U')).toEqual(-1); // -1 (hit wall)
+----
+
+_Common in interviews at: Amazon, Bloomberg, Apple_
+// end::queue-q-design-snake-game[]
+
+[source, javascript]
+----
+include::../../interview-questions/design-snake-game.js[tag=description]
+----
+
+_Solution: <<queue-q-design-snake-game>>_
diff --git a/book/content/part02/stack.asc b/book/content/part02/stack.asc
new file mode 100644
index 00000000..e08cc965
--- /dev/null
+++ b/book/content/part02/stack.asc
@@ -0,0 +1,148 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[stack]]
+=== Stack
+(((Stack)))
+(((Data Structures, Linear, Stack)))
+(((Last-In First-out)))
+(((LIFO)))
+The stack is a data structure that restricts the way you add and remove data. It only allows you to insert and retrieve in a *Last-In-First-Out* (LIFO) fashion.
+
+An analogy is to think that the stack is a rod, and the data are discs. You can only take out the last one you put in.
+
+.Stack data structure is like a stack of disks: the last element in is the first element out
+image::image29.png[image,width=240,height=238]
+
+// #Change image from https://www.khanacademy.org/computing/computer-science/algorithms/towers-of-hanoi/a/towers-of-hanoi[Khan Academy]#
+
+As you can see in the image above, If you insert the disks in the order `5`, `4`, `3`, `2`, `1`, then you can remove them in `1`, `2`, `3`, `4`, `5`.
+
+The stack inserts items to the end of the collection and also removes it from the rear. Both an array and linked list would do it in constant time. However, since we don’t need the Array’s random access, a linked list makes more sense.
+
+.Stack's constructor
+[source, javascript]
+----
+include::{codedir}/data-structures/stacks/stack.js[tag=constructor]
+  // ... methods goes here ...
+}
+----
+
+As you can see in the stack constructor, we use a  linked list as the underlying data structure.
+
+Let's now develop the insert and remove operations in a stack.
+
+==== Insertion
+
+We can insert into a stack using the linked list’s `addLast` method.
+
+.Stack's add
+[source, javascript]
+----
+include::{codedir}/data-structures/stacks/stack.js[tag=add, indent=0]
+----
+
+We are returning `this`, in case we want to chain multiple add commands.
+
+==== Deletion
+
+Deleting is straightforward, as well.
+
+.Stack's remove
+[source, javascript]
+----
+include::{codedir}/data-structures/stacks/stack.js[tag=remove, indent=0]
+----
+
+This time we used the linked list’s `removeLast` method. That’s all we need for a stack implementation. Check out the https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/stacks/stack.js#L6[full implementation].
+
+==== Implementation Usage
+
+We can use our stack implementation as follows:
+
+.Stack usage example
+[source, javascript]
+----
+include::{codedir}/data-structures/stacks/stack.js[tag=snippet, indent=0]
+----
+
+As you can see, if we add new items, they will be the first to go out to honor LIFO.
+
+==== Stack Complexity
+
+Implementing the stack with an array and linked list would lead to the same time complexity:
+(((Tables, Linear DS, Stack complexities)))
+
+// tag::table[]
+.Time/Space complexity for the stack operations
+|===
+.2+.^s| Data Structure 2+^s| Searching By 3+^s| Inserting at the 3+^s| Deleting from .2+.^s| Space
+^|_Index/Key_ ^|_Value_ ^|_beginning_ ^|_middle_ ^|_end_ ^|_beginning_ ^|_middle_ ^|_end_
+| Stack ^|- ^|- ^|- ^|- ^|O(1) ^|- ^|- ^|O(1) ^|O(n)
+|===
+// end::table[]
+
+It's not very common to search for values on a stack (other Data Structures are better suited for this). Stacks are especially useful for implementing <<part03-graph-data-structures#dfs-tree, Depth-First Search>>.
+
+
+==== Practice Questions
+(((Interview Questions, Stack)))
+
+// tag::stack-q-valid-parentheses[]
+===== Validate Parentheses / Braces / Brackets
+
+*ST-1*) _Given a string with three types of brackets: `()`, `{}`, and `[]`. Validate they are correctly closed and opened._
+
+Examples:
+
+[source, javascript]
+----
+isParenthesesValid('(){}[]'); // true
+isParenthesesValid('('); // false (closing parentheses is missing)
+isParenthesesValid('([{}])'); // true
+isParenthesesValid('[{]}'); // false (brakets are not closed in the right order)
+isParenthesesValid('([{)}]'); // false (closing is out of order)
+----
+
+_Common in interviews at: Amazon, Bloomberg, Facebook, Citadel_
+// end::stack-q-valid-parentheses[]
+
+[source, javascript]
+----
+include::../../interview-questions/valid-parentheses.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<stack-q-valid-parentheses>>_
+
+
+
+
+
+// tag::stack-q-daily-temperatures[]
+===== Daily Temperaturs
+
+*ST-2*) _Given an array of integers from 30 to 100 (daily temperatures), return another array that, for each day in the input, tells you how many days you would have to wait until warmer weather. If no warmer climate is possible, then return `0` for that element._
+
+Examples:
+
+[source, javascript]
+----
+dailyTemperatures([30, 28, 50, 40, 30]); // [2 (to 50), 1 (to 28), 0, 0, 0]
+dailyTemperatures([73, 69, 72, 76, 73, 100]); // [3, 1, 1, 0, 1, 100]
+----
+
+_Common in interviews at: Amazon, Adobe, Cisco_
+// end::stack-q-daily-temperatures[]
+
+[source, javascript]
+----
+include::../../interview-questions/daily-temperatures.js[tag=description]
+  // write you code here
+}
+----
+
+_Solution: <<stack-q-daily-temperatures>>_
diff --git a/book/content/part03/binary-search-tree-traversal.asc b/book/content/part03/binary-search-tree-traversal.asc
new file mode 100644
index 00000000..512a6cbe
--- /dev/null
+++ b/book/content/part03/binary-search-tree-traversal.asc
@@ -0,0 +1,231 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Binary Tree Traversal
+(((Binary Tree Traversal)))
+In this section, we are going to focus on depth-first tree traversal.
+
+==== In Order Traversal
+(((Tree Traversal, In Order)))
+(((In Order Traversal)))
+If your tree happens to be a binary search tree (BST), then you can use "in order" traversal to get the values sorted in ascending order. To accomplish this, you have to visit the nodes in a `left-root-right` order.
+
+If we have the following tree:
+----
+         10
+       /    \
+      5      30
+    /       /  \
+   4       15   40
+ /
+3
+----
+
+In-order traverval will return `3, 4, 5, 10, 15, 30, 40`.
+
+Check out the implementation:
+
+.In-order traversal implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/trees/binary-search-tree.js[tag=inOrderTraversal, indent=0]
+----
+
+This function gets the leftmost element (recursively) and then yields that node, then we go to the right child (if any) and repeat the process. This method will get us the values ordered.
+
+==== Pre Order Traversal
+(((Tree Traversal, Pre Order)))
+(((Pre Order Traversal)))
+Pre-order traversal visits nodes in this order `root-left-right` recursively.
+
+.Usage of pre-order traversal:
+- Create a copy of the tree.
+- Get prefix expression on of an expression tree used in the https://en.wikipedia.org/wiki/Polish_notation[polish notation].
+
+
+.Pre-order traversal implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/trees/binary-search-tree.js[tag=preOrderTraversal, indent=0]
+----
+
+If we have the following tree:
+----
+         10
+       /    \
+      5      30
+    /       /  \
+   4       15   40
+ /
+3
+----
+
+Pre-order traverval will return `10, 5, 4, 3, 30, 15, 40`.
+
+==== Post-order Traversal
+(((Tree Traversal, Post Order)))
+(((Post Order Traversal)))
+Post-order traversal goes to each node in this order `left-right-root` recursively.
+
+.Usages of the post-order tree traversal
+- Traversal is used to delete the tree because you visit the children before removing the parent.
+- Get the postfix expression of an expression tree used in the http://en.wikipedia.org/wiki/Reverse_Polish_notation[reverse polish notation].
+
+.Post-order traversal implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/trees/binary-search-tree.js[tag=postOrderTraversal, indent=0]
+----
+
+
+If we have the following tree:
+----
+         10
+       /    \
+      5      30
+    /       /  \
+   4       15   40
+ /
+3
+----
+
+Post-order traverval will return `3, 4, 5, 15, 40, 30, 10`.
+
+
+==== Practice Questions
+(((Interview Questions, Binary Tree)))
+
+
+// tag::binary-tree-q-diameter-of-binary-tree[]
+===== Binary Tree Diameter
+
+*BT-1*) _Find the diameter of a binary tree. A tree's diameter is the longest possible path from two nodes (it doesn't need to include the root). The length of a diameter is calculated by counting the number of edges on the path._
+
+// end::binary-tree-q-diameter-of-binary-tree[]
+
+_Common in interviews at: FAANG_
+
+// Example 1:
+// [graphviz, tree-diameter-example-1, png]
+// ....
+// graph G {
+//     1 -- 2 [color=red]
+//     1 -- 3 [color=red]
+
+//     2 -- 4
+//     2 -- 5 [color=red]
+// }
+// ....
+
+// Example 2:
+// [graphviz, tree-diameter-example-2, png]
+// ....
+// graph G {
+//     1
+//     2
+//     3
+//     4
+//     5
+//     6
+//     "null" [color=white, fontcolor = white]
+//     "null." [color=white, fontcolor = white]
+//     7
+//     8
+//     "null.." [color=white, fontcolor = white]
+//     "null..." [color=white, fontcolor = white]
+//     9
+
+//     1 -- 2
+//     1 -- 3
+
+//     3 -- 4 [color=red]
+//     3 -- 5 [color=red]
+
+//     4 -- 6 [color=red]
+//     4 -- "null." [color=white]
+
+//     5 -- "null" [color=white]
+//     5 -- 7 [color=red]
+
+//     6 -- 8 [color=red]
+//     6 -- "null.." [color=white]
+
+//     7 -- "null..." [color=white]
+//     7 -- 9 [color=red]
+// }
+// ....
+
+Examples:
+
+[source, javascript]
+----
+/*           1
+            / \
+           2   3
+          / \
+         4   5          */
+diameterOfBinaryTree(toBinaryTree([1,2,3,4,5])); // 3
+// For len 3, the path could be 3-1-2-5 or 3-1-2-4
+
+/*           1
+            / \
+           2   3
+              / \
+             4   5
+            /     \
+           6       7
+          /         \
+         8           9      */
+const array = [1,2,3,null,null,4,5,6,null,null,7,8,null,null,9];
+const tree = BinaryTreeNode.from(array);
+diameterOfBinaryTree(tree); // 6 (path: 8-6-4-3-5-7-9)
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/diameter-of-binary-tree.js[tags=description;placeholder]
+----
+
+
+_Solution: <<binary-tree-q-diameter-of-binary-tree>>_
+
+
+
+
+// tag::binary-tree-q-binary-tree-right-side-view[]
+===== Binary Tree from right side view
+
+*BT-2*) _Imagine that you are viewing the tree from the right side. What nodes would you see?_
+
+// end::binary-tree-q-binary-tree-right-side-view[]
+
+_Common in interviews at: Facebook, Amazon, ByteDance (TikTok)._
+
+Examples:
+
+[source, javascript]
+----
+/*
+ 1      <- 1 (only node on level)
+ /   \
+2     3   <- 3 (3 is the rightmost)
+ \
+  4       <- 4 (only node on level) */
+rightSideView(BinaryTreeNode.from([1, 2, 3, null, 4])); // [1, 3, 4]
+
+rightSideView(BinaryTreeNode.from([])); // []
+rightSideView(BinaryTreeNode.from([1, 2, 3, null, 5, null, 4, 6])); // [1, 3, 4, 6]
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/binary-tree-right-side-view.js[tags=description;placeholder]
+----
+
+_Solution: <<binary-tree-q-binary-tree-right-side-view>>_
diff --git a/book/chapters/tree--binary-search-tree.adoc b/book/content/part03/binary-search-tree.asc
similarity index 50%
rename from book/chapters/tree--binary-search-tree.adoc
rename to book/content/part03/binary-search-tree.asc
index 4241335d..312421fa 100644
--- a/book/chapters/tree--binary-search-tree.adoc
+++ b/book/content/part03/binary-search-tree.asc
@@ -1,14 +1,24 @@
-= Binary Search Tree
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
 
-.The Binary Search Tree (BST) is a tree data structure that keeps the following constraints:
-* Each node must have at most two children. Usually referred to as "left" and "right".
-* All trees must a have a "root" node.
-* The order of nodes values must be: left child < parent < right child.
-* Nodes might need re-ordering after each insert/delete operation to keep the `left < parent < right` constraint.
+(((Binary Search Tree)))
+(((BST)))
+(((Data Structures, Non-Linear, Binary Search Tree)))
+[[binary-search-tree-chap]]
+=== Binary Search Tree
 
-== Implementing a Binary Search Tree
 
-The first step is to implement the Binary Tree Node, which can hold 0, 1 or 2 children.
+.To recap, the Binary Search Tree (BST) is a tree data structure that keeps the following constraints:
+* Each node must have at most two children. Usually referred to as "left" and "right."
+* All trees must have a "root" node.
+* The order of nodes values must be: `left child < parent < right child`.
+* Nodes might need re-ordering after each insert/delete operation to keep the `left <= parent < right` constraint.
+
+==== Implementing a Binary Search Tree
+
+The first step is to implement the Binary Tree Node, which can hold 0, 1, or 2 children.
 
 .Binary Tree Node's constructor
 [source, javascript]
@@ -22,7 +32,7 @@ Does this look familiar to you? It’s almost like the linked list node, but ins
 
 We also added the `meta` object to hold some metadata about the node, like duplicity, color (for red-black trees), or any other data needed for future algorithms.
 
-We implemented the node, now let’s layout other methods that we can implement for a BST:
+We implemented the node; now, let’s layout other methods that we can implement for a BST:
 
 .Binary Search Tree's class
 [source, javascript]
@@ -37,31 +47,34 @@ include::{codedir}/data-structures/trees/binary-search-tree.js[tag=snippet, inde
 }
 ----
 
-With the methods `add` and `remove` we have to guarantee that our tree always has one root element from where we can navigate left or right based on the value that we are looking for. Let's implement those `add` method first:
+With the methods `add` and `remove`, we have to guarantee that our tree always has one root element from where we can navigate left or right based on the value we are looking for. Let's implement those `add` method first:
 
-=== Inserting new elements in a BST
+===== Inserting new elements in a BST
 
-.For inserting an element, in a BST, we have two scenarios:
-1.  If the tree is empty (root element is null), we add the newly created node as root, and we are done!
-2.  If the root is not null. Start from the root, then compare the node’s value against the new element. If the node has higher than a new item, we move to the right child, otherwise to the left. We check each node recursively until we find an empty spot where we can put the new element and keep the rule `right < parent < left`.
-3.  If we insert the same value multiple times, we don’t want duplicates. So, we can keep track of multiples using a duplicity counter.
+.For inserting an element in a BST, we have two scenarios:
+. If the tree is empty (root element is null), we add the newly created node as root, and that's it!
+. If the tree has a root, compare the new value with the root. Then we have three possibilities:
+.. `root == newValue`: we increase the duplicity counter in that case, and done!
+.. `root > newValue`, we search on the left side of the root.
+.. `root < newValue`, we search on the right side of the root.
+. Repeat the comparison between the current node and `newValue`, until we find the value or (null) space.
 
-For instance, let’s say that we want to insert the values 19, 21, 10, 2, 8 in a BST:
+For instance, let’s say that we want to insert the values 19, 21, 10, 2, 18 in a BST:
 
 .Inserting values on a BST.
-image:image36.png[image,width=528,height=329]
+image::image36.png[image,width=528,height=329]
 
-In the last box of the image above, when we are inserting node 18, we start by the root (19). Since 18 is less than 19, then we move left. Node 18 is greater than 10, so we move right. There’s an empty spot, and we place it there. Let’s code it up:
+In the last box of the image above, we start by the root when we insert node 18 (19). Since 18 is less than 19, then we move left. Node 18 is greater than 10, so we move right. There’s an empty spot, and we place it there. Let’s code it up:
 
 .Binary Search Tree's class
 [source, javascript]
 ----
 include::{codedir}/data-structures/trees/binary-search-tree.js[tag=add, indent=0]
 ----
-<1> We are using a helper function `findNodeAndParent` to iterate through the tree finding a node with current value “found” and its parent (implementation on the next section).
-<2> We are taking care of duplicates. Instead of inserting duplicates we are keeping a multiplicity tally. We have to decrease it when removing nodes.
+<1> We are using a helper function `findNodeAndParent` to iterate through the tree, finding a node with the current value “found” and its parent (implementation on the next section).
+<2> We are taking care of duplicates. Instead of inserting duplicates, we are keeping a multiplicity tally. We have to decrease it when removing nodes.
 
-=== Finding a value in a BST
+===== Finding a value in a BST
 
 We can implement the find method using the helper `findNodeAndParent` as follows:
 
@@ -71,50 +84,50 @@ We can implement the find method using the helper `findNodeAndParent` as follows
 include::{codedir}/data-structures/trees/binary-search-tree.js[tag=find, indent=0]
 ----
 
-`findNodeAndParent` is a recursive function that goes to the left child or right depending on the value. However, if the value already exists, it will return it in `found` variable.
+`findNodeAndParent` is a recursive function that goes to the left or right child, depending on the value. However, if the value already exists, it will return it in `found` variable.
 
-=== Removing elements from a BST
+===== Removing elements from a BST
 
-Deleting a node from a BST have three cases.
+Deleting a node from a BST has three cases.
 
 .The node is a
 1. leaf
 2. parent with one child
 3. parent with two children/root.
 
-==== Removing a leaf (Node with 0 children)
+====== Removing a leaf (Node with 0 children)
 
-Deleting a leaf is the easiest, we look for their parent and set the child to null.
+Deleting a leaf is the easiest; we look for their parent and set the child to null.
 
 .Removing node without children from a BST.
-image:image37.png[image,width=528,height=200]
+image::image37.png[image,width=528,height=200]
 
 
-Node 18, will be hanging around until the garbage collector is run. However, there’s no node referencing to it so it won’t be reachable from the tree anymore.
+Node 18, will be hanging around until the garbage collector is run. However, there’s no node referencing to it to no longer be reachable from the tree.
 
-==== Removing a parent (Node with 1 children)
+====== Removing a parent (Node with 1 children)
 
 Removing a parent is not as easy since you need to find new parents for its children.
 
 .Removing node with 1 children from a BST.
-image:image38.png[image,width=528,height=192]
+image::image38.png[image,width=528,height=192]
 
 
 In the example, we removed node `10` from the tree, so its child (node 2) needs a new parent. We made node 19 the new parent for node 2.
 
-==== Removing a full parent (Node with 2 children) or root
+====== Removing a full parent (Node with 2 children) or root
 
-Removing a parent of two children is the trickiest of all cases because we need to find new parents for two children. (This sentence sounds tragic out of context 😂)
+Removing a parent of two children is the trickiest of all cases because we need to find new parents. (This sentence might sound tragic out of context 😂)
 
 .Removing node with two children from a BST.
-image:image39.png[image,width=528,height=404]
+image::image39.png[image,width=528,height=404]
 
 
-In the example, we delete the root node 19. This leaves the two orphans (node 10 and node 21). There are no more parents because node 19 was the *root* element. One way to solve this problem is to *combine* the left subtree (Node 10 and descendants) into the right subtree (node 21). The final result is node 21 is the new root.
+In the example, we delete the root node 19. This deletion leaves two orphans (node 10 and node 21). There are no more parents because node 19 was the *root* element. One way to solve this problem is to *combine* the left subtree (Node 10 and descendants) into the right subtree (node 21). The final result is node 21 is the new root.
 
 What would happen if node 21 had a left child (e.g., node 20)? Well, we would move node 10 and its descendants' bellow node 20.
 
-==== Implementing removing elements from a BST
+====== Implementing removing elements from a BST
 
 All the described scenarios removing nodes with zero, one and two children can be sum up on this code:
 
@@ -124,13 +137,13 @@ All the described scenarios removing nodes with zero, one and two children can b
 include::{codedir}/data-structures/trees/binary-search-tree.js[tag=remove, indent=0]
 ----
 <1> Try to find if the value exists on the tree.
-<2> If the value doesn’t exist we are done!
-<3> Create new subtree without the value to delete
-<4> Check the multiplicity (duplicates) and decrement the count in case we have multiple nodes with the same value
-<5> If the `nodeToRemove` was the root, then we move the removed node’s children as the new root.
-<6> If it was not the root, then we go to the deleted node’s parent and put their children there.
+<2> If the value doesn’t exist, we are done!
+<3> Create a new subtree without the value to delete
+<4> Check the multiplicity (duplicates) and decrement the count if we have multiple nodes with the same value
+<5> If the `nodeToRemove` was the root, we wouldwould move the removed node’s children as the new root.
+<6> If it was not the root, we will go to the deleted node’s parent and put their children there.
 
-We compute `removedNodeChildren`, which is the resulting subtree after combining the children of the deleted node.
+We compute `removedNodeChildren`, which is the resulting subtree after combining the deleted node children.
 
 The method to combine subtrees is the following:
 
@@ -140,7 +153,7 @@ The method to combine subtrees is the following:
 include::{codedir}/data-structures/trees/binary-search-tree.js[tag=combine, indent=0]
 ----
 
-Take a look at the code above and the example. You will see how to remove node `30` and combine both children subtree and keeping the BST rules. Also, this method uses a helper to get the left-most node. We can implement it like this:
+Take a look at the code above and the example. You will see how to remove node `30` and combine both children's subtree, and keeping the BST rules. Also, this method uses a helper to get the left-most node. We can implement it like this:
 
 .Binary Search Tree's get the leftmost node
 [source, javascript]
@@ -150,19 +163,21 @@ include::{codedir}/data-structures/trees/binary-search-tree.js[tag=leftMost, ind
 
 That’s all we need to remove elements from a BST. Check out the complete BST implementation https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/trees/binary-search-tree.js#L5[here].
 
-== Differentiating a balanced and non-balanced Tree
+==== Differentiating a balanced and non-balanced Tree
 
-As we insert and remove nodes from a BST we could end up like the tree on the left:
+As we insert and remove nodes from a BST, we could end up like the tree on the left:
 
 .Balanced vs. Unbalanced Tree.
-image:image40.png[image,width=454,height=201]
+image::image40.png[image,width=454,height=201]
 
-The tree on the left is unbalanced. It looks like a Linked List and has the same runtime! Searching for an element would be *O(n)*, yikes! However, on a balanced tree, the search time is *O(log n)*, which is pretty good! That’s why we always want to keep the tree balanced. In further chapters, we are going to explore how to keep a tree balanced after each insert/delete.
+The tree on the left is unbalanced. It looks like a Linked List and has the same runtime! Searching for an element would be *O(n)*, yikes! However, on a balanced tree, the search time is *O(log n)*, which is pretty good! That’s why we always want to keep the tree balanced. In further chapters, we will explore how to keep a tree balanced after each insert/delete.
 
-== Tree Complexity
+==== Tree Complexity
 
 We can sum up the tree operations using Big O notation:
+(((Tables, Non-Linear DS, Binary Search Tree complexities)))
 
+// tag::table[]
 .Time complexity for a Binary Search Tree (BST)
 |===
 .2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
@@ -170,5 +185,6 @@ We can sum up the tree operations using Big O notation:
 | BST (**un**balanced) ^|- ^|O(n) ^|O(n) ^|O(n) ^|O(n)
 | BST (balanced) ^|- ^|O(log n) ^|O(log n) ^|O(log n) ^|O(n)
 |===
+// end::table[]
 indexterm:[Runtime, Linear]
 indexterm:[Runtime, Logarithmic]
diff --git a/book/content/part03/graph-search.asc b/book/content/part03/graph-search.asc
new file mode 100644
index 00000000..af7d5969
--- /dev/null
+++ b/book/content/part03/graph-search.asc
@@ -0,0 +1,221 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Graph Search
+
+Graph search allows you to visit search elements.
+
+WARNING:  Graph search is very similar to <<Tree Search & Traversal>>. So, if you read that section, some of the concepts here will be familiar to you.
+
+There are two ways to navigate the graph, one is using Depth-First Search (DFS), and the other one is Breadth-First Search (BFS). Let's see the difference using the following graph.
+
+image::directed-graph.png[directed graph]
+
+// [graphviz, directed graph, png]
+// ....
+// digraph G {
+
+//      node [fillcolor="#F8E71C" style=filled shape=circle] 0;
+//      node [fillcolor="#F5A623"] 1;
+//      node [fillcolor="#B8E986"] 2;
+//      node [fillcolor="#BD10E0"] 3;
+//      node [fillcolor="#50E3C2"] 4;
+//      node [fillcolor="#4A90E2"] 5;
+//     //  node [fillcolor="#FF5252"] 6;
+
+//         0 -> 5
+//         0 -> 4
+//         0 -> 1
+//         1 -> 4
+//         1 -> 3
+//         2 -> 1
+//         3 -> 4
+//         3 -> 2
+
+//     // label="DFS"
+
+//     { rank=same; 3, 1 }
+//     { rank=same; 0, 4 }
+
+// }
+// ....
+
+==== Depth-First Search for Graphs
+
+With Depth-First Search (DFS), we go deep before going wide.
+
+We use DFS on the graph shown above, starting with node `0`.
+A DFS will probably visit 5, then visit `1` and continue going down `3` and `2`. As you can see, we need to keep track of visited nodes, since, in graphs, we can have cycles like `1-3-2`.
+Finally, we back up to the remaining node `0` children: node `4`.
+
+So, DFS would visit the graph: `[0, 5, 1, 3, 2, 4]`.
+
+// TODO: add arrows to show DFS and create another one for BFS
+
+==== Breadth-First Search for Graphs
+
+With Breadth-First Search (BFS), we go wide before going deep.
+
+// TODO: BFS traversal
+We use BFS on the graph shown above, starting with the same node `0`.
+A BFS will visit 5 as well, then visit `1` and not go down to its children.
+It will first finish all the children of node `0`, so it will visit node `4`.
+After all the children of node `0` are visited, it will continue with all the children of node `5`, `1`, and `4`.
+
+In summary, BFS would visit the graph: `[0, 5, 1, 4, 3, 2]`
+
+==== Depth-First Search vs. Breadth-First Search in a Graph
+
+DFS and BFS can implementation can be almost identical; the difference is the underlying data structured. In our implementation, we have a generic `graphSearch` where we pass the first element to start the search the data structure that we can to use:
+
+.DFS and BFS implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/graphs/graph.js[tag=graphSearch,indent=0]
+----
+
+Using an <<part02-linear-data-structures#stack>> (LIFO) for DFS will make use keep visiting the last node children while having a <<part02-linear-data-structures#queue>> (FIFO) will allow to visit adjacent nodes first and "queue" their children for later visiting.
+
+TIP: you can also implement the DFS as a recursive function, similar to what we did in the <<part03-graph-data-structures#dfs-tree, DFS for trees>>.
+
+You might wonder what the difference between search algorithms in a tree and a graph is? Check out the next section.
+
+==== DFS/BFS on Tree vs Graph
+
+The difference between searching a tree and a graph is that the tree always has a starting point (root node). However, in a graph, you can start searching anywhere. There's no root.
+
+NOTE: Every tree is a graph, but not every graph is a tree. Only acyclic directed graphs (DAG) are trees.
+
+
+==== Practice Questions
+(((Interview Questions, graph)))
+
+
+
+
+// tag::graph-q-course-schedule[]
+===== Course Schedule
+
+*gr-1*) _Check if it's possible to take all courses while satisfying their prerequisites._
+
+// end::graph-q-course-schedule[]
+
+_Common in interviews at: Amazon, Facebook, Bytedance (TikTok)._
+
+
+*Starter code*:
+
+[source, javascript]
+----
+include::../../interview-questions/course-schedule.js[tags=description;placeholder]
+----
+
+
+*Examples*:
+
+[source, javascript]
+----
+canFinish(2, [[1, 0]]); // true
+// 2 courses: 0 and 1. One prerequisite: 0 -> 1
+// To take course 1 you need to take course 0.
+// Course 0 has no prerequisite, so you can take 0 and then 1.
+
+canFinish(2, [[1, 0], [0, 1]]); // false
+// 2 courses: 0 and 1. Two prerequisites: 0 -> 1 and 1 -> 0.
+// To take course 1, you need to take course 0.
+// To Course 0, you need course 1, so you can't any take them!
+
+canFinish(3, [[2, 0], [1, 0], [2, 1]]); // true
+// 3 courses: 0, 1, 2. Three prerequisites: 0 -> 2 and 0 -> 1 -> 2
+// To take course 2 you need course 0, course 0 has no prerequisite.
+// So you can take course 0 first, then course 1, and finally course 2.
+
+canFinish(4, [[1, 0], [2, 1], [3, 2], [1, 3]]); // false
+// 4 courses: 0, 1, 2, 3. Prerequisites: 0 -> 1 -> 2 -> 3 and 3 -> 1.
+// You can take course 0 first since it has no prerequisite.
+// For taking course 1, you need course 3. However, for taking course 3
+// you need 2 and 1. You can't finish then!
+----
+
+
+_Solution: <<graph-q-course-schedule>>_
+
+
+
+
+
+
+
+// tag::graph-q-critical-connections-in-a-network[]
+===== Critical Network Paths
+
+*gr-2*) _Given `n` servers and the connections between them, return the critical paths._
+
+// end::graph-q-critical-connections-in-a-network[]
+
+_Common in interviews at: FAANG._
+
+Examples:
+
+[graphviz, critical-path-examples, png]
+....
+graph G {
+  subgraph cluster_1 {
+    a0 -- a1 -- a2 [color=firebrick1]
+    label = "Example A";
+  }
+
+  subgraph cluster_0 {
+    b0 -- b1 [color=blue]
+    b1 -- b2 [color=blue]
+    b2 -- b0 [color=blue]
+    b1 -- b3 [color=blue]
+    b3 -- b2 [color=blue]
+    label = "Example B";
+    b0, b1, b2, b3 [color=midnightblue]
+  }
+
+  subgraph cluster_3 {
+    c0 -- c1 [color=blue]
+    c1 -- c2 [color=blue]
+    c2 -- c0 [color=blue]
+    c1 -- c3 [color=firebrick1]
+    c3 -- c2 [color=transparent] // removed
+    label = "Example C";
+    c0, c1, c2 [color=midnightblue]
+    // c3 [color=red]
+  }
+
+  a0, b0, c0 [label = 0]
+  a1, b1, c1 [label = 1]
+  a2, b2, c2 [label = 2]
+  b3, c3 [label = 3]
+}
+....
+
+[source, javascript]
+----
+// Example A
+criticalConnections(3, [[0, 1], [1, 2]]);// [[0, 1], [1, 2]]
+// if you remove any link, there will be stranded servers.
+
+// Example B
+criticalConnections(4, [[0, 1], [1, 2], [2, 0], [1, 3], [3, 2]]);// []
+// you can remove any connection and all servers will be reachable.
+
+// Example C
+criticalConnections(4, [[0, 1], [1, 2], [2, 0], [1, 3]]); // [[1, 3]]
+// if you remove [1, 3], then server 3 won't be reachable.
+// If you remove any other link. It will be fine.
+----
+
+Starter code:
+
+[source, javascript]
+----
+include::../../interview-questions/critical-connections-in-a-network.js[tags=description;placeholder]
+----
+
+_Solution: <<graph-q-critical-connections-in-a-network>>_
diff --git a/book/chapters/graph.adoc b/book/content/part03/graph.asc
similarity index 59%
rename from book/chapters/graph.adoc
rename to book/content/part03/graph.asc
index b26e972e..3e2fff43 100644
--- a/book/chapters/graph.adoc
+++ b/book/content/part03/graph.asc
@@ -1,12 +1,19 @@
-= Graph
-
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[graph]]
+=== Graph
+(((Graph)))
+(((Data Structures, Non-Linear, Graph)))
 Graphs are one of my favorite data structures.
-They have a lot of cool applications like optimizing routes, social network analysis to name a few. You are probably using apps that use graphs every day.
+They have many exciting applications like optimizing routes and social network analysis, to name a few. You are probably using apps that use graphs every day.
 First, let’s start with the basics.
 
 TIP: A graph is a non-linear data structure where a node can have zero or more connected nodes.
 
-You can think of graph like an extension of a Linked List. Instead of having a `next` or `previous` reference, you can have as many as you want. You can implement a graph node as an array of associated nodes.
+You can think of a Graph as an extension of a Linked List. Instead of having a `next` or `previous` reference, you can have as many as you want. You can implement a graph node as an array of associated nodes.
 
 .Node's constructor
 [source, javascript]
@@ -17,66 +24,68 @@ include::{codedir}/data-structures/graphs/node.js[tag=constructor]
 As you can see, it’s pretty similar to the Linked List node.
 The only difference is that it uses an *array* of adjacent nodes instead of just one or two.
 
-Other difference between a linked list and graph is that a linked list always has a root node, while the graph doesn’t.
+Another difference between a linked list and a Graph is that a linked list always has a root node (or first element), while the Graph doesn’t.
 You can start traversing a graph from anywhere. Let’s examine these graph properties!
 
-== Graph Properties
+==== Graph Properties
 
 The connection between two nodes is called *edge*.
 Also, nodes might be called *vertex*.
 
 .Graph is composed of vertices/nodes and edges
-image:image42.png[image,width=305,height=233]
+image::image42.png[image,width=305,height=233]
 
-=== Directed Graph vs Undirected
+===== Directed Graph vs Undirected
 
 A graph can be either *directed* or *undirected*.
 
 .Graph: directed vs undirected
-image:image43.jpg[image,width=469,height=192]
+image::image43.jpg[image,width=469,height=192]
 
 
-An *undirected graph* has edges that are *two-way street*. E.g., On the undirected example, you can traverse from the green node to the orange and vice versa.
+An *undirected graph* has edges that are *two-way street*. E.g., On the undirected example, you can traverse from the green Node to the orange and vice versa.
 
-A *directed graph (digraph)* has edges that are *one-way street*. E.g., On the directed example, you can only go from green node to orange and not the other way around. When one node has an edge to itself is called a *self-loop*.
+A *directed graph (digraph)* has edges that are *one-way street*. E.g., On the directed example, you can only go from green Node to orange and not the other way around. When one Node has an edge to itself is called a *self-loop*.
 
-=== Graph Cycles
+===== Graph Cycles
 
 A graph can have *cycles* or not.
 
-.Cyclic vs Acyclic Graphs.
-image:image44.jpg[image,width=444,height=194]
-
+.Cyclic vs. Acyclic Graphs.
+image::image44.jpg[image,width=444,height=194]
 
+(((Cyclic Graph)))
 A *cyclic graph* is the one that you can pass through a node more than once.
-E.g., On the cyclic illustration, if you start in the green node, then go the orange and purple, finally, you could come back to green again.
+E.g., On the cyclic illustration, if you start in the green Node, go the orange and purple; finally, you could come back to green again.
 Thus, it has a *cycle*.
+(((Acyclic Graph)))
+An acyclic graph is the one that you can’t pass through a node more than once. E.g., in the acyclic illustration, can you find a path where you can pass through the same vertex more than one?
 
-An acyclic graph is the one that you can’t pass through a node more than once. E.g., in the acyclic illustration, can you to find a path where you can pass through the same vertex more than one?
+(((Directed Acyclic Graph)))
+(((DAG)))
+The *Directed Acyclic Graph (DAG)* is unique. It has many applications like scheduling tasks, spreadsheets' change propagation, and so forth. DAG is also called *Tree* data structure only when each Node has only *one parent*.
 
-The *Directed Acyclic Graph (DAG)* is unique. It has many applications like scheduling tasks, spreadsheets change propagation, and so forth. DAG is also called *Tree* data structure only when each node has only *one parent*.
-
-=== Connected vs Disconnected vs Complete Graphs
+===== Connected vs Disconnected vs Complete Graphs
 
 .Different kinds of graphs: disconnected, connected, and complete.
-image:image45.png[image,width=1528,height=300]
-
-A *disconnected graph* is one that has one or more subgraph. In other words, a graph is *disconnected* if two nodes don’t have a path between them.
+image::image45.png[image,width=1528,height=300]
 
-A *connected graph* is the opposite to disconnected, there’s a path between every node. No one is left behind.
+A *disconnected graph* is one that has one or more subgraphs. In other words, a graph is *disconnected* if two nodes don’t have a path between them.
 
-A *complete graph* is where every node is adjacent to all the other nodes in the graph. E.g., If there are seven nodes, every node has six edges.
+A *connected graph* is the opposite of disconnected; there’s a path between every Node. No one is left stranded.
 
-=== Weighted Graphs
+A *complete graph* is where every Node is adjacent to all the other nodes in the Graph. E.g., If there are seven nodes, every Node has six edges.
 
+===== Weighted Graphs
+(((Weighted Graphs)))
 Weighted graphs have labels in the edges (a.k.a *weight* or *cost*). The link weight can represent many things like distance, travel time, or anything else.
 
 .Weighted Graph representing USA airports distance in miles.
-image:image46.png[image,width=528,height=337]
+image::image46.png[image,width=528,height=337]
 
 For instance, a weighted graph can have a distance between nodes. So, algorithms can use the weight and optimize the path between them.
 
-== Exciting Graph applications in real-world
+==== Exciting Graph applications in real-world
 
 Now that we know what graphs are and some of their properties. Let’s discuss some real-life usages of graphs.
 
@@ -97,28 +106,28 @@ Graphs become a metaphor where nodes and edges model something from our physical
 ** Edge = data link
 ** Weight = connection speed
 
-There are endless applications for graphs in electronics, social networks, recommendation systems and many more. That’s cool and all, but how do we represent graphs in code? Let’s see that in the next section.
+There are endless applications for graphs in electronics, social networks, recommendation systems, and many more. That’s cool and all, but how do we represent graphs in code? Let’s see that in the next section.
 
-== Representing Graphs
+==== Representing Graphs
 
 There are two main ways to graphs one is:
 
 * Adjacency Matrix
 * Adjacency List
 
-=== Adjacency Matrix
-
-Representing graphs as adjacency matrix is done using a two-dimensional array. For instance, let’s say we have the following graph:
+===== Adjacency Matrix
+(((Adjacency Matrix)))
+Representing graphs as adjacency matrix is done using a two-dimensional array. For instance, let’s say we have the following Graph:
 
 .Graph and its adjacency matrix.
-image:image47.png[image,width=438,height=253]
+image::image47.png[image,width=438,height=253]
 
-The number of vertices |V| define the size of the matrix. In the example, we have five vertices, so we have a 5x5 matrix.
+The number of vertices, |V|, defines the size of the matrix. In the example, we have five vertices, so we have a 5x5 matrix.
 
 We fill up the matrix row by row. Mark with 1 (or any other weight) when you find an edge. E.g.
 
 * *Row 0:* It has a self-loop, so it has a `1` in the coordinate 0,0. Node 0 also has an edge to 1 and 4, so we mark it.
-* *Row 1:* The node 1 has one edge to 3, so we check it.
+* *Row 1:* node 1 has one edge to 3, so we check it.
 * *Row 2:* Node 2 goes to Node 4, so we note the insertion with 1.
 * etc.
 
@@ -145,26 +154,26 @@ digraph[2][3]; //=> 0
 digraph[3][2]; //=> 1
 ----
 
-As you can see, we don’t have a link from node 2 to 3, but we do in the opposite direction. Querying arrays is constant time *O(1)*, so no bad at all.
-
-The issue with the adjacency matrix is the space it takes. Let’s say you want to represent the entire Facebook network on a digraph. You would have a massive matrix of 1.2 billion x 1.2 billion. The worst part is that most of it would be empty (zeros) since people are friends to at most few thousands.
+As you can see, we don’t have a link from node 2 to 3, but we do in the opposite direction. Querying arrays is constant time *O(1)*, so not bad at all.
 
-TIP: When the graph has few connections compared to the number of nodes we say that we have a *sparse graph*. On the opposite, if we have almost complete graphs, we say we have a *dense graph*.
+The issue with the adjacency matrix is the space it takes. Let’s say you want to represent the entire Facebook network on a digraph. You would have a massive matrix of 1.2 billion x 1.2 billion. The worst part is that most of it would be empty (zeros) since people are friends to at most a few thousands.
 
-The space complexity of the adjacency matrix is *O(|V|^2^)*, where |V| is the number of vertices/nodes.
+TIP: When the Graph has few connections compared to the number of nodes, we say that we have a *sparse graph*. Conversely, if we have almost complete maps, we say we have a *dense graph*.
 
-=== Adjacency List
+The adjacency matrix's space complexity is *O(|V|^2^)*, where |V| is the number of vertices/nodes.
 
-Another way to represent a graph is by using an adjacency list. This time instead of using an array (matrix) we use a list.
+===== Adjacency List
+(((Adjacency List)))
+Another way to represent a graph is by using an adjacency list. This time instead of using an array (matrix), we use a list.
 
 .Graph represented as an Adjacency List.
-image:image48.png[image,width=528,height=237]
+image::image48.png[image,width=528,height=237]
 
-If we want to add a new node to the list, we can do it by adding one element to the end of the array of nodes *O(1)*. In the next section, we are going to explore the running times of all operations in an adjacency list.
+If we want to add a new node to the list, we can do it by adding one element to the end of the array of nodes *O(1)*. In the next section, we will explore the running times of all operations in an adjacency list.
 
-== Implementing a Graph data structure
+==== Implementing a Graph data structure
 
-Since adjacency lists are more efficient (than adjacency matrix), we are going to use to implement a graph data structure.
+Since adjacency lists are more efficient (than adjacency matrix), we will use to implement a graph data structure.
 
 Let's start by creating the constructor of the Graph class.
 
@@ -174,11 +183,11 @@ Let's start by creating the constructor of the Graph class.
 include::{codedir}/data-structures/graphs/graph.js[tag=constructor]
 ----
 
-Notice that the constructor takes a parameter. The `edgeDirection` allow us to use one class for both undirected and directed graphs.
+Notice that the constructor takes a parameter. The `edgeDirection` allows us to use one class for both undirected and directed graphs.
 
-== Adding a vertex
+==== Adding a vertex
 
-For adding a vertex, we first need to check if the node already exists. If so, we return the node.
+For adding a vertex, we first need to check if the Node already exists. If so, we return the Node.
 
 .Graphs's `addVertex` method
 [source, javascript]
@@ -189,11 +198,11 @@ include::{codedir}/data-structures/graphs/graph.js[tag=addVertex, indent=0]
 <2> Create new `Node` with the given value.
 <3> Set `hashMap` with value and node pair.
 
-If the node doesn't exist, then we create the new node and add it to a `HashMap`.
+If the Node doesn't exist, we create the new Node and add it to a `HashMap`.
 
-TIP: <<Map>> stores key/pair value very efficiently. Lookup is `O(1)`.
+TIP: <<tree-map-chap>> stores key/pair value very efficiently. Lookup is `O(1)`.
 
-The `key` is the node's value, while the `value` is the newly created node.
+The `key` is the Node's value, while the `value` is the newly created Node.
 
 The `Node` class is constructed as follows:
 
@@ -204,7 +213,7 @@ include::{codedir}/data-structures/graphs/node.js[tag=constructor, indent=0]
 ----
 
 
-== Deleting a vertex
+==== Deleting a vertex
 
 .Graphs's `removeVertex` method
 [source, javascript]
@@ -213,9 +222,9 @@ include::{codedir}/data-structures/graphs/graph.js[tag=removeVertex, indent=0]
 ----
 <1> Try to find if node exists.
 <2> Remove related edges. See `removeAdjacent` below.
-<3> Remove node with the given value.
+<3> Remove Node with the given value.
 
-Notice on the callout 2, that we visit every edge on the graph and remove the ones that contain the node to remove.
+Notice on callout 2 that we visit every edge on the Graph and remove the ones that contain the Node to remove.
 
 For removing adjacent nodes, we use Node's method called `removeAdjacent` that can be implemented as follows:
 
@@ -225,13 +234,13 @@ For removing adjacent nodes, we use Node's method called `removeAdjacent` that c
 include::{codedir}/data-structures/graphs/node.js[tag=removeAdjacent, indent=0]
 ----
 
-All adjacencies are stored as a HashSet to provide constant time deletion.
+All adjacencies are stored as a HashSet to provide constant-time deletion.
 
-== Adding an edge
+==== Adding an edge
 
-An edge is a connection between two nodes (vertices). If the graph is undirected means that every link is a two-way street. When we create the edge from node 1 to node 2, we also need to establish a connection between node 2 and 1 for undirected graphs.
+An edge is a connection between two nodes (vertices). If the Graph is undirected means that every link is a two-way street. When we create the edge from node 1 to node 2, we also need to establish a connection between nodes 2 and 1 for undirected graphs.
 
-If we are dealing with a digraph (directed graph), then we create one edge.
+If we are dealing with a digraph (directed Graph), then we create one edge.
 
 .Graphs's `addEdge` method
 [source, javascript]
@@ -240,7 +249,7 @@ include::{codedir}/data-structures/graphs/graph.js[tag=addEdge, indent=0]
 ----
 <1> Find or create nodes if they don't exists yet.
 <2> Create edge from source to destination.
-<3> If us a undirected graph, create the edge on the other direction.
+<3> If it's an undirected graph, create the edge in the other direction.
 
 We can add adjacencies using the `addAdjacent` method from the Node class.
 
@@ -250,7 +259,7 @@ We can add adjacencies using the `addAdjacent` method from the Node class.
 include::{codedir}/data-structures/graphs/node.js[tag=addAdjacent, indent=0]
 ----
 
-== Querying Adjacency
+==== Querying Adjacency
 
 .Graphs's `areAdjacents` method
 [source, javascript]
@@ -265,7 +274,7 @@ include::{codedir}/data-structures/graphs/node.js[tag=isAdjacent, indent=0]
 ----
 
 
-== Deleting an edge
+==== Deleting an edge
 
 .Graphs's `removeEdge` method
 [source, javascript]
@@ -278,16 +287,19 @@ include::{codedir}/data-structures/graphs/graph.js[tag=removeEdge, indent=0]
 ----
 include::{codedir}/data-structures/graphs/node.js[tag=removeAdjacent, indent=0]
 ----
+<<<
+==== Graph Complexity
+(((Tables, Non-Linear DS, Graph adjacency matrix/list complexities)))
 
-== Graph Complexity
-
+// tag::table[]
 .Time complexity for a Graph data structure
 |===
 .2+.^s| Data Structure 2+^s| Vertices 2+^s| Edges .2+^.^s| Space Complexity
 ^|_Add_ ^|_Remove_ ^|_Add_ ^|_Remove_
 | Graph (adj. matrix) ^| O(\|V\|^2^) ^| O(\|V\|^2^) ^|O(1) ^|O(1) ^|O(\|V\|^2^)
-| Graph (adj. list w/array) ^| O(1) ^| O(\|V\| + \|E\|)) ^|O(1) ^|O(\|V\| + \|E\|) ^|O(\|V\| + \|E\|)
-| Graph (adj. list w/HashSet) ^| O(1) ^| O(\|V\|)) ^|O(1) ^|O(\|V\|) ^|O(\|V\| + \|E\|)
+| Graph (adj. list w/array) ^| O(1) ^| O(\|V\| + \|E\|)) ^|O(1) ^|O(\|E\|) ^|O(\|V\| + \|E\|)
+| Graph (adj. list w/HashSet) ^| O(1) ^| O(\|V\|)) ^|O(1) ^|O(1) ^|O(\|V\| + \|E\|)
 |===
+// end::table[]
 
-As you can see using a `HashSet` on for the adjacency list make a performance improvement.
+As you can see, using a `HashSet` on for the adjacency list make a performance improvement if you need to query for connectivity. However, this is rarely required. Most graph algorithms visit all adjacent nodes one by one.
diff --git a/book/content/part03/time-complexity-graph-data-structures.asc b/book/content/part03/time-complexity-graph-data-structures.asc
new file mode 100644
index 00000000..366b2111
--- /dev/null
+++ b/book/content/part03/time-complexity-graph-data-structures.asc
@@ -0,0 +1,25 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Summary
+
+In this section, we learned about Graphs, applications, properties, and how we can create them. We mention that you can represent a graph as a matrix or as a list of adjacencies. We went for implementing the latter since it's more space-efficient. We cover the basic graph operations like adding and removing nodes and edges.  In the algorithms section, we are going to cover searching values in the graph.
+(((Tables, Non-Linear DS, BST/Maps/Sets Complexities)))
+
+// tag::table[]
+.Time and Space Complexity for Graph-based Data Structures
+|===
+.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
+^|_Index/Key_ ^|_Value_
+| <<part03-graph-data-structures#bst, BST (unbalanced)>> ^|- ^|O(n) ^|O(n) ^|O(n) ^|O(n)
+| <<b-self-balancing-binary-search-trees, BST (balanced)>> ^|- ^|O(log n) ^|O(log n) ^|O(log n) ^|O(n)
+| Hash Map (naïve) ^|O(n) ^|O(n) ^|O(n) ^|O(n) ^|O(n)
+| <<hashmap-chap, HashMap>> (optimized) ^|O(1) ^|O(n) ^|O(1)* ^|O(1) ^|O(n)
+| <<tree-map-chap, TreeMap>> (Red-Black Tree) ^|O(log n) ^|O(n) ^|O(log n) ^|O(log n) ^|O(n)
+| <<hash-set-chap, HashSet>> ^|O(1) ^|- ^|O(1)* ^|O(1) ^|O(n)
+| <<tree-set-chap, TreeSet>> ^|O(log n) ^|- ^|O(log n) ^|O(log n) ^|O(n)
+|===
+{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
+// end::table[]
diff --git a/book/chapters/tree.adoc b/book/content/part03/tree-intro.asc
similarity index 51%
rename from book/chapters/tree.adoc
rename to book/content/part03/tree-intro.asc
index 08db9eb0..94f4b88b 100644
--- a/book/chapters/tree.adoc
+++ b/book/content/part03/tree-intro.asc
@@ -1,15 +1,22 @@
-= Tree
-
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[tree]]
+=== Tree
+(((Tree)))
+(((Data Structures, Non-Linear, Tree)))
 A tree is a non-linear data structure where a node can have zero or more connections. The topmost node in a tree is called *root*. The linked nodes to the root are called *children* or *descendants*.
 
 .Tree Data Structure: root node and descendants.
-image:image31.jpg[image,width=404,height=240]
+image::image31.jpg[image,width=404,height=240]
 
-As you can see in the picture above, this data structure resembles an inverted tree hence the name. It starts with a *root* node and *branch* off with its descendants, and finally *leaves*.
+As you can see in the picture above, this data structure resembles an inverted tree, hence the name. It starts with a *root* node and *branch* off with its descendants, and finally *leaves*.
 
-== Implementing a Tree
+==== Implementing a Tree
 
-Implementing a tree is not too hard. It’s similar to a <<Linked List>>. The main difference is that instead of having a `next` and `previous` links, we have an infinite number of linked nodes (children/descendants).
+Implementing a tree is not too hard. It’s similar to a <<part02-linear-data-structures#linked-list>>. The main difference is that instead of having the `next` and `previous` links, we have an 0 or more number of linked nodes (children/descendants).
 
 .Tree's node constructor
 [source, javascript]
@@ -20,15 +27,15 @@ include::{codedir}/data-structures/trees/tree-node.js[tag=snippet]
 Simple! Right? But there are some constraints that you have to keep at all times.
 
 .Tree data structures constraints
-1. *Loops*: You have to be careful *not* to make a circular loop. Otherwise, this wouldn’t be a tree anymore but a <<Graph, graph data structure>>! E.g., Node A has B as a child, then Node B list Node A as its descendant forming a loop. ‍️
-2.  *Parents*: A node with more than two parents. Again, if that happens is no longer a tree but a <<Graph, graph>>.
-3.  *Root*: a tree must have only one root. Two non-connected parts are not a tree. <<Graph>> can have non-connected portions and doesn’t have root.
+1. *Loops*: You have to be careful *not* to make a circular loop. Otherwise, this wouldn’t be a tree anymore but a <<part03-graph-data-structures#graph, graph data structure>>! E.g., Node A has B as a child, then Node B list Node A as its descendant forming a loop. ‍️
+2.  *Parents*: A node with more than two parents. Again, if that happens is no longer a tree but a <<part03-graph-data-structures#graph>>.
+3.  *Root*: a tree must have only one root. Two non-connected parts are not a tree. <<part03-graph-data-structures#graph>> can have non-connected portions and doesn’t have root.
 
-== Basic concepts
+==== Basic concepts
 
 .Here’s a summary of the three basic concepts:
 * The topmost node is called *root*.
-* A node’s immediate linked nodes are called *children*.
+* A node’s primary linked nodes are called *children*.
 * A *leaf* or *terminal node* is a node without any descendant or children.
 * A node immediate ancestor is called *parent*. Yep, and like a family tree, a node can have *uncles* and *siblings*, and *grandparents*.
 * *Internal nodes* are all nodes except for the leaf nodes and the root node.
@@ -43,57 +50,62 @@ Simple! Right? But there are some constraints that you have to keep at all times
 * The *depth of a tree* is the distance (edge count) from the root to the farthest leaf.
 
 .Tree anatomy
-image:image31.jpg[image]
-
-== Types of Binary Trees
+image::image31.jpg[image]
 
-There are different kinds of trees depending on the restrictions. E.g. The trees that have two children or less are called *binary tree*, while trees with at most three children are called *Ternary Tree*. Since binary trees are most common we are going to cover them here and others in another chapter.
+==== Types of Binary Trees
 
-=== Binary Tree
+There are different kinds of trees, depending on the restrictions. E.g. The trees with two children or less are called *binary tree*, while trees with at most three children are *Ternary Tree*. Since binary trees are the most common, we will cover them here and others in another chapter.
 
-The binary restricts the nodes to have at most two children. Trees, in general, can have 3, 4, 23 or more, but not binary trees.
+===== Binary Tree
+(((Binary Tree)))
+(((Data Structures, Non-Linear, Binary Tree)))
+The binary restricts the nodes to have at most two children. Trees can have 0, 1, 2, 7, or more, but not binary trees.
 
-.Binary tree has at most 2 children while non-binary trees can have more.
-image:image32.png[image,width=321,height=193]
+.Binary tree has at most two children while non-binary trees can have more.
+image::image32.png[image,width=321,height=193]
 
-Binary trees are the one of the most common types and it's used to build other data structures and applications.
+Binary trees are one of the most used kinds of trees, and they are used to build other data structures.
 
 .Binary Tree Applications
-- <<Map>>
-- <<Set>>
+- <<tree-map-chap>>
+- <<tree-set-chap>>
 - Priority Queues
-- <<Binary Search Tree (BST)>>
-
-
-=== Binary Search Tree (BST)
+- <<part03-graph-data-structures#bst>>
 
+[[bst]]
+===== Binary Search Tree (BST)
+(((Binary Search Tree)))
+(((Data Structures, Non-Linear, Binary Search Tree)))
 The Binary Search Tree (BST) is a specialization of the binary tree. BST has the same restriction as a binary tree; each node has at most two children. However, there’s another restriction: the values are ordered. It means the left child’s value has to be less or equal than the parent. In turn, the right child’s value has to be bigger than the parent.
 
 > BST: left ≤ parent < right
 
 .BST or ordered binary tree vs. non-BST.
-image:image33.png[image,width=348,height=189]
-
+image::image33.png[image,width=348,height=189]
 
-=== Binary Heap
 
-The heap (max-heap) is a type of binary tree where the children's values are higher than the parent. Opposed to the BST, the left child doesn’t have to be smaller than the right child.
+===== Binary Heap
+(((Binary Heap)))
+(((Heap)))
+(((Max-Heap)))
+(((Min-Heap)))
+(((Data Structures, Non-Linear, Binary Heap)))
+The heap (max-heap) is a binary tree where the parent's value is higher than both children's value. Opposed to the BST, the left child doesn’t have to be smaller than the right child.
 
 .Heap vs BST
-image:image34.png[image,width=325,height=176]
-
+image::image34.png[image,width=325,height=176]
 
 The (max) heap has the maximum value in the root, while BST doesn’t.
 
-There is two kind of heaps: min-heap and max-heap.
+There are two kinds of heaps: min-heap and max-heap.
 For a *max-heap*, the root has the highest value. The heap guarantee that as you move away from the root, the values get smaller. The opposite is true for a *min-heap*. In a min-heap, the lowest value is at the root, and as you go down the lower to the descendants, they will keep increasing values.
 
 .Max-heap keeps the highest value at the top while min-heap keep the lowest at the root.
-image:image35.png[image,width=258,height=169]
+image::image35.png[image,width=258,height=169]
 
 
 .Heap vs. Binary Search Tree
 ****
-Heap is better at finding max or min values in constant time *O(1)*, while a balanced BST is good a finding any element in *O(log n)*. Heaps are often used to implement priority queues while BST is used when you need every value sorted.
+Heap is better at finding max or min values in constant time *O(1)*, while a balanced BST is good at finding any element in *O(log n)*. Heaps are often used to implement priority queues, while BST is used when you need every value sorted.
 ****
 indexterm:[Runtime, Logarithmic]
diff --git a/book/content/part03/tree-map.asc b/book/content/part03/tree-map.asc
new file mode 100644
index 00000000..8820212a
--- /dev/null
+++ b/book/content/part03/tree-map.asc
@@ -0,0 +1,123 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((TreeMap))) (((Data Structures, Non-Linear, TreeMap))) (((Binary Search Tree))) (((BST)))
+[[tree-map-chap]]
+=== Tree Map
+
+A Map is an abstract data structure to store pairs of data: *key* and *value*. It also has a fast key lookup of `O(1)` for <<hashmap-chap>> or `O(log n)` for <<tree-map-chap>>.
+
+We can implement a Map using two different underlying data structures: Hash Map or Tree Map.
+
+
+==== HashMap vs TreeMap
+
+.A map can be implemented using hash functions or a binary search tree:
+* *HashMap*: it’s a map implementation using an *array* and a *hash function*. The hash function's job is to convert the `key` into an index that maps to the `value`. HashMap has an average runtime of *O(1)*.
+* *TreeMap*: it’s a map implementation that uses a self-balanced Binary Search Tree (like <<c-avl-tree>> or Red-Black Tree). The BST nodes store the key, and the value and nodes are sorted by key guaranteeing an *O(log n)* lookup time.
+
+.When to use a TreeMap vs. HashMap?
+* `HashMap` is more time-efficient. A `TreeMap` is more space-efficient.
+* `TreeMap` search complexity is *O(log n)*, while an optimized `HashMap` is *O(1)* on average.
+* `HashMap`’s keys are in insertion order (or random depending on the implementation). `TreeMap`’s keys are always sorted.
+* `TreeMap` offers some statistical data for free such as: get minimum, get maximum, median, find ranges of keys. `HashMap` doesn’t.
+* `TreeMap` has a guarantee always an *O(log n)*, while `HashMap`s has an amortized time of *O(1)* but in the rare case of a rehash, it would take an *O(n)*.
+
+==== TreeMap Time complexity vs HashMap
+
+As we discussed so far, there is a trade-off between the implementations.
+(((Tables, Non-Linear DS, HashMap/TreeMap complexities))) (((Linear))) (((Runtime, Linear))) (((Logarithmic))) (((Runtime, Logarithmic)))
+
+// also on: book/content/part03/time-complexity-graph-data-structures.asc
+// tag::table[]
+.Time complexity for different Maps implementations
+|===
+.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
+^|_Index/Key_ ^|_Value_
+| <<hashmap-chap, Hash Map>> ^|O(1) ^|O(n) ^|O(1)* ^|O(1) ^|O(n)
+| <<tree-map-chap, Tree Map (Red-Black Tree)>> ^|O(log n) ^|O(n) ^|O(log n) ^|O(log n) ^|O(n)
+|===
+{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
+// end::table[]
+
+We already covered <<hashmap-chap, Hash Map>>, so in this chapter, we will focus on TreeMap.
+
+TIP: JavaScript only provides (Hash) `Map`. That's enough for most needs. But we will implement a TreeMap so it's more clear how it works and when it should be used.
+
+Ok, now that you know the advantages, let’s implement it!
+
+Let’s get started with the essential functions. They have the same interface as the `HashMap` (but the implementation is different).
+
+.TreeMap class overview
+[source, javascript]
+----
+class TreeMap {
+  constructor(){}
+  set(key, value) {}
+  get(key) {}
+  has(key) {}
+  delete(key) {}
+}
+----
+
+==== Inserting values into a TreeMap
+
+For inserting a value on a TreeMap, we first need to initialize the tree:
+
+.TreeMap constructor
+[source, javascript]
+----
+include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=constructor, indent=0]
+----
+
+The tree can be an instance of any Binary Search Tree that we implemented so far. For better performance, it should be a self-balanced tree like a https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/trees/red-black-tree.js#L20[Red-Black Tree] or https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/trees/avl-tree.js#L64[AVL Tree].
+
+Let's implement the method to add values to the tree.
+
+.TreeMap `add` method and `size` attribute
+[source, javascript]
+----
+include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=set, indent=0]
+----
+
+Adding values is very easy (once we have the underlying tree implementation).
+
+==== Getting values out of a TreeMap
+
+When we search by key in a treemap, it takes *O(log n)*. The following is a possible implementation:
+
+.TreeMap `get` and `has` method
+[source, javascript]
+----
+include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=get, indent=0]
+----
+
+One side effect of storing keys in a tree is that they don't come up in insertion order. Instead, they ordered by value.
+
+.TreeMap iterators
+[source, javascript]
+----
+include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=iterators, indent=0]
+----
+<1> We implemented the default iterator using the in-order traversal. That's useful for getting the keys sorted.
+
+.JavaScript Iterators and Generators
+****
+Generators are useful for producing values that can you can iterate in a `for...of` loop. Generators use the `function*` syntax, which expects to have a `yield` with a value.
+****
+
+==== Deleting values from a TreeMap
+
+Removing elements from TreeMap is simple.
+
+.TreeMap `delete` method
+[source, javascript]
+----
+include::{codedir}/data-structures/maps/tree-maps/tree-map.js[tag=delete, indent=0]
+----
+
+The BST implementation does all the heavy lifting.
+
+That’s it! To see the full file in context, click here: https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/maps/tree-maps/tree-map.js#L22[here]
diff --git a/book/content/part03/tree-search-traversal.asc b/book/content/part03/tree-search-traversal.asc
new file mode 100644
index 00000000..dbb9c797
--- /dev/null
+++ b/book/content/part03/tree-search-traversal.asc
@@ -0,0 +1,121 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Tree Search & Traversal
+
+So far, we have covered how to insert/delete/search values in a binary search tree (BST).
+However, not all binary trees are BST, so there are other ways to look for values or visit all nodes in a particular order.
+
+If we have the following tree:
+----
+         10
+       /    \
+      5      30
+    /       /  \
+   4       15   40
+ /
+3
+----
+
+Depending on what traversal methods we used, we will have a different visiting order.
+(((Tree Traversal)))
+(((Tree, Traversal)))
+
+.Tree traversal methods
+- Breadth-first traversal (a.k.a level order traversal): `10, 5, 30, 4, 15, 40, 3`
+- Depth-first traversal
+** In-order (left-root-right): `3, 4, 5, 10, 15, 30, 40`
+** Pre-order (root-left-right): `10, 5, 4, 3, 30, 15, 40`
+** Post-order (left-right-root): `3, 4, 5, 15, 40, 30, 10`
+
+Why do we care? Well, there are specific problems that you can solve more optimally using one or another traversal method. For instance, to get the size of a subtree, finding maximums/minimums, and so on.
+
+Let's cover the Breadth-first search (BFS) and the Depth-first search (DFS).
+
+[[bfs-tree]]
+==== Breadth-First Search for Binary Tree
+(((BFS)))
+(((Breadth-First Search)))
+(((Tree, Breadth-First Search)))
+The breadth-first search goes wide (breadth) before going deep. Hence, the name. In other words, it goes level by level. It visits all the immediate nodes or children and then moves on to the children's children.
+Let's how can we implement it!
+
+.Breath-First Search (BFS) Implementation
+[source, javascript]
+----
+include::{codedir}/data-structures/trees/binary-search-tree.js[tag=bfs,indent=0]
+----
+
+As you see, the BFS uses a <<part02-linear-data-structures#queue>> data structure. We enqueue all the children of the current node and then dequeue them as we visit them.
+
+Note the asterisk (`*`) in front of the function means that this function is a generator that yields values.
+(((JavaScript Notes, Generators)))
+(((Generators)))
+
+.JavaScript Generators
+****
+
+JavaScript generators were added as part of ES6; they allow process possibly expensive operations one by one. You can convert any function into a generator by adding the asterisk in front and `yield`ing a value.
+
+Then you can use `next()` to get the value and also `done` to know if it's the last value. Here are some examples:
+
+[source, javascript]
+----
+function* dummyIdMaker() {
+  yield 0;
+  yield 1;
+  yield 2;
+}
+
+const generator = dummyIdMaker()
+
+// getting values
+console.log(generator.next()); // ↪️ {value: 0, done: false}
+console.log(generator.next()); // ↪️ {value: 1, done: false}
+console.log(generator.next()); // ↪️ {value: 2, done: false}
+console.log(generator.next()); // ↪️ {value: undefined, done: true}
+
+// iterating generator values with for..of loops
+for(const n of dummyIdMaker()) {
+  console.log(n);
+}
+
+// converting a generator to an array
+console.log(Array.from(dummyIdMaker())); // [0, 1, 2]
+----
+
+****
+
+[[dfs-tree]]
+==== Depth-First Search for Binary Tree
+(((DFS)))
+(((Depth-First Search)))
+(((Tree, Depth-First Search)))
+Depth-First search goes deep (depth) before going wide. It means that starting for the root, it goes as deep as it can until it found a leaf node (node without children), then it visits all the remaining nodes in the path.
+
+.Depth-First Search (DFS) Implementation with a Stack
+[source, javascript]
+----
+include::{codedir}/data-structures/trees/binary-search-tree.js[tag=dfs,indent=0]
+----
+
+This is an iterative implementation of a DFS using an <<part02-linear-data-structures#stack>>.
+It's almost identical to the BFS, but instead of using a <<part02-linear-data-structures#queue>> we use a Stack.
+We can also implement it as recursive functions we will see in the <<Binary Tree Traversal>> section.
+
+==== Depth-First Search vs. Breadth-First Search
+
+We can see visually the difference between how the DFS and BFS search for nodes:
+
+.Depth-First Search vs. Breadth-First Search
+image::depth-first-search-dfs-breadth-first-search-bfs.jpg[]
+
+As you can see, the DFS in two iterations is already at one of the farthest nodes from the root while BFS search nearby nodes first.
+
+.Use DFS when:
+- The node you are looking for is likely to be *far* from the root.
+
+.Use BFS when:
+- The node you are looking for is *nearby* the root.
diff --git a/book/content/part03/tree-set.asc b/book/content/part03/tree-set.asc
new file mode 100644
index 00000000..60fb0676
--- /dev/null
+++ b/book/content/part03/tree-set.asc
@@ -0,0 +1,141 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((TreeSet)))
+(((Data Structures, Non-Linear, TreeSet)))
+[[tree-set-chap]]
+=== Tree Set
+
+A tree set is a data structure that stores unique values and keeps them sorted. You can get check if a value exists in `O(log n)` time.
+
+Another way to implement a Set is by using a hash function, as we covered on <<hash-set-chap, Hash Set>>. There are some critical differences between the two implementations.
+
+==== HashSet vs TreeSet
+
+We can implement a `map` using a *<<b-self-balancing-binary-search-trees, balanced binary search tree>>* or a *<<hashmap-chap,hash function>>*. If we use them to implement a `Set`, we would have a `HashSet` and `TreeSet`. As with all data structures, there are trade-offs. Here are some key differences:
+
+* `TreeSet`, would return the values sorted in ascending order.
+* `HashSet`, would return the values in insertion order.
+* Operations on a `HashSet` would take on average O(1), and in the worst case (rehash is due), it would take O(n).
+* Operation on a `TreeSet` is always O(log n).
+
+==== Time Complexity Hash Set vs Tree Set
+
+(((Tables, Non-Linear DS, HashSet/TreeSet complexities)))
+indexterm:[Runtime, Linear]
+(((Logarithmic)))
+(((Runtime, Logarithmic)))
+
+// also on: book/content/part03/time-complexity-graph-data-structures.asc
+// tag::table[]
+.Time complexity HashSet vs TreeSet
+|===
+.2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
+^|_Index/Key_ ^|_Value_
+| <<hash-set-chap, Hash Set>> ^|O(1) ^|- ^|O(1)* ^|O(1) ^|O(n)
+| <<tree-set-chap, Tree Set>> ^|O(log n) ^|- ^|O(log n) ^|O(log n) ^|O(n)
+|===
+{empty}* = Amortized run time. E.g. rehashing might affect run time to *O(n)*.
+// end::table[]
+
+
+TIP: JavaScript only provides (Hash) `Set` that's enough for most needs. But we will implement a Tree Set so it's more clear how it works and when it should be used.
+
+==== Implementing a Tree Set
+
+
+.TreeSet's constructor method and size attribute
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/tree-set.js[tag=constructor]
+}
+----
+<1> Converts an array or any iterable data structure to a set.
+
+An everyday use case for Sets is to remove duplicated values from an array. We can do that by bypassing them in the constructor as follows:
+
+.Removing duplicates from an Array using a Set
+[source, javascript]
+----
+set = new TreeSet([1, 2, 3, 2, 1]);
+expect(set.size).toBe(3);
+expect(Array.from(set.keys())).toEqual([1, 2, 3]);
+----
+
+Ok, now let’s implement the add method.
+
+===== Adding elements to a TreeSet
+
+For adding values to the set, we `Tree.add` method.
+
+.TreeSet's constructor method and size attribute
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/tree-set.js[tag=add,indent=0]
+----
+
+Our <<Inserting new elements in a BST, BST implementation>> can hold duplicated values. It has a multiplicity tally to keep track of duplicates. However, we don’t dupe in a set. For that, we check if the value is already in the tree.
+Don’t worry about adding extra lookups. The
+`Tree.has` is also very performant *O(log n)*.
+
+===== Searching for values in a TreeSet
+
+Again, we rely on the Tree implementation to do the heavy lifting:
+
+.TreeSet's `has` method
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/tree-set.js[tag=has, indent=0]
+----
+
+===== Deleting elements from a TreeSet
+
+We delete the elements from the TreeSet using the remove method of the BST.
+
+.TreeSet's `delete` method
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/tree-set.js[tag=delete, indent=0]
+----
+
+Voilà! That’s it!
+
+===== Converting TreeSet to Array
+
+Another use case for a Set is to convert it to an array or use an iterator (for loops, forEach, …). Let’s provide the method for that:
+
+.TreeSet's iterator
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/tree-set.js[tag=iterator, indent=0]
+----
+
+We are using the `inOrderTraversal` method of the BST to go each key in an
+ascending order.
+
+.JavaScript Built-in `Symbol` iterator
+****
+The `Symbol.iterator` built-in symbol specifies the default iterator for
+an object. Used by `for...of`, `Array.from`, and others.
+****
+
+Now we can convert from set to array and vice versa easily. For
+instance:
+
+.TreeSet's iterator
+[source, javascript]
+----
+const array = [1, 1, 2, 3, 5];
+
+// array to set
+const set = new TreeSet(array);
+
+// set to array
+Array.from(set); //↪️ (4) [1, 2, 3, 5]
+----
+
+No more duplicates in our array!
+
+Check out our https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/sets/tree-set.js#L12[GitHub repo for the full TreeSet implementation].
diff --git a/book/content/part04/algorithmic-toolbox.asc b/book/content/part04/algorithmic-toolbox.asc
new file mode 100644
index 00000000..2d0f6b8a
--- /dev/null
+++ b/book/content/part04/algorithmic-toolbox.asc
@@ -0,0 +1,39 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[algorithms-toolbox]]
+=== Algorithmic Toolbox
+
+Have you ever given a programming problem and freeze without knowing where to start?
+Well, in this section, we are going to give some tips so you don't get stuck while coding.
+
+TIP: TL;DR: Don't start coding right away. First, solve the problem, then write the code. Make it work first; make it better later.
+
+.Steps to solve algorithmic problems
+. *Understand* the requirements. Reframe it in your own words.
+. Draw a *simple example* (no edge cases yet)
+. *Brainstorm* possible solutions
+.. How would you solve this problem *manually*? (without a computer) Is there any formula or theorem you can use?
+.. Is there any heuristics (largest, smallest, best ratio), or can you spot a pattern to solve this problem using a <<Greedy Algorithms, greedy algorithm>>?
+.. Can you address the simple base case and generalize for other cases using a *recursive solution*? Can you divide the problem into subproblems? Try <<Divide and Conquer>>.
+.. Do you have to generate multiple solutions or try different paths? Try <<Backtracking>>.
+.. List all the data structures that you know that might solve this problem.
+.. If anything else fails, how would you solve it the dumbest way possible (brute force). We can optimize it later.
+. *Test* your algorithm idea with multiple examples
+. *Optimize* the solution –Only optimize when you have something working. Don't try to do both at the same time!
+.. Can you trade-off space for speed? Use a <<hashmap-chap>> to speed up results!
+.. Do you have a bunch of recursive and overlapping problems? Try <<dynamic-programming-chap>>.
+.. Re-read requirements and see if you can take advantage of anything. E.g. is the array sorted?
+. *Write Code*, yes, now you can code.
+.. Modularize your code with functions (don't do it all in one giant function, please 🙏)
+.. Comment down edge cases but don't address until the basic cases are working.
+. *Test* your code.
+.. Choose a typical input and test against your code.
+.. Brainstorm about edge cases (empty, null values, overflows, largest supported inputs)
+.. How would your code scale beyond the current boundaries?
+
+These steps should get you going even with the most challenging algorithmic problems.
+
+Stay effective!
diff --git a/book/chapters/backtracking.adoc b/book/content/part04/backtracking.asc
similarity index 63%
rename from book/chapters/backtracking.adoc
rename to book/content/part04/backtracking.asc
index 790662d4..81c81445 100644
--- a/book/chapters/backtracking.adoc
+++ b/book/content/part04/backtracking.asc
@@ -1,49 +1,54 @@
-= Backtracking
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
 
-Backtracking algorithms are used to find *all (or some)* solutions that satisfy a contraint.
+=== Backtracking
+(((Backtracking)))
+(((Algorithmic Techniques, Backtracking)))
+Backtracking algorithms are used to find *all (or some)* solutions that satisfy a constraint.
 
-Bracktracking builds a solution step by step using recursion.
+Backtracking builds a solution step by step, using recursion.
 If during the process it realizes a given path is not going to lead to a solution,
-it stops and step back (backtracking) to try a different alternative.
+it stops and steps back (backtracks) to try another alternative.
 
-Some examples that use backtracking is a solving Sudoku/crosswords puzzle, and graph operations.
+Some examples that use backtracking is solving Sudoku/crosswords puzzle and graph operations.
 
 ifndef::backend-pdf[]
-image:Sudoku_solved_by_bactracking.gif[]
+image::Sudoku_solved_by_bactracking.gif[]
 endif::backend-pdf[]
 
-Listing all possible solutions might sound like a brute force.
-However, is not the same.
-Backtracking algorithms are faster than brute force one.
+Listing all possible solutions might sound like brute force.
+However, it is not the same.
+Backtracking algorithms are faster because it tests if a path will lead to a solution or not.
 
-.Brute Force vs Backtracking Algorithms
+.Brute Force vs. Backtracking Algorithms
 ****
-*Brute force* evaluates every possiblity.
-*Bracktracking* is an optimized brute force.
-It stops evaluating a path as soon as some of the conditions are broken and move on to the next.
-However, it an only be applied if a quick test can be run to tell if a candidate will contribute to a valid solution.
+*Brute force* evaluates every possibility.
+*Backtracking* is an optimized brute force.
+It stops evaluating a path as soon as some of the conditions are broken. However, it can only be applied if a quick test can be run to tell if a candidate will contribute to a correct solution.
 ****
 
-== How to develop backtracking algorithms?
+==== How to develop backtracking algorithms?
 
-Backtracking algorithms can be tricky to get right or reason about but we are going to follow this recipe to make it easier.
+Backtracking algorithms can be tricky to get right or reason about, but we will follow this recipe to make it easier.
 
 .Steps to create backtracking algorithms
-. Iterate through all the elements in the input
+. Iterate through the given input
 . Make a change
-. Recursive function moving to the next element
-. Test if the current change is a solution
-. Revert back the change (backtracking)
+. Recursively move to the next element.
+. Test if the current change is a possible solution
+. Revert the change (backtracking) and try with the next item
 
 Let's do an exercise to explain better how backtracking works.
 
 // https://leetcode.com/problems/combination-sum/description/
 
-== Permutations
+==== Permutations
+(((Permutations)))
+(((Words permutations)))
+> Return all the permutations (without repetitions) of a word.
 
-> Return all the permutations (without repetitions) of a given word.
-
-indexterm:[Permutation]
 For instace, if you are given the word `art` these are the possible permutations:
 
 ----
@@ -57,25 +62,24 @@ For instace, if you are given the word `art` these are the possible permutations
 
 Now, let's implement the program to generate all permutations of a word.
 
-
-NOTE: We already solved this problem using an <<Getting all permutations of a word, iterative program>>, now let's do it using backtracking.
+NOTE: We already solved this problem using an <<part01-algorithms-analysis#factorial-example, iterative program>>, now let's do it using backtracking.
 
 .Word permutations using backtracking
 [source, javascript]
 ----
 include::{codedir}/algorithms/permutations-backtracking.js[tag=snippet,indent=0]
 ----
-<1> Iterate through all the elements in the input
+<1> Iterate through all the elements
 <2> Make a change: swap letters
 <3> Recursive function moving to the next element
 <4> Test if the current change is a solution: reached the end of the string.
-<5> Revert back the change (backtracking): Undo swap from step 2
+<5> Revert the change (backtracking): Undo swap from step 2
 
-As you can see, we iterate through each letter and swap with the following letters until we reach the end of the string. Then, we rollback the change and try another path.
+As you can see, we iterate through each element and swap with the following letters until we reach the end of the string. Then, we roll back the change and try another path.
 
-In the following tree, you can visualize how the backtracking algorithm is swaping the letters.
+In the following tree, you can visualize how the backtracking algorithm is swapping the letters. We are taking `art` as an example.
 
-[graphviz, Words Permutations, svg]
+[graphviz, Words-Permutations, png]
 ....
 digraph g {
   node [shape = record,height=.1];
@@ -115,12 +119,12 @@ digraph g {
 ....
 
 .Legend:
-- The [red]#red# words are the iterations added to the solution array.
+- The asterisk (`*`) indicates `start` index.
 - *Black* arrows indicate the `swap` operations.
 - *Grey* arrows indicate the _backtracking_ operation (undo swap).
-- The asterisk (`*`) indicates `start` index.
+- The [red]#red# words are the iterations added to the solution array.
 
-Most of backtracking algorihtms do something similar. What changes is the test function to determine if a current iteration is a solution or not.
+Most of the backtracking algorithms do something similar. What changes is the test function or base case to determine if a current iteration is a solution or not.
 
 
 
diff --git a/book/content/part04/bubble-sort.asc b/book/content/part04/bubble-sort.asc
new file mode 100644
index 00000000..d9494cd1
--- /dev/null
+++ b/book/content/part04/bubble-sort.asc
@@ -0,0 +1,81 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[bubble-sort]]
+==== Bubble Sort
+(((Bubble Sort)))
+(((Sorting, Bubble Sort)))
+(((Sinking Sort)))
+(((Sorting, Sinking Sort)))
+Bubble sort is a simple sorting algorithm that "bubbles up" the biggest values to the array's right side.
+It's also called _sinking sort_ because of the most significant values "sink" to the array's right side.
+This algorithm is adaptive, which means that if the array is already sorted, it will take only _O(n)_ to "sort".
+However, if the array is entirely out of order, it will require _O(n^2^)_ to sort.
+(((Quadratic)))
+(((Runtime, Quadratic)))
+
+===== Bubble Sort Implementation
+
+.Bubble Sort implementation in JavaScript
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/bubble-sort.js[tag=sort, indent=0]
+----
+<1> Convert any iterable (array, sets, etc.) into an array or if it's already an array, it clones it, so the input is not modified.
+<2> Starting from index 0 compare current and next element
+<3> If they are out of order, swap the pair
+<4> Repeat pair comparison until the last element that has been bubbled up to the right side `array.length - i`.
+<5> (optimization) If there were no swaps, this means that the array is sorted. This single-pass makes this sorting _adaptive_, and it will only require _O(n)_ operations.
+<6> Each step moves the largest element from where it was to the right side. We need to do this `n - 1`  times to cover all items.
+
+.The `swap` function is implemented as follows:
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/sorting-common.js[tag=swap, indent=0]
+----
+
+It uses JavaScript ES6 destructing arrays.
+
+.JavaScript Array destructuring
+****
+*Assignment separate from declaration*
+
+A variable can be assigned to its values using the destructing syntax.
+
+[source, javascript]
+----
+let a, b;
+
+[a, b] = [1, 2];
+console.log(a); //↪️ 1
+console.log(b); //️↪️ 2
+----
+
+*Swapping variables*
+
+Two variables' values can be swapped in one line using the destructuring expression.
+
+[source, javascript]
+----
+[a, b] = [b, a];
+console.log(a); //↪️ 2
+console.log(b); //️↪️ 1
+----
+
+Without the destructuring assignment, swapping two values requires a temporary variable.
+****
+
+Bubble sort has a <<part01-algorithms-analysis#quadratic>> running time, as you might infer from the nested for-loop.
+
+===== Bubble Sort Properties
+
+- <<Stable>>: [big]#✅# Yes
+- <<In-place>>: [big]#✅# Yes
+- <<Online>>: [big]#✅# Yes
+- <<Adaptive>>: [big]#✅# Yes, _O(n)_ when already sorted
+- Time Complexity: [big]#⛔️# <<part01-algorithms-analysis#quadratic>> _O(n^2^)_
+- Space Complexity: [big]#✅# <<part01-algorithms-analysis#constant>> _O(1)_
+
+indexterm:[Runtime, Quadratic]
diff --git a/book/content/part04/divide-and-conquer.asc b/book/content/part04/divide-and-conquer.asc
new file mode 100644
index 00000000..d6e7c083
--- /dev/null
+++ b/book/content/part04/divide-and-conquer.asc
@@ -0,0 +1,168 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Divide and Conquer
+
+(((Divide and Conquer)))
+(((Algorithmic Techniques, Divide and Conquer)))
+Divide and conquer is a strategy for solving algorithmic problems.
+It splits the input into manageable parts recursively and finally joins solved pieces to form the solution.
+
+We have already implemented some algorithms using the divide and conquer technique.
+
+.Examples of divide and conquer algorithms:
+- <<part04-algorithmic-toolbox#merge-sort>>: *divides* the input into pairs, sort them, and them *join* all the pieces in ascending order.
+- <<quicksort-chap>>: *splits* the data by a random number called "pivot," then move everything smaller than the pivot to the left and anything more significant to the right. Repeat the process on the left and right sides. Note: since this works in place doesn't need a "join" part.
+- <<part01-algorithms-analysis#logarithmic-example, Binary Search>>: find a value in a sorted collection by *splitting* the data in half until it sees the value.
+- <<part01-algorithms-analysis#factorial-example, Permutations>>: *Take out* the first element from the input and solve permutation for the remainder of the data recursively, then *join* results and append the items that were taken out.
+
+.In general, the divide and conquer algorithms have the following pattern:
+1. *Divide* data into subproblems.
+2. *Conquer* each subproblem.
+3. *Combine* results.
+
+As you might know, there are multiple ways to solve a problem. Let's solve the Fibonacci numbers using a divide and conquer algorithm. Later we are going to provide a more performant solution using dynamic programming.
+
+==== Recursive Fibonacci Numbers
+(((Fibonacci)))
+To illustrate how we can solve a problem using divide and conquer, let's write a program to find the n-th Fibonacci number.
+
+.Fibonacci Numbers
+****
+Fibonacci sequence is a series of numbers that starts with `0, 1`; the following values are calculated as the sum of the previous two. So, we have:
+
+`0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, ...`
+****
+
+We can get the n-th Fibonacci number with the following recursive program:
+
+.Recursive Fibonacci implemenation
+[source, javascript]
+----
+include::{codedir}/algorithms/fibonacci-recursive.js[tag=snippet,indent=0]
+----
+
+.Let's see how this fit divide and conquer:
+- *Divide*: `n` is divided in two subproblems `f(n-1)` and `f(n-2)`.
+- *Conquer*: solve each subproblem independently
+- *Combine*: sum the subproblem results to get the final solution.
+
+The implementation above does the job, but what's the runtime?
+
+For that, let's take a look at the job performed by calculating the `fib(5)` number. Since `fib(5) = fib(4) + fib(3)`, we need to find the answer for `fib(4)` and `fib(3)`. We do that recursively until we reach the base cases of `fib(1)` and `fib(0)`. If we represent the calls in a tree, we would have the following:
+
+// http://bit.ly/2UmwzZV
+[graphviz, recursive-fibonacci-call-tree, png]
+....
+graph G {
+    "fib(5)" -- { "fib(4)", "fib(3)" }
+    "fib(4)" -- { "fib(3)*", "fib(2)" }
+    "fib(3)" -- { "fib(2)*", "fib(1)" }
+    "fib(2)" -- { "fib(1)*", "fib(0)" }
+    "fib(2)*" -- { "fib(1)***", "fib(0)*" }
+    "fib(3)*" -- { "fib(2)**", "fib(1)**" }
+    "fib(2)**" -- { "fib(1)****", "fib(0)**" }
+
+    // red colors
+    "fib(0)*" [color="#FF5252" label="fib(0)"];
+    "fib(0)**" [color="#FF5252" label="fib(0)"];
+    "fib(1)*" [color="#FF5252" label="fib(1)"];
+    "fib(1)**" [color="#FF5252" label="fib(1)"];
+    "fib(1)***" [color="#FF5252" label="fib(1)"];
+    "fib(1)****" [color="#FF5252" label="fib(1)"];
+    "fib(2)*" [color="#FF5252" label="fib(2)"];
+    "fib(2)**" [color="#FF5252" label="fib(2)"];
+    "fib(3)*" [color="#FF5252" label="fib(3)"];
+}
+....
+
+In the diagram, we see the two recursive calls needed to compute each number. So if we follow the _O(branches^depth^)_, we get O(2^n^). [big]#🐢#
+(((Exponential)))
+(((Runtime, Exponential)))
+NOTE: Fibonacci is not a perfect binary tree since some nodes only have one child instead of two. The exact runtime for recursive Fibonacci is _O(1.6^n^)_ (still exponential time complexity).
+
+Exponential time complexity is pretty bad. Can we do better?
+
+You can notice every element in red, and with asterisks `*`, it's called more than once in the call tree. We are repeating calculations too many times!
+
+(((quotes)))
+[quote, Dynamic Programming]
+Those who cannot remember the past are condemned to repeat it.
+
+For these cases, when subproblems repeat themselves, we can optimize them using <<Dynamic Programming, dynamic programming>>. Let's do that in the next section.
+
+
+
+
+
+
+
+// But before doing dynamic programming. Let's do this divide and conquer exercises.
+
+// ==== Practice Questions
+// (((Interview Questions, Divide and Conquer)))
+
+
+
+
+
+// // https://leetcode.com/problems/merge-k-sorted-lists/
+
+// // tag::divide-and-conquer-q-FILENAME[]
+// ===== TITLE
+
+// *DC-1*) _._
+
+// // end::divide-and-conquer-q-FILENAME[]
+
+// _Common in interviews at: X._
+
+// *Starter code*:
+
+// [source, javascript]
+// ----
+// include::../../interview-questions/FILENAME.js[tags=description;placeholder]
+// ----
+
+// *Examples*:
+
+// [source, javascript]
+// ----
+// FN([]); // 3 (EXPLANATION)
+// ----
+
+
+// _Solution: <<divide-and-conquer-q-FILENAME>>_
+
+
+
+
+// // https://leetcode.com/problems/search-a-2d-matrix-ii/
+
+// // tag::divide-and-conquer-q-FILENAME[]
+// ===== TITLE
+
+// *DC-2*) _._
+
+// // end::divide-and-conquer-q-FILENAME[]
+
+// _Common in interviews at: X._
+
+// *Starter code*:
+
+// [source, javascript]
+// ----
+// include::../../interview-questions/FILENAME.js[tags=description;placeholder]
+// ----
+
+// *Examples*:
+
+// [source, javascript]
+// ----
+// FN([]); // 3 (EXPLANATION)
+// ----
+
+
+// _Solution: <<divide-and-conquer-q-FILENAME>>_
diff --git a/book/content/part04/dynamic-programming.asc b/book/content/part04/dynamic-programming.asc
new file mode 100644
index 00000000..147a92f1
--- /dev/null
+++ b/book/content/part04/dynamic-programming.asc
@@ -0,0 +1,63 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Dynamic Programming)))
+(((Algorithmic Techniques, Dynamic Programming)))
+[[dynamic-programming-chap]]
+=== Dynamic Programming
+
+Dynamic programming (DP) is a way to solve algorithmic problems with *overlapping subproblems*. Algorithms using DP find the base case and building a solution from the ground-up. Dp _keep track_ of previous results to avoid re-computing the same operations.
+
+// https://twitter.com/iAmAdrianMejia/status/1103050924933726208
+// https://www.quora.com/How-should-I-explain-dynamic-programming-to-a-4-year-old/answer/Jonathan-Paulson
+// https://medium.com/@codingfreak/top-50-dynamic-programming-practice-problems-4208fed71aa3
+// https://www.slideshare.net/balamoorthy39/greedy-algorithm-knapsack-problem
+
+.How to explain dynamic programming to kids? 👶
+****
+
+$$*$$*_Write down 1+1+1+1+1+1+1+1+1+1_*$$*$$
+
+--{sp} What's that equal to?
+
+--{sp} $$*$$*_Kid counting one by one_*$$*$$ Ten!
+
+--{sp} Add another "+1". What's the total now?
+
+--{sp} $$*$$*_Quickly_*$$*$$  Eleven!
+
+--{sp} Why you get the result so quickly? Ah, you got it faster by adding one to the memorized previous answer. So Dynamic Programming is a fancy way of saying: "remembering past solutions to save time later."
+****
+
+==== Fibonacci Sequence with Dynamic Programming
+(((Fibonacci)))
+Let's solve the same Fibonacci problem but this time with dynamic programming.
+
+When we have recursive functions, doing duplicated work is the perfect place for dynamic programming optimization. We can save (or cache) the results of previous operations and speed up future computations.
+
+.Recursive Fibonacci Implemenation using Dynamic Programming
+[source, javascript]
+----
+include::{codedir}/algorithms/fibonacci-dynamic-programming.js[tag=snippet,indent=0]
+----
+
+This implementation checks if we already calculated the value, if so it will save it for later use.
+
+[graphviz, Recursive-Fibonacci-call-tree-with-dp, png]
+....
+graph G {
+    "fib(5)" -- { "fib(4)" }
+    "fib(4)" -- { "fib(3)" }
+    "fib(3)" -- { "fib(2)" }
+    "fib(2)" -- { "fib(1)", "fib(0)" }
+}
+....
+
+This graph looks pretty linear now. It's runtime _O(n)_!
+(((Linear)))
+(((Runtime, Linear)))
+
+(((Memoization)))
+TIP: Saving previous results for later is a technique called "memoization". This is very common to optimize recursive algorithms with overlapping subproblems. It can make exponential algorithms linear!
diff --git a/book/content/part04/greedy-algorithms.asc b/book/content/part04/greedy-algorithms.asc
new file mode 100644
index 00000000..7063348f
--- /dev/null
+++ b/book/content/part04/greedy-algorithms.asc
@@ -0,0 +1,105 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Greedy Algorithms
+
+(((Greedy Algorithms)))
+(((Algorithmic Techniques, Greedy Algorithms)))
+Greedy algorithms are designed to find a solution by going one step at a time and using heuristics to determine the best choice.
+They are quick but not always lead to the most optimum results since it might not take into consideration all the options to give a solution.
+
+An excellent example of a greedy algorithm that doesn't work well is finding the largest sum on a tree.
+
+[graphviz, Find-the-largest-sum, png]
+....
+graph G {
+  5 -- 3 [color="#B8E986", penwidth=2]
+  5 -- 7 [color="#FF5252", penwidth=2]
+  3 -- 87 [color="#B8E986", penwidth=2]
+  3 -- 1
+  7 -- 2
+  7 -- 4 [color="#FF5252", penwidth=2]
+
+  label="Optimal vs. Greedy path"
+}
+....
+
+Let's say the greedy heuristics are set to take the more significant value. The greedy algorithm will start at the root and say, "Which number is bigger 3 or 7?" Then go with 7 and later 4. As you can see in the diagram, the most significant sum would be the path `7 - 3 - 87`. A greedy algorithm never goes back on its options. This greedy choice makes it different from dynamic programming, which is exhaustive and guaranteed to find the best option. However, when they work well, they are usually faster than other options.
+
+Greedy algorithms are well suited when an optimal local solution is also a globally optimal solution.
+
+[TIP]
+=======
+Greedy algorithms make the choice that looks best at the moment based on a heuristic, such as the smallest, largest, best ratio, and so on.
+This algorithm only gives one shot at finding the solution and never goes back to consider other options.
+=======
+
+Don't get the wrong idea; some greedy algorithms work very well if they are designed correctly.
+
+.Some examples of greedy algorithms that work well:
+- <<part04-algorithmic-toolbox#selection-sort>>: we select the best (minimum value) remove it from the input and then select the next minimum until everything is processed.
+- <<part04-algorithmic-toolbox#merge-sort>>: the "merge" uses a greedy algorithm, where it combines two sorted arrays by looking at their current values and choosing the best (minimum) at every time.
+indexterm:[Merge Sort]
+
+
+.In general, we can follow these steps to design Greedy Algorithms:
+1. Take a sample from the input data (usually in a data structure like array/list, tree, graph).
+2. Greedy choice: use a heuristic function that will choose the best candidate. E.g., Largest/smallest number, best ratio, etc.
+3. Reduce the processed input and repeat step #1 and #2 until all data is gone.
+4. Return solution.
+5. Check correctness with different examples and edge cases.
+
+==== Fractional Knapsack Problem
+
+We are going to use the "Fractional Knapsack Problem" to learn how to design greedy algorithms. The problem is the following:
+
+> You are going to resell legumes (rice, beans, chickpeas, lentils), and you only brought a knapsack. What proportion of items can you choose to get the highest loot without exceeding the bag's maximum weight?
+
+Let's say we have the following items available.
+
+.Knpasack Input
+[source, javascript]
+----
+const items = [
+  { value: 1, weight: 1},
+  { value: 4, weight: 3 },
+  { value: 5, weight: 4 },
+  { value: 7, weight: 5 },
+];
+
+const maxWeight = 7;
+----
+
+So, we have four items that we can choose from. We can't take them all because the total weight is `13` and the maximum we can carry is `7`. We can't just take the first one because with value `1` because it is not the best profit.
+
+How would you solve this problem?
+
+First, we have to define what parameters we will use to make our *greedy choice*. This some ideas:
+
+- We can take items with the *largest* value in hopes to maximize profit. Based on that, we can take the last and the first to have a total weight of 7 and a total cost of 8.
+
+We could also take items with the *smallest* weight so we can fit as much as possible in the knapsack. Let's analyze both options. So we can choose the first two items for a total value of 5 and a total weight of 4. This option is worse than picking the most significant amount! [big]#👎#
+
+- One last idea, we can take items based on the *best* value/weight ratio and take fractions of an article to fill up the knapsack to maximum weight. In that case, we can buy the last item in full and 2/3 of the 2nd item. We get a total value of `9.67` and a total weight of `7`. These heuristics seem to be the most profitable. [big]#👍#
+
+.Items value/weight ratio
+----
+  { value: 1, weight: 1 }, // 1/1  = 1
+  { value: 4, weight: 3 }, // 4/3 = 1.33 ✅
+  { value: 5, weight: 4 }, // 5/4 = 1.25
+  { value: 7, weight: 5 }, // 7/5 = 1.4 ✅
+----
+
+Let's implement this algorithm!
+
+.Factional Knapsack Problem Implementation
+[source, javascript]
+----
+include::{codedir}/algorithms/knapsack-fractional.js[tag=snippet,indent=0]
+----
+
+What's the runtime of this algorithm?
+
+We have to sort the array based on the value/weight ratio. Sorting runtime is O(n log n). The rest is linear operations, so the answer is _O(n log n)_ for our greedy algorithm.
diff --git a/book/content/part04/insertion-sort.asc b/book/content/part04/insertion-sort.asc
new file mode 100644
index 00000000..0c02cfc6
--- /dev/null
+++ b/book/content/part04/insertion-sort.asc
@@ -0,0 +1,42 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[insertion-sort]]
+==== Insertion Sort
+
+(((Sorting, Insertion Sort)))
+(((Insertion Sort)))
+Insertion sort is a simple sorting algorithm. It is one of the most natural ways of sorting. If I give you some cards to sort, you will probably use this algorithm without knowing.
+
+// Good illustration on of sorting a deck of cards:  https://www.khanacademy.org/computing/computer-science/algorithms/insertion-sort/a/insertion-sort
+
+===== Insertion Sort Implementation
+
+Insertion sort does the following: It starts from the 2nd element, and it tries to find anything to the left that could be bigger than the current item.
+ It will swap all the elements with higher value
+ and insert the current element where it belongs.
+
+.Insertion sort
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/insertion-sort.js[tag=sort, indent=0]
+----
+<1> Convert to an array or clone the array.
+<2> Start with the 2nd element. Everything on the left is considered sorted.
+<3> Compare the current element (2nd) to the previous one. If `left - 1` is bigger, it will swap places. If not, it will continue checking the next one to the left.
+<4> We check every element on the left side and swap any of them that are out of order
+
+
+===== Insertion Sort Properties
+
+- <<Stable>>: [big]#✅# Yes
+- <<In-place>>: [big]#✅# Yes
+- <<Online>>: [big]#✅# Yes
+- <<Adaptive>>: [big]#✅# Yes
+- Time Complexity: [big]#⛔️# <<part01-algorithms-analysis#quadratic>> _O(n^2^)_
+- Space Complexity: [big]#✅# <<part01-algorithms-analysis#constant>> _O(1)_
+
+(((Quadratic)))
+(((Runtime, Quadratic)))
diff --git a/book/content/part04/merge-sort.asc b/book/content/part04/merge-sort.asc
new file mode 100644
index 00000000..3edf462b
--- /dev/null
+++ b/book/content/part04/merge-sort.asc
@@ -0,0 +1,64 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[merge-sort]]
+==== Merge Sort
+
+(((Sorting, Merge Sort)))
+(((Merge Sort)))
+
+Merge Sort is an efficient sorting algorithm that uses <<Divide and Conquer, divide and conquer>> paradigm to accomplish its task faster. However, It uses auxiliary memory in the process of sorting.
+
+indexterm:[Divide and Conquer]
+Merge sort algorithm splits the array into halves until two or fewer elements are left. It sorts these two elements and then merges back all halves until the whole collection is sorted.
+
+image::image11.png[Mergesort visualization,width=500,height=600]
+
+===== Merge Sort Implementation
+
+.Merge Sort implementation in JavaScript (mergeSort)
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/merge-sort.js[tag=sort, indent=0]
+----
+<1> Convert any kind of iterable (array, sets, etc.) into an array
+
+As you can see, this function is just a wrapper to transform things into an array. The heavy lifting is done in `splitSort`, as you can see below.
+
+.Merge Sort implementation in JavaScript (splitSort)
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/merge-sort.js[tag=splitSort, indent=0]
+----
+<1> Base case: Sort two or less items manually.
+<2> Recursively divide the array in half until two or fewer elements are left.
+<3> Merge back the sorted halves in ascending order.
+
+Let's take a look at the merge function:
+
+.Merge Sort implementation in JavaScript (merge)
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/merge-sort.js[tag=merge, indent=0]
+----
+<1> We need to keep track of 3 arrays indices: `index` which keeps track of the combined array position, `i1` which is the `array1` index and `i2` for `array2`.
+<2> If `array1` current element (`i1`) has the lowest value, we insert it into the `mergedArray`. If not we then insert the `array2` element.
+<3> `mergedArray` is `array1` and `array2` combined in ascending order (sorted).
+
+Merge sort has an _O(n log n)_  running time. For more details about how to extract the runtime, go to <<part01-algorithms-analysis#linearithmic>> section.
+
+===== Merge Sort Properties
+
+- <<Stable>>: [big]#✅# Yes
+- <<In-place>>: [big]#️❌# No, it requires auxiliary memory O(n).
+- <<Online>>: [big]#️❌# No, new elements will require to sort the whole array.
+- <<Adaptive>>: [big]#️❌# No, mostly sorted array takes the same time O(n log n).
+- Recursive: Yes
+- Time Complexity: [big]#✅# <<part01-algorithms-analysis#linearithmic>> _O(n log n)_
+- Space Complexity: [big]#⚠️# <<part01-algorithms-analysis#linear>> _O(n)_, use auxiliary memory
+
+(((Linearithmic)))
+(((Runtime, Linearithmic)))
+(((Space complexity, Linear)))
diff --git a/book/content/part04/quick-sort.asc b/book/content/part04/quick-sort.asc
new file mode 100644
index 00000000..e1a1de65
--- /dev/null
+++ b/book/content/part04/quick-sort.asc
@@ -0,0 +1,99 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Sorting, QuickSort)))
+(((QuickSort)))
+[[quicksort-chap]]
+==== Quicksort
+
+Quicksort is an efficient recursive sorting algorithm that uses <<Divide and Conquer, divide and conquer>> paradigm to sort faster. It can be implemented in-place, so it doesn't require additional memory.
+
+indexterm:[Divide and Conquer]
+In practice, quicksort outperforms other sorting algorithms like <<part04-algorithmic-toolbox#merge-sort>>. And, of course, It also outperforms simple sorting algorithms like <<part04-algorithmic-toolbox#selection-sort>>, <<part04-algorithmic-toolbox#insertion-sort>> and <<part04-algorithmic-toolbox#insertion-sort>>.
+
+Quicksort picks a "pivot" element randomly and moves all the smaller parts than the pivot to the left and the ones that are bigger to the right. It does this recursively until all the array is sorted.
+
+===== Quicksort Implementation
+
+Quicksort implementation uses the divide-and-conquer in the following way:
+
+.Quicksort Algorithm
+. Pick a "pivot" element (at random).
+. Move everything lower than the pivot to the left, and everything more significant than the pivot to the right.
+. Recursively repeat step #1 and #2 in the sub-arrays on the left and on the right WITHOUT including the pivot.
+
+Let's convert these words into code!
+
+.Quicksort implementation in JavaScript (QuickSort)
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/quick-sort.js[tag=quickSort, indent=0]
+----
+<1> Partition: picks a pivot and find the index where the pivot will be when the array is sorted.
+<2> Do the partition of the sub-array at the left of the pivot.
+<3> Do the partition of the sub-array at the right of the pivot.
+<4> Only do the partition when there's something to divide.
+
+The `partition` function does the real heavy lifting. 🏋️‍♀️
+
+.Quicksort implementation in JavaScript (partition)
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/quick-sort.js[tag=partition, indent=0]
+----
+<1> Take the leftmost element as the pivot.
+<2> `pivotFinalIndex` is the placeholder for the final position where the pivot will be placed when the array is sorted.
+<3> Check all values other than the pivot to see if any value is smaller than the pivot.
+<4> If the `current` element's value is less than the pivot, then increment `pivotFinalIndex` to make room on the left side.
+<5> We also swap the smaller element to the left side since it's smaller than the pivot.
+<6> Finally, we move the pivot to its final position. Everything to the left is smaller than the pivot, and everything to the right is bigger.
+
+*What would happen if use Quicksort for an array in reverse order?*
+
+E.g. `[10, 7, 5, 4, 2, 1]`, if we always choose the first element as the pivot, we would have to swap everything to the left of `10`.
+
+So in the first partition, we would have `[7, 5, 4, 2, 1, 10]`.
+Then, we take `7` would be the next pivot, and we have to swap everything to the left.
+Descending arrays are the worst-case for this Quicksort since it will perform O(n^2^) work.
+If we do it by the middle (or even better at random) instead of partitioning by the first element, we would have better performance. That's why we usually shuffle the array before doing Quicksort to avoid edge cases.
+
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/quick-sort.js[tag=sort, indent=0]
+----
+<1> Convert to array (or clone array). If you want to modify the input, directly remove this line.
+<2> Shuffle array to avoid edge cases (desc order arrays)
+
+And you can see the implementation of `shuffle` below:
+
+.Shuffling an array
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/sorting-common.js[tag=shuffle, indent=0]
+----
+
+With the optimization, Quicksort has an _O(n log n)_ running time. Similar to the merge sort, we divide the array into halves each time. For more details about how to extract the runtime, go to <<part01-algorithms-analysis#linearithmic>>.
+
+===== Quicksort Properties
+
+- <<Stable>>: [big]#❌# No
+- <<In-place>>: [big]#✅# Yes
+- <<Adaptive>>: [big]#️❌# No, mostly sorted array takes the same time O(n log n).
+- <<Online>>: [big]#️❌# No, the pivot element can be choose at random.
+- Recursive: Yes
+- Time Complexity: [big]#✅# <<part01-algorithms-analysis#linearithmic>> _O(n log n)_
+- Space Complexity: [big]#✅# <<part01-algorithms-analysis#logarithmic>> _O(log n)_, because of recursion.
+
+(((Linearithmic)))
+(((Runtime, Linearithmic)))
+(((Space complexity, Logarithmic)))
+
+// Resources:
+// https://www.khanacademy.org/computing/computer-science/algorithms/quick-sort/a/linear-time-partitioning
+// https://www.khanacademy.org/computing/computer-science/algorithms/quick-sort/a/overview-of-quicksort
+// https://algs4.cs.princeton.edu/23quicksort/
+// https://twitter.com/mathias/status/1036626116654637057?lang=en
+// https://www.toptal.com/developers/sorting-algorithms/quick-sort
+// https://stackoverflow.com/q/19255999/684957 // Is Quicksort “adaptive” and “online”?
diff --git a/book/content/part04/selection-sort.asc b/book/content/part04/selection-sort.asc
new file mode 100644
index 00000000..6f9f1a12
--- /dev/null
+++ b/book/content/part04/selection-sort.asc
@@ -0,0 +1,61 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+(((Sorting, Selection Sort)))
+(((Selection Sort)))
+[[selection-sort]]
+==== Selection Sort
+
+The selection sort is a simple sorting algorithm. As its name indicates, it _selects_ the lowest element from the list and moves it where it should be.
+
+.Selection sort algorithm
+. Start with the element in position 0.
+. Find the minimum item in the rest of the array. If a new minimum is found, swap them.
+. Repeat step #1 and #2 with the next element until the last one.
+
+image::selection-sort.gif[]
+
+===== Selection sort implementation
+For implementing the selection sort, we need two indexes.
+
+.Selection sort
+[source, javascript]
+----
+include::{codedir}/algorithms/sorting/selection-sort.js[tag=sort, indent=0]
+----
+<1> Converts any collection to an array or clone existing array.
+<2> Visit all elements in the array starting from the 1st element (index 0).
+<3> Everything to the left side is considered sorted in its final position. So, select `left` as the initial minimum value.
+<4> Compare the `selection` to every element to the right side.
+<5> If it finds a value _smaller_ than the selection, then update the `selection`.
+<6> Put the next smallest item to its final position
+
+TIP: Selection sort minimizes the number of swaps. It does one swap per iteration while insertion sort and bubble sort could swap many times with the same array.
+
+One index is for the position in question (selection/left) and another for finding the minimum in the rest of the array (right).
+
+===== Selection Sort Properties
+
+- <<In-place>>: [big]#✅# Yes
+- <<Stable>>: [big]#️️❌# No
+- <<Adaptive>>: [big]#️️❌# No
+- <<Online>>: [big]#️️❌# No
+- Time Complexity: [big]#⛔️# <<part01-algorithms-analysis#quadratic>> _O(n^2^)_
+- Space Complexity: [big]#✅# <<part01-algorithms-analysis#constant>> _O(1)_
+
+*Why selection sort is not stable?*
+
+To recap, _stable_ means that items with the same value keep their initial position.
+Let's see what would happen with the selection sort if we (select) sort the following array `2, 5, 2, 1`. To distinguish them, let's say `2a` and `2b`, so `2a, 5, 2b, 1`.
+
+Initially, we select the first element, `2a` and check if there's anything less than 2 in the array. We find out that position 3 has an item with a smaller value (`1`), so we swap them.
+
+Now, we have: `1, 5, 2b, 2a`.
+There you have it, `2b` now comes before `2a`.
+
+// CAUTION: In practice, selection sort performance is the worst compared <<part04-algorithmic-toolbox#insertion-sort>> and <<part04-algorithmic-toolbox#insertion-sort>>. The only advantage of selection sort is that it minimizes the number of swaps. If swapping is expensive, it could make sense to use this one over the others.
+
+(((Quadratic)))
+(((Runtime, Quadratic)))
diff --git a/book/content/part04/sorting-algorithms.asc b/book/content/part04/sorting-algorithms.asc
new file mode 100644
index 00000000..cf19cd33
--- /dev/null
+++ b/book/content/part04/sorting-algorithms.asc
@@ -0,0 +1,216 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+=== Sorting Algorithms
+
+Sorting is one of the most common solutions when we want to extract some insights about data.
+We can sort to get the maximum or minimum value, and many algorithmic problems can benefit from sorting.
+
+.We are going to explore three basic sorting algorithms _O(n^2^)_ which have low overhead:
+- <<part04-algorithmic-toolbox#bubble-sort>>
+- <<part04-algorithmic-toolbox#selection-sort>>
+- <<part04-algorithmic-toolbox#insertion-sort>>
+
+.and then discuss efficient sorting algorithms _O(n log n)_ such as:
+- <<part04-algorithmic-toolbox#merge-sort>>
+- <<quicksort-chap>>
+
+Before we dive into the most well-known sorting algorithms, let's discuss the sorting properties.
+
+==== Sorting Properties
+
+Sorting implementations with the same time complexity might manipulate the data differently. We want to understand these differences to be aware of the side effects on data or extra resources they will require. For instance, some solutions will need auxiliary memory to store temporary data while sorting, while others can do it in place.
+
+Sorting properties are stable, adaptive, online, and in-place. Let's go one by one.
+
+===== Stable
+(((Sorting, stable)))
+An ((stable sorting)) algorithms keep the relative order of items with the same comparison criteria.
+
+This incredibly useful when you want to sort on multiple phases.
+
+.Let's say you have the following data:
+[source, javascript]
+----
+const users = [
+  { name: 'Bob', age: 32 },
+  { name: 'Alice', age: 30 },
+  { name: 'Don', age: 30 },
+  { name: 'Charly', age: 32 },
+];
+----
+
+.If you sort by `name`, you would have:
+[source, javascript]
+----
+[
+  { name: 'Alice', age: 30 },
+  { name: 'Bob', age: 32 },
+  { name: 'Charly', age: 32 },
+  { name: 'Don', age: 30 },
+];
+----
+
+Then, here comes the _critical_ part; if you sort by `age`, you might get (at least two) different results.
+
+.If the sorting algorithm is *stable*; it should keep the items with the same age ordered by `name`:
+[source, javascript]
+----
+[
+  { name: 'Alice', age: 30 },
+  { name: 'Don', age: 30 },
+  { name: 'Bob', age: 32 },
+  { name: 'Charly', age: 32 },
+];
+----
+
+.However, if the sorting is *not stable*, then you will lose the relative order of the items and get something like this:
+[source, javascript]
+----
+[
+  { name: 'Don', age: 30 },
+  { name: 'Alice', age: 30 },
+  { name: 'Charly', age: 32 },
+  { name: 'Bob', age: 32 },
+];
+----
+
+Both results are sorted by `age`; however, having a stable sorting is better if you want to keep the relative position of data with the same value.
+
+===== In-place
+(((Sorting, in-place)))
+An ((in-place sorting)) algorithm would have a _space complexity_ of O(1). In other words, it does not use any additional memory because it moves the items in-place.
+No extra memory for sorting is especially useful for large amounts of data or in memory constraint environments like robotics, smart devices, or embedded systems in appliances.
+
+===== Online
+(((Sorting, online)))
+It can sort a list as it receives it.
+((Online sorting)) algorithms don't have to re-sort the whole collection for every new item added.
+
+===== Adaptive
+(((Sorting, adaptive)))
+Algorithms with ((adaptive sorting)) run faster, close to _O(n)_, on an already sorted (or partially sorted) collection.
+
+<<<
+include::bubble-sort.asc[]
+
+<<<
+include::insertion-sort.asc[]
+
+<<<
+include::selection-sort.asc[]
+
+<<<
+include::merge-sort.asc[]
+
+<<<
+include::quick-sort.asc[]
+
+<<<
+==== Summary
+
+We explored the most common sorting algorithms, some of which are simple and others more performant. Also, we cover the properties of sorting algorithms such as stable, in-place, online, and adaptive.
+(((Tables, Algorithms, Sorting Complexities)))
+(((Tables, Algorithms, Sorting Summary)))
+
+// tag::table[]
+.Sorting algorithms comparison
+[cols="20,80"]
+|===
+| Algorithms         | Comments
+| <<part04-algorithmic-toolbox#bubble-sort>>    | Swap pairs bubbling up largest numbers to the right
+| <<part04-algorithmic-toolbox#insertion-sort>> | Look for biggest number to the left and swap it with current
+| <<part04-algorithmic-toolbox#selection-sort>> | Iterate array looking for smallest value to the right
+| <<part04-algorithmic-toolbox#merge-sort>>     | Split numbers in pairs, sort pairs and join them in ascending order
+| <<quicksort-chap>>      | Choose a pivot, set smaller values to the left and bigger to the right.
+// | Tim sort        | Hybrid of merge sort and insertion sort
+|===
+
+.Sorting algorithms time/space complexity and properties
+|===
+| Algorithms         | Avg        | Best | Worst | Space       | Stable | In-place | Online | Adaptive
+| <<part04-algorithmic-toolbox#bubble-sort>>    | O(n^2^)    | O(n) | O(n^2^) | O(1)      | Yes    | Yes      | Yes    | Yes
+| <<part04-algorithmic-toolbox#insertion-sort>> | O(n^2^)    | O(n) | O(n^2^) | O(1)      | Yes    | Yes      | Yes    | Yes
+| <<part04-algorithmic-toolbox#selection-sort>> | O(n^2^)    | O(n^2^) | O(n^2^) | O(1)        | No     | Yes      | No    | No
+| <<part04-algorithmic-toolbox#merge-sort>>     | O(n log n) | O(n log n) | O(n log n) | O(n)        | Yes     | No       | No     | No
+| <<quicksort-chap>>      | O(n log n) | O(n log n) | O(n^2^) | O(log n) | No     | Yes      | No     | No
+// | Tim sort       | O(n log n) | O(log n)    | Yes    | No       | No     | Yes
+|===
+// end::table[]
+
+
+
+
+
+
+
+
+
+==== Practice Questions
+(((Interview Questions, sorting)))
+
+
+
+
+
+// tag::sorting-q-merge-intervals[]
+===== Merge Intervals
+
+*so-1*) _Given an array of intervals `[start, end]`, merge all overlapping intervals._
+
+// end::sorting-q-merge-intervals[]
+
+_Common in interviews at: Facebook, Amazon, Bloomberg._
+
+*Starter code*:
+
+[source, javascript]
+----
+include::../../interview-questions/merge-intervals.js[tags=description;placeholder]
+----
+
+*Examples*:
+
+[source, javascript]
+----
+merge([[0, 2], [2, 4]]); // [[0, 4]] (0-2 overlaps with 2-4)
+merge([[2, 2], [3, 4]]); // [[2, 2], [3, 4]] (no overlap)
+merge([[1, 10], [3, 4]]); // [[1, 10]] (1-10 covers the other)
+----
+
+
+_Solution: <<sorting-q-merge-intervals>>_
+
+
+
+
+
+
+// tag::sorting-q-sort-colors[]
+===== Sort Colors (The Dutch flag problem)
+
+*so-2*) _Given an array with three possible values (0, 1, 2), sort them in linear time, and in-place. Hint: similar to quicksort, where the pivot is 1._
+
+// end::sorting-q-sort-colors[]
+
+_Common in interviews at: Amazon, Microsoft, Facebook._
+
+*Starter code*:
+
+[source, javascript]
+----
+include::../../interview-questions/sort-colors.js[tags=description;placeholder]
+----
+
+*Examples*:
+
+[source, javascript]
+----
+sortColors([0, 2, 1]); // [0, 1, 2]
+sortColors([2, 0, 2, 1, 0, 1, 0]); // [0, 0, 0, 1, 1, 2, 2]
+sortColors([1, 1, 1]); // [1, 1, 1]
+----
+
+_Solution: <<sorting-q-sort-colors>>_
diff --git a/book/content/preface.asc b/book/content/preface.asc
new file mode 100644
index 00000000..82f78390
--- /dev/null
+++ b/book/content/preface.asc
@@ -0,0 +1,85 @@
+[preface]
+== Preface
+
+=== What is in this book?
+
+_{doctitle}_ is a book that can be read from cover to cover. Each section builds on top of the previous one. Also, you can use it as a reference manual. Developers can refresh specific topics before an interview or look for ideas to solve a problem optimally. (Check out the <<a-time-complexity-cheatsheet,Time Complexity Cheatsheet>> and <<index, topical index>>)
+
+This publication is designed to be concise, intending to serve software developers looking to get a firm conceptual understanding of data structures in a quick yet in-depth fashion. After reading this book, the reader should have a fundamental knowledge of algorithms, including when and where to apply it, what are the trade-offs of using one data structure over the other. The reader will then be able to make intelligent decisions about algorithms and data structures in their projects.
+
+=== Who this book is for
+
+This book is for software developers familiar with JavaScript looking to improve their problem-solving skills or preparing for a job interview.
+
+NOTE: You can apply the concepts in this book to any programming language. However, instead of doing examples in pseudo-code, we are going to use JavaScript to implement the code examples.
+
+=== What you need for this book
+
+You will need Node.js. The book code was tested against Node.js v14.8, but newer versions should also work.
+
+All the code examples used in this book can be found on:
+https://github.com/amejiarosario/dsa.js
+
+=== Conventions
+
+We use some typographical conventions within this book that distinguish between different kinds of information.
+
+The code in the text, including commands, variables, file names, and property names are shown as follows:
+
+[example]
+Repeat pair comparison until the last element that has been bubbled up to the right side `array.length - i`.
+
+A block of code is set out as follows. It may be colored, depending on the format in which you're reading this book.
+
+[source, javascript]
+----
+function* dummyIdMaker() {
+  yield 0;
+  yield 1;
+  yield 2;
+}
+
+const generator = dummyIdMaker()
+
+// getting values
+console.log(generator.next()); // ↪️ {value: 0, done: false}
+----
+
+When we want to draw your attention to specific code lines, those lines are annotated using numbers accompanied by brief descriptions.
+
+.Quicksort implementation in JavaScript (QuickSort)
+[source, javascript]
+----
+include::../../src/algorithms/sorting/quick-sort.js[tag=quickSort, indent=0]
+----
+<1> Partition: picks a pivot and find the index where the pivot will be when the array is sorted.
+<2> Do the partition of the sub-array at the left of the pivot.
+<3> Do the partition of the sub-array at the right of the pivot.
+<4> Only do the partition when there's something to divide.
+
+
+The following admonitions are used to highlight content.
+
+IMPORTANT: Reword essential concepts. Useful for memorizing, tweeting, and sharing.
+
+TIP: Tips are shown using callouts like this.
+
+WARNING: Warnings are shown using callouts like this.
+
+NOTE: This is a side note
+
+.Sidebar
+****
+Additional information about a certain topic may be displayed in a sidebar like this one.
+****
+
+Finally, this text shows what a quote looks like:
+
+[quote, H. J. Harrington]
+Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t manage it, you can’t improve it.
+
+=== Reader feedback
+
+Your feedback is very welcome and valuable. Let us know your thoughts about this book — what you like or ideas to make it better.
+
+To send us feedback, e-mail us at hello+dsajs@adrianmejia.com, send a tweet to https://twitter.com/iAmAdrianMejia[@iAmAdrianMejia], or using the hash tag `#dsaJS`.
diff --git a/book/chapters/map-hashmap.adoc b/book/deprecated/old-hashmap.asc
similarity index 65%
rename from book/chapters/map-hashmap.adoc
rename to book/deprecated/old-hashmap.asc
index a1a81c84..ded40a63 100644
--- a/book/chapters/map-hashmap.adoc
+++ b/book/deprecated/old-hashmap.asc
@@ -1,10 +1,18 @@
-= HashMap
-
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[hashmap-advanced]]
+==== HashMap
+(((HashMap)))
+(((HashTable)))
+(((Data Structures, Non-Linear, HashMap)))
 A HashMap is a Map implementation. HashMaps are composed of two things:
-1) a hash function and
-2) a bucket array to store values.
+1) a _hash function_ and
+2) a bucket _array_ to store values.
 
-Before going into the implementation details let’s give an overview of how it works. Let’s say we want to keep a tally of things and animals:
+Before going into the implementation details, let’s give an overview of how it works. Let’s say we want to keep a tally of things and animals:
 
 .HashMap example
 [source, javascript]
@@ -16,26 +24,26 @@ How are the keys mapped to their values?
 Using a hash function. Here’s an illustration:
 
 .Internal HashMap representation
-image:image41.png[image,width=528,height=299]
+image::image41.png[image,width=528,height=299]
 
 
 .This is the main idea:
-1.  We use a *hash function* to transform the keys (e.g., dog, cat, rat, …) into an array index. This array is called *bucket*.
-2.  The bucket holds the values (linked list in case of collisions).
+1.  We use a *hash function* to transform the keys (e.g., dog, cat, rat, …) into an array index. This _array_ is called *bucket*.
+2.  The bucket holds the values or list of values in case of collisions.
 
-In the illustration, we have a bucket size of 10. In bucket 0, we have a collision. Both `cat` and `art` keys map to the same bucket even thought their hash codes are different.
+In the illustration, we have a bucket size of 10. In bucket 0, we have a collision. Both `cat` and `art` keys map to the same bucket even though their hash codes are different.
 
 In a HashMap, a *collision* is when different keys lead to the same index. They are nasty for performance since it can reduce the search time from *O(1)* to *O(n)*.
 
-Having a big bucket size can avoid a collision but also can waste too much memory. We are going to build an _optimized_ HashMap that re-sizes itself when it is getting full. This avoids collisions and doesn’t spend too much memory upfront. Let’s start with the hash function.
+Having a big bucket size can avoid a collision but also can waste too much memory. We are going to build an _optimized_ HashMap that resizes itself when it is getting full. This auto-resizing avoids collisions and don't need to allocate too much memory upfront. Let’s start with the *hash function*.
 
-== Designing an optimized hash function
+===== Designing an optimized hash function
 
 To minimize collisions, we need to create an excellent hash function.
 
 IMPORTANT: A *perfect* hash function is one that assigns a unique array index for every different key.
 
-It’s no practical and memory-wise wasteful to have a perfect hash function, so we are going to shoot for a cost-effective hash function instead.
+It’s no practical and memory-wise wasteful to have a perfect hash function, so we will shoot for a cost-effective hash function instead.
 
 .To recap:
 - A hash function converts keys into array indices.
@@ -43,11 +51,11 @@ It’s no practical and memory-wise wasteful to have a perfect hash function, so
 1.  *Hash Code*: maps any key into an integer (unbonded)
 2.  *Compression function*: maps an arbitrary integer to integer in the range of [0… BUCKET_SIZE -1].
 
-Before doing a great hash function, let's see what a lousy hash function looks like. 😉
+Before doing an excellent hash function, let's see what a lousy hash function looks like. 😉
 
-=== Analysing collisions on bad hash code functions
+====== Analysing collisions on bad hash code functions
 
-The goal of a hash code function is to convert any value given into a positive integer — a common way to accomplish with summing each string’s Unicode value.
+A hash code function's goal is to convert any value given into a positive integer — a common way to accomplish with summing each string’s Unicode value.
 
 .Naïve hashing function implementation
 [source, javascript]
@@ -64,7 +72,7 @@ The `charCodeAt()` method returns an integer between `0` and `65535` representin
 
 The `codePointAt()` method returns a non-negative integer that is the Unicode code point value.
 ****
-With this function we have the can convert some keys to numbers as follows:
+With this function, we have the can convert some keys to numbers as follows:
 
 .Hashing examples
 [source, javascript]
@@ -74,7 +82,7 @@ include::{codedir}/data-structures/maps/hash-maps/hashing.js[tag=naiveHashCodeEx
 
 Notice that `rat` and `art` have the same hash code! These are collisions that we need to solve.
 
-Collisions happened because we are adding the letter's unicode and are not taking the order into account nor the type. We can do better by offsetting the character value based on their position in the string. We can also add the object type, so number `10` produce different output than string `'10'`.
+Collisions happened because we add the letter's Unicode and are not taking the order into account or the type. We can do better by offsetting the character value based on their position in the string. We can also add the object type, so number `10` produce different output than the string `'10'`.
 
 .Hashing function implementation that offset character value based on the position
 [source, javascript]
@@ -86,14 +94,14 @@ Since Unicode uses 20 bits, we can offset each character by 20 bits based on the
 
 .JavaScript built-in `BigInt`
 ****
-BigInt allows to operate beyond the maximum safe limit of integers.
+BigInt allows operating beyond the maximum safe limit of integers.
 
 [source, javascript]
 ----
 Number.MAX_SAFE_INTEGER // => 9,007,199,254,740,991
 ----
 
-BigInt has no virtually limits (until you run out of memory). It uses the suffix `n`.
+BigInt has no virtual limits (until you run out of physical memory). It uses the suffix `n`.
 
 [source, javascript]
 ----
@@ -101,7 +109,7 @@ BigInt has no virtually limits (until you run out of memory). It uses the suffix
 ----
 ****
 
-As you can imagine, summing 20bits per letter leads to a humongous number! That's the case even for 3 letters words. We are using `BigInt` so it doesn’t overflow.
+As you can imagine, summing 20bits per letter leads to a massive number! That's the case even for three-letter words. We are using `BigInt`, so it doesn’t overflow.
 
 .Verifying there's not hashing code duplicates
 [source, javascript]
@@ -109,9 +117,9 @@ As you can imagine, summing 20bits per letter leads to a humongous number! That'
 include::{codedir}/data-structures/maps/hash-maps/hashing.js[tag=hashCodeOffsetExample, indent=0]
 ----
 
-We don’t have duplicates anymore! If the keys have different content or type they have a different hash code. However, we need to represent these unbounded integers to finite buckets in an array. We do that using *compression function*. This function can be as simple as `% BUCKET_SIZE`.
+We don’t have duplicates anymore! If the keys have different content or type, they have distinct hash codes. However, we need to represent these unbounded integers to finite buckets in an array. We do that using *compression function*. This function can be as simple as `% BUCKET_SIZE`.
 
-However, there’s an issue with the last implementation. It doesn’t matter how big (and different) is the hash code number if we at the end use the modulus to get an array index. The part of the hash code that truly matters is the last bits.
+However, there’s an issue with the last implementation. It doesn’t matter how enormous (and different) is the hash code number if we, in the end, use the modulus to get an array index. The part of the hash code that truly matters is the last bits.
 
 .Look at this example with a bucket size of 4.
 [source, javascript]
@@ -123,9 +131,9 @@ However, there’s an issue with the last implementation. It doesn’t matter ho
 50 % 4 //↪️ 2
 ----
 
-All the hash codes are different and still we get many collisions! [big]#😱#
+All the hash codes are different, and still, we get many collisions! [big]#😱#
 
-Based on numbers properties, using a prime number as the modulus produce fewer collisions.
+Based on numbers properties, using a prime number as the modulus produces fewer collisions.
 
 .Let’s see what happens if the bucket size is a prime number:
 [source, javascript]
@@ -141,13 +149,13 @@ Now it’s more evenly distributed!! [big]#😎👍#
 
 .So, to sum up:
 * Bucket size should always be a *prime number*, so data is distributed more evenly and minimized collisions.
-* Hash code doesn’t have to be too big. At the end what matters is the few last digits.
+* Hash code doesn’t have to be too big. In the end, what matters is the few last digits.
 
 Let’s design a better HashMap with what we learned.
 
-=== Implementing an optimized hash function
+====== Implementing an optimized hash function
 
-We are going to use a battle tested non-cryptographic hash function called FNV Hash.
+We are going to use a battle-tested non-cryptographic hash function called FNV Hash.
 
 .FNV (Fowler/Noll/Vo) Hash
 ****
@@ -163,9 +171,9 @@ Take a look at the following function:
 include::{codedir}/data-structures/maps/hash-maps/hash-map.js[tag=hashFunction, indent=0]
 ----
 
-Is somewhat similar to what we did before, in the sense that we use each letter’s Unicode is used to compute the hash. The difference is:
+It is somewhat similar to what we did before, in the sense that we use each letter’s Unicode to compute the hash. The difference is:
 
-1.  We are using the XOR bitwise operation (`^`) to produce an *avalanche effect*, where a small change in two strings produces completely different hash codes. E.g.
+1.  We are using the XOR bitwise operation (`^`) to produce an *avalanche effect*, where a small change in two strings makes completely different hash codes. E.g.
 
 .Hash Code example using FVN1a
 [source, javascript]
@@ -174,15 +182,15 @@ hashCode('cat') //↪️ 4201630708
 hashCode('cats') //↪️ 3304940933
 ----
 
-A one letter change produce a totally different output.
+A one-letter change produces a very different output.
 
-We are using the FVN-1a prime number (`16777619`) and the offset (`2166136261`) to reduce collisions even further. If you are curious where these numbers come from check out this http://bit.ly/fvn-1a[link].
+We are using the FVN-1a prime number (`16777619`) and the offset (`2166136261`) to reduce collisions even further if you are curious where these numbers come from, check out this http://bit.ly/fvn-1a[link].
 
 FVN-1a hash function is a good trade-off between speed and collision prevention.
 
 Now that we have a proper hash function. Let’s move on with the rest of the HashMap implementation.
 
-== Implementing a HashMap in JavaScript
+===== Implementing a HashMap in JavaScript
 
 Let’s start by creating a class and its constructor to initialize the hash map. We are going to have an array called `buckets` to hold all the data.
 
@@ -200,9 +208,9 @@ include::{codedir}/data-structures/maps/hash-maps/hash-map.js[tag=getLoadFactor,
 }
 ----
 
-Notice that we are also keeping track of collisions (for benchmarking purposes) and a load factor. *The load factor* measures how full the hash map is. We don’t want to be fuller than 75%. If the HashMap is getting too full, then we are going to fix it doing a *rehash* (more on that later).
+Notice that we are also keeping track of collisions (for benchmarking purposes) and a load factor. *The load factor* measures how full the hash map is. We don’t want to be fuller than 75%. If the HashMap is getting too full, we will fix it doing a *rehash* (more on that later).
 
-=== Inserting elements in a HashMap
+====== Inserting elements in a HashMap
 
 To insert values into a HashMap, we first convert the *key* into an *array index* using the hash and compression function. Each bucket of the array will have an object with the shape of `{key, value}`.
 
@@ -217,11 +225,11 @@ include::{codedir}/data-structures/maps/hash-maps/hash-map.js[tag=set, indent=0]
 <1>  Key doesn’t exist yet, so we create the new key/value pair.
 <2>  Key already exists, then we will replace the value.
 <3>  Key doesn’t exist, but the bucket already has other data, this is a collision! We push the new element to the bucket.
-<4> To keep insertion order, we keep track of the order of the keys using `keysTrackerArray` and `keysTrackerIndex`.
+<4> To keep insertion order, we keep track of the keys' order using `keysTrackerArray` and `keysTrackerIndex`.
 
-Notice, that we are using a function called `getEntry` to check if the key already exists. It gets the index of the bucket corresponding to the key and then checks if the entry with the given key exists. We are going to implement this function in a bit.
+Notice that we are using a function called `getEntry` to check if the key already exists. It gets the bucket's index corresponding to the access and then checks if the entry with the given key exists. We are going to implement this function in a bit.
 
-=== Getting values out of a HashMap
+====== Getting values out of a HashMap
 
 For getting values out of the Map, we do something similar to inserting. We convert the key into an `index` using the hash function, then we that `index` we look for the value in the bucket.
 
@@ -232,10 +240,10 @@ include::{codedir}/data-structures/maps/hash-maps/hash-map.js[tag=getEntry, inde
 ----
 <1> Convert key to an array index.
 <2> If the bucket is empty create a new linked list
-<3> Use Linked list's <<Searching by value>> method to find value on the bucket.
+<3> Use Linked list's <<part02-linear-data-structures#array-search-by-value>> method to find value on the bucket.
 <4> Return `bucket` and `entry` if found.
 
-With the help of the `getEntry` method, we can do the `HashMap.get` and `HashMap.has` methods:
+With the `getEntry` method, we can do the `HashMap.get` and `HashMap.has` methods:
 
 .HashMap's get method
 [source, javascript]
@@ -253,7 +261,7 @@ include::{codedir}/data-structures/maps/hash-maps/hash-map.js[tag=has, indent=0]
 
 For `HashMap.has` we only care if the value exists or not, while that for `HashMap.get` we want to return the value or `undefined` if it doesn’t exist.
 
-=== Deleting from a HashMap
+====== Deleting from a HashMap
 
 Removing items from a HashMap is not too different from what we did before.
 
@@ -267,9 +275,9 @@ If the bucket doesn’t exist or is empty, we don't have to do anything else. If
 https://github.com/amejiarosario/dsa.js/blob/7694c20d13f6c53457ee24fbdfd3c0ac57139ff4/src/data-structures/linked-lists/linked-list.js#L218[`LinkedList.remove` ]
 method.
 
-== Rehashing a HashMap
+===== Rehashing a HashMap
 
-Rehashing is a technique to minimize collisions when a hash map is getting full. It doubles the size of the map and recomputes all the hash codes and insert data in the new buckets.
+Rehashing is a technique to minimize collisions when a hash map is getting full. It doubles the map's size and recomputes all the hash codes, and inserts data in the new buckets.
 
 When we increase the map size, we try to find the next prime. We explained that keeping the bucket size a prime number is beneficial for minimizing collisions.
 
@@ -282,18 +290,21 @@ include::{codedir}/data-structures/maps/hash-maps/hash-map.js[tag=rehash, indent
 In the
 https://github.com/amejiarosario/dsa.js/blob/7694c20d13f6c53457ee24fbdfd3c0ac57139ff4/src/data-structures/maps/hash-maps/primes.js#L33[prime.js] file you can find the implementation for finding the next prime. Also, you can see the full HashMap implementation on this file: https://github.com/amejiarosario/dsa.js/blob/f69b744a1bddd3d99243ca64b3ad46f3f2dd7342/src/data-structures/maps/hash-maps/hash-map.js#L1[hashmap.js]
 
-== HashMap time complexity
+===== HashMap time complexity
 
-Hash Map it’s very optimal for searching values by key in constant time *O(1)*. However, searching by value is not any better than an array since we have to visit every value *O(n)*.
+Hash Map is optimal for searching values by key in constant time *O(1)*. However, searching by value is not any better than an array since we have to visit every value *O(n)*.
+(((Tables, Non-Linear DS, HashMap complexities)))
 
+// tag::table[]
 .Time complexity for a Hash Map
 |===
 .2+.^s| Data Structure 2+^s| Searching By .2+^.^s| Insert .2+^.^s| Delete .2+^.^s| Space Complexity
 ^|_Index/Key_ ^|_Value_
 | Hash Map (naïve) ^|O(n) ^|O(n) ^|O(n) ^|O(n) ^|O(n)
-| Hash Map (optimized) ^|O(1)* ^|O(n) ^|O(1)* ^|O(1)* ^|O(1)*
+| Hash Map (optimized) ^|O(1) ^|O(n) ^|O(1)* ^|O(1) ^|O(n)
 |===
 {empty}* = Amortized run time. E.g. rehashing might affect run time.
+// end::table[]
 
 indexterm:[Runtime, Linear]
-As you can notice we have amortized times since, in the unfortunate case of a rehash, it will take O(n) while it resizes. After that, it will be *O(1)*.
+As you can notice, we have amortized times since it will take O(n) while it resizes in the unfortunate case of a rehash. After that, it will be *O(1)*.
diff --git a/book/deprecated/old-set.asc b/book/deprecated/old-set.asc
new file mode 100644
index 00000000..f07a72c8
--- /dev/null
+++ b/book/deprecated/old-set.asc
@@ -0,0 +1,73 @@
+ifndef::imagesdir[]
+:imagesdir: ../../images
+:codedir: ../../../src
+endif::[]
+
+[[hashset]]
+==== Hash Set Implementation
+(((HashSet)))
+(((Data Structures, Non-Linear, HashSet)))
+The *HashSet* is the set implementation using a HashMap as its underlying data structure.
+
+The HashSet interface will be the same as the built-in `Set` or our previously implemented `TreeSet`.
+
+.HashSet's constructor method and size attribute
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/hash-set.js[tag=constructor]
+}
+----
+
+This constructor is useful for converting an array to set and initializing the `HashMap`.
+
+===== Inserting values to a HashSet
+
+To insert items in a HashSet, we use the `set` method of the `HashMap`:
+
+.HashSet's `add` method
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/hash-set.js[tag=add, indent=0]
+}
+----
+
+`HashMap` stores key/value pairs, but we only need the keys for Set, so we ignore the value.
+
+===== Finding values in a HashSet
+
+We use the method `has` to check if a value is on the `Set` or not.
+
+.HashSet's `has` method
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/hash-set.js[tag=has, indent=0]
+----
+
+Internally, the `HashMap` will convert the key into an array index using a hash function. If there’s something in the array index bucket, it will return
+true, and if it’s empty, it will be false.
+
+===== Deleting values from a HashSet
+
+For deleting a value from a hashSet, we use the HashMap’s delete method:
+
+.HashSet's `delete` method
+[source, javascript]
+----
+include::{codedir}/data-structures/sets/hash-set.js[tag=delete, indent=0]
+----
+
+This method has an average runtime of *O(1)*.
+
+==== HashSet vs HashMap Time Complexity
+
+We can say that `HashMap` in on average, more performant O(1) vs. O(log n). However, if a
+rehash happens, it will take *O(n)* instead of *O(1)*. A `TreeSet` is always *O(log n)*.
+
+
+
+To recap, HashSet and TreeSet will keep data without duplicates. The
+difference besides runtime is that:
+
+.TreeSet vs HashSet
+* HashSet keeps data in insertion order
+* TreeSet keeps data sorted in ascending order.
diff --git a/book/images/Find largest sum.svg b/book/images/Find largest sum.svg
new file mode 100644
index 00000000..595fe27a
--- /dev/null
+++ b/book/images/Find largest sum.svg	
@@ -0,0 +1,86 @@
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+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
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+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 206)">
+<title>G</title>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-206 274,-206 274,4 -4,4"/>
+<text text-anchor="middle" x="135" y="-6.8" font-family="Times,serif" font-size="14.00" fill="#000000">Optimal vs. Greedy path</text>
+<!-- 5 -->
+<g id="node1" class="node">
+<title>5</title>
+<ellipse fill="none" stroke="#000000" cx="135" cy="-184" rx="27" ry="18"/>
+<text text-anchor="middle" x="135" y="-179.8" font-family="Times,serif" font-size="14.00" fill="#000000">5</text>
+</g>
+<!-- 3 -->
+<g id="node2" class="node">
+<title>3</title>
+<ellipse fill="none" stroke="#000000" cx="99" cy="-112" rx="27" ry="18"/>
+<text text-anchor="middle" x="99" y="-107.8" font-family="Times,serif" font-size="14.00" fill="#000000">3</text>
+</g>
+<!-- 5&#45;&#45;3 -->
+<g id="edge1" class="edge">
+<title>5&#45;&#45;3</title>
+<path fill="none" stroke="#b8e986" stroke-width="2" d="M126.2854,-166.5708C120.6253,-155.2506 113.2784,-140.5568 107.6343,-129.2687"/>
+</g>
+<!-- 7 -->
+<g id="node3" class="node">
+<title>7</title>
+<ellipse fill="none" stroke="#000000" cx="171" cy="-112" rx="27" ry="18"/>
+<text text-anchor="middle" x="171" y="-107.8" font-family="Times,serif" font-size="14.00" fill="#000000">7</text>
+</g>
+<!-- 5&#45;&#45;7 -->
+<g id="edge2" class="edge">
+<title>5&#45;&#45;7</title>
+<path fill="none" stroke="#ff5252" stroke-width="2" d="M143.7146,-166.5708C149.3747,-155.2506 156.7216,-140.5568 162.3657,-129.2687"/>
+</g>
+<!-- 87 -->
+<g id="node4" class="node">
+<title>87</title>
+<ellipse fill="none" stroke="#000000" cx="27" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="27" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">87</text>
+</g>
+<!-- 3&#45;&#45;87 -->
+<g id="edge3" class="edge">
+<title>3&#45;&#45;87</title>
+<path fill="none" stroke="#b8e986" stroke-width="2" d="M83.7307,-96.7307C71.512,-84.512 54.4602,-67.4602 42.2473,-55.2473"/>
+</g>
+<!-- 1 -->
+<g id="node5" class="node">
+<title>1</title>
+<ellipse fill="none" stroke="#000000" cx="99" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="99" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">1</text>
+</g>
+<!-- 3&#45;&#45;1 -->
+<g id="edge4" class="edge">
+<title>3&#45;&#45;1</title>
+<path fill="none" stroke="#000000" d="M99,-93.8314C99,-83 99,-69.2876 99,-58.4133"/>
+</g>
+<!-- 2 -->
+<g id="node6" class="node">
+<title>2</title>
+<ellipse fill="none" stroke="#000000" cx="171" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="171" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">2</text>
+</g>
+<!-- 7&#45;&#45;2 -->
+<g id="edge5" class="edge">
+<title>7&#45;&#45;2</title>
+<path fill="none" stroke="#000000" d="M171,-93.8314C171,-83 171,-69.2876 171,-58.4133"/>
+</g>
+<!-- 4 -->
+<g id="node7" class="node">
+<title>4</title>
+<ellipse fill="none" stroke="#000000" cx="243" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="243" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">4</text>
+</g>
+<!-- 7&#45;&#45;4 -->
+<g id="edge6" class="edge">
+<title>7&#45;&#45;4</title>
+<path fill="none" stroke="#ff5252" stroke-width="2" d="M186.2693,-96.7307C198.488,-84.512 215.5398,-67.4602 227.7527,-55.2473"/>
+</g>
+</g>
+</svg>
diff --git a/book/images/Find the largest sum.svg b/book/images/Find the largest sum.svg
new file mode 100644
index 00000000..595fe27a
--- /dev/null
+++ b/book/images/Find the largest sum.svg	
@@ -0,0 +1,86 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.40.1 (20161225.0304)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="278pt" height="210pt"
+ viewBox="0.00 0.00 278.00 210.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 206)">
+<title>G</title>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-206 274,-206 274,4 -4,4"/>
+<text text-anchor="middle" x="135" y="-6.8" font-family="Times,serif" font-size="14.00" fill="#000000">Optimal vs. Greedy path</text>
+<!-- 5 -->
+<g id="node1" class="node">
+<title>5</title>
+<ellipse fill="none" stroke="#000000" cx="135" cy="-184" rx="27" ry="18"/>
+<text text-anchor="middle" x="135" y="-179.8" font-family="Times,serif" font-size="14.00" fill="#000000">5</text>
+</g>
+<!-- 3 -->
+<g id="node2" class="node">
+<title>3</title>
+<ellipse fill="none" stroke="#000000" cx="99" cy="-112" rx="27" ry="18"/>
+<text text-anchor="middle" x="99" y="-107.8" font-family="Times,serif" font-size="14.00" fill="#000000">3</text>
+</g>
+<!-- 5&#45;&#45;3 -->
+<g id="edge1" class="edge">
+<title>5&#45;&#45;3</title>
+<path fill="none" stroke="#b8e986" stroke-width="2" d="M126.2854,-166.5708C120.6253,-155.2506 113.2784,-140.5568 107.6343,-129.2687"/>
+</g>
+<!-- 7 -->
+<g id="node3" class="node">
+<title>7</title>
+<ellipse fill="none" stroke="#000000" cx="171" cy="-112" rx="27" ry="18"/>
+<text text-anchor="middle" x="171" y="-107.8" font-family="Times,serif" font-size="14.00" fill="#000000">7</text>
+</g>
+<!-- 5&#45;&#45;7 -->
+<g id="edge2" class="edge">
+<title>5&#45;&#45;7</title>
+<path fill="none" stroke="#ff5252" stroke-width="2" d="M143.7146,-166.5708C149.3747,-155.2506 156.7216,-140.5568 162.3657,-129.2687"/>
+</g>
+<!-- 87 -->
+<g id="node4" class="node">
+<title>87</title>
+<ellipse fill="none" stroke="#000000" cx="27" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="27" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">87</text>
+</g>
+<!-- 3&#45;&#45;87 -->
+<g id="edge3" class="edge">
+<title>3&#45;&#45;87</title>
+<path fill="none" stroke="#b8e986" stroke-width="2" d="M83.7307,-96.7307C71.512,-84.512 54.4602,-67.4602 42.2473,-55.2473"/>
+</g>
+<!-- 1 -->
+<g id="node5" class="node">
+<title>1</title>
+<ellipse fill="none" stroke="#000000" cx="99" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="99" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">1</text>
+</g>
+<!-- 3&#45;&#45;1 -->
+<g id="edge4" class="edge">
+<title>3&#45;&#45;1</title>
+<path fill="none" stroke="#000000" d="M99,-93.8314C99,-83 99,-69.2876 99,-58.4133"/>
+</g>
+<!-- 2 -->
+<g id="node6" class="node">
+<title>2</title>
+<ellipse fill="none" stroke="#000000" cx="171" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="171" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">2</text>
+</g>
+<!-- 7&#45;&#45;2 -->
+<g id="edge5" class="edge">
+<title>7&#45;&#45;2</title>
+<path fill="none" stroke="#000000" d="M171,-93.8314C171,-83 171,-69.2876 171,-58.4133"/>
+</g>
+<!-- 4 -->
+<g id="node7" class="node">
+<title>4</title>
+<ellipse fill="none" stroke="#000000" cx="243" cy="-40" rx="27" ry="18"/>
+<text text-anchor="middle" x="243" y="-35.8" font-family="Times,serif" font-size="14.00" fill="#000000">4</text>
+</g>
+<!-- 7&#45;&#45;4 -->
+<g id="edge6" class="edge">
+<title>7&#45;&#45;4</title>
+<path fill="none" stroke="#ff5252" stroke-width="2" d="M186.2693,-96.7307C198.488,-84.512 215.5398,-67.4602 227.7527,-55.2473"/>
+</g>
+</g>
+</svg>
diff --git a/book/images/Find-the-largest-sum.png b/book/images/Find-the-largest-sum.png
new file mode 100644
index 00000000..2131f997
Binary files /dev/null and b/book/images/Find-the-largest-sum.png differ
diff --git a/book/images/Recursive Fibonacci call tree with dp (sample).svg b/book/images/Recursive Fibonacci call tree with dp (sample).svg
new file mode 100644
index 00000000..43875cdc
--- /dev/null
+++ b/book/images/Recursive Fibonacci call tree with dp (sample).svg	
@@ -0,0 +1,74 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.40.1 (20161225.0304)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="146pt" height="332pt"
+ viewBox="0.00 0.00 145.52 332.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 328)">
+<title>G</title>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-328 141.517,-328 141.517,4 -4,4"/>
+<!-- fib(5) -->
+<g id="node1" class="node">
+<title>fib(5)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-306" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-301.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(5)</text>
+</g>
+<!-- fib(4) -->
+<g id="node2" class="node">
+<title>fib(4)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-234" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-229.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(4)</text>
+</g>
+<!-- fib(5)&#45;&#45;fib(4) -->
+<g id="edge1" class="edge">
+<title>fib(5)&#45;&#45;fib(4)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-287.8314C68.7585,-277 68.7585,-263.2876 68.7585,-252.4133"/>
+</g>
+<!-- fib(3) -->
+<g id="node3" class="node">
+<title>fib(3)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-162" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-157.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(3)</text>
+</g>
+<!-- fib(4)&#45;&#45;fib(3) -->
+<g id="edge2" class="edge">
+<title>fib(4)&#45;&#45;fib(3)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-215.8314C68.7585,-205 68.7585,-191.2876 68.7585,-180.4133"/>
+</g>
+<!-- fib(2) -->
+<g id="node4" class="node">
+<title>fib(2)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-90" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-85.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(2)</text>
+</g>
+<!-- fib(3)&#45;&#45;fib(2) -->
+<g id="edge3" class="edge">
+<title>fib(3)&#45;&#45;fib(2)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-143.8314C68.7585,-133 68.7585,-119.2876 68.7585,-108.4133"/>
+</g>
+<!-- fib(1) -->
+<g id="node5" class="node">
+<title>fib(1)</title>
+<ellipse fill="none" stroke="#000000" cx="29.7585" cy="-18" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="29.7585" y="-13.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(1)</text>
+</g>
+<!-- fib(2)&#45;&#45;fib(1) -->
+<g id="edge4" class="edge">
+<title>fib(2)&#45;&#45;fib(1)</title>
+<path fill="none" stroke="#000000" d="M59.3177,-72.5708C53.1859,-61.2506 45.2267,-46.5568 39.1124,-35.2687"/>
+</g>
+<!-- fib(0) -->
+<g id="node6" class="node">
+<title>fib(0)</title>
+<ellipse fill="none" stroke="#000000" cx="107.7585" cy="-18" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="107.7585" y="-13.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(0)</text>
+</g>
+<!-- fib(2)&#45;&#45;fib(0) -->
+<g id="edge5" class="edge">
+<title>fib(2)&#45;&#45;fib(0)</title>
+<path fill="none" stroke="#000000" d="M78.1993,-72.5708C84.3311,-61.2506 92.2902,-46.5568 98.4046,-35.2687"/>
+</g>
+</g>
+</svg>
diff --git a/book/images/Recursive Fibonacci call tree with dp.svg b/book/images/Recursive Fibonacci call tree with dp.svg
new file mode 100644
index 00000000..43875cdc
--- /dev/null
+++ b/book/images/Recursive Fibonacci call tree with dp.svg	
@@ -0,0 +1,74 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.40.1 (20161225.0304)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="146pt" height="332pt"
+ viewBox="0.00 0.00 145.52 332.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 328)">
+<title>G</title>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-328 141.517,-328 141.517,4 -4,4"/>
+<!-- fib(5) -->
+<g id="node1" class="node">
+<title>fib(5)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-306" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-301.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(5)</text>
+</g>
+<!-- fib(4) -->
+<g id="node2" class="node">
+<title>fib(4)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-234" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-229.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(4)</text>
+</g>
+<!-- fib(5)&#45;&#45;fib(4) -->
+<g id="edge1" class="edge">
+<title>fib(5)&#45;&#45;fib(4)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-287.8314C68.7585,-277 68.7585,-263.2876 68.7585,-252.4133"/>
+</g>
+<!-- fib(3) -->
+<g id="node3" class="node">
+<title>fib(3)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-162" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-157.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(3)</text>
+</g>
+<!-- fib(4)&#45;&#45;fib(3) -->
+<g id="edge2" class="edge">
+<title>fib(4)&#45;&#45;fib(3)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-215.8314C68.7585,-205 68.7585,-191.2876 68.7585,-180.4133"/>
+</g>
+<!-- fib(2) -->
+<g id="node4" class="node">
+<title>fib(2)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-90" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-85.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(2)</text>
+</g>
+<!-- fib(3)&#45;&#45;fib(2) -->
+<g id="edge3" class="edge">
+<title>fib(3)&#45;&#45;fib(2)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-143.8314C68.7585,-133 68.7585,-119.2876 68.7585,-108.4133"/>
+</g>
+<!-- fib(1) -->
+<g id="node5" class="node">
+<title>fib(1)</title>
+<ellipse fill="none" stroke="#000000" cx="29.7585" cy="-18" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="29.7585" y="-13.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(1)</text>
+</g>
+<!-- fib(2)&#45;&#45;fib(1) -->
+<g id="edge4" class="edge">
+<title>fib(2)&#45;&#45;fib(1)</title>
+<path fill="none" stroke="#000000" d="M59.3177,-72.5708C53.1859,-61.2506 45.2267,-46.5568 39.1124,-35.2687"/>
+</g>
+<!-- fib(0) -->
+<g id="node6" class="node">
+<title>fib(0)</title>
+<ellipse fill="none" stroke="#000000" cx="107.7585" cy="-18" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="107.7585" y="-13.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(0)</text>
+</g>
+<!-- fib(2)&#45;&#45;fib(0) -->
+<g id="edge5" class="edge">
+<title>fib(2)&#45;&#45;fib(0)</title>
+<path fill="none" stroke="#000000" d="M78.1993,-72.5708C84.3311,-61.2506 92.2902,-46.5568 98.4046,-35.2687"/>
+</g>
+</g>
+</svg>
diff --git a/book/images/Recursive fibonacci call tree.png b/book/images/Recursive fibonacci call tree.png
new file mode 100644
index 00000000..9e18a2ab
Binary files /dev/null and b/book/images/Recursive fibonacci call tree.png differ
diff --git a/book/images/Recursive fibonacci call tree.svg b/book/images/Recursive fibonacci call tree.svg
new file mode 100644
index 00000000..43875cdc
--- /dev/null
+++ b/book/images/Recursive fibonacci call tree.svg	
@@ -0,0 +1,74 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.40.1 (20161225.0304)
+ -->
+<!-- Title: G Pages: 1 -->
+<svg width="146pt" height="332pt"
+ viewBox="0.00 0.00 145.52 332.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 328)">
+<title>G</title>
+<polygon fill="#ffffff" stroke="transparent" points="-4,4 -4,-328 141.517,-328 141.517,4 -4,4"/>
+<!-- fib(5) -->
+<g id="node1" class="node">
+<title>fib(5)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-306" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-301.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(5)</text>
+</g>
+<!-- fib(4) -->
+<g id="node2" class="node">
+<title>fib(4)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-234" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-229.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(4)</text>
+</g>
+<!-- fib(5)&#45;&#45;fib(4) -->
+<g id="edge1" class="edge">
+<title>fib(5)&#45;&#45;fib(4)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-287.8314C68.7585,-277 68.7585,-263.2876 68.7585,-252.4133"/>
+</g>
+<!-- fib(3) -->
+<g id="node3" class="node">
+<title>fib(3)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-162" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-157.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(3)</text>
+</g>
+<!-- fib(4)&#45;&#45;fib(3) -->
+<g id="edge2" class="edge">
+<title>fib(4)&#45;&#45;fib(3)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-215.8314C68.7585,-205 68.7585,-191.2876 68.7585,-180.4133"/>
+</g>
+<!-- fib(2) -->
+<g id="node4" class="node">
+<title>fib(2)</title>
+<ellipse fill="none" stroke="#000000" cx="68.7585" cy="-90" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="68.7585" y="-85.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(2)</text>
+</g>
+<!-- fib(3)&#45;&#45;fib(2) -->
+<g id="edge3" class="edge">
+<title>fib(3)&#45;&#45;fib(2)</title>
+<path fill="none" stroke="#000000" d="M68.7585,-143.8314C68.7585,-133 68.7585,-119.2876 68.7585,-108.4133"/>
+</g>
+<!-- fib(1) -->
+<g id="node5" class="node">
+<title>fib(1)</title>
+<ellipse fill="none" stroke="#000000" cx="29.7585" cy="-18" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="29.7585" y="-13.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(1)</text>
+</g>
+<!-- fib(2)&#45;&#45;fib(1) -->
+<g id="edge4" class="edge">
+<title>fib(2)&#45;&#45;fib(1)</title>
+<path fill="none" stroke="#000000" d="M59.3177,-72.5708C53.1859,-61.2506 45.2267,-46.5568 39.1124,-35.2687"/>
+</g>
+<!-- fib(0) -->
+<g id="node6" class="node">
+<title>fib(0)</title>
+<ellipse fill="none" stroke="#000000" cx="107.7585" cy="-18" rx="29.5189" ry="18"/>
+<text text-anchor="middle" x="107.7585" y="-13.8" font-family="Times,serif" font-size="14.00" fill="#000000">fib(0)</text>
+</g>
+<!-- fib(2)&#45;&#45;fib(0) -->
+<g id="edge5" class="edge">
+<title>fib(2)&#45;&#45;fib(0)</title>
+<path fill="none" stroke="#000000" d="M78.1993,-72.5708C84.3311,-61.2506 92.2902,-46.5568 98.4046,-35.2687"/>
+</g>
+</g>
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diff --git a/book/images/Recursive-Fibonacci-call-tree-with-dp.png b/book/images/Recursive-Fibonacci-call-tree-with-dp.png
new file mode 100644
index 00000000..98e89c5f
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diff --git a/book/images/Sudoku_solved_by_bactracking.gif b/book/images/Sudoku_solved_by_bactracking.gif
new file mode 100644
index 00000000..cf62c1dd
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diff --git a/book/images/Words Permutations.svg b/book/images/Words Permutations.svg
new file mode 100644
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--- /dev/null
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+<!-- art -->
+<g id="node1" class="node">
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+<text text-anchor="middle" x="295.3002" y="-153.3" font-family="Times,serif" font-size="14.00" fill="#000000">A*</text>
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+<text text-anchor="middle" x="324.5244" y="-153.3" font-family="Times,serif" font-size="14.00" fill="#000000">R</text>
+<polyline fill="none" stroke="#000000" points="337.1933,-146.5 337.1933,-168.5 "/>
+<text text-anchor="middle" x="349.4692" y="-153.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
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+<!-- art1 -->
+<g id="node2" class="node">
+<title>art1</title>
+<polygon fill="none" stroke="#000000" points="67.7451,-73.5 67.7451,-95.5 150.7451,-95.5 150.7451,-73.5 67.7451,-73.5"/>
+<text text-anchor="middle" x="80.8002" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">A</text>
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+<text text-anchor="middle" x="110.0244" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">R*</text>
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+<text text-anchor="middle" x="138.4692" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
+</g>
+<!-- art&#45;&gt;art1 -->
+<g id="edge1" class="edge">
+<title>art:f0&#45;&gt;art1:f0</title>
+<path fill="none" stroke="#000000" d="M278.2451,-157.5C235.7566,-157.5 103.0952,-143.6966 83.6373,-105.2321"/>
+<polygon fill="#000000" stroke="#000000" points="87.031,-104.3754 81.2451,-95.5 80.2334,-106.0464 87.031,-104.3754"/>
+<text text-anchor="middle" x="146.0537" y="-116.8" font-family="Times,serif" font-size="14.00" fill="#000000">1. swap A/A</text>
+</g>
+<!-- rat -->
+<g id="node5" class="node">
+<title>rat</title>
+<polygon fill="none" stroke="#000000" points="305.7451,-73.5 305.7451,-95.5 388.7451,-95.5 388.7451,-73.5 305.7451,-73.5"/>
+<text text-anchor="middle" x="318.414" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">R</text>
+<polyline fill="none" stroke="#000000" points="331.083,-73.5 331.083,-95.5 "/>
+<text text-anchor="middle" x="347.6381" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">A*</text>
+<polyline fill="none" stroke="#000000" points="364.1933,-73.5 364.1933,-95.5 "/>
+<text text-anchor="middle" x="376.4692" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
+</g>
+<!-- art&#45;&gt;rat -->
+<g id="edge7" class="edge">
+<title>art:f1&#45;&gt;rat:f0</title>
+<path fill="none" stroke="#000000" d="M324.2451,-146.5C324.2451,-130.1318 300.5654,-142.1863 292.4003,-128 289.2965,-122.6072 289.556,-119.534 292.4003,-114 296.6381,-105.7551 306.4442,-106.4479 312.6851,-103.9061"/>
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+<text text-anchor="middle" x="327.6674" y="-116.8" font-family="Times,serif" font-size="14.00" fill="#000000">7. swap A/R</text>
+</g>
+<!-- tra -->
+<g id="node8" class="node">
+<title>tra</title>
+<polygon fill="none" stroke="#000000" points="525.7451,-73.5 525.7451,-95.5 608.7451,-95.5 608.7451,-73.5 525.7451,-73.5"/>
+<text text-anchor="middle" x="538.0209" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
+<polyline fill="none" stroke="#000000" points="550.2968,-73.5 550.2968,-95.5 "/>
+<text text-anchor="middle" x="566.4658" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">R*</text>
+<polyline fill="none" stroke="#000000" points="582.6347,-73.5 582.6347,-95.5 "/>
+<text text-anchor="middle" x="595.6899" y="-80.3" font-family="Times,serif" font-size="14.00" fill="#000000">A</text>
+</g>
+<!-- art&#45;&gt;tra -->
+<g id="edge13" class="edge">
+<title>art:f2&#45;&gt;tra:f0</title>
+<path fill="none" stroke="#000000" d="M362.2451,-157.5C438.0571,-157.5 445.3343,-90.9113 514.9889,-84.9287"/>
+<polygon fill="#000000" stroke="#000000" points="515.4,-88.4147 525.2451,-84.5 515.1076,-81.4208 515.4,-88.4147"/>
+<text text-anchor="middle" x="489.7744" y="-116.8" font-family="Times,serif" font-size="14.00" fill="#000000">13. swap A/T</text>
+</g>
+<!-- art1&#45;&gt;art -->
+<g id="edge6" class="edge">
+<title>art1:f1&#45;&gt;art:f0</title>
+<path fill="none" stroke="#c0c0c0" d="M110.2451,-95.5C110.2451,-129.8324 152.8743,-102.5611 185.2451,-114 224.7399,-127.9563 231.6406,-152.9457 267.8048,-156.9516"/>
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+<text text-anchor="middle" x="218.4951" y="-116.8" font-family="Times,serif" font-size="14.00" fill="#c0c0c0">6.</text>
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+<!-- art2 -->
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+<title>art2</title>
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+<!-- art1&#45;&gt;art2 -->
+<g id="edge2" class="edge">
+<title>art1:f0&#45;&gt;art2:f0</title>
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+<!-- atr -->
+<g id="node4" class="node">
+<title>atr</title>
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+<text text-anchor="middle" x="139.3002" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">A</text>
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+<text text-anchor="middle" x="164.6313" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
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+<text text-anchor="middle" x="189.5761" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">R</text>
+</g>
+<!-- art1&#45;&gt;atr -->
+<g id="edge4" class="edge">
+<title>art1:f2&#45;&gt;atr:f0</title>
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+</g>
+<!-- art2&#45;&gt;art1 -->
+<g id="edge3" class="edge">
+<title>art2:f2&#45;&gt;art1:f1</title>
+<path fill="none" stroke="#c0c0c0" d="M80.2451,-22.5C80.2451,-38.3169 86.3632,-41.5867 94.7451,-55 97.9425,-60.1169 102.4472,-61.9368 105.7767,-64.4589"/>
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+</g>
+<!-- atr&#45;&gt;art1 -->
+<g id="edge5" class="edge">
+<title>atr:f2&#45;&gt;art1:f2</title>
+<path fill="none" stroke="#c0c0c0" d="M189.2451,-22.5C189.2451,-37.2757 203.2674,-41.9997 196.2451,-55 186.6553,-72.7533 178.2372,-81.5494 161.4758,-83.8647"/>
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+<text text-anchor="middle" x="203.4951" y="-43.8" font-family="Times,serif" font-size="14.00" fill="#c0c0c0">5.</text>
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+<!-- rat&#45;&gt;art -->
+<g id="edge12" class="edge">
+<title>rat:f2&#45;&gt;art:f2</title>
+<path fill="none" stroke="#c0c0c0" d="M376.2451,-95.5C376.2451,-111.4087 368.9267,-114.0688 361.2451,-128 358.7311,-132.5593 355.5015,-134.7296 353.0261,-137.2318"/>
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+<!-- rat1 -->
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+<title>rat1</title>
+<polygon fill="none" stroke="#ff0000" points="255.2451,-.5 255.2451,-22.5 331.2451,-22.5 331.2451,-.5 255.2451,-.5"/>
+<text text-anchor="middle" x="267.914" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">R</text>
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+<text text-anchor="middle" x="318.9692" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
+</g>
+<!-- rat&#45;&gt;rat1 -->
+<g id="edge8" class="edge">
+<title>rat:f0&#45;&gt;rat1:f0</title>
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+<!-- rta -->
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+<title>rta</title>
+<polygon fill="none" stroke="#ff0000" points="388.2451,-.5 388.2451,-22.5 464.2451,-22.5 464.2451,-.5 388.2451,-.5"/>
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+<polyline fill="none" stroke="#ff0000" points="438.1347,-.5 438.1347,-22.5 "/>
+<text text-anchor="middle" x="451.1899" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">A</text>
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+<!-- rat&#45;&gt;rta -->
+<g id="edge10" class="edge">
+<title>rat:f2&#45;&gt;rta:f0</title>
+<path fill="none" stroke="#000000" d="M376.2451,-73.5C376.2451,-58.9497 373.3987,-53.87 380.1865,-41 383.728,-34.2851 390.7731,-33.5104 395.7368,-31.1107"/>
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+<!-- rat1&#45;&gt;rat -->
+<g id="edge9" class="edge">
+<title>rat1:f2&#45;&gt;rat:f1</title>
+<path fill="none" stroke="#c0c0c0" d="M319.2451,-22.5C319.2451,-44.5198 339.5495,-47.269 345.5774,-63.5678"/>
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+<!-- rta&#45;&gt;rat -->
+<g id="edge11" class="edge">
+<title>rta:f2&#45;&gt;rat:f2</title>
+<path fill="none" stroke="#c0c0c0" d="M451.2451,-22.5C451.2451,-37.0534 463.1626,-42.7887 455.2451,-55 439.6772,-79.0106 424.9736,-83.6521 399.2449,-84.3802"/>
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+<!-- tra&#45;&gt;art -->
+<g id="edge18" class="edge">
+<title>tra:f2&#45;&gt;art:f2</title>
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+<!-- tra1 -->
+<g id="node9" class="node">
+<title>tra1</title>
+<polygon fill="none" stroke="#ff0000" points="528.2451,-.5 528.2451,-22.5 604.2451,-22.5 604.2451,-.5 528.2451,-.5"/>
+<text text-anchor="middle" x="540.5209" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
+<polyline fill="none" stroke="#ff0000" points="552.7968,-.5 552.7968,-22.5 "/>
+<text text-anchor="middle" x="565.4658" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">R</text>
+<polyline fill="none" stroke="#ff0000" points="578.1347,-.5 578.1347,-22.5 "/>
+<text text-anchor="middle" x="591.1899" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">A</text>
+</g>
+<!-- tra&#45;&gt;tra1 -->
+<g id="edge14" class="edge">
+<title>tra:f0&#45;&gt;tra1:f0</title>
+<path fill="none" stroke="#000000" d="M538.2451,-73.5C538.2451,-61.4442 523.2143,-66.0559 518.4072,-55 515.9261,-49.2938 515.7908,-46.6454 518.4072,-41 521.7501,-33.7868 529.4448,-33.3324 534.7457,-30.9004"/>
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+<text text-anchor="middle" x="557.164" y="-43.8" font-family="Times,serif" font-size="14.00" fill="#000000">14. swap R/R</text>
+</g>
+<!-- tar -->
+<g id="node10" class="node">
+<title>tar</title>
+<polygon fill="none" stroke="#ff0000" points="669.2451,-.5 669.2451,-22.5 745.2451,-22.5 745.2451,-.5 669.2451,-.5"/>
+<text text-anchor="middle" x="681.5209" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">T</text>
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+<text text-anchor="middle" x="706.852" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">A</text>
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+<text text-anchor="middle" x="732.5761" y="-7.3" font-family="Times,serif" font-size="14.00" fill="#000000">R</text>
+</g>
+<!-- tra&#45;&gt;tar -->
+<g id="edge16" class="edge">
+<title>tra:f2&#45;&gt;tar:f0</title>
+<path fill="none" stroke="#000000" d="M609.2451,-84.5C638.4961,-84.5 636.3362,-59.9314 658.6347,-41 664.9756,-35.6166 672.4716,-34.867 677.0403,-31.6097"/>
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+<text text-anchor="middle" x="697.5502" y="-43.8" font-family="Times,serif" font-size="14.00" fill="#000000">16. swap R/A</text>
+</g>
+<!-- tra1&#45;&gt;tra -->
+<g id="edge15" class="edge">
+<title>tra1:f2&#45;&gt;tra:f2</title>
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+<g id="edge3" class="edge">
+<title>art2:f2&#45;&gt;art1:f1</title>
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+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="417px" height="310px" version="1.1"><defs/><g transform="translate(0.5,0.5)"><rect x="25" y="127" width="80" height="20" fill="#9ac7bf" stroke="#000000" pointer-events="none"/><g transform="translate(60.5,128.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">a</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">a</text></switch></g><rect x="35" y="145" width="60" height="20" fill="#fff2cc" stroke="#000000" pointer-events="none"/><g transform="translate(60.5,146.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; 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font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">b</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">b</text></switch></g><rect x="25" y="28" width="80" height="20" fill="#9ac7bf" stroke="#000000" pointer-events="none"/><g transform="translate(60.5,29.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">a</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">a</text></switch></g><rect x="110" y="28" width="50" height="20" fill="#fff2cc" stroke="#000000" pointer-events="none"/><g transform="translate(130.5,29.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">b</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">b</text></switch></g><g transform="translate(167.5,30.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="182" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">=&gt; 'a' and 'b' do not overlap</div></div></foreignObject><text x="91" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">=&amp;gt; 'a' and 'b' do not overlap</text></switch></g><g transform="translate(167.5,76.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="233" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">=&gt; 'a' &amp; 'b' overlap, 'b' ends after 'a'</div></div></foreignObject><text x="117" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">=&amp;gt; 'a' &amp;amp; 'b' overlap, 'b' ends after 'a'</text></switch></g><g transform="translate(167.5,125.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="191" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">=&gt; 'a' completely overlaps 'b'</div></div></foreignObject><text x="96" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">=&amp;gt; 'a' completely overlaps 'b'</text></switch></g><g transform="translate(1.5,28.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="13" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; font-weight: bold;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">1)</div></div></foreignObject><text x="7" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica" font-weight="bold">1)</text></switch></g><g transform="translate(1.5,76.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="13" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; font-weight: bold;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">2)</div></div></foreignObject><text x="7" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica" font-weight="bold">2)</text></switch></g><g transform="translate(1.5,129.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="13" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; font-weight: bold;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">3)</div></div></foreignObject><text x="7" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica" font-weight="bold">3)</text></switch></g><path d="M 25 8 L 75 8 Q 85 8 95 8 L 138.63 8" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 143.88 8 L 136.88 11.5 L 138.63 8 L 136.88 4.5 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(70.5,-0.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="28" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;background-color:#ffffff;">time</div></div></foreignObject><text x="14" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">time</text></switch></g><rect x="56" y="180" width="80" height="20" fill="#9ac7bf" stroke="#000000" pointer-events="none"/><g transform="translate(91.5,181.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">a</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">a</text></switch></g><rect x="26" y="196" width="60" height="20" fill="#fff2cc" stroke="#000000" pointer-events="none"/><g transform="translate(51.5,197.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">b</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">b</text></switch></g><g transform="translate(167.5,178.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="233" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">=&gt; 'a' &amp; 'b' overlap, 'a' ends after 'b'</div></div></foreignObject><text x="117" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">=&amp;gt; 'a' &amp;amp; 'b' overlap, 'a' ends after 'b'</text></switch></g><g transform="translate(1.5,182.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="13" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; font-weight: bold;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">4)</div></div></foreignObject><text x="7" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica" font-weight="bold">4)</text></switch></g><rect x="26" y="250" width="60" height="20" fill="#fff2cc" stroke="#000000" pointer-events="none"/><g transform="translate(51.5,251.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">b</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">b</text></switch></g><g transform="translate(167.5,230.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="191" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">=&gt; 'b' completely overlaps 'a'</div></div></foreignObject><text x="96" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">=&amp;gt; 'b' completely overlaps 'a'</text></switch></g><g transform="translate(1.5,234.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="13" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; font-weight: bold;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">5)</div></div></foreignObject><text x="7" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica" font-weight="bold">5)</text></switch></g><rect x="41" y="233" width="30" height="20" fill="#9ac7bf" stroke="#000000" pointer-events="none"/><g transform="translate(51.5,234.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">a</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">a</text></switch></g><rect x="25" y="287" width="60" height="20" fill="#fff2cc" stroke="#000000" pointer-events="none"/><g transform="translate(50.5,288.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">b</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">b</text></switch></g><g transform="translate(168.5,287.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="182" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">=&gt; 'a' and 'b' do not overlap</div></div></foreignObject><text x="91" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">=&amp;gt; 'a' and 'b' do not overlap</text></switch></g><g transform="translate(2.5,291.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="13" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; white-space: nowrap; font-weight: bold;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">6)</div></div></foreignObject><text x="7" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica" font-weight="bold">6)</text></switch></g><rect x="90" y="287" width="30" height="20" fill="#9ac7bf" stroke="#000000" pointer-events="none"/><g transform="translate(100.5,288.5)"><switch><foreignObject style="overflow:visible;" pointer-events="all" width="8" height="16" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: inline-block; font-size: 15px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; vertical-align: top; width: 10px; white-space: nowrap; overflow-wrap: normal; text-align: center;"><div xmlns="http://www.w3.org/1999/xhtml" style="display:inline-block;text-align:inherit;text-decoration:inherit;">a</div></div></foreignObject><text x="4" y="16" fill="#000000" text-anchor="middle" font-size="15px" font-family="Helvetica">a</text></switch></g></g></svg>
\ No newline at end of file
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diff --git a/book/images/time-complexity-manual.png b/book/images/time-complexity-manual.png
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diff --git a/book/images/tree-diameter-example-1.png b/book/images/tree-diameter-example-1.png
new file mode 100644
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diff --git a/book/images/tree-diameter-example-2.png b/book/images/tree-diameter-example-2.png
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diff --git a/book/index.asc b/book/index.asc
new file mode 100644
index 00000000..d07d9fba
--- /dev/null
+++ b/book/index.asc
@@ -0,0 +1,4 @@
+[index]
+== Index [[index]]
+
+ifndef::backend-pdf[Topical index only available on the PDF version.]
diff --git a/book/interview-questions/binary-tree-right-side-view.js b/book/interview-questions/binary-tree-right-side-view.js
new file mode 100644
index 00000000..2e489077
--- /dev/null
+++ b/book/interview-questions/binary-tree-right-side-view.js
@@ -0,0 +1,59 @@
+const { Queue } = require('../../src/index');
+
+// tag::description[]
+/**
+ * Find the rightmost nodes by level.
+ *
+ * @example rightSideView(bt([1,2,3,4])); // [1, 3, 4]
+ *     1      <- 1
+ *   /   \
+ *  2     3   <- 3
+ *   \
+ *    4       <- 4
+ *
+ * @param {BinaryTreeNode} root - The root of the binary tree.
+ * @returns {number[]} - array with the rightmost nodes values.
+ */
+function rightSideView(root) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  if (!root) return [];
+  const queue = new Queue([root]);
+  const ans = [];
+
+  while (queue.size) {
+    const { size } = queue;
+    for (let i = 0; i < size; i++) {
+      const node = queue.dequeue();
+      if (i === size - 1) ans.push(node.value);
+      if (node.left) queue.enqueue(node.left);
+      if (node.right) queue.enqueue(node.right);
+    }
+  }
+
+  return ans;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+// tag::dfs[]
+function rightSideViewDfs(root) {
+  const ans = [];
+
+  const dfs = (node, level = 0) => {
+    if (!node) return;
+    if (level === ans.length) ans.push(node.value);
+    dfs(node.right, level + 1); // right side first!
+    dfs(node.left, level + 1);
+  };
+
+  dfs(root);
+  return ans;
+}
+// end::dfs[]
+
+module.exports = { rightSideView, rightSideViewDfs };
diff --git a/book/interview-questions/binary-tree-right-side-view.spec.js b/book/interview-questions/binary-tree-right-side-view.spec.js
new file mode 100644
index 00000000..59b0417f
--- /dev/null
+++ b/book/interview-questions/binary-tree-right-side-view.spec.js
@@ -0,0 +1,24 @@
+const { rightSideView, rightSideViewDfs } = require('./binary-tree-right-side-view');
+const { BinaryTreeNode } = require('../../src/index');
+
+[rightSideView, rightSideViewDfs].forEach((fn) => {
+  describe(`Binary Tree: ${fn.name}`, () => {
+    it('should work with null', () => {
+      const actual = null;
+      const expected = [];
+      expect(fn(actual)).toEqual(expected);
+    });
+
+    it('should work with small case', () => {
+      const actual = BinaryTreeNode.from([1, 2, 3, null, 4]);
+      const expected = [1, 3, 4];
+      expect(fn(actual)).toEqual(expected);
+    });
+
+    it('should work with other case', () => {
+      const actual = BinaryTreeNode.from([1, 2, 3, null, 5, null, 4, 6]);
+      const expected = [1, 3, 4, 6];
+      expect(fn(actual)).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/buy-sell-stock.js b/book/interview-questions/buy-sell-stock.js
new file mode 100644
index 00000000..b12c11d4
--- /dev/null
+++ b/book/interview-questions/buy-sell-stock.js
@@ -0,0 +1,37 @@
+// tag::description[]
+/**
+ * Find the max profit from buying and selling a stock given their daily prices.
+ * @examples
+ *    maxProfit([5, 10, 5, 10]); // => 5
+ *    maxProfit([1, 2, 3]); // => 2
+ *    maxProfit([3, 2, 1]); // => 0
+ * @param {number[]} prices - Array with daily stock prices
+ * @returns {number} - Max profit
+ */
+function maxProfit(prices) {
+// end::description[]
+// tag::solution[]
+  let max = 0;
+  let local = Infinity;
+  for (let i = 0; i < prices.length; i++) {
+    local = Math.min(local, prices[i]);
+    max = Math.max(max, prices[i] - local);
+  }
+  return max;
+}
+// end::solution[]
+
+// tag::maxProfitBrute1[]
+function maxProfitBrute1(prices) {
+  let max = 0;
+  for (let i = 0; i < prices.length; i++) {
+    for (let j = i + 1; j < prices.length; j++) {
+      max = Math.max(max, prices[j] - prices[i]);
+    }
+  }
+  return max;
+}
+// end::maxProfitBrute1[]
+
+
+module.exports = { maxProfitBrute1, maxProfit };
diff --git a/book/interview-questions/buy-sell-stock.spec.js b/book/interview-questions/buy-sell-stock.spec.js
new file mode 100644
index 00000000..bce80375
--- /dev/null
+++ b/book/interview-questions/buy-sell-stock.spec.js
@@ -0,0 +1,19 @@
+const { maxProfitBrute1, maxProfit } = require('./buy-sell-stock');
+
+describe('Best Time to Buy and Sell Stocks', () => {
+  [maxProfitBrute1, maxProfit].forEach((fn) => {
+    describe(`with ${fn.name}`, () => {
+      it('should work with bullish markets', () => {
+        expect(fn([1, 2, 3])).toEqual(2);
+      });
+
+      it('should work with bearish markets', () => {
+        expect(fn([3, 2, 1])).toEqual(0);
+      });
+
+      it('should work with zig-zag markets', () => {
+        expect(fn([5, 10, 5, 10, 5, 10, 5, 10])).toEqual(5);
+      });
+    });
+  });
+});
diff --git a/book/interview-questions/course-schedule.js b/book/interview-questions/course-schedule.js
new file mode 100644
index 00000000..c3e8b354
--- /dev/null
+++ b/book/interview-questions/course-schedule.js
@@ -0,0 +1,61 @@
+// tag::description[]
+/**
+ * Check if you can finish all courses with their prerequisites.
+ * @param {number} n - The number of courses
+ * @param {[number, number][]} prerequisites - Array of courses pairs.
+ *  E.g. [[200, 101]], to take course 202 you need course 101 first.
+ * @returns {boolean} - True = can finish all courses, False otherwise
+ */
+function canFinish(n, prerequisites) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  const graph = new Map(Array(n).fill().map((_, i) => ([i, []])));
+  prerequisites.forEach(([u, v]) => graph.get(v).push(u));
+
+  const seen = [];
+  const hasCycle = (node) => {
+    if (seen[node] === 1) return true; // if visiting, it's a cycle!
+    if (seen[node] === 2) return false; // if visited, skip it.
+
+    seen[node] = 1; // mark as visiting.
+    for (const adj of graph.get(node)) if (hasCycle(adj)) return true;
+    seen[node] = 2; // mark as visited.
+    return false;
+  };
+
+  for (let i = 0; i < n; i++) if (hasCycle(i)) return false;
+  return true;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+
+// tag::brute1[]
+function canFinishBrute1(n, prerequisites) {
+  const graph = new Map(); // inialize adjacency list as map of arrays
+  for (let i = 0; i < n; i++) graph.set(i, []); // build nodes
+  prerequisites.forEach(([u, v]) => graph.get(v).push(u)); // edges
+
+  const hasCycles = (node, parent = node, seen = []) => {
+    for (const next of graph.get(node)) {
+      if (next === parent) return true;
+      if (seen[next]) continue;
+      seen[next] = true;
+      if (hasCycles(next, parent, seen)) return true;
+    }
+    return false;
+  };
+
+  for (let i = 0; i < n; i++) {
+    if (hasCycles(i)) return false;
+  }
+
+  return true;
+}
+// end::brute1[]
+
+module.exports = { canFinish, canFinishBrute1 };
diff --git a/book/interview-questions/course-schedule.spec.js b/book/interview-questions/course-schedule.spec.js
new file mode 100644
index 00000000..61bb9a5f
--- /dev/null
+++ b/book/interview-questions/course-schedule.spec.js
@@ -0,0 +1,54 @@
+const { canFinish, canFinishBrute1 } = require('./course-schedule');
+// const {  } = require('../../src/index');
+
+[canFinish, canFinishBrute1].forEach((fn) => {
+  describe(`TOPIC: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = [];
+      const expected = true;
+      expect(fn(0, actual)).toEqual(expected);
+    });
+
+    it('should work basic case', () => {
+      const actual = [[1, 0]];
+      const courses = 2;
+      const expected = true;
+      expect(fn(courses, actual)).toEqual(expected);
+    });
+
+    it('should detect cycle', () => {
+      const actual = [[0, 1], [1, 0]];
+      const courses = 2;
+      const expected = false;
+      expect(fn(courses, actual)).toEqual(expected);
+    });
+
+    it('multiple links to a node without cycle', () => {
+      const actual = [[2, 1], [1, 0], [2, 0]];
+      const courses = 3;
+      const expected = true;
+      expect(fn(courses, actual)).toEqual(expected);
+    });
+
+    it('multiple links to a node without cycle (different order)', () => {
+      const actual = [[2, 0], [1, 0], [2, 1]];
+      const courses = 3;
+      const expected = true;
+      expect(fn(courses, actual)).toEqual(expected);
+    });
+
+    it('indirect cycle', () => {
+      const actual = [[1, 0], [2, 1], [0, 2]];
+      const courses = 3;
+      const expected = false;
+      expect(fn(courses, actual)).toEqual(expected);
+    });
+
+    it('indirect cycle with nodes without indegrees', () => {
+      const actual = [[1, 0], [2, 1], [3, 2], [1, 3]];
+      const courses = 4;
+      const expected = false;
+      expect(fn(courses, actual)).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/critical-connections-in-a-network.js b/book/interview-questions/critical-connections-in-a-network.js
new file mode 100644
index 00000000..bd7bfe37
--- /dev/null
+++ b/book/interview-questions/critical-connections-in-a-network.js
@@ -0,0 +1,76 @@
+const { Queue } = require('../../src/index');
+
+// tag::description[]
+function criticalConnections(n, connections) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  const critical = [];
+  const graph = new Map(Array(n).fill(0).map((_, i) => [i, []]));
+  connections.forEach(([u, v]) => {
+    graph.get(u).push(v);
+    graph.get(v).push(u);
+  });
+
+  const dfs = (node, parent = null, depth = 0, group = []) => {
+    group[node] = depth;
+    for (const adj of (graph.get(node) || [])) {
+      if (adj === parent) continue; // skip parent node
+      if (group[adj] === undefined) dfs(adj, node, depth + 1, group);
+      group[node] = Math.min(group[node], group[adj]); // update group.
+      if (group[adj] >= depth + 1) critical.push([node, adj]);
+    }
+  };
+
+  dfs(0);
+  return critical;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+// tag::criticalConnectionsBrute1[]
+function areAllNodesReachable(n, graph) {
+  const seen = Array(n).fill(false);
+  const queue = new Queue([0]);
+
+  while (queue.size) {
+    const node = queue.dequeue();
+    if (seen[node]) continue;
+    seen[node] = true;
+
+    for (const adj of (graph.get(node) || [])) {
+      queue.enqueue(adj);
+    }
+  }
+
+  return !seen.some((s) => !s);
+}
+
+function criticalConnectionsBrute1(n, connections) {
+  const critical = [];
+  const graph = new Map(Array(n).fill(0).map((_, i) => [i, []]));
+  connections.forEach(([u, v]) => {
+    graph.get(u).push(v);
+    graph.get(v).push(u);
+  });
+
+  for (const [u, v] of connections) {
+    // remove edge
+    graph.set(u, (graph.get(u) || []).filter((e) => e !== v));
+    graph.set(v, (graph.get(v) || []).filter((e) => e !== u));
+
+    if (!areAllNodesReachable(n, graph)) critical.push([u, v]);
+
+    // add it back
+    graph.get(u).push(v);
+    graph.get(v).push(u);
+  }
+
+  return critical;
+}
+// end::criticalConnectionsBrute1[]
+
+module.exports = { criticalConnections, criticalConnectionsBrute1 };
diff --git a/book/interview-questions/critical-connections-in-a-network.spec.js b/book/interview-questions/critical-connections-in-a-network.spec.js
new file mode 100644
index 00000000..003374d7
--- /dev/null
+++ b/book/interview-questions/critical-connections-in-a-network.spec.js
@@ -0,0 +1,37 @@
+const { criticalConnections, criticalConnectionsBrute1 } = require('./critical-connections-in-a-network');
+// const {  } = require('../../src/index');
+
+[criticalConnections, criticalConnectionsBrute1].forEach((fn) => {
+  describe(`Graph: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = fn(0, []);
+      const expected = [];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with critical path', () => {
+      const actual = fn(4, [[0, 1], [1, 2], [2, 0], [1, 3]]);
+      const expected = [[1, 3]];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work without critical path', () => {
+      const actual = fn(4, [[0, 1], [1, 2], [2, 0], [1, 3], [3, 2]]);
+      const expected = [];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with other case', () => {
+      const actual = fn(3, [[0, 1], [1, 2]]);
+      const expected = [[0, 1], [1, 2]];
+      expect(actual).toEqual(expect.arrayContaining(expected));
+    });
+
+
+    it('should work with 2 SCC', () => {
+      const actual = fn(6, [[0, 1], [1, 2], [2, 0], [1, 3], [3, 4], [4, 5], [5, 3]]);
+      const expected = [[1, 3]];
+      expect(actual).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/daily-temperatures.js b/book/interview-questions/daily-temperatures.js
new file mode 100644
index 00000000..82f01cf5
--- /dev/null
+++ b/book/interview-questions/daily-temperatures.js
@@ -0,0 +1,49 @@
+// tag::description[]
+/**
+ * Given an array with daily temperatures (30 °C to 100 °C),
+ * return an array with the days count until a warmer temperature
+ * for each elem from the input.
+ *
+ * @examples
+ *  dailyTemperatures([30, 28, 50, 40, 30]); // [2, 1, 0, 0, 0]
+ *  dailyTemperatures([73, 69, 72, 76, 73]); // [3, 1, 1, 0, 0]
+ *
+ * @param {number[]} t - Daily temperatures
+ * @returns {number[]} - Array with count of days to warmer temp.
+ */
+function dailyTemperatures(t) {
+  // end::description[]
+  // tag::solution[]
+  const last = (arr) => arr[arr.length - 1];
+  const stack = [];
+  const ans = [];
+
+  for (let i = t.length - 1; i >= 0; i--) {
+    while (stack.length && t[i] >= t[last(stack)]) stack.pop();
+    ans[i] = stack.length ? last(stack) - i : 0;
+    stack.push(i);
+  }
+
+  return ans;
+}
+// end::solution[]
+
+// tag::dailyTemperaturesBrute1[]
+function dailyTemperaturesBrute1(t) {
+  const ans = [];
+
+  for (let i = 0; i < t.length; i++) {
+    ans[i] = 0;
+    for (let j = i + 1; j < t.length; j++) {
+      if (t[j] > t[i]) {
+        ans[i] = j - i;
+        break;
+      }
+    }
+  }
+
+  return ans;
+}
+// end::dailyTemperaturesBrute1[]
+
+module.exports = { dailyTemperatures, dailyTemperaturesBrute1 };
diff --git a/book/interview-questions/daily-temperatures.spec.js b/book/interview-questions/daily-temperatures.spec.js
new file mode 100644
index 00000000..7d5de523
--- /dev/null
+++ b/book/interview-questions/daily-temperatures.spec.js
@@ -0,0 +1,22 @@
+/* eslint-disable max-len */
+const { dailyTemperatures } = require('./daily-temperatures');
+
+describe('Stack: Daily Temperatures', () => {
+  it('should work', () => {
+    expect(dailyTemperatures([30, 28, 50, 40, 30])).toEqual([2, 1, 0, 0, 0]);
+  });
+
+  it('should work 2', () => {
+    expect(dailyTemperatures([73, 74, 75, 71, 69, 72, 76, 73])).toEqual([1, 1, 4, 2, 1, 1, 0, 0]);
+  });
+
+  it('should work 3', () => {
+    expect(dailyTemperatures([89, 62, 70, 58, 47, 47, 46, 76, 100, 70])).toEqual([8, 1, 5, 4, 3, 2, 1, 1, 0, 0]);
+  });
+
+  it('should work with large data', () => {
+    const input = [64, 40, 49, 73, 72, 35, 68, 83, 35, 73, 84, 88, 96, 43, 74, 63, 41, 95, 48, 46, 89, 72, 34, 85, 72, 59, 87, 49, 30, 32, 47, 34, 74, 58, 31, 75, 73, 88, 64, 92, 83, 64, 100, 99, 81, 41, 48, 83, 96, 92, 82, 32, 35, 68, 68, 92, 73, 92, 52, 33, 44, 38, 47, 88, 71, 50, 57, 95, 33, 65, 94, 44, 47, 79, 41, 74, 50, 67, 97, 31, 68, 50, 37, 70, 77, 55, 48, 30, 77, 100, 31, 100, 69, 60, 47, 95, 68, 47, 33, 64];
+    const output = [3, 1, 1, 4, 3, 1, 1, 3, 1, 1, 1, 1, 30, 1, 3, 2, 1, 25, 2, 1, 19, 2, 1, 3, 2, 1, 11, 5, 1, 1, 2, 1, 3, 2, 1, 2, 1, 2, 1, 3, 2, 1, 0, 46, 3, 1, 1, 1, 30, 18, 5, 1, 1, 2, 1, 12, 1, 10, 5, 1, 2, 1, 1, 4, 3, 1, 1, 11, 1, 1, 8, 1, 1, 5, 1, 3, 1, 1, 11, 1, 3, 2, 1, 1, 5, 3, 2, 1, 1, 0, 1, 0, 3, 2, 1, 0, 0, 2, 1, 0];
+    expect(dailyTemperatures(input)).toEqual(output);
+  });
+});
diff --git a/book/interview-questions/design-snake-game.js b/book/interview-questions/design-snake-game.js
new file mode 100644
index 00000000..9a327e4f
--- /dev/null
+++ b/book/interview-questions/design-snake-game.js
@@ -0,0 +1,86 @@
+const { Queue } = require('../../src/index');
+
+// tag::description[]
+/**
+ * The snake game stars with a snake of length 1 at postion 0,0.
+ * Only one food position is shown at a time. Once it's eaten the next one shows up.
+ * The snake can move in four directions up, down, left and right.
+ * If the snake go out of the boundaries (width x height) the game is over.
+ * If the snake hit itself the game is over.
+ * When the game is over, the `move` method returns -1 otherwise, return the current score.
+ *
+ * @example
+ *  const snakeGame = new SnakeGame(3, 2, [[1, 2], [0, 1]]);
+ *  snakeGame.move('R'); //  0
+ *  snakeGame.move('D'); //  0
+ *  snakeGame.move('R'); //  0
+ *  snakeGame.move('U'); //  1
+ *  snakeGame.move('L'); //  2
+ *  snakeGame.move('U'); // -1
+ */
+class SnakeGame {
+// end::description[]
+// tag::solution[]
+
+  // end::solution[]
+  // tag::description[]
+  /**
+   * Initialize game with grid's dimension and food order.
+   * @param {number} width - The screen width (grid's columns)
+   * @param {number} height - Screen height (grid's rows)
+   * @param {number[]} food - Food locations.
+   */
+  constructor(width, height, food) {
+    // end::description[]
+    // tag::solution[]
+    this.width = width;
+    this.height = height;
+    this.food = new Queue(food);
+    this.snake = new Queue([[0, 0]]);
+    this.tail = new Set([[0, 0]]);
+    this.dirs = {
+      U: [-1, 0], D: [1, 0], R: [0, 1], L: [0, -1],
+    };
+    // end::solution[]
+    // tag::description[]
+  }
+  // end::description[]
+
+  // tag::description[]
+  /**
+   * Move snake 1 position into the given direction.
+   * It returns the score or game over (-1) if the snake go out of bound or hit itself.
+   * @param {string} direction - 'U' = Up, 'L' = Left, 'R' = Right, 'D' = Down.
+   * @returns {number} - The current score (snake.length - 1).
+   */
+  move(direction) {
+    // end::description[]
+    // tag::solution[]
+    let [r, c] = this.snake.back(); // head of the snake
+    [r, c] = [r + this.dirs[direction][0], c + this.dirs[direction][1]];
+
+    // check wall collision
+    if (r < 0 || c < 0 || r >= this.height || c >= this.width) return -1;
+
+    const [fr, fc] = this.food.front() || []; // peek
+    if (r === fr && c === fc) {
+      this.food.dequeue(); // remove eaten food.
+    } else {
+      this.snake.dequeue(); // remove snake's if not food was eaten
+      this.tail.delete(this.tail.keys().next().value);
+    }
+
+    // check collision with snake's tail
+    if (this.tail.has(`${r},${c}`)) return -1; // O(1)
+
+    this.snake.enqueue([r, c]); // add new position
+    this.tail.add(`${r},${c}`);
+
+    return this.snake.size - 1; // return score (length of the snake - 1)
+    // end::solution[]
+    // tag::description[]
+  }
+}
+// end::description[]
+
+module.exports = { SnakeGame };
diff --git a/book/interview-questions/design-snake-game.spec.js b/book/interview-questions/design-snake-game.spec.js
new file mode 100644
index 00000000..acfcbd1f
--- /dev/null
+++ b/book/interview-questions/design-snake-game.spec.js
@@ -0,0 +1,31 @@
+const { SnakeGame } = require('./design-snake-game');
+
+describe('Queue: Design Snake Game', () => {
+  it('should game over when hits wall', () => {
+    const snakeGame = new SnakeGame(4, 2, [[1, 2], [0, 1]]);
+    expect(snakeGame.move('R')).toEqual(0); //  0
+    expect(snakeGame.move('D')).toEqual(0); //  0
+    expect(snakeGame.move('R')).toEqual(1); //  1 (ate food1)
+    expect(snakeGame.move('U')).toEqual(1); //  1
+    expect(snakeGame.move('L')).toEqual(2); //  2 (ate food2)
+    expect(snakeGame.move('U')).toEqual(-1); // -1 (hit wall)
+  });
+
+  it('should circle around without eating itself', () => {
+    const snakeGame = new SnakeGame(2, 2, [[0, 1], [1, 1], [1, 0]]);
+    expect(snakeGame.move('R')).toEqual(1);
+    expect(snakeGame.move('D')).toEqual(2);
+    expect(snakeGame.move('L')).toEqual(3);
+    expect(snakeGame.move('U')).toEqual(3);
+    expect(snakeGame.move('R')).toEqual(3);
+  });
+
+  it('should game over when hit itself', () => {
+    const snakeGame = new SnakeGame(3, 2, [[0, 1], [0, 2], [1, 2], [1, 1]]);
+    expect(snakeGame.move('R')).toEqual(1);
+    expect(snakeGame.move('R')).toEqual(2);
+    expect(snakeGame.move('D')).toEqual(3);
+    expect(snakeGame.move('L')).toEqual(4);
+    expect(snakeGame.move('U')).toEqual(-1);
+  });
+});
diff --git a/book/interview-questions/diameter-of-binary-tree.js b/book/interview-questions/diameter-of-binary-tree.js
new file mode 100644
index 00000000..520fff44
--- /dev/null
+++ b/book/interview-questions/diameter-of-binary-tree.js
@@ -0,0 +1,31 @@
+// tag::description[]
+/**
+ * Find the length of the binary tree diameter.
+ *
+ * @param {BinaryTreeNode | null} root - Binary Tree's root.
+ * @returns {number} tree's diameter (longest possible path on the tree)
+ */
+function diameterOfBinaryTree(root) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  let diameter = 0;
+
+  const height = (node) => {
+    if (!node) return 0;
+    const left = height(node.left);
+    const right = height(node.right);
+    diameter = Math.max(diameter, left + right);
+    return 1 + Math.max(left, right);
+  };
+
+  height(root);
+  return diameter;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+module.exports = { diameterOfBinaryTree };
diff --git a/book/interview-questions/diameter-of-binary-tree.spec.js b/book/interview-questions/diameter-of-binary-tree.spec.js
new file mode 100644
index 00000000..2bdba6d9
--- /dev/null
+++ b/book/interview-questions/diameter-of-binary-tree.spec.js
@@ -0,0 +1,14 @@
+const { diameterOfBinaryTree } = require('./diameter-of-binary-tree');
+const { BinaryTreeNode } = require('../../src/index');
+
+describe('Binary Tree: Diameter', () => {
+  it('should find the diameter', () => {
+    const actual = BinaryTreeNode.from([1, 2, 3, 4, 5]);
+    expect(diameterOfBinaryTree(actual)).toEqual(3);
+  });
+
+  it('should find the diameter when does not pass through the root node', () => {
+    const arr = [1, 2, 3, null, null, 4, 5, 6, null, null, 7, 8, null, null, 9];
+    expect(diameterOfBinaryTree(BinaryTreeNode.from(arr))).toEqual(6);
+  });
+});
diff --git a/book/interview-questions/linkedlist-find-cycle-start.js b/book/interview-questions/linkedlist-find-cycle-start.js
new file mode 100644
index 00000000..56afa687
--- /dev/null
+++ b/book/interview-questions/linkedlist-find-cycle-start.js
@@ -0,0 +1,39 @@
+// tag::fn[]
+/**
+ * Find where the cycle starts or null if no loop.
+ * @param {Node} head - The head of the list
+ * @returns {Node|null}
+ */
+function findCycleStart(head) {
+  let slow = head;
+  let fast = head;
+  while (fast && fast.next) {
+    slow = slow.next; // slow moves 1 by 1.
+    fast = fast.next.next; // slow moves 2 by 2.
+    if (fast === slow) { // detects loop!
+      slow = head; // reset pointer to begining.
+      while (slow !== fast) { // find intersection
+        slow = slow.next;
+        fast = fast.next; // move both pointers one by one this time.
+      }
+      return slow; // return where the loop starts
+    }
+  }
+  return null; // not found.
+}
+// end::fn[]
+
+// tag::brute[]
+function findCycleStartBrute(head) {
+  const visited = new Set();
+  let curr = head;
+  while (curr) {
+    if (visited.has(curr)) return curr;
+    visited.add(curr);
+    curr = curr.next;
+  }
+  return null;
+}
+// end::brute[]
+
+module.exports = { findCycleStart, findCycleStartBrute };
diff --git a/book/interview-questions/linkedlist-find-cycle-start.spec.js b/book/interview-questions/linkedlist-find-cycle-start.spec.js
new file mode 100644
index 00000000..3f1ab06b
--- /dev/null
+++ b/book/interview-questions/linkedlist-find-cycle-start.spec.js
@@ -0,0 +1,25 @@
+const { findCycleStart, findCycleStartBrute } = require('./linkedlist-find-cycle-start');
+const { LinkedList } = require('../../src/index');
+
+[findCycleStart, findCycleStartBrute].forEach((fn) => {
+  describe(`findCycleStart: ${fn.name}`, () => {
+    it('should work without loop', () => {
+      const head = new LinkedList([1, 2, 3]).first;
+      expect(fn(head)).toEqual(null);
+    });
+
+    it('should work with loop on first', () => {
+      const list = new LinkedList([1, 2, 3]);
+      const n1 = list.first;
+      list.last.next = n1;
+      expect(fn(list.first)).toEqual(n1);
+    });
+
+    it('should work with loop on second', () => {
+      const list = new LinkedList([1, 2, 3]);
+      const n2 = list.first.next;
+      list.last.next = n2;
+      expect(fn(list.first)).toEqual(n2);
+    });
+  });
+});
diff --git a/book/interview-questions/linkedlist-flatten-multilevel.js b/book/interview-questions/linkedlist-flatten-multilevel.js
new file mode 100644
index 00000000..30f6e07c
--- /dev/null
+++ b/book/interview-questions/linkedlist-flatten-multilevel.js
@@ -0,0 +1,45 @@
+// tag::fn[]
+/**
+ * Flatten a multi-level to a single level
+ * @param {Node} head
+ * @return {Node}
+ */
+function flatten(head) {
+  for (let curr = head; curr; curr = curr.next) {
+    if (!curr.child) continue;
+
+    let last = curr.child;
+    while (last && last.next) last = last.next; // find "child"'s last
+    if (curr.next) { // move "next" to "child"'s last postion
+      last.next = curr.next;
+      curr.next.previous = last;
+    }
+    curr.next = curr.child; // override "next" with "child".
+    curr.child.previous = curr;
+    curr.child = null; // clean "child" pointer.
+  }
+
+  return head;
+}
+// end::fn[]
+
+// tag::fn2[]
+function flattenBrute(head) {
+  const stack = [];
+  for (let curr = head; curr; curr = curr.next) {
+    if (!curr.next && stack.length) {
+      curr.next = stack.pop(); // merge main thread with saved nodes.
+      curr.next.previous = curr;
+    }
+    if (!curr.child) continue;
+    if (curr.next) stack.push(curr.next); // save "next" nodes.
+    curr.next = curr.child; // override next pointer with "child"
+    curr.child.previous = curr;
+    curr.child = null; // clear child pointer (was moved to "next").
+  }
+
+  return head;
+}
+// end::fn2[]
+
+module.exports = { flatten, flattenBrute };
diff --git a/book/interview-questions/linkedlist-flatten-multilevel.spec.js b/book/interview-questions/linkedlist-flatten-multilevel.spec.js
new file mode 100644
index 00000000..8115f035
--- /dev/null
+++ b/book/interview-questions/linkedlist-flatten-multilevel.spec.js
@@ -0,0 +1,79 @@
+/* eslint-disable one-var, one-var-declaration-per-line, prefer-destructuring */
+const { flatten, flattenBrute } = require('./linkedlist-flatten-multilevel');
+const { LinkedList } = require('../../src/index');
+const { ListNode } = require('../../src/index');
+
+class Node extends ListNode {
+  constructor(value) {
+    super(value);
+    this.child = null;
+  }
+}
+
+// print linked list node with (previous and child)
+const toString = (head) => {
+  const arr = [];
+  for (let i = head; i; i = i.next) {
+    arr.push(`${i.value}(${(i.previous && i.previous.value) || ''},${(i.child && i.child.value) || ''})`);
+  }
+  return `{ ${arr.join(' -> ')} }`;
+};
+
+const ll = (nums) => Array.from(new LinkedList(nums, Node));
+
+[flatten, flattenBrute].forEach((fn) => {
+  describe(`flatten: ${fn.name}`, () => {
+    let l1, l2, l3, l4;
+
+    beforeEach(() => {
+      l1 = ll([1, 2, 3]);
+      l2 = ll([10, 12, 14, 16]);
+      l3 = ll([21, 23]);
+      l4 = ll([36, 37]);
+    });
+
+    it('works with flat 1 level', () => {
+      // 1--- 2--- 3
+      expect(toString(fn(l1[0]))).toEqual('{ 1(,) -> 2(1,) -> 3(2,) }');
+    });
+
+    it('works with flat 2 levels', () => {
+      // 21--23
+      //      |
+      //      36--37
+      l3[1].child = l4[0];
+      expect(toString(l3[0])).toEqual('{ 21(,) -> 23(21,36) }');
+      expect(toString(fn(l3[0]))).toEqual('{ 21(,) -> 23(21,) -> 36(23,) -> 37(36,) }');
+    });
+
+    it('works with flat 2 levels and reminder', () => {
+      // 1--- 2--- 3
+      //      |
+      //      36--37
+      l1[1].child = l4[0];
+      expect(toString(l1[0])).toEqual('{ 1(,) -> 2(1,36) -> 3(2,) }');
+
+      expect(toString(fn(l1[0]))).toEqual('{ 1(,) -> 2(1,) -> 36(2,) -> 37(36,) -> 3(37,) }');
+    });
+
+    it('should flatten 3 levels', () => {
+      // 1--- 2--- 3
+      //      |
+      //     10---12---14---16
+      //           |    |
+      //           |   36---37
+      //           |
+      //           21--23
+      l1[1].child = l2[0];
+      l2[1].child = l3[0];
+      l2[2].child = l4[0];
+
+      // verify list children are present
+      expect(toString(l1[0])).toEqual('{ 1(,) -> 2(1,10) -> 3(2,) }');
+      expect(toString(l2[0])).toEqual('{ 10(,) -> 12(10,21) -> 14(12,36) -> 16(14,) }');
+
+      // run
+      expect(toString(fn(l1[0]))).toEqual('{ 1(,) -> 2(1,) -> 10(2,) -> 12(10,) -> 21(12,) -> 23(21,) -> 14(23,) -> 36(14,) -> 37(36,) -> 16(37,) -> 3(16,) }');
+    });
+  });
+});
diff --git a/book/interview-questions/linkedlist-is-palindrome.js b/book/interview-questions/linkedlist-is-palindrome.js
new file mode 100644
index 00000000..6eb65f3f
--- /dev/null
+++ b/book/interview-questions/linkedlist-is-palindrome.js
@@ -0,0 +1,39 @@
+// tag::fn[]
+function isPalindrome(head) {
+  let slow = head;
+  let fast = head;
+  while (fast) { // use slow/fast pointers to find the middle.
+    slow = slow.next;
+    fast = fast.next && fast.next.next;
+  }
+
+  const reverseList = (node) => { // use 3 pointers to reverse a linked list
+    let prev = null;
+    let curr = node;
+    while (curr) {
+      const { next } = curr; // same as: "const next = curr.next;"
+      curr.next = prev;
+      prev = curr;
+      curr = next;
+    }
+    return prev;
+  };
+
+  const reversed = reverseList(slow); // head of the reversed half
+  for (let i = reversed, j = head; i; i = i.next, j = j.next) if (i.value !== j.value) return false;
+  return true;
+}
+// end::fn[]
+
+// tag::fn2[]
+function isPalindromeBrute(head) {
+  const arr = [];
+  for (let i = head; i; i = i.next) arr.push(i.value); // <1>
+  let lo = 0;
+  let hi = arr.length - 1;
+  while (lo < hi) if (arr[lo++] !== arr[hi--]) return false; // <2>
+  return true;
+}
+// end::fn2[]
+
+module.exports = { isPalindrome, isPalindromeBrute };
diff --git a/book/interview-questions/linkedlist-is-palindrome.spec.js b/book/interview-questions/linkedlist-is-palindrome.spec.js
new file mode 100644
index 00000000..c57c300f
--- /dev/null
+++ b/book/interview-questions/linkedlist-is-palindrome.spec.js
@@ -0,0 +1,19 @@
+const { isPalindrome, isPalindromeBrute } = require('./linkedlist-is-palindrome');
+const { LinkedList } = require('../../src');
+
+const toList = (arr) => new LinkedList(arr).first;
+
+[isPalindrome, isPalindromeBrute].forEach((fn) => {
+  describe(`isPalindrome: ${fn.name}`, () => {
+    it('should work', () => {
+      expect(fn()).toEqual(true);
+    });
+
+    it('should work different cases', () => {
+      expect(fn(toList([1, 2, 3]))).toEqual(false);
+      expect(fn(toList([1, 2, 3, 2, 1]))).toEqual(true);
+      expect(fn(toList([1, 1, 2, 1]))).toEqual(false);
+      expect(fn(toList([1, 2, 2, 1]))).toEqual(true);
+    });
+  });
+});
diff --git a/book/interview-questions/linkedlist-same-data.js b/book/interview-questions/linkedlist-same-data.js
new file mode 100644
index 00000000..ca99badc
--- /dev/null
+++ b/book/interview-questions/linkedlist-same-data.js
@@ -0,0 +1,84 @@
+// const ListNode = require('../../src/data-structures/linked-lists/node');
+
+// tag::description[]
+/**
+ * Check if two lists has the same string data.
+ * Note: each lists can be huge, they have up to 10 million nodes.
+ *
+ * @examples
+ *    hasSameData(['he', 'll', 'o'], ['hel', 'lo']); // true
+ *    hasSameData(['hel', 'lo'], ['hi']); // false
+ *
+ * @param {ListNode} l1 - The root node of list 1.
+ * @param {ListNode} l2 - The root node of list 2.
+ * @returns {boolean} - true if has same data, false otherwise.
+ */
+function hasSameData(l1, l2) {
+  // end::description[]
+  // tag::solution[]
+  let p1 = l1;
+  let p2 = l2;
+  let i1 = -1;
+  let i2 = -1;
+
+  const findNextPointerIndex = (p, i) => {
+    let node = p;
+    let index = i;
+    while (node && index >= node.value.length) {
+      node = node.next;
+      index = 0;
+    }
+    return [node, index];
+  };
+
+  while (p1 && p2) {
+    [p1, i1] = findNextPointerIndex(p1, i1 + 1);
+    [p2, i2] = findNextPointerIndex(p2, i2 + 1);
+    if ((p1 && p2 && p1.value[i1] !== p2.value[i2])
+      || ((!p1 || !p2) && p1 !== p2)) return false;
+  }
+  return true;
+}
+// end::solution[]
+
+// tag::hasSameDataBrute1[]
+function hasSameDataBrute1(l1, l2) {
+  function toString(node) {
+    const str = [];
+    for (let curr = node; curr; curr = curr.next) {
+      str.push(curr.value);
+    }
+    return str.join('');
+  }
+
+  // console.log({s1: toString(l1), s2: toString(l2) });
+  return toString(l1) === toString(l2);
+}
+// end::hasSameDataBrute1[]
+
+function hasSameData1(l1, l2) {
+  let p1 = l1;
+  let p2 = l2;
+
+  let i1 = 0;
+  let i2 = 0;
+
+  while (p1 || p2) {
+    if (!p1 || !p2 || p1.value[i1] !== p2.value[i2]) return false;
+
+    if (i1 < p1.value.length - 1) i1++;
+    else {
+      p1 = p1.next;
+      i1 = 0;
+    }
+
+    if (i2 < p2.value.length - 1) i2++;
+    else {
+      p2 = p2.next;
+      i2 = 0;
+    }
+  }
+  return true;
+}
+
+module.exports = { hasSameData, hasSameDataBrute1, hasSameData1 };
diff --git a/book/interview-questions/linkedlist-same-data.spec.js b/book/interview-questions/linkedlist-same-data.spec.js
new file mode 100644
index 00000000..a9933aaa
--- /dev/null
+++ b/book/interview-questions/linkedlist-same-data.spec.js
@@ -0,0 +1,41 @@
+const { hasSameData } = require('./linkedlist-same-data');
+const LinkedList = require('../../src/data-structures/linked-lists/linked-list');
+
+describe('Linked List: has same data', () => {
+  it('should work with same data and shape', () => {
+    const l1 = new LinkedList(['hi']).first;
+    const l2 = new LinkedList(['hi']).first;
+    expect(hasSameData(l1, l2)).toEqual(true);
+  });
+
+  it('should work with different data', () => {
+    const l1 = new LinkedList(['ab']).first;
+    const l2 = new LinkedList(['a']).first;
+    expect(hasSameData(l1, l2)).toEqual(false);
+  });
+
+  it('should work with same data and but different shape', () => {
+    const l1 = new LinkedList(['h', 'e', 'l', 'l', 'o']).first;
+    const l2 = new LinkedList(['hello']).first;
+    expect(hasSameData(l1, l2)).toEqual(true);
+  });
+
+  it('should work with different data separated', () => {
+    const l1 = new LinkedList(['he', 'll', 'o']).first;
+    const l2 = new LinkedList(['ho', 'la']).first;
+    expect(hasSameData(l1, l2)).toEqual(false);
+  });
+
+  it('should handle empty', () => {
+    const l1 = new LinkedList(['hi']).first;
+    const l2 = new LinkedList(['', 'h', '', 'i']).first;
+    expect(hasSameData(l1, l2)).toEqual(true);
+  });
+
+  xit('should work with large data', () => {
+    const size = 1e6; // 1e7 takes 4sec.
+    const l1 = new LinkedList(Array(size).fill('x')).first;
+    const l2 = new LinkedList(Array(size).fill('z')).first;
+    expect(hasSameData(l1, l2)).toEqual(false);
+  });
+});
diff --git a/book/interview-questions/longest-substring-without-repeating-characters.js b/book/interview-questions/longest-substring-without-repeating-characters.js
new file mode 100644
index 00000000..3c3606a6
--- /dev/null
+++ b/book/interview-questions/longest-substring-without-repeating-characters.js
@@ -0,0 +1,29 @@
+// tag::description[]
+/**
+ * Find the length of the longest substring without duplicates.
+ * @example lenLongestSubstring('abccxyz'); // => 4 (cxyz)
+ * @param {string} s - The string.
+ * @returns {number} - The length of the longest unique substring.
+ */
+function lenLongestSubstring(s) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  let max = 0;
+  const set = new Set();
+
+  for (let i = 0, j = 0; j < s.length; j++) {
+    while (set.has(s[j])) set.delete(s[i++]);
+    set.add(s[j]);
+    max = Math.max(max, set.size);
+  }
+
+  return max;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+module.exports = { lenLongestSubstring };
diff --git a/book/interview-questions/longest-substring-without-repeating-characters.spec.js b/book/interview-questions/longest-substring-without-repeating-characters.spec.js
new file mode 100644
index 00000000..91df0b9a
--- /dev/null
+++ b/book/interview-questions/longest-substring-without-repeating-characters.spec.js
@@ -0,0 +1,30 @@
+const { lenLongestSubstring } = require('./longest-substring-without-repeating-characters');
+// const {  } = require('../../src/index');
+
+[lenLongestSubstring].forEach((fn) => {
+  describe(`Set: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = '';
+      const expected = 0;
+      expect(fn(actual)).toEqual(expected);
+    });
+
+    it('should work with small case', () => {
+      const actual = 'abc';
+      const expected = 3;
+      expect(fn(actual)).toEqual(expected);
+    });
+
+    it('should work with other case', () => {
+      const actual = 'abccdefg';
+      const expected = 5;
+      expect(fn(actual)).toEqual(expected);
+    });
+
+    it('should work with example', () => {
+      const actual = 'abcdaefg';
+      const expected = 7;
+      expect(fn(actual)).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/max-subarray.data.js b/book/interview-questions/max-subarray.data.js
new file mode 100644
index 00000000..9222bb6e
--- /dev/null
+++ b/book/interview-questions/max-subarray.data.js
@@ -0,0 +1,2 @@
+/* eslint-disable max-len */
+module.exports = [-57, 9, -72, -72, -62, 45, -97, 24, -39, 35, -82, -4, -63, 1, -93, 42, 44, 1, -75, -25, -87, -16, 9, -59, 20, 5, -95, -41, 4, -30, 47, 46, 78, 52, 74, 93, -3, 53, 17, 34, -34, 34, -69, -21, -87, -86, -79, 56, -9, -55, -69, 3, 5, 16, 21, -75, -79, 2, -39, 25, 72, 84, -52, 27, 36, 98, 20, -90, 52, -85, 44, 94, 25, 51, -27, 37, 41, -6, -30, -68, 15, -23, 11, -79, 93, -68, -78, 90, 11, -41, -8, -17, -56, 17, 86, 56, 15, 7, 66, -56, -2, -13, -62, -77, -62, -12, 37, 55, 81, -93, 86, -27, -39, -3, -30, -46, 6, -8, -79, -83, 50, -10, -24, 70, -93, -38, 27, -2, 45, -7, 42, -57, 79, 56, -57, 93, -56, 79, 48, -98, 62, 11, -48, -77, 84, 21, -47, -10, -87, -49, -17, 40, 40, 35, 10, 23, 97, -63, -79, 19, 6, 39, 62, -38, -27, 81, -68, -7, 60, 79, -28, -1, -33, 23, 22, -48, -79, 51, 18, -66, -98, -98, 50, 41, 13, -63, -59, 10, -49, -38, -70, 56, 77, 68, 95, -73, 26, -73, 20, -14, 83, 91, 61, -50, -9, -40, 1, 11, -88, -80, 21, 89, 97, -29, 8, 10, -15, 48, 97, 35, 86, -96, -9, 64, 48, -37, 90, -26, -10, -13, 36, -27, -45, -3, -1, 45, 34, 77, -66, 22, 73, 54, 11, 70, -97, -81, -43, -13, 44, -69, -78, 30, -66, -11, -29, 58, 52, -61, -68, -81, 25, 44, -32, 57, -81, 66, 2, 52, 43, 35, -26, 16, -33, 61, -37, -54, 80, -3, 32, 24, 27, 30, -69, 38, -81, 2, -4, 47, 17, 5, 42, -58, -51, -90, 98, -33, 76, -22, 95, -4, 89, -31, -87, -44, -69, -48, 1, 87, 48, -90, -12, -24, 39, 18, -86, 35, 96, -14, -41, 13, 90, -98, 32, -83, -89, 7, -17, 63, 84, -21, -40, 51, 24, -51, 83, 31, 0, -38, -5, -74, -29, 59, 1, 87, -22, -9, -1, -49, 76, 57, 41, 44, 35, -27, 60, 23, 56, -80, -14, 41, -2, 22, -31, 99, 47, -48, 7, -75, 13, -97, -50, 61, 61, 27, 48, -84, 94, -76, -56, 70, 57, 84, -9, -7, -66, -49, -84, 89, -29, -22, 7, 45, -99, 75, 21, 24, -95, -71, 48, 17, -92, 74, -22, 45, 1, -97, 61, -5, -74, 81, -57, 83, 42, 33, -47, 75, 61, -55, 41, -68, 22, -51, 53, -1, -99, -25, -76, -95, 3, 48, -1, -13, 23, 53, -68, -76, 33, 92, -4, 35, 50, 38, 18, -8, -52, 47, -33, -91, 91, 85, -60, 14, -89, 93, 89, -89, -55, 89, 92, 47, 38, -9, -66, -39, -79, -58, -39, 53, -65, 56, -11, 61, -29, 83, -46, 19, 31, -3, 27, -1, -18, 67, -87, -8, 37, 79, -20, 58, 68, -28, -18, -17, 39, -8, 43, 59, 33, 81, 13, 44, 37, -98, 6, 85, 84, 59, 4, -8, -44, -69, 91, 15, 74, 80, 83, -12, 59, -37, -54, 5, 34, 27, 87, -50, -81, 8, -90, 52, -11, -1, -4, -97, 0, 78, 87, -39, 37, -32, 30, 70, -1, 21, -38, -50, -22, -55, 15, -85, 8, 60, 19, -81, -35, -17, -31, -40, 90, -45, -88, -44, 53, -15, -41, -70, -37, -77, -33, 77, -9, 96, 24, 66, -6, 85, 92, 72, -70, 7, 86, 14, -32, -18, 33, 9, 64, 78, 68, 32, -90, 57, 87, 62, -58, -77, 68, -19, -54, -65, -42, 13, -68, 58, -44, 25, 43, -52, -26, 73, 55, -63, -13, -77, 18, 96, 31, -40, 51, -1, 91, 60, -44, 55, 22, -26, 78, -10, 32, -99, 2, 66, 13, 33, 25, 68, -65, -32, -84, -14, -82, 70, 22, 5, 69, -59, -22, -23, 0, -70, 53, -32, 89, 85, -77, -11, -40, 77, 55, 68, 77, -43, 34, -33, 66, -41, -88, -98, 27, -72, -13, 21, 74, 85, -74, 21, -74, -19, 97, 2, 10, 50, 46, -1, 13, 69, 87, 72, 23, 20, 40, 1, 76, -49, 67, 43, 10, 79, 21, -86, 83, 84, 34, 34, 69, 37, -45, 72, -82, -70, -26, 27, 56, 97, -97, -31, 66, 67, -82, -11, -13, 57, 66, -37, 85, 11, 82, -5, -33, 3, -15, -50, -13, 95, 60, -66, 9, -84, -94, 26, -78, -44, -70, 77, -47, -90, -53, 95, 76, -36, -38, -60, 98, -72, -21, 83, 15, -38, -45, 81, 41, 16, -69, -94, 11, 91, -84, -79, 83, -79, 23, -95, -24, 30, 58, 6, 39, -95, 1, -8, -54, 62, 31, -56, 67, 86, -96, -18, -75, -42, -36, 66, 73, -29, 48, -39, -61, 63, -42, 98, 60, 81, -97, -64, 11, 61, 18, -73, 42, -80, 18, 87, 58, -51, -69, 2, -88, -66, 84, -63, -32, -75, 79, -82, -28, 27, -21, 11, -33, 13, 9, -73, -6, -11, -61, 81, -73, 57, -92, 45, 53, 25, 33, 11, 50, 40, 90, 62, 51, 74, 75, -81, 75, 54, -86, -53, -42, -8, 34, 1, -95, -79, 27, -24, -14, 42, -66, 12, -24, -58, -66, -71, 43, 66, 17, -29, -16, 7, -90, -65, -42, 84, -70, -90, 15, -57, -67, 49, 11, 67, -50, -7, 64, 53, 68, -50, -5, 78, 38, 71, 96, 71, 76, 40, 15, -7, 87, 98, 76, 96, -90, -66, 57, -61, -57, -51, -41, -47, 97, 69, -80, -53, -61, 83, 76, 83, -90, -29, 62, 47, -81, 58, 18, 95, -2, -67, -12, -38, -92, -35, -65, -83, -25, 91, -44, -5, -83, -9, 47, -86, -40, 43, -63, -1, 3, -87, -18, 12, -39, -79, -41, -21, 79, 53, -26, -46, 63, 39, 16, 70, 80, 50, 87, -45, 19, -80, 26, 35, 10, -27, 26, 46, 92, 62, -55, -5, 52, 4, -93, -87, 1, -58, -9, -20, 95, 42, 34, 58, -19, -73, 5, -39, 53, -31, -8, -28, -12, 95, 84, 97, -55, 10, 44, -62, -51, 65, 32, -99, -54, 16, 89, 47, 57, -42, -96, 52, 99, 14, -13, -43, 40, 69, -6, -6, -62, 85, 42, 26, 80, 26, 0, -74, -87, -79, -60, -38, 63, 71, -61, 85, -13, -71, 9, -78, -14, 13, 50, -38, -73, -85, 18, 44, 83, -88, -85, -79, 73, 56, 23, 31, -40, -99, 33, -51, 97, 72, -13, 60, 20, 26, 46, 84, 31, -45, -94, 93, 67, 55, -45, 71, 69, 49, 15, 52, 37, 29, 50, -13, -38, -50, -82, -2, -73, 27, 47, -75, -24, -66, 84, 96, 36, 7, 80, -56, 62, 62, -63, 6, 17, -32, -46, -13, 93, 45, -84, 30, -26, 42, -82, 13, 92, -88, -89, -81, 16, 34, -57, 91, 45, -95, 87, -42, 11, 44, 2, -50, 6, 15, 33, -76, 83, 86, -13, 76, 32, -21, -16, 82, -78, -22, -28, 90, -34, -40, -91, 81, 93, -71, 73, 15, -90, 37, 73, -3, -41, -48, 47, 64, 66, -43, 64, 49, -57, -72, 3, 51, 7, 63, 11, 28, -82, 82, 18, -17, -58, 3, -58, -87, 8, -85, 27, 17, 28, -23, -85, 86, 28, 38, 28, -5, 94, -31, -79, -86, -3, 0, 65, 80, -60, -24, 8, -43, -65, -97, 40, -23, -18, 81, -11, 90, 72, 92, -16, 0, -30, -25, -36, 97, -87, 68, -31, 83, -63, -33, 97, 10, 66, 39, -10, -93, 91, 74, -37, -74, 53, 79, -21, -64, 37, 67, -74, 9, 60, 9, 86, -70, 84, -73, -96, 73, 94, -50, 57, -69, 16, 31, 18, -18, -53, -92, -35, -62, 59, 5, -60, 12, -16, 19, 47, -78, -14, 49, 7, -77, -64, -7, -71, 96, 19, -67, 69, -10, -18, 3, -2, 97, -89, -84, -44, -43, 99, -2, -6, 58, -97, 11, -29, -14, -70, 94, -16, -8, 44, 91, 15, 79, -39, 20, 75, 57, 52, 21, -53, -89, -98, 44, 84, -88, 36, -82, -31, 36, 15, 39, -29, 17, -50, 41, 79, -21, 13, -36, 71, -66, -68, -37, 89, -8, 82, 41, -74, 12, -38, -50, -1, -37, 70, -39, -48, 7, -22, 20, -57, 69, -41, 13, -14, -14, -68, -58, 64, 21, 5, 12, 54, 13, 51, 43, -94, 11, -16, -92, 99, 22, -43, -2, 62, -72, 58, -86, 11, -87, 33, 53, 81, 68, -57, -56, -46, -49, -14, 95, 71, 67, -16, 2, -19, -87, -78, -37, 0, -18, -30, -1, -95, 4, 96, 66, 31, 32, 79, -81, 44, -11, 48, 3, -66, 90, 46, -12, -81, -91, -40, 66, 76, 20, -54, -43, 9, -33, 19, -91, 49, 88, 7, 30, -8, -19, -4, 99, -87, -48, -82, 33, 40, 65, -64, 73, 33, 59, -62, 28, 67, -26, -29, 43, 71, 16, 99, -20, 83, 18, -11, 9, -16, 72, -61, 52, -47, 34, 29, -58, 85, 23, 75, 2, -34, 87, -48, 75, 46, -33, 3, -9, 40, 73, -66, -12, -10, -89, 68, -50, 5, -66, 58, 88, 82, 96, 18, -64, 7, -53, -23, -31, 69, -71, 47, -88, -83, 98, 86, 39, -35, -34, -70, 82, -60, -36, -30, 6, -26, -85, 55, 55, -75, -10, 44, 84, -37, -38, -80, 69, -15, -27, -85, -69, -21, 61, -57, -5, 59, -71, -66, -98, -5, -59, 60, 11, 4, -93, 93, 54, 98, 48, 9, 99, -85, -70, 83, -23, -32, 79, -77, 52, -47, -63, 60, 8, 97, -97, -97, 33, -92, -87, 11, -21, -47, -29, 66, 33, -45, 59, -36, -47, -16, 50, -48, -2, 79, -64, 51, -75, -85, 73, 76, -56, -90, 13, 51, 83, -8, 30, 17, -23, 20, -72, 55, 49, -24, -1, -17, 7, -42, 23, 59, 42, -27, 87, -83, -47, 99, 68, -46, 91, 18, -93, -88, 28, 20, 40, -12, -88, -30, -95, -12, 66, -90, -79, 16, -38, 19, 75, 68, 76, -2, 27, -5, 71, -9, 12, -99, -32, -43, -46, -41, 74, -40, -53, -21, 79, 86, 67, 68, -66, 48, -67, 99, 57, -47, 15, -81, 71, -33, 86, 25, 65, -10, 96, 36, 58, -15, 13, -74, 41, 66, -39, -7, -97, 7, 71, 59, -6, 15, 27, 4, -36, 59, 3, -79, 89, 95, -83, 37, -38, 79, -38, -96, -53, -41, 39, -95, 43, -71, -93, -38, 71, -33, 54, 74, 50, 2, 10, -79, -82, -86, 24, -19, 49, -95, 1, 38, 99, -6, -24, -62, -26, 14, -58, 20, 49, 57, 1, -7, 63, -16, 31, 34, 50, -15, -15, -23, 86, 94, -2, -96, -92, 98, -39, 34, -97, 62, -28, 78, -67, 24, 93, 6, -61, -65, -97, 87, 68, -20, -43, 31, 63, 87, -57, -10, -51, 27, 67, -87, -1, -35, -84, -17, -60, -23, -83, -57, -84, -34, -79, -52, 89, -86, 31, -95, -75, 10, 69, 70, 90, -97, 1, 53, 67, 43, -56, -84, -52, 87, -72, 46, -71, -79, -71, -32, -26, -77, 10, -34, -12, 8, -10, -46, -2, -79, -41, 0, 8, -95, -30, -2, 83, 47, -72, 50, -9, -29, 43, 15, -65, 70, -39, -37, 67, -34, 31, -59, -12, -82, 6, 75, 25, 96, -70, -99, 93, -35, 0, 1, -54, 69, 75, -71, 16, -96, 56, 83, -49, -1, -2, -14, -31, 35, 48, -86, -98, -21, -46, -34, -3, 37, -58, 98, 10, -52, 98, 3, -11, -2, 81, 11, -33, 56, 16, 60, 36, -28, 43, 87, 47, -81, -50, 93, 53, 97, -93, 31, -46, -40, 97, 27, 73, -84, 25, -17, -60, 1, 63, 5, 98, 44, -84, -57, -23, 8, 79, 90, 57, 22, 54, 4, 17, -96, -3, -29, -99, 3, 78, -69, 40, 52, 57, 13, 67, -40, 73, 83, 60, 36, -12, 35, -43, -20, 54, 10, 88, 33, 0, 45, -67, -46, -51, 49, -43, 23, 96, -65, -74, 52, -35, 42, 4, 99, -67, -28, -41, -94, -45, -81, 18, 43, 53, 74, 99, -15, -39, 87, -82, 61, 9, -73, 91, 58, 76, -74, -19, 49, -63, -17, 1, 1, -97, -94, -23, -65, -46, 35, -83, 8, 53, 34, -72, -16, -15, -95, 68, 45, 91, 62, -17, 1, 89, -48, -64, 42, -46, -7, -9, -10, 52, 69, 67, 54, 74, -55, 65, -72, 79, 58, 12, 10, -31, 17, 70, 53, 21, 38, -24, -11, -23, 35, 89, -34, 86, -98, -92, -60, -6, -24, 6, -53, -55, -26, 77, -81, 18, 20, -77, -26, -22, 11, 60, 47, -72, 30, -23, 25, -55, 52, -85, 22, -12, 80, 87, -49, 59, 72, -32, -47, -52, 73, -24, -8, -76, -69, -13, 18, 50, 9, 92, -95, 96, 52, 51, -98, -40, -71, 26, 4, 57, 17, -74, -78, -25, 90, -50, -66, 39, 17, -37, 86, -33, 39, -45, -9, 69, 41, -91, -4, -73, 77, 0, -77, 7, -48, -76, 66, -43, 50, -30, 90, -56, -27, -87, -5, -37, -38, 28, -98, 55, 91, 64, -78, 7, -81, 12, -47, 36, -2, 48, 62, -25, -75, 84, 81, -47, -91, 24, -14, 35, 94, -23, 78, -56, -34, -49, -17, 27, 78, -16, -18, 46, -75, -20, -70, -80, 92, -18, 55, -10, -93, 17, 41, -68, 1, 0, -39, -14, -76, 47, -79, 94, -76, 76, -62, -11, -73, 20, 92, 81, 80, -49, 28, -95, 30, 34, -99, 22, -83, 55, 88, 99, -28, 7, -69, 50, -93];
diff --git a/book/interview-questions/max-subarray.js b/book/interview-questions/max-subarray.js
new file mode 100644
index 00000000..6fb62d5d
--- /dev/null
+++ b/book/interview-questions/max-subarray.js
@@ -0,0 +1,57 @@
+// tag::description[]
+/**
+ * Find the maximun sum of contiguous elements in an array.
+ *
+ * @examples
+ *  maxSubArray([1, -3, 10, -5]); // => 10
+ *  maxSubArray([-3,4,-1,2,1,-5]); // => 6
+ *
+ * @param {number[]} a - Array
+ * @returns {number} - max sum
+ */
+function maxSubArray(a) {
+// end::description[]
+// tag::solution[]
+  let max = -Infinity;
+  let local = 0;
+
+  a.forEach((n) => {
+    local = Math.max(n, local + n);
+    max = Math.max(max, local);
+  });
+
+  return max;
+}
+// end::solution[]
+
+// tag::maxSubArrayBrute1[]
+function maxSubArrayBrute1(nums) {
+  let max = -Infinity;
+
+  for (let i = 0; i < nums.length; i++) { // O(n^3)
+    for (let j = i + 1; j <= nums.length; j++) { // O(n^2)
+      const sum = nums.slice(i, j).reduce((a, n) => n + a, 0); // O(n)
+      max = Math.max(max, sum); // O(1)
+    }
+  }
+
+  return max;
+}
+// end::maxSubArrayBrute1[]
+
+// tag::maxSubArrayBrute2[]
+function maxSubArrayBrute2(nums) {
+  let max = -Infinity;
+
+  for (let i = 0; i < nums.length; i++) { // O(n) * O(n)
+    let local = 0;
+    for (let j = i; j < nums.length; j++) { // O(n)
+      local += nums[j];
+      max = Math.max(max, local);
+    }
+  }
+  return max;
+}
+// end::maxSubArrayBrute2[]
+
+module.exports = { maxSubArrayBrute1, maxSubArrayBrute2, maxSubArray };
diff --git a/book/interview-questions/max-subarray.spec.js b/book/interview-questions/max-subarray.spec.js
new file mode 100644
index 00000000..6abfb033
--- /dev/null
+++ b/book/interview-questions/max-subarray.spec.js
@@ -0,0 +1,20 @@
+const { maxSubArray, maxSubArrayBrute1, maxSubArrayBrute2 } = require('./max-subarray');
+const largeArray = require('./max-subarray.data');
+
+describe('Max Subarray Sum', () => {
+  [maxSubArray, maxSubArrayBrute1, maxSubArrayBrute2].forEach((fn) => {
+    describe(`with ${fn.name}`, () => {
+      it('should work with large arrays', () => {
+        expect(fn([-2, 1, -3, 4, -1, 2, 1, -5, 4])).toEqual(6);
+      });
+
+      it('should work with small arrays', () => {
+        expect(fn([1, -3, 10, -5])).toEqual(10);
+      });
+
+      it('should work with humongous arrays', () => {
+        expect(fn(largeArray)).toEqual(4853);
+      });
+    });
+  });
+});
diff --git a/book/interview-questions/merge-intervals.js b/book/interview-questions/merge-intervals.js
new file mode 100644
index 00000000..10192cbb
--- /dev/null
+++ b/book/interview-questions/merge-intervals.js
@@ -0,0 +1,32 @@
+// const {  } = require('../../src/index');
+
+// tag::description[]
+/**
+ * Merge overlapping intervals.
+ * @param {[numer, number][]} intervals - Array with pairs [start, end]
+ * @returns {[numer, number][]} - Array of merged pairs [start, end]
+ */
+function merge(intervals) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  const ans = [];
+
+  intervals.sort((a, b) => a[0] - b[0]); // sort by start time
+
+  for (let i = 0; i < intervals.length; i++) {
+    const last = ans[ans.length - 1];
+    const curr = intervals[i];
+    if (last && last[1] >= curr[0]) { // check for overlaps
+      last[1] = Math.max(last[1], curr[1]);
+    } else ans.push(curr);
+  }
+  return ans;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+module.exports = { merge };
diff --git a/book/interview-questions/merge-intervals.spec.js b/book/interview-questions/merge-intervals.spec.js
new file mode 100644
index 00000000..efae90e9
--- /dev/null
+++ b/book/interview-questions/merge-intervals.spec.js
@@ -0,0 +1,30 @@
+const { merge } = require('./merge-intervals');
+// const {  } = require('../../src/index');
+
+[merge].forEach((fn) => {
+  describe(`TOPIC: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = fn([]);
+      const expected = [];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with small case', () => {
+      const actual = fn([[1, 3]]);
+      const expected = [[1, 3]];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with other case', () => {
+      const actual = fn([[0, 1], [1, 3], [3, 5], [6, 6]]);
+      const expected = [[0, 5], [6, 6]];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with other case with large numbers', () => {
+      const actual = fn([[10, 99], [20, 50], [9, 11], [98, 100]]);
+      const expected = [[9, 100]];
+      expect(actual).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/merge-lists.js b/book/interview-questions/merge-lists.js
new file mode 100644
index 00000000..b50f7f33
--- /dev/null
+++ b/book/interview-questions/merge-lists.js
@@ -0,0 +1,38 @@
+const ListNode = require('../../src/data-structures/linked-lists/node');
+// tag::description[]
+/**
+ * Given two sorted linked lists merge them while keeping the asc order.
+ * @examples
+ *    mergeTwoLists([2,4,6], [1,3]); // => [1,2,3,4,6]
+ *    mergeTwoLists([2,4,6], []); // => [2,4,6]
+ *    mergeTwoLists([], [1,3]); // => [1,3]
+ *
+ * @param {ListNode} l1 - The root node of list 1
+ * @param {ListNode} l2 - The root node of list 2
+ * @returns {ListNode} - The root of the merged list.
+ */
+function mergeTwoLists(l1, l2) {
+  // end::description[]
+  // tag::solution[]
+  const sentinel = new ListNode();
+  let p0 = sentinel;
+  let p1 = l1;
+  let p2 = l2;
+
+  while (p1 || p2) {
+    if (!p1 || (p2 && p1.value > p2.value)) {
+      p0.next = p2;
+      p2 = p2.next;
+    } else {
+      p0.next = p1;
+      p1 = p1.next;
+    }
+    p0 = p0.next;
+  }
+
+  return sentinel.next;
+}
+// end::solution[]
+
+
+module.exports = { mergeTwoLists };
diff --git a/book/interview-questions/merge-lists.spec.js b/book/interview-questions/merge-lists.spec.js
new file mode 100644
index 00000000..650aaea0
--- /dev/null
+++ b/book/interview-questions/merge-lists.spec.js
@@ -0,0 +1,44 @@
+const { mergeTwoLists } = require('./merge-lists');
+const LinkedList = require('../../src/data-structures/linked-lists/linked-list');
+
+describe('Linked List: Merge Lists', () => {
+  function asString(root) {
+    const values = [];
+    for (let curr = root; curr; curr = curr.next) {
+      values.push(curr.value);
+    }
+    return values.join(' -> ');
+  }
+
+  it('should merge in asc order', () => {
+    const l1 = new LinkedList([2, 3, 4]).first;
+    const l2 = new LinkedList([1, 2]).first;
+    const actual = mergeTwoLists(l1, l2);
+    const expected = '1 -> 2 -> 2 -> 3 -> 4';
+    expect(asString(actual)).toEqual(expected);
+  });
+
+  it('should handle empty list 1', () => {
+    const l1 = new LinkedList().first;
+    const l2 = new LinkedList([1, 2]).first;
+    const actual = mergeTwoLists(l1, l2);
+    const expected = '1 -> 2';
+    expect(asString(actual)).toEqual(expected);
+  });
+
+  it('should handle empty list 2', () => {
+    const l1 = new LinkedList([2, 3, 4]).first;
+    const l2 = new LinkedList().first;
+    const actual = mergeTwoLists(l1, l2);
+    const expected = '2 -> 3 -> 4';
+    expect(asString(actual)).toEqual(expected);
+  });
+
+  it('should handle empty lists', () => {
+    const l1 = new LinkedList().first;
+    const l2 = new LinkedList().first;
+    const actual = mergeTwoLists(l1, l2);
+    const expected = '';
+    expect(asString(actual)).toEqual(expected);
+  });
+});
diff --git a/book/interview-questions/most-common-word.js b/book/interview-questions/most-common-word.js
new file mode 100644
index 00000000..b2c3f58f
--- /dev/null
+++ b/book/interview-questions/most-common-word.js
@@ -0,0 +1,52 @@
+// tag::description[]
+/**
+ * Find the most common word that is not banned.
+ * @example mostCommonWord("It's blue and it's round", ['and']) // it
+ * @param {string} paragraph - The text to sanitize and search on.
+ * @param {string[]} banned - List of banned words (lowercase)
+ * @returns {string} - The first word that is the most repeated.
+ */
+function mostCommonWord(paragraph, banned) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  const words = paragraph.toLowerCase().replace(/\W+/g, ' ').split(/\s+/);
+  const b = new Set(banned);
+  const map = words.reduce((m, w) => (b.has(w) ? m : m.set(w, 1 + (m.get(w) || 0))), new Map());
+  const max = Math.max(...map.values());
+  for (const [w, c] of map.entries()) if (c === max) return w;
+  return '';
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+
+// tag::explicit[]
+function mostCommonWordExplicit(paragraph, banned) {
+  const words = paragraph
+    .toLowerCase()
+    .replace(/\W+/g, ' ')
+    .split(/\s+/);
+  const exclude = new Set(banned);
+
+  const wordsCount = words.reduce((map, word) => {
+    if (exclude.has(word)) return map;
+    const count = map.get(word) || 0;
+    return map.set(word, 1 + count);
+  }, new Map());
+
+  const max = Math.max(...wordsCount.values());
+
+  for (const [word, count] of wordsCount.entries()) {
+    if (count === max) {
+      return word;
+    }
+  }
+  return '';
+}
+// end::explicit[]
+
+module.exports = { mostCommonWord, mostCommonWordExplicit };
diff --git a/book/interview-questions/most-common-word.spec.js b/book/interview-questions/most-common-word.spec.js
new file mode 100644
index 00000000..a0a501de
--- /dev/null
+++ b/book/interview-questions/most-common-word.spec.js
@@ -0,0 +1,41 @@
+const { mostCommonWord, mostCommonWordExplicit } = require('./most-common-word');
+// const {  } = require('../../src/index');
+
+[mostCommonWord, mostCommonWordExplicit].forEach((fn) => {
+  describe(`Set: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = '';
+      const expected = '';
+      expect(fn(actual, [])).toEqual(expected);
+    });
+
+    it('should work with small case', () => {
+      const actual = 'a';
+      const expected = '';
+      expect(fn(actual, ['a'])).toEqual(expected);
+    });
+
+    it('should work with small case 2', () => {
+      const actual = 'a';
+      const expected = 'a';
+      expect(fn(actual, ['b'])).toEqual(expected);
+    });
+
+    it('should work with other case', () => {
+      expect(fn(
+        `How much wood, would a Woodchuck chuck,
+      if a woodchuck could chuck?`,
+        ['a'],
+      )).toEqual({
+        asymmetricMatch: (actual) => ['woodchuck', 'chuck'].includes(actual),
+      });
+    });
+
+    it('should work with \' case', () => {
+      expect(fn(
+        'It\'s a blue ball and its shade... Very BLUE!',
+        ['and'],
+      )).toEqual('blue');
+    });
+  });
+});
diff --git a/book/interview-questions/most-common-words-ii.js b/book/interview-questions/most-common-words-ii.js
new file mode 100644
index 00000000..3b118ea1
--- /dev/null
+++ b/book/interview-questions/most-common-words-ii.js
@@ -0,0 +1,47 @@
+/**
+ * Given text and banned words,
+ * return the most common words in descending order.
+ * @param {string} text - The text to parse.
+ * @param {number} n - The number of results.
+ * @return {string[]}
+ */
+// tag::map[]
+function mostCommonWords(text, n = 1) {
+  const words = text.toLowerCase().split(/\W+/);
+
+  const map = words
+    .reduce((m, w) => m.set(w, 1 + (m.get(w) || 0)), new Map());
+
+  return Array.from(map.entries())
+    .sort((a, b) => b[1] - a[1])
+    .slice(0, n)
+    .map((w) => w[0]);
+}
+// end::map[]
+
+// tag::brute[]
+function mostCommonWordsBrute(text, n = 1) {
+  const words = text.toLowerCase().split(/\W+/);
+  const entries = []; // array of [word, count] pairs
+
+  for (let i = 0; i < words.length; i++) {
+    if (!words[i]) continue;
+    let count = 1;
+    for (let j = i + 1; j < words.length; j++) {
+      if (words[i] === words[j]) {
+        count++;
+        words[j] = null; // removed letter once it's counted.
+      }
+    }
+    entries.push([words[i], count]);
+  }
+
+  return entries
+    .sort((a, b) => b[1] - a[1])
+    .slice(0, n)
+    .map((w) => w[0]);
+}
+// end::brute[]
+
+
+module.exports = { mostCommonWords, mostCommonWordsBrute };
diff --git a/book/interview-questions/most-common-words-ii.spec.js b/book/interview-questions/most-common-words-ii.spec.js
new file mode 100644
index 00000000..6c443366
--- /dev/null
+++ b/book/interview-questions/most-common-words-ii.spec.js
@@ -0,0 +1,26 @@
+const { mostCommonWords, mostCommonWordsBrute } = require('./most-common-words-ii');
+
+[mostCommonWordsBrute, mostCommonWords].forEach((fn) => {
+  describe(`Most Common words: ${fn.name}`, () => {
+    it('should work', () => {
+      expect(fn(
+        'The map, maps keys to values; Keys can be anything.',
+        1,
+      )).toEqual(['keys']);
+    });
+
+    it('should work 2', () => {
+      expect(fn(
+        'Look at it! What is it? It does look like my code from 1 year ago',
+        2,
+      )).toEqual(['it', 'look']);
+    });
+
+    it('should work all puntuations', () => {
+      expect(fn(
+        'a; a,b, a\'s c a!; b,b, c.',
+        4,
+      )).toEqual(['a', 'b', 'c', 's']);
+    });
+  });
+});
diff --git a/book/interview-questions/network-delay-time.js b/book/interview-questions/network-delay-time.js
new file mode 100644
index 00000000..97a00c32
--- /dev/null
+++ b/book/interview-questions/network-delay-time.js
@@ -0,0 +1,57 @@
+const { PriorityQueue, Queue } = require('../../src/index');
+
+// tag::description[]
+function networkDelayTime(times, N, K) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  const graph = new Map(Array(N).fill(0).map((_, i) => [i + 1, []]));
+  times.forEach(([u, v, w]) => graph.get(u).push([v, w]));
+
+  const q = new PriorityQueue([[0, K]]);
+  const dist = new Map();
+
+  while (q.size) {
+    const [d, n] = q.dequeue();
+
+    if (dist.has(n)) continue;
+    dist.set(n, d);
+
+    for (const [adj, w] of graph.get(n)) {
+      if (!dist.has(adj)) q.enqueue([d + w, adj]);
+    }
+  }
+
+  return dist.size === N ? Math.max(...dist.values()) : -1;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+// tag::networkDelayTimeQueue[]
+function networkDelayTimeQueue(times, N, K) {
+  const graph = new Map(Array(N).fill(0).map((_, i) => [i + 1, []]));
+  times.forEach(([u, v, w]) => graph.get(u).push([v, w]));
+
+  const q = new Queue([[0, K]]);
+  const dist = new Map();
+
+  while (q.size) {
+    const [d, n] = q.dequeue();
+
+    dist.set(n, dist.has(n) ? Math.min(dist.get(n), d) : d);
+
+    for (const [adj, w] of graph.get(n)) {
+      if (!dist.has(adj) || dist.get(adj) > d + w) {
+        q.enqueue([d + w, adj]);
+      }
+    }
+  }
+
+  return dist.size === N ? Math.max(...dist.values()) : -1;
+}
+// end::networkDelayTimeQueue[]
+
+module.exports = { networkDelayTime, networkDelayTimeQueue };
diff --git a/book/interview-questions/network-delay-time.spec.js b/book/interview-questions/network-delay-time.spec.js
new file mode 100644
index 00000000..bb3440e7
--- /dev/null
+++ b/book/interview-questions/network-delay-time.spec.js
@@ -0,0 +1,48 @@
+/* eslint-disable max-len */
+const { networkDelayTime, networkDelayTimeQueue } = require('./network-delay-time');
+
+[networkDelayTime, networkDelayTimeQueue].forEach((fn) => {
+  describe(`Graph/PriorityQueue: ${fn.name}`, () => {
+    it('should work with simple case', () => {
+      const times = [[2, 1, 1], [2, 3, 1], [3, 4, 1]];
+      const n = 4;
+      const k = 2;
+      expect(fn(times, n, k)).toEqual(2);
+    });
+
+    it('should work with loops', () => {
+      const times = [[1, 2, 0], [1, 5, 10], [1, 4, 1], [2, 3, 100], [4, 5, 1], [5, 6, 1], [6, 7, 1], [7, 3, 1], [7, 5, 1]];
+      const n = 7;
+      const k = 1;
+      expect(fn(times, n, k)).toEqual(5);
+    });
+
+    it('should work with loops and dead starts', () => {
+      const times = [[1, 2, 0], [1, 5, 10], [1, 4, 1], [2, 3, 100], [4, 5, 1], [5, 6, 1], [6, 7, 1], [7, 3, 1], [7, 5, 1]];
+      const n = 7;
+      const k = 3;
+      expect(fn(times, n, k)).toEqual(-1);
+    });
+
+    it('should work', () => {
+      const times = [[2, 1, 15], [2, 3, 85], [1, 3, 0], [1, 2, 91], [3, 2, 78], [3, 1, 36]];
+      const n = 3;
+      const k = 2;
+      expect(fn(times, n, k)).toEqual(15);
+    });
+
+    it('should work with highly connected networks', () => {
+      const times = [[14, 1, 8], [11, 2, 25], [14, 15, 37], [3, 7, 70], [11, 7, 60], [13, 11, 87], [15, 10, 67], [13, 10, 58], [5, 4, 56], [9, 3, 26], [5, 11, 51], [11, 4, 92], [7, 6, 8], [7, 10, 95], [14, 9, 0], [4, 13, 1], [7, 9, 89], [3, 14, 24], [11, 15, 30], [13, 2, 91], [15, 8, 60], [1, 4, 96], [8, 2, 71], [6, 8, 38], [14, 13, 46], [2, 12, 48], [10, 11, 92], [8, 12, 28], [8, 7, 12], [9, 13, 82], [8, 6, 27], [3, 2, 65], [4, 10, 62], [11, 13, 55], [1, 2, 52], [8, 3, 98], [7, 12, 85], [6, 12, 97], [9, 4, 90], [2, 4, 23], [9, 11, 20], [1, 14, 61], [8, 9, 77], [6, 5, 80], [14, 11, 33], [9, 8, 54], [13, 1, 42], [13, 8, 13], [10, 14, 40], [9, 7, 18], [14, 3, 50], [14, 6, 83], [14, 8, 14], [2, 1, 86], [9, 5, 54], [11, 5, 29], [9, 12, 43], [9, 2, 74], [14, 4, 87], [12, 7, 98], [7, 14, 13], [4, 12, 33], [5, 2, 60], [15, 11, 33], [8, 4, 99], [9, 6, 98], [4, 6, 57], [6, 11, 5], [9, 15, 37], [1, 3, 30], [9, 10, 60], [13, 12, 73], [13, 14, 56], [1, 11, 13], [14, 2, 8], [4, 15, 60], [11, 3, 90], [2, 5, 86], [11, 1, 1], [13, 4, 2], [15, 7, 91], [15, 4, 51], [11, 6, 70], [2, 7, 51], [11, 9, 37], [4, 2, 92], [10, 4, 4], [7, 2, 30], [13, 9, 79], [8, 15, 41], [11, 8, 18], [15, 2, 4], [12, 14, 88], [12, 6, 9], [12, 9, 44], [1, 6, 87], [15, 14, 42], [4, 9, 41], [7, 15, 90], [4, 1, 84], [7, 11, 9], [3, 11, 75], [5, 9, 2], [2, 11, 96], [12, 5, 89], [6, 15, 25], [5, 13, 7], [15, 5, 32], [13, 5, 84], [7, 5, 9], [15, 3, 14], [12, 13, 4], [5, 3, 73], [6, 9, 85], [6, 10, 29], [1, 8, 24], [12, 3, 85], [4, 3, 60], [1, 13, 6], [1, 5, 58], [2, 3, 29], [14, 5, 67], [13, 15, 70], [5, 14, 94], [15, 1, 95], [3, 1, 17], [10, 2, 6], [11, 10, 44], [9, 14, 62], [4, 11, 32], [15, 13, 48], [2, 10, 77], [3, 13, 90], [5, 7, 68], [10, 6, 78], [3, 6, 95], [10, 12, 68], [13, 6, 73], [10, 1, 8], [10, 7, 18], [10, 5, 64], [5, 1, 55], [13, 7, 90], [1, 9, 67], [3, 12, 76], [14, 10, 22], [12, 8, 83], [4, 7, 76], [8, 13, 25], [5, 6, 57], [13, 3, 90], [6, 2, 96], [11, 14, 61], [12, 1, 94], [12, 15, 12], [4, 8, 88], [4, 14, 27], [7, 4, 25], [3, 9, 57], [2, 15, 90], [1, 12, 85], [12, 11, 44], [5, 10, 13], [5, 12, 96], [14, 7, 24], [14, 12, 98], [10, 9, 36], [15, 6, 17], [8, 10, 11], [2, 13, 5], [10, 3, 78], [6, 13, 11], [5, 15, 34], [12, 10, 12], [9, 1, 68], [10, 13, 1], [7, 13, 86], [1, 7, 62], [2, 14, 53], [8, 14, 75], [2, 6, 49], [10, 15, 83], [7, 8, 88], [6, 1, 87], [8, 1, 38], [8, 11, 73], [3, 15, 1], [3, 8, 93], [2, 8, 26], [4, 5, 26], [3, 4, 58], [7, 1, 55], [7, 3, 84], [5, 8, 97], [12, 4, 42], [6, 3, 71], [6, 7, 48], [15, 12, 3], [1, 15, 30], [10, 8, 11], [2, 9, 49], [6, 14, 95], [3, 10, 68], [6, 4, 14], [11, 12, 29], [1, 10, 93], [8, 5, 55], [12, 2, 86], [3, 5, 26], [15, 9, 12]];
+      const n = 15;
+      const k = 11;
+      expect(fn(times, n, k)).toEqual(38);
+    });
+
+    it('should work with large highly connected networks', () => {
+      const times = [[15, 8, 1], [7, 10, 41], [7, 9, 34], [9, 4, 31], [12, 13, 50], [14, 3, 52], [4, 11, 99], [4, 7, 86], [10, 13, 57], [9, 6, 10], [1, 7, 51], [7, 15, 38], [1, 9, 11], [12, 7, 94], [9, 13, 34], [11, 7, 79], [7, 6, 28], [5, 3, 34], [2, 6, 97], [14, 1, 97], [6, 10, 90], [12, 10, 37], [13, 3, 73], [11, 14, 7], [15, 1, 39], [6, 5, 90], [13, 6, 43], [6, 9, 32], [4, 6, 45], [11, 10, 2], [2, 13, 4], [14, 15, 29], [1, 14, 88], [14, 6, 19], [6, 2, 29], [3, 14, 72], [1, 15, 4], [11, 5, 2], [6, 7, 56], [8, 7, 88], [13, 14, 70], [14, 12, 58], [14, 2, 86], [11, 3, 57], [5, 2, 56], [3, 10, 26], [2, 11, 21], [14, 5, 54], [5, 12, 40], [14, 4, 81], [15, 2, 99], [5, 7, 57], [13, 12, 5], [4, 9, 60], [12, 15, 48], [6, 14, 1], [9, 7, 44], [13, 7, 69], [5, 13, 42], [4, 1, 7], [11, 9, 76], [8, 1, 76], [5, 14, 29], [2, 3, 69], [7, 3, 23], [12, 14, 28], [11, 4, 85], [10, 1, 10], [15, 12, 36], [1, 11, 69], [15, 10, 96], [11, 13, 69], [7, 12, 49], [1, 2, 95], [6, 4, 46], [8, 12, 94], [12, 4, 93], [13, 5, 31], [12, 2, 60], [6, 1, 87], [4, 14, 20], [5, 11, 89], [4, 15, 88], [4, 10, 21], [1, 6, 5], [10, 8, 26], [8, 2, 51], [3, 15, 23], [7, 2, 12], [11, 1, 47], [2, 1, 75], [3, 8, 63], [8, 10, 19], [6, 8, 18], [4, 2, 55], [14, 11, 80], [10, 3, 73], [3, 5, 22], [12, 3, 61], [1, 13, 33], [9, 3, 98], [9, 12, 69], [15, 9, 6], [7, 13, 76], [11, 12, 22], [11, 15, 51], [13, 15, 46], [5, 10, 58], [1, 10, 26], [13, 4, 85], [7, 14, 58], [5, 8, 46], [11, 6, 32], [10, 9, 41], [9, 14, 35], [14, 13, 60], [3, 9, 97], [2, 5, 39], [7, 11, 19], [1, 12, 27], [7, 5, 13], [8, 4, 34], [9, 15, 25], [5, 1, 93], [15, 13, 97], [14, 9, 35], [8, 6, 67], [9, 5, 39], [13, 11, 35], [7, 4, 21], [12, 9, 64], [14, 8, 8], [10, 12, 94], [8, 9, 76], [8, 5, 71], [2, 9, 64], [10, 14, 59], [1, 4, 74], [7, 1, 69], [15, 5, 55], [6, 15, 80], [13, 8, 84], [8, 13, 63], [8, 3, 91], [10, 4, 87], [1, 5, 39], [8, 11, 0], [1, 3, 79], [4, 5, 82], [4, 12, 87], [3, 11, 29], [7, 8, 92], [10, 7, 77], [6, 12, 42], [13, 2, 40], [9, 10, 13], [4, 13, 65], [2, 4, 34], [3, 13, 44], [2, 14, 69], [3, 4, 42], [5, 15, 98], [14, 7, 6], [15, 3, 94], [10, 2, 37], [15, 11, 7], [9, 2, 15], [13, 9, 66], [4, 8, 83], [8, 15, 23], [13, 1, 50], [6, 13, 57], [2, 10, 37], [10, 6, 38], [2, 7, 45], [9, 8, 8], [3, 12, 28], [3, 2, 83], [2, 12, 75], [1, 8, 91], [4, 3, 70], [12, 6, 48], [3, 1, 13], [5, 6, 42], [6, 11, 96], [3, 6, 22], [15, 6, 34], [11, 8, 43], [15, 7, 40], [9, 11, 57], [11, 2, 11], [2, 8, 22], [9, 1, 73], [2, 15, 40], [12, 11, 10], [15, 4, 78], [12, 8, 75], [10, 15, 37], [13, 10, 44], [8, 14, 33], [3, 7, 82], [5, 4, 46], [12, 5, 79], [15, 14, 43], [10, 5, 65], [5, 9, 34], [12, 1, 54], [6, 3, 16], [14, 10, 83], [10, 11, 67]];
+      const n = 15;
+      const k = 8;
+      expect(fn(times, n, k)).toEqual(34);
+    });
+  });
+});
diff --git a/book/interview-questions/recent-counter.js b/book/interview-questions/recent-counter.js
new file mode 100644
index 00000000..c30b4948
--- /dev/null
+++ b/book/interview-questions/recent-counter.js
@@ -0,0 +1,56 @@
+const { Queue } = require('../../src/index');
+
+// tag::description[]
+/**
+ * Counts the most recent requests within a time window.
+ * Each request has its timestamp.
+ * If the time window is 2 seconds,
+ * any requests that happened more than 2 seconds before the most
+ * recent request should not count.
+ *
+ * @example - The time window is 3 sec. (3000 ms)
+ *  const counter = new RecentCounter(3000);
+ *  counter.request(100); // 1
+ *  counter.request(1000); // 2
+ *  counter.request(3000); // 3
+ *  counter.request(3100); // 4
+ *  counter.request(3101); // 4
+ *
+ */
+class RecentCounter {
+  // end::description[]
+  // tag::solution[]
+  // end::solution[]
+  // tag::description[]
+  /**
+   * @param {number} maxWindow - Max. time window (in ms) for counting requests
+   *  Defaults to 1 second (1000 ms)
+   */
+  constructor(maxWindow = 1000) {
+  // end::description[]
+  // tag::solution[]
+    this.window = maxWindow;
+    this.queue = new Queue();
+  // end::solution[]
+  // tag::description[]
+  }
+
+  /**
+   * Add new request and calculate the current count within the window.
+   * @param {number} timestamp - The current timestamp (increasing order)
+   * @return {number} - The number of requests within the time window.
+   */
+  request(timestamp) {
+  // end::description[]
+  // tag::solution[]
+    this.queue.enqueue(timestamp);
+    while (timestamp - this.queue.peek() > this.window) this.queue.dequeue();
+
+    return this.queue.size;
+  // end::solution[]
+  // tag::description[]
+  }
+}
+// end::description[]
+
+module.exports = { RecentCounter };
diff --git a/book/interview-questions/recent-counter.spec.js b/book/interview-questions/recent-counter.spec.js
new file mode 100644
index 00000000..5ed65746
--- /dev/null
+++ b/book/interview-questions/recent-counter.spec.js
@@ -0,0 +1,21 @@
+const { RecentCounter } = require('./recent-counter');
+
+describe('Queue: Recent Counter', () => {
+  it('should count requests within the window', () => {
+    const counter = new RecentCounter(3000);
+    expect(counter.request(100)).toEqual(1); // 1
+    expect(counter.request(1000)).toEqual(2); // 2
+    expect(counter.request(3000)).toEqual(3); // 3
+    expect(counter.request(3100)).toEqual(4); // 4
+    expect(counter.request(3101)).toEqual(4); // 4 (request at time 100 is out of the 3000 window).
+  });
+
+  it('should NOT count requests out of the window', () => {
+    const counter = new RecentCounter(10);
+    expect(counter.request(100)).toEqual(1);
+    expect(counter.request(1000)).toEqual(1);
+    expect(counter.request(3000)).toEqual(1);
+    expect(counter.request(3100)).toEqual(1);
+    expect(counter.request(3101)).toEqual(2);
+  });
+});
diff --git a/book/interview-questions/sort-colors.js b/book/interview-questions/sort-colors.js
new file mode 100644
index 00000000..d4f331ae
--- /dev/null
+++ b/book/interview-questions/sort-colors.js
@@ -0,0 +1,51 @@
+/* eslint-disable no-return-assign */
+// const {  } = require('../../src/index');
+
+// tag::description[]
+/**
+ * Sort array of 0's, 1's and 2's in linear time and in-place.
+ * @param {numbers[]} nums - Array of number (0, 1, or 2).
+ * @returns {void} Don't return anything, modify the input array.
+ */
+function sortColors(nums) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  let left = 0;
+  let right = nums.length - 1;
+  let curr = 0;
+
+  while (curr <= right) {
+    if (nums[curr] < 1) {
+      [nums[curr], nums[left]] = [nums[left], nums[curr]]; // swap
+      left++;
+      curr++;
+    } else if (nums[curr] > 1) {
+      [nums[curr], nums[right]] = [nums[right], nums[curr]]; // swap
+      right--;
+    } else {
+      curr++;
+    }
+  }
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+// tag::compact[]
+function sortColorsCompact(nums) {
+  let i = 0; let lo = 0; let
+    hi = nums.length - 1;
+  const swap = (k, j) => [nums[k], nums[j]] = [nums[j], nums[k]];
+
+  while (i <= hi) {
+    if (nums[i] < 1) swap(i++, lo++);
+    else if (nums[i] > 1) swap(i, hi--);
+    else i++;
+  }
+}
+// end::compact[]
+
+module.exports = { sortColors, sortColorsCompact };
diff --git a/book/interview-questions/sort-colors.spec.js b/book/interview-questions/sort-colors.spec.js
new file mode 100644
index 00000000..9e0fdc47
--- /dev/null
+++ b/book/interview-questions/sort-colors.spec.js
@@ -0,0 +1,55 @@
+const { sortColors, sortColorsCompact } = require('./sort-colors');
+// const {  } = require('../../src/index');
+
+[sortColors, sortColorsCompact].forEach((fn) => {
+  describe(`Sorting: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = [];
+      fn(actual);
+      const expected = [];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with small case', () => {
+      const actual = [0, 2, 1];
+      fn(actual);
+      const expected = [0, 1, 2];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with small case1', () => {
+      const actual = [2, 1, 2];
+      fn(actual);
+      const expected = [1, 2, 2];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with small case2', () => {
+      const actual = [1, 0, 2];
+      fn(actual);
+      const expected = [0, 1, 2];
+      expect(actual).toEqual(expected);
+    });
+
+    it('should work with small case3', () => {
+      const actual = [2, 0, 1];
+      fn(actual);
+      const expected = [0, 1, 2];
+      expect(actual).toEqual(expected);
+    });
+
+    it('all numbers the same', () => {
+      const actual = Array(3).fill(1);
+      fn(actual);
+      const expected = [1, 1, 1];
+      expect(actual).toEqual(expected);
+    });
+
+    it('larger cases', () => {
+      const actual = [2, 0, 2, 1, 0, 1, 0];
+      fn(actual);
+      const expected = [0, 0, 0, 1, 1, 2, 2];
+      expect(actual).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/subarray-sum-equals-k.js b/book/interview-questions/subarray-sum-equals-k.js
new file mode 100644
index 00000000..61aa74f0
--- /dev/null
+++ b/book/interview-questions/subarray-sum-equals-k.js
@@ -0,0 +1,65 @@
+// tag::description[]
+/**
+ * Find the number of subarrays that add up to k.
+ * @example subarraySum([1, -1, 1], 0); // 3 ([1,-1,1], [1], [1])
+ * @param {number[]} nums - Array of integers.
+ * @param {number} k - The target sum.
+ * @returns {number} - The number of solutions.
+ */
+function subarraySum(nums, k) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  let ans = 0;
+  let sum = 0;
+  const map = new Map([[0, 1]]);
+
+  for (let i = 0; i < nums.length; i++) {
+    sum += nums[i];
+    if (map.has(sum - k)) ans += map.get(sum - k);
+    map.set(sum, 1 + (map.get(sum) || 0));
+  }
+
+  return ans;
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+// tag::subarraySumBrute1[]
+function subarraySumBrute1(nums, k) {
+  let ans = 0;
+
+  for (let i = 0; i < nums.length; i++) {
+    for (let j = i; j < nums.length; j++) {
+      let sum = 0;
+      for (let n = i; n <= j; n++) {
+        sum += nums[n];
+      }
+      if (sum === k) ans++;
+    }
+  }
+
+  return ans;
+}
+// end::subarraySumBrute1[]
+
+// tag::subarraySumBrute2[]
+function subarraySumBrute2(nums, k) {
+  let ans = 0;
+
+  for (let i = 0; i < nums.length; i++) {
+    let sum = 0;
+    for (let j = i; j < nums.length; j++) {
+      sum += nums[j];
+      if (sum === k) ans++;
+    }
+  }
+
+  return ans;
+}
+// end::subarraySumBrute2[]
+
+module.exports = { subarraySum, subarraySumBrute1, subarraySumBrute2 };
diff --git a/book/interview-questions/subarray-sum-equals-k.spec.js b/book/interview-questions/subarray-sum-equals-k.spec.js
new file mode 100644
index 00000000..c3670295
--- /dev/null
+++ b/book/interview-questions/subarray-sum-equals-k.spec.js
@@ -0,0 +1,24 @@
+const { subarraySum, subarraySumBrute1, subarraySumBrute2 } = require('./subarray-sum-equals-k');
+// const {  } = require('../../src/index');
+
+[subarraySum, subarraySumBrute1, subarraySumBrute2].forEach((fn) => {
+  describe(`HashMap: ${fn.name}`, () => {
+    it('should work with null/empty', () => {
+      const actual = [];
+      const expected = 0;
+      expect(fn(actual, 0)).toEqual(expected);
+    });
+
+    it('should work with small case', () => {
+      const actual = [1, -1, 1];
+      const expected = 3;
+      expect(fn(actual, 1)).toEqual(expected);
+    });
+
+    it('should work with other case', () => {
+      const actual = [1, 2, 3, 0, 1, 4, 0, 5];
+      const expected = 8;
+      expect(fn(actual, 5)).toEqual(expected);
+    });
+  });
+});
diff --git a/book/interview-questions/two-sum.js b/book/interview-questions/two-sum.js
new file mode 100644
index 00000000..37d75204
--- /dev/null
+++ b/book/interview-questions/two-sum.js
@@ -0,0 +1,42 @@
+// tag::description[]
+/**
+ * Find two numbers that add up to the target value.
+ * Return empty array if not found.
+ * @example twoSum([19, 7, 3], 10) // => [1, 2]
+ * @param {number[]} nums - Array of integers
+ * @param {number} target - The target sum.
+ * @returns {[number, number]} - Array with index 1 and index 2
+ */
+function twoSum(nums, target) {
+  // end::description[]
+  // tag::placeholder[]
+  // write your code here...
+  // end::placeholder[]
+  // tag::solution[]
+  const map = new Map();
+
+  for (let i = 0; i < nums.length; i++) {
+    const complement = target - nums[i];
+    if (map.has(nums[i])) return [map.get(nums[i]), i];
+    map.set(complement, i);
+  }
+
+  return [];
+  // end::solution[]
+  // tag::description[]
+}
+// end::description[]
+
+// tag::twoSumBrute[]
+function twoSumBrute(nums, target) {
+  for (let i = 0; i < nums.length; i++) {
+    for (let j = i + 1; j < nums.length; j++) {
+      if (nums[i] + nums[j] === target) return [i, j];
+    }
+  }
+
+  return [];
+}
+// end::twoSumBrute[]
+
+module.exports = { twoSum, twoSumBrute };
diff --git a/book/interview-questions/two-sum.spec.js b/book/interview-questions/two-sum.spec.js
new file mode 100644
index 00000000..7507ac18
--- /dev/null
+++ b/book/interview-questions/two-sum.spec.js
@@ -0,0 +1,28 @@
+const { twoSum } = require('./two-sum');
+// const {  } = require('../../src/index');
+
+describe('HashMap: Two Sum', () => {
+  it('should work with null/empty', () => {
+    const actual = [];
+    const expected = [];
+    expect(twoSum(actual, 0)).toEqual(expected);
+  });
+
+  it('should work with small case', () => {
+    const actual = [150, 100, 200];
+    const expected = [1, 2];
+    expect(twoSum(actual, 300)).toEqual(expected);
+  });
+
+  it('should work with small invalid case', () => {
+    const actual = [150, 100, 200];
+    const expected = [];
+    expect(twoSum(actual, 150)).toEqual(expected);
+  });
+
+  it('should work with other case', () => {
+    const actual = [113, 248, 80, 200, 91, 201, 68];
+    const expected = [1, 6];
+    expect(twoSum(actual, 316)).toEqual(expected);
+  });
+});
diff --git a/book/interview-questions/valid-parentheses.js b/book/interview-questions/valid-parentheses.js
new file mode 100644
index 00000000..2e097cc0
--- /dev/null
+++ b/book/interview-questions/valid-parentheses.js
@@ -0,0 +1,28 @@
+// tag::description[]
+/**
+ * Validate if the parentheses are opened and closed in the right order.
+ *
+ * @examples
+ *  isParenthesesValid('(){}[]'); // true
+ *  isParenthesesValid('([{}])'); // true
+ *  isParenthesesValid('([{)}]'); // false
+ *
+ * @param {string} string - The string
+ * @returns {boolean} - True if valid, false otherwise.
+ */
+function isParenthesesValid(string) {
+  // end::description[]
+  // tag::solution[]
+  const map = new Map([['(', ')'], ['{', '}'], ['[', ']']]);
+  const stack = [];
+
+  for (const c of string) {
+    if (map.has(c)) stack.push(map.get(c));
+    else if (c !== stack.pop()) return false;
+  }
+
+  return stack.length === 0;
+}
+// end::solution[]
+
+module.exports = { isParenthesesValid };
diff --git a/book/interview-questions/valid-parentheses.spec.js b/book/interview-questions/valid-parentheses.spec.js
new file mode 100644
index 00000000..fcb392f3
--- /dev/null
+++ b/book/interview-questions/valid-parentheses.spec.js
@@ -0,0 +1,23 @@
+const { isParenthesesValid } = require('./valid-parentheses');
+
+describe('Stack: Valid Parentheses', () => {
+  it('should be valid', () => {
+    expect(isParenthesesValid('()')).toEqual(true);
+  });
+
+  it('should be valid with different kinds', () => {
+    expect(isParenthesesValid('()[]{}')).toEqual(true);
+  });
+
+  it('should be valid with different nested kinds', () => {
+    expect(isParenthesesValid('([{}])')).toEqual(true);
+  });
+
+  it('should not be valid if incomplete', () => {
+    expect(isParenthesesValid('()(')).toEqual(false);
+  });
+
+  it('should not be valid if invalid character is present', () => {
+    expect(isParenthesesValid('()-')).toEqual(false);
+  });
+});
diff --git a/book/lab/greetings.pdf b/book/lab/greetings.pdf
deleted file mode 100644
index fab4624f..00000000
Binary files a/book/lab/greetings.pdf and /dev/null differ
diff --git a/book/lab/prawn_test.rb b/book/lab/prawn_test.rb
deleted file mode 100644
index 01c0165d..00000000
--- a/book/lab/prawn_test.rb
+++ /dev/null
@@ -1,6 +0,0 @@
-require 'prawn'
-
-pdf = Prawn::Document.new
-# pdf.text 'Hello World ✌️😄' # Prawn::Errors::IncompatibleStringEncoding
-pdf.text 'Hello World'
-pdf.render_file 'test.pdf'
diff --git a/book/lab/prawn_view.rb b/book/lab/prawn_view.rb
deleted file mode 100644
index 3e864625..00000000
--- a/book/lab/prawn_view.rb
+++ /dev/null
@@ -1,37 +0,0 @@
-require 'prawn'
-
-class Greeter
-  include Prawn::View
-
-  def initialize(name)
-    @name = name
-    set_font
-  end
-
-  def set_font
-    file = '/Users/admejiar/Library/Fonts/TwitterColorEmoji.ttf'
-    font_families["emoji"] = {
-      normal: {file: file, font: 'emoji'}
-    }
-  end
-
-  def say_hello
-    text "Hello, #{@name}!"
-    text "Hello emoji 😊🤯✅↪️🐢💀⏱🚀", fallback_fonts: ["emoji"]
-  end
-
-  def say_goodbye
-    font('Courier') do
-      text "Goodbye, #{@name}!"
-    end
-  end
-end
-
-greeter = Greeter.new('Adrian')
-
-greeter.say_hello
-greeter.say_goodbye
-
-greeter.save_as('greetings.pdf')
-
-# https://github.com/mitre-cyber-academy/ctf-scoreboard/commit/e4c46d126f1578ee950a01604801f2e987491ccd
diff --git a/book/lab/test.pdf b/book/lab/test.pdf
deleted file mode 100644
index c49d9d93..00000000
Binary files a/book/lab/test.pdf and /dev/null differ
diff --git a/book/part01-algorithms-analysis.asc b/book/part01-algorithms-analysis.asc
new file mode 100644
index 00000000..9a4399e0
--- /dev/null
+++ b/book/part01-algorithms-analysis.asc
@@ -0,0 +1,12 @@
+[[part01-algorithms-analysis]]
+== Algorithms Analysis
+
+In this part, we are going to cover the basics of algorithms analysis. We will also discuss the most common runtimes of algorithms and provide a code example for each one.
+
+include::content/part01/algorithms-analysis.asc[]
+
+<<<
+include::content/part01/how-to-big-o.asc[]
+
+<<<
+include::content/part01/big-o-examples.asc[]
diff --git a/book/part02-linear-data-structures.asc b/book/part02-linear-data-structures.asc
new file mode 100644
index 00000000..ab8b99da
--- /dev/null
+++ b/book/part02-linear-data-structures.asc
@@ -0,0 +1,48 @@
+[[part02-linear-data-structures]]
+== Linear Data Structures
+
+Data Structures come in many flavors. There's no one to rule them all. You have to know the tradeoffs so you can choose the right one for the job.
+
+In your day-to-day work, you might not need to re-implement basic data structures. However, knowing how they work internally can help you understand their time complexity better (Remember the chapter <<big-o-function-statement, How to determine Big-O from code?>>).
+
+When you are aware of the data structures implementations, you spot when to use one over the other or even extend them to create a new one. We are going to explore the most common data structures' time and space complexity.
+
+.In this part we are going to learn about the following linear data structures:
+- <<array-chap>>
+- <<hashmap-chap>>
+- <<hash-set-chap>>
+- <<part02-linear-data-structures#linked-list>>
+- <<part02-linear-data-structures#stack>>
+- <<part02-linear-data-structures#queue>>
+
+Later, in the next part, we are going to explore non-linear data structures like <<part03-graph-data-structures#graph, Graphs>> and <<part03-graph-data-structures#tree, Trees>>.
+
+ifdef::backend-html5[]
+If you want to have a general overview of each one, take a look at the following interactive diagram:
++++
+<iframe
+  src="https://embed.kumu.io/85f1a4de5fb8430a10a1bf9c5118e015"
+  width="940" height="600" frameborder="0"></iframe>
++++
+endif::[]
+
+<<<
+include::content/part02/array.asc[]
+
+<<<
+include::content/part02/hash-map.asc[]
+
+<<<
+include::content/part02/hash-set.asc[]
+
+<<<
+include::content/part02/linked-list.asc[]
+
+<<<
+include::content/part02/stack.asc[]
+
+<<<
+include::content/part02/queue.asc[]
+
+<<<
+include::content/part02/array-vs-list-vs-queue-vs-stack.asc[]
diff --git a/book/part03-graph-data-structures.asc b/book/part03-graph-data-structures.asc
new file mode 100644
index 00000000..c9b7e820
--- /dev/null
+++ b/book/part03-graph-data-structures.asc
@@ -0,0 +1,37 @@
+[[part03-graph-data-structures]]
+== Graph & Tree Data Structures
+
+Graph-based data structures are everywhere, whether you realize it or not. You can find them in databases, Web (HTML DOM tree), search algorithms, finding the best route to get home, and many more uses. We are going to learn the basic concepts and when to choose one over the other.
+
+.In this chapter we are going to learn:
+- Exciting <<part03-graph-data-structures#graph>> data structure applications
+- Searching efficiently with a <<part03-graph-data-structures#tree>> data structures.
+- One of the most versatile data structure of all <<hashmap-chap>>.
+- Keeping duplicates out with a <<tree-set-chap>>.
+By the end of this section, you will know the data structures trade-offs and when to use one over the other.
+
+include::content/part03/tree-intro.asc[]
+
+<<<
+include::content/part03/binary-search-tree.asc[]
+
+<<<
+include::content/part03/tree-search-traversal.asc[]
+
+<<<
+include::content/part03/binary-search-tree-traversal.asc[]
+
+<<<
+include::content/part03/tree-map.asc[]
+
+<<<
+include::content/part03/tree-set.asc[]
+
+<<<
+include::content/part03/graph.asc[]
+
+<<<
+include::content/part03/graph-search.asc[]
+
+<<<
+include::content/part03/time-complexity-graph-data-structures.asc[]
diff --git a/book/part04-algorithmic-toolbox.asc b/book/part04-algorithmic-toolbox.asc
new file mode 100644
index 00000000..3508f0cd
--- /dev/null
+++ b/book/part04-algorithmic-toolbox.asc
@@ -0,0 +1,38 @@
+[[part04-algorithmic-toolbox]]
+== Algorithmic Toolbox
+
+In this part of the book, we will cover examples of classical algorithms in more detail. Also, we will provide algorithmic tools for improving your problem-solving skills.
+
+
+IMPORTANT: There's no single approach to solve all problems, but knowing well-known techniques can help you build your own faster.
+
+We are going to start with <<Sorting Algorithms>>
+//  and searching algorithms,
+such as <<part04-algorithmic-toolbox#insertion-sort>>, <<part04-algorithmic-toolbox#merge-sort>> and some others.
+Later, you will learn some algorithmic paradigms that will help you identify common patterns and solve problems from different angles.
+
+
+.We are going to discuss the following techniques for solving algorithms problems:
+- <<Greedy Algorithms>>: makes greedy choices using heuristics to find the best solution without looking back.
+- <<dynamic-programming-chap>>: a technique for speeding up recursive algorithms when there are many _overlapping subproblems_. It uses _memoization_ to avoid duplicating work.
+- <<Divide and Conquer>>: _divide_ problems into smaller pieces, _conquer_ each subproblem, and then _join_ the results.
+- <<Backtracking>>: search _all (or some)_ possible paths. However, it stops and _go back_ as soon as notice the current solution is not working.
+- _Brute Force_: generate all possible solutions and tries all of them. (Use it as a last resort or as the starting point and to later optimize it).
+
+
+include::content/part04/sorting-algorithms.asc[]
+
+<<<
+include::content/part04/divide-and-conquer.asc[]
+
+<<<
+include::content/part04/dynamic-programming.asc[]
+
+<<<
+include::content/part04/greedy-algorithms.asc[]
+
+<<<
+include::content/part04/backtracking.asc[]
+
+<<<
+include::content/part04/algorithmic-toolbox.asc[]
diff --git a/book/readme.asc b/book/readme.asc
new file mode 100644
index 00000000..8c3416d0
--- /dev/null
+++ b/book/readme.asc
@@ -0,0 +1,69 @@
+DSA.js
+=======
+Adrian Mejia
+:doctitle: Data Structures & Algorithms in JavaScript
+:doctype: book
+:author: Adrian Mejia
+:email:  hello+dsajs@adrianmejia.com
+:producer: {author}
+:subject: Algorithms
+:keywords: dsa.js, Algorithms, Data Structures, JavaScript, Coding Interviews, Computer Science, Time Complexity, Linked Lists, Graphs, Binary Search Trees
+:revdate: {docdate}
+:docinfo:
+:toc:
+:toclevels: 4
+:sectnums:
+:chapter-label:
+:pagenums:
+:front-cover-image: image:cover.png[width=1050,height=1600]
+:icons: font
+//
+// custom variables (no blank lines)
+//
+:codedir: ../../../src
+:imagesdir: images
+//
+// highlighter
+:source-highlighter: rouge
+// :source-highlighter: coderay
+// :source-highlighter: pygments
+// :pygments-style: pastie
+//
+// PDF
+:pdf-fontsdir: fonts
+:pdf-stylesdir: _resources/pdfstyles
+:pdf-style: adrian-screen
+//
+// :stem:
+// :plantuml-config: ./config/_conf/umlconfig.txt
+// ---
+
+ifdef::ebook-format[:leveloffset: -1]
+
+include::content/colophon.asc[]
+
+include::content/dedication.asc[]
+
+include::content/preface.asc[]
+
+include::content/introduction.asc[]
+
+include::part01-algorithms-analysis.asc[]
+
+include::part02-linear-data-structures.asc[]
+
+include::part03-graph-data-structures.asc[]
+
+include::part04-algorithmic-toolbox.asc[]
+
+include::A-time-complexity-cheatsheet.asc[]
+
+include::B-self-balancing-binary-search-trees.asc[]
+
+include::C-AVL-tree.asc[]
+
+include::D-interview-questions-solutions.asc[]
+
+include::index.asc[]
+
+// ifndef::ebook-format[include::index.asc[]]
diff --git a/book/sample-o.adoc b/book/sample-o.adoc
deleted file mode 100644
index 983c90fa..00000000
--- a/book/sample-o.adoc
+++ /dev/null
@@ -1,47 +0,0 @@
-include::_conf/variables.adoc[]
-
-= {doctitle}
-
-// remove numbering from titles, and sub-titles e.g. 1.1
-:sectnums!:
-
-// Copyright © 2018 Adrian Mejia (g)
-include::chapters/colophon.adoc[]
-
-// Abstract and Dedication MUST have a level-0 heading in EPUB and Kindle
-// but level-1 in PDF and HTML
-// ifndef::backend-epub3[:leveloffset: +1]
-// include::chapters/dedication.adoc[]
-// ifndef::backend-epub3[:leveloffset: -1]
-include::chapters/dedication.adoc[]
-
-// (g)
-include::chapters/preface.adoc[]
-
-// add sections to chapters
-:sectnums:
-
-
-//-----------------------------------
-// TODO: commment out sample on final
-//-----------------------------------
-
-include::chapters/sample.adoc[]
-
-//-----------------------------------
-// TODO: end remove ------
-//-----------------------------------
-
-
-// --- end algorithms ---
-
-
-//
-// end chapters
-//
-:sectnums!:
-
-include::chapters/epigraph.adoc[]
-
-[index]
-= Index
diff --git a/book/sample.adoc b/book/sample.adoc
deleted file mode 100644
index cda039b7..00000000
--- a/book/sample.adoc
+++ /dev/null
@@ -1,70 +0,0 @@
-= Data Structures and Algorithms Explained in JavaScript
-:book-title: {doctitle}
-:author: Adrian Mejia
-:email:  hello+dsajs@adrianmejia.com
-:revnumber: 1.0.0
-:revdate: {docdate}
-:revyear: 2019
-:revremark: First Edition
-:subject: Algorithms
-:keywords: Algorithms, Data Structures, JavaScript, Coding Interviews, Computer Science, Time Complexity, Linked Lists, Graphs, Binary Search Trees
-:doctype: book
-:media: screen
-:imagesdir: {docdir}/images
-ifeval::["{media}" != "prepress"]
-:front-cover-image: image:cover.png[Front Cover,1050,2600]
-endif::[]
-:toc:
-:toclevels: 3
-:icons: font
-:lang: en
-:language: javascript
-:experimental:
-:pdf-fontsdir: ./fonts
-:pdf-stylesdir: ./_resources/pdfstyles
-:pdf-style: adrian-screen
-:title-logo-image: image:logo.png[Logo,50,50]
-
-// custom variables
-:codedir: ../../src
-:datadir: {docdir}/data
-:source-highlighter: pygments
-:pygments-style: xcode
-
-
-ifndef::ebook-format[:leveloffset: 1]
-
-[colophon#colophon%nonfacing]
-include::chapters/colophon.adoc[]
-
-[dedication]
-include::chapters/dedication.adoc[]
-
-// [%nonfacing]
-// include::chapters/acknowledgements.adoc[]
-
-[preface]
-include::chapters/preface.adoc[]
-
-// include::chapters/introduction.adoc[]
-
-include::chapters/chapter1.adoc[]
-
-include::chapters/sample.adoc[]
-
-// include::chapters/chapter2.adoc[]
-
-// include::chapters/chapter3.adoc[]
-
-// include::chapters/chapter4.adoc[]
-
-include::chapters/appendix.adoc[]
-
-// include::chapters/epigraph.adoc[]
-
-// include::chapters/about.adoc[]
-
-ifdef::backend-pdf,backend-docbook[]
-[index]
-= Index
-endif::[]
diff --git a/deprecated-README.adoc b/deprecated-README.adoc
new file mode 100644
index 00000000..27200775
--- /dev/null
+++ b/deprecated-README.adoc
@@ -0,0 +1,63 @@
+= Data Structures and Algorithms in JavaScript
+:toc: macro
+:toclevels: 2
+Adrian Mejia <https://github.com/amejiarosario[@amejiarosario]>
+
+image::https://travis-ci.com/amejiarosario/dsa.js.svg?branch=master["Build Status", link="https://travis-ci.com/amejiarosario/dsa.js"]
+image::https://badge.fury.io/js/dsa.js.svg["npm version", link="https://badge.fury.io/js/dsa.js"]
+
+This repository covers the implementation of the classical algorithms and data structures in JavaScript.
+
+toc::[]
+
+## Book
+
+.You can check out the book that goes deeper into each topic and provide addtional illustrations and explanations.
+- Algorithmic toolbox to avoid getting stuck while coding.
+- Explains data structures similarities and differences.
+- Algorithm analysis fundamentals (Big O notation, Time/Space complexity) and examples.
+- Time/space complexity cheatsheet.
+
+image::book/cover.png[link=https://books.adrianmejia.com/dsajs-data-structures-algorithms-javascript/, height=400]
+
+== Data Structures
+We are covering the following data structures.
+
+image::https://user-images.githubusercontent.com/418605/46118890-ba721180-c1d6-11e8-82bc-6a671428b422.png[link=https://embed.kumu.io/85f1a4de5fb8430a10a1bf9c5118e015]
+
+### Linear Data Structures
+1. **Arrays**: Built-in in most languages so not implemented here. https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Array[Post].
+2. **Linked Lists**: each data node has a link to the next (and previous). https://github.com/amejiarosario/dsa.js/blob/master/src/data-structures/linked-lists/linked-list.js[Code] | https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Linked-Lists[Post].
+3. **Queue**: data flows in a "first-in, first-out" (FIFO) manner. https://github.com/amejiarosario/dsa.js/blob/master/src/data-structures/queues/queue.js[Code] | https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Queues[Post]
+4. **Stacks**:  data flows in a "last-in, first-out" (LIFO) manner. https://github.com/amejiarosario/dsa.js/blob/master/src/data-structures/stacks/stack.js[Code] | https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Stacks[Post].
+
+### Non-Linear Data Structures
+1. **Trees**: data nodes has zero or more adjacent nodes a.k.a. children. Each node can only have one parent node otherwise is a graph not a tree. https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/trees[Code] | https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/[Post]
+	.. **Binary Trees**: same as tree but only can have two children at most. https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/trees[Code] | https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Trees[Post]
+	.. **Binary Search Trees** (BST): same as binary tree, but the nodes value keep this order `left < parent < rigth`. https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/binary-search-tree.js[Code]] | https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Search-Tree-BST[Post]
+	.. **AVL Trees**: Self-balanced BST to maximize look up time. https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/avl-tree.js[Code] | https://adrianmejia.com/blog/2018/07/16/self-balanced-binary-search-trees-with-avl-tree-data-structure-for-beginners/[Post]
+	.. **Red-Black Trees**:  Self-balanced BST more loose than AVL to maximize insertion speed. https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/red-black-tree.js[Code]
+2. **Maps**: key-value store.
+	.. **Hash Maps**: implements map using a hash function. https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/hash-maps/hashmap.js[Code] | https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#HashMaps[Post]
+	.. **Tree Maps**: implement map using a self-balanced BST. https://github.com/amejiarosario/dsa.js/blob/master/src/data-structures/maps/tree-maps/tree-map.js[Code]
+3. **Graphs**: data *nodes* that can have a connection or *edge* to zero or more adjacent nodes. Unlike trees, nodes can have multiple parents, loops. https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/graphs/graph.js[Code] | https://adrianmejia.com/blog/2018/05/14/data-structures-for-beginners-graphs-time-complexity-tutorial/[Post]
+
+## Algorithms
+
+.We cover the following algorithms and techniques.
+* Sorting algorithms
+  ** Bubble Sort. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/sorting/bubble-sort.js[Code]
+  ** Insertion Sort. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/sorting/insertion-sort.js[Code]
+  ** Selection Sort. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/sorting/selection-sort.js[Code]
+  ** Merge Sort. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/sorting/merge-sort.js[Code]
+  ** Quicksort. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/sorting/quick-sort.js[Code]
+* Greedy Algorithms
+	** Fractional Knapsack Problem. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/knapsack-fractional.js[Code]
+* Divide and Conquer
+  ** Fibonacci Numbers. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/fibonacci-recursive.js[Code]
+* Dynamic Programming
+	** Fibonacci with memoization. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/fibonacci-dynamic-programming.js[Code]
+* Backtracking algorithms
+	** Word permutations. https://github.com/amejiarosario/dsa.js/blob/master/src/algorithms/permutations-backtracking.js[Code]
+
+
diff --git a/docs/kumu-adriansky-data-structures-algorithms-java.json b/docs/kumu-adriansky-data-structures-algorithms-java.json
deleted file mode 100644
index 0effd5ab..00000000
--- a/docs/kumu-adriansky-data-structures-algorithms-java.json
+++ /dev/null
@@ -1,826 +0,0 @@
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-      "description": "# Data Structures and Algorithms in JavaScript\n\nThis [repository](https://github.com/amejiarosario/algorithms.js) covers the implementation of the most important algorithms and data structures.\n\nThis goes along with [these posts series](https://adrianmejia.com/tags/tutorial-algorithms/) that explain each implementation in details.\n\n## Data Structures\nWe are covering the following data structures.\n\n### Linear Data Structures\n1. **Arrays**: Built-in in most languages so not implemented here. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/arrays/array.js) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Array).\n2. **Linked Lists**: each data node has a link to the next (and previous). [Code](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/linked-lists) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Linked-Lists).\n3. **Queue**: data flows in a \"first-in, first-out\" (FIFO) manner. [Code](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/queues) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Queues).\n4. **Stacks**:  data flows in a \"last-in, first-out\" (LIFO) manner. [Code](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#Stacks) | [Details](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/stacks).\n\n### Non-Linear Data Structures\n1. **Trees**: data nodes has zero or more adjacent nodes a.k.a. children. Each node can only have one parent node otherwise is a graph not a tree. [Code](https://github.com/amejiarosario/algorithms.js/tree/master/src/data-structures/trees) | [Details](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/)\n\t1. **Binary Trees**: same as tree but only can have two children at most. [Details](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Trees)\n\t1. **Binary Search Trees** (BST): same as binary tree, but the nodes value keep this order `left < parent < rigth`. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/binary-search-tree.js) | [Details](https://adrianmejia.com/blog/2018/06/11/data-structures-for-beginners-trees-binary-search-tree-tutorial/#Binary-Search-Tree-BST)\n\t2. **AVL Trees**: Self-balanced BST to maximize look up time. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/avl-tree.js) | [Details](https://adrianmejia.com/blog/2018/07/16/self-balanced-binary-search-trees-with-avl-tree-data-structure-for-beginners/)\n\t3. **Red-Black Trees**:  Self-balanced BST more loose than AVL to maximize insertion speed. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/trees/red-black-tree.js) | Details\n2. **Maps**: key-value store.\n\t1. **Hash Maps**: implements map using a hash function. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/hash-maps/hashmap.js) | [Details](https://adrianmejia.com/blog/2018/04/28/data-structures-time-complexity-for-beginners-arrays-hashmaps-linked-lists-stacks-queues-tutorial/#HashMaps)\n\t2. **Tree Maps**: implement map using a self-balanced BST. WIP\n3. **Graphs**: data *nodes* that can have a connection or *edge* to zero or more adjacent nodes. Unlike trees, nodes can have multiple parents, loops. [Code](https://github.com/amejiarosario/algorithms.js/blob/master/src/data-structures/graphs/graph.js) | [Details](https://adrianmejia.com/blog/2018/05/14/data-structures-for-beginners-graphs-time-complexity-tutorial/)",
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diff --git a/docs/kumu-adriansky-data-structures-algorithms.xlsx b/docs/kumu-adriansky-data-structures-algorithms.xlsx
deleted file mode 100644
index 38fd6864..00000000
Binary files a/docs/kumu-adriansky-data-structures-algorithms.xlsx and /dev/null differ
diff --git a/editorconfig b/editorconfig
deleted file mode 100644
index c08160c4..00000000
--- a/editorconfig
+++ /dev/null
@@ -1,12 +0,0 @@
-# Cross-editor coding style settings.
-# See http://editorconfig.org/ for details.
-
-root = true
-
-[*]
-charset = utf-8
-indent_style = space
-indent_size = 2
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-trim_trailing_whitespace = true
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diff --git a/jest-all.config.js b/jest-all.config.js
new file mode 100644
index 00000000..d7ad87ae
--- /dev/null
+++ b/jest-all.config.js
@@ -0,0 +1,4 @@
+module.exports = {
+  name: 'dsa.js',
+  // testPathIgnorePatterns: ['/node_modules/', '/dist/', '/lab/', '/benchmarks/', '/coverage/'],
+};
diff --git a/jest.config.js b/jest.config.js
new file mode 100644
index 00000000..59b40aec
--- /dev/null
+++ b/jest.config.js
@@ -0,0 +1,4 @@
+module.exports = {
+  name: 'dsa.js',
+  testPathIgnorePatterns: ['/node_modules/', '/dist/', '/lab/', '/benchmarks/', '/coverage/'],
+};
diff --git a/src/runtimes/array10.txt b/lab/array10.txt
similarity index 100%
rename from src/runtimes/array10.txt
rename to lab/array10.txt
diff --git a/src/runtimes/array1000.txt b/lab/array1000.txt
similarity index 100%
rename from src/runtimes/array1000.txt
rename to lab/array1000.txt
diff --git a/src/runtimes/array100k.txt b/lab/array100k.txt
similarity index 100%
rename from src/runtimes/array100k.txt
rename to lab/array100k.txt
diff --git a/src/runtimes/array1M.txt b/lab/array1M.txt
similarity index 100%
rename from src/runtimes/array1M.txt
rename to lab/array1M.txt
diff --git a/lab/exercises/01-arrays/longest-unique-characters.js b/lab/exercises/01-arrays/longest-unique-characters.js
new file mode 100644
index 00000000..895e4e91
--- /dev/null
+++ b/lab/exercises/01-arrays/longest-unique-characters.js
@@ -0,0 +1,72 @@
+/**
+ * @param {string} s
+ * @return {number}
+ */
+function lengthOfLongestSubstring(s) {
+  let max = 0;
+  let start = 0;
+  const map = {};
+
+  for (let i = 0; i < s.length; i++) {
+    const char = s[i];
+
+    if (map[char]) {
+      start = map[char] + 1;
+    }
+
+    map[char] = i;
+    max = Math.max(1 + i - start, max);
+  }
+
+  return max;
+}
+
+const assert = require('assert');
+
+const testCases = { abcabcbb: 3 };
+
+for (const [string, unique] of Object.entries(testCases)) {
+  assert.equal(lengthOfLongestSubstring(string), unique);
+}
+
+/*
+  Longest string without duplicate chars.
+
+  "abcabcbb"
+  3 (abc)
+
+  i=6
+  c=b
+  s=5
+  h={a:3,b:4,c:2}
+  m=3
+
+  ---
+  a
+  1
+
+  aa
+  1
+
+  ab
+  2
+
+  abc
+  3
+
+  aab
+  2
+
+  aabca
+  3
+
+  "dvdf"
+  3 (vdf)
+
+  "abcabcbb"
+  3 (abc)
+  ---
+
+  map: O(n)
+  backtracking
+*/
diff --git a/lab/exercises/01-arrays/powerset/powerset-backtrack.js b/lab/exercises/01-arrays/powerset/powerset-backtrack.js
new file mode 100644
index 00000000..b76994ca
--- /dev/null
+++ b/lab/exercises/01-arrays/powerset/powerset-backtrack.js
@@ -0,0 +1,18 @@
+/**
+ *
+ * @param {*} nums
+ */
+function subsetBacktrack(nums, start = 0, current = [], result = []) {
+  // console.log({ nums, start, current, result });
+  result.push([...current]);
+
+  for (let i = start; i < nums.length; i++) {
+    current.push(nums[i]);
+    subsetBacktrack(nums, i + 1, current, result);
+    current.pop();
+    // console.log(current.pop());
+  }
+  return result;
+}
+
+module.exports = subsetBacktrack;
diff --git a/lab/exercises/01-arrays/powerset/powerset-binaryNumbers.js b/lab/exercises/01-arrays/powerset/powerset-binaryNumbers.js
new file mode 100644
index 00000000..c5ec6197
--- /dev/null
+++ b/lab/exercises/01-arrays/powerset/powerset-binaryNumbers.js
@@ -0,0 +1,27 @@
+/**
+ * Bitmask approach
+ * @param {number[]} nums
+ * @return {number[][]}
+ */
+function subsetsBinary(nums) {
+  const len = nums.length;
+  const max = 2 ** len;
+  const result = [];
+
+  for (let bin = 0; bin < max; bin++) {
+    const bitmask = bin.toString(2).padStart(len, '0');
+
+    const el = Array.from(bitmask).reduce((a, bit, i) => {
+      if (bit === '1') {
+        return a.concat(nums[i]);
+      }
+      return a;
+    }, []);
+
+    result.push(el);
+  }
+
+  return result;
+}
+
+module.exports = subsetsBinary;
diff --git a/lab/exercises/01-arrays/powerset/powerset-concat.js b/lab/exercises/01-arrays/powerset/powerset-concat.js
new file mode 100644
index 00000000..17f1e782
--- /dev/null
+++ b/lab/exercises/01-arrays/powerset/powerset-concat.js
@@ -0,0 +1,16 @@
+/**
+ *
+ * https://leetcode.com/problems/subsets
+ * @param {number[]} nums
+ * @return {number[][]}
+ */
+function subsetsConcat(nums) {
+  return nums.reduce((result, n) => {
+    const newSet = result.map(r => r.concat(n));
+    return result.concat(newSet); // O(n * n^2) bcuz copy
+  }, [
+    [],
+  ]);
+}
+
+module.exports = subsetsConcat;
diff --git a/lab/exercises/01-arrays/powerset/powerset.spec.js b/lab/exercises/01-arrays/powerset/powerset.spec.js
new file mode 100644
index 00000000..b308d9c2
--- /dev/null
+++ b/lab/exercises/01-arrays/powerset/powerset.spec.js
@@ -0,0 +1,46 @@
+// npx jest lab/exercises/01-arrays/powerset/powerset.spec.js --watch
+
+const backtrack = require('./powerset-backtrack');
+const subsetsBinary = require('./powerset-binaryNumbers');
+const subsetsConcat = require('./powerset-concat');
+
+const approaches = [backtrack, subsetsBinary, subsetsConcat];
+
+approaches.forEach((approach) => {
+  describe(`Powerset ${approach.name}`, () => {
+    it('should work with 0 elements', () => {
+      expect(approach([])).toEqual([
+        [],
+      ]);
+    });
+
+    it('should work with 1 element', () => {
+      expect(approach([1])).toEqual([
+        [],
+        [1],
+      ]);
+    });
+
+    it('should work with 2 elements', () => {
+      expect(approach([1, 2])).toEqual(expect.arrayContaining([
+        [],
+        [1],
+        [2],
+        [1, 2],
+      ]));
+    });
+
+    it('should work with 3 elements', () => {
+      expect(approach([1, 2, 3])).toEqual(expect.arrayContaining([
+        [],
+        [1],
+        [2],
+        [1, 2],
+        [3],
+        [2, 3],
+        [1, 3],
+        [1, 2, 3],
+      ]));
+    });
+  });
+});
diff --git a/lab/exercises/01-arrays/rotate-array-left.js b/lab/exercises/01-arrays/rotate-array-left.js
new file mode 100644
index 00000000..236cfc73
--- /dev/null
+++ b/lab/exercises/01-arrays/rotate-array-left.js
@@ -0,0 +1,34 @@
+// tag::description[]
+/**
+ * Rotate an array left by k number of times.
+ *
+ * @example
+ *    rotateLeft([1,2,3], 1); // [2,3,1]
+ *    rotateLeft([1,2,3,4,5], 4); // [5,1,2,3,4]
+ *
+ *    rotateLeft(Array(1e6).fill(1), 1e4); // <scale testing>
+ *
+ * @param a - The array
+ * @param k - The number of times the array is rotated
+ */
+function rotateLeft(a, k) {
+// end::description[]
+// tag::solution[]
+  const moves = k % a.length;
+  for (let i = 0; i < moves; i++) {
+    a.push(a.shift());
+  }
+  return a;
+}
+// end::solution[]
+
+// tag::bruteForce[]
+function rotateLeftBruteForce(a, k) {
+  for (let i = 0; i < k; i++) {
+    a.push(a.shift());
+  }
+  return a;
+}
+// end::bruteForce[]
+
+module.exports = { rotateLeft, rotateLeftBruteForce };
diff --git a/lab/exercises/01-arrays/rotate-array-left.spec.js b/lab/exercises/01-arrays/rotate-array-left.spec.js
new file mode 100644
index 00000000..990e1454
--- /dev/null
+++ b/lab/exercises/01-arrays/rotate-array-left.spec.js
@@ -0,0 +1,23 @@
+const { rotateLeft, rotateLeftBruteForce } = require('./rotate-array-left');
+
+const largeArray = Array(1e6).fill(1).map((t) => t * Math.random());
+
+[rotateLeft, rotateLeftBruteForce].forEach((fn) => {
+  xdescribe(`Rotate Left ${fn.name}`, () => {
+    describe('when data is small', () => {
+      it('should work with 1', () => {
+        expect(fn([1, 2, 3], 1)).toEqual([2, 3, 1]);
+      });
+
+      it('should work with 4', () => {
+        expect(fn([1, 2, 3, 4, 5], 4)).toEqual([5, 1, 2, 3, 4]);
+      });
+    });
+
+    xdescribe('when data is large', () => {
+      it('should work at scale', () => {
+        expect(fn(largeArray, 75863)).toEqual(largeArray);
+      });
+    });
+  });
+});
diff --git a/lab/exercises/01-arrays/sum-arrays.js b/lab/exercises/01-arrays/sum-arrays.js
new file mode 100644
index 00000000..24d3576a
--- /dev/null
+++ b/lab/exercises/01-arrays/sum-arrays.js
@@ -0,0 +1,13 @@
+/**
+ * Return the sum of two arrays as a new array.
+ *
+ * @example
+ *    sum([1,2,3], [1,1,1]); // [2,3,4]
+ *    sum([1], [9,9,9]); // [1,0,0,0]
+ *
+ * @param {number[]} a - Array of numbers.
+ * @param {number[]} b - Array of numbers.
+ * @returns {number[]} the sum array.
+ */
+function sum(a, b) {
+}
diff --git a/lab/exercises/05-bits/02-binary-to-string.js b/lab/exercises/05-bits/02-binary-to-string.js
index 8dd528be..24b57948 100644
--- a/lab/exercises/05-bits/02-binary-to-string.js
+++ b/lab/exercises/05-bits/02-binary-to-string.js
@@ -58,7 +58,7 @@ module.exports = binaryToString;
 
 // 17 = 7 * 10^0 + 1 * 10^1
 
-// 0.893 = 8*10^-1 + 9*10^-2 + 3*10^-1 
+// 0.893 = 8*10^-1 + 9*10^-2 + 3*10^-1
 // 0.10010 => 1*2^-1 + 0*2^-2 + 0*2^-3 + 1*2^-4 =
 
 
diff --git a/lab/exercises/09-backtracking/generate-parentheses.js b/lab/exercises/09-backtracking/generate-parentheses.js
new file mode 100644
index 00000000..7fee361b
--- /dev/null
+++ b/lab/exercises/09-backtracking/generate-parentheses.js
@@ -0,0 +1,28 @@
+/**
+ * https://leetcode.com/submissions/detail/313704254/
+ * @param {number} n
+ * @return {string[]}
+ */
+function generateParenthesis(n, result = [], open = 0, close = 0, curr = '') {
+  if (curr.length === n * 2) {
+    result.push(curr);
+  } else {
+    if (open < n) {
+      generateParenthesis(n, result, open + 1, close, `${curr}(`);
+    }
+    if (close < n) {
+      generateParenthesis(n, result, open, close + 1, `${curr})`);
+    }
+  }
+
+  return result;
+}
+
+module.exports = generateParenthesis;
+
+/*
+0: [""]
+1: ["()"]
+2: ["(())", "()()"]
+3: ["((()))", "()()()", "(())()", "()(())"]
+*/
diff --git a/lab/exercises/09-backtracking/generate-parentheses.spec.js b/lab/exercises/09-backtracking/generate-parentheses.spec.js
new file mode 100644
index 00000000..22285d55
--- /dev/null
+++ b/lab/exercises/09-backtracking/generate-parentheses.spec.js
@@ -0,0 +1,27 @@
+const generateParenthesis = require('./generate-parentheses');
+
+describe('Generate Parenthesis', () => {
+  it('should work with 0', () => {
+    expect(generateParenthesis(1)).toEqual(expect.arrayContaining([
+    ]));
+  });
+
+  it('should work with 1', () => {
+    expect(generateParenthesis(1)).toEqual(expect.arrayContaining([
+      '()',
+    ]));
+  });
+
+  it('should work with 2', () => {
+    expect(generateParenthesis(2)).toEqual(expect.arrayContaining([
+      '(())',
+      '()()',
+    ]));
+  });
+
+  it('should work with 3', () => {
+    expect(generateParenthesis(3)).toEqual(expect.arrayContaining(
+      ['((()))', '(()())', '(())()', '()(())', '()()()'],
+    ));
+  });
+});
diff --git a/lab/exercises/09-backtracking/powerset-backtrack.js b/lab/exercises/09-backtracking/powerset-backtrack.js
new file mode 120000
index 00000000..7cce8c0f
--- /dev/null
+++ b/lab/exercises/09-backtracking/powerset-backtrack.js
@@ -0,0 +1 @@
+../01-arrays/powerset/powerset-backtrack.js
\ No newline at end of file
diff --git a/lab/exercises/10-mixed/2sum-1.js b/lab/exercises/10-mixed/2sum-1.js
new file mode 100644
index 00000000..81ed21c8
--- /dev/null
+++ b/lab/exercises/10-mixed/2sum-1.js
@@ -0,0 +1,31 @@
+
+/**
+ * Given a SORTED array and a target, return the indices of the 2 number that sum up target
+ *
+ * @param {number[]} nums
+ * @param {numer} target
+ * @returns
+ *
+ * @runtime O(n)
+ */
+function twoSum(nums, target) {
+  const len = nums.length - 1;
+  let lo = 0;
+  let hi = len;
+
+  while (lo < hi && hi > 0 && lo < len) {
+    const sum = nums[lo] + nums[hi];
+    if (sum === target) {
+      return [lo, hi];
+    }
+    if (sum > target) {
+      hi--;
+    } else {
+      lo++;
+    }
+  }
+
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/lab/exercises/10-mixed/2sum.js b/lab/exercises/10-mixed/2sum.js
new file mode 100644
index 00000000..5cc6ff63
--- /dev/null
+++ b/lab/exercises/10-mixed/2sum.js
@@ -0,0 +1,25 @@
+
+/**
+ * Given an array and a target, return the indices of the 2 number that sum up target
+ *
+ * @param {number[]} nums
+ * @param {numer} target
+ * @returns
+ *
+ * @runtime O(n)
+ */
+function twoSum(nums, target) {
+  const map = new Map();
+
+  for (let i = 0; i < nums.length; i++) {
+    const diff = target - nums[i];
+    if (map.has(diff)) {
+      return [map.get(diff), i];
+    }
+    map.set(nums[i], i);
+  }
+
+  return [];
+}
+
+module.exports = twoSum;
diff --git a/lab/exercises/10-mixed/2sum.spec.js b/lab/exercises/10-mixed/2sum.spec.js
new file mode 100644
index 00000000..74832238
--- /dev/null
+++ b/lab/exercises/10-mixed/2sum.spec.js
@@ -0,0 +1,23 @@
+const fn = require('./2sum');
+
+describe('2 sum', () => {
+  it('should work', () => {
+    expect(fn([-1, 0, 1], 0)).toEqual(expect.arrayContaining([0, 2]));
+  });
+
+  it('should work', () => {
+    expect(fn([2, 7, 11, 15], 9)).toEqual([0, 1]);
+  });
+
+  it('should work', () => {
+    expect(fn([2, 7, 11, 15], 18)).toEqual([1, 2]);
+  });
+
+  it('should be empty', () => {
+    expect(fn([2, 7, 11, 15], 1)).toEqual([]);
+  });
+
+  it('should should work with non-sorted', () => {
+    expect(fn([3, 2, 4], 6)).toEqual([1, 2]);
+  });
+});
diff --git a/lab/exercises/10-mixed/3sum-1.js b/lab/exercises/10-mixed/3sum-1.js
new file mode 100644
index 00000000..33527a16
--- /dev/null
+++ b/lab/exercises/10-mixed/3sum-1.js
@@ -0,0 +1,29 @@
+/**
+ * @param {number[]} nums
+ * @return {number[][]}
+ *
+ * @runtime O(n^3)
+ */
+function threeSum(nums) {
+  const ans = new Set();
+
+  for (let i = 0; i < nums.length; i++) {
+    for (let j = i + 1; j < nums.length; j++) {
+      for (let k = j + 1; k < nums.length; k++) {
+        if (nums[i] + nums[j] + nums[k] === 0) {
+          ans.add(JSON.stringify([nums[i], nums[j], nums[k]].sort()));
+        }
+      }
+    }
+  }
+
+  return Array.from(ans).map((s) => JSON.parse(s));
+}
+
+module.exports = threeSum;
+
+// Given an array find the unique triplets elements that sum zero.
+
+// Brute force: O(n^3)
+// Using twoSum: O(n^2)
+//
diff --git a/lab/exercises/10-mixed/3sum-2.js b/lab/exercises/10-mixed/3sum-2.js
new file mode 100644
index 00000000..02389971
--- /dev/null
+++ b/lab/exercises/10-mixed/3sum-2.js
@@ -0,0 +1,47 @@
+/**
+ * @param {number[]} nums
+ * @return {number[][]}
+ *
+ * @runtime O(n^2)
+ * @space O(n)
+ */
+const threeSum = function (nums) {
+  const array = nums.reduce((acc, n, i) => { // O(n^2)
+    if (i > nums.length - 2) return acc;
+    const partial = twoSum(nums, -n, i + 1);
+    const res = partial.map((p) => [n, ...p]);
+    // console.log({i, n, partial, res, acc})
+    return acc.concat(res);
+  }, []);
+
+  // remove dups
+  const set = array.reduce((acc, n) => {
+    const str = n.sort((a, b) => a - b).toString();
+    return acc.add(str);
+  }, new Set());
+
+  // convert to array of nums
+  return [...set].map((a) => a.split(',').map((n) => +n));
+};
+
+function twoSum(nums, target, start) { // O(n)
+  const ans = [];
+  const map = new Map();
+
+  for (let i = start; i < nums.length; i++) {
+    if (map.has(target - nums[i])) {
+      ans.push([target - nums[i], nums[i]]);
+    }
+    map.set(nums[i], i);
+  }
+
+  return ans;
+}
+
+module.exports = threeSum;
+
+// Given an array find the unique triplets elements that sum zero.
+
+// Brute force: O(n^3)
+// Using twoSum: O(n^2)
+//
diff --git a/lab/exercises/10-mixed/3sum.js b/lab/exercises/10-mixed/3sum.js
new file mode 100644
index 00000000..6837b34c
--- /dev/null
+++ b/lab/exercises/10-mixed/3sum.js
@@ -0,0 +1,47 @@
+/**
+ * @param {number[]} nums
+ * @return {number[][]}
+ *
+ * @runtime O(n^2) and skips duplicates
+ * @space O(1)
+ */
+function threeSum(nums) {
+  const ans = [];
+
+  nums.sort((a, b) => a - b); // sort: O(n log n)
+
+  for (let i = 0; i < nums.length - 2; i++) { // O(n^2)
+    if (i > 0 && nums[i - 1] === nums[i]) continue; // skip duplicates
+
+    let lo = i + 1;
+    let hi = nums.length - 1;
+
+    while (lo < hi) {
+      const sum = nums[i] + nums[lo] + nums[hi];
+      if (sum === 0) {
+        ans.push([nums[i], nums[lo], nums[hi]]);
+        // console.log([nums[i], nums[lo], nums[hi]]);
+        lo++;
+        hi--;
+        while (lo < hi && nums[lo - 1] === nums[lo]) lo++; // skip duplicates
+        while (lo < hi && nums[hi + 1] === nums[hi]) hi--; // skip duplicates
+      } else if (sum < 0) {
+        lo++;
+        while (lo < hi && nums[lo - 1] === nums[lo]) lo++; // skip duplicates
+      } else {
+        hi--;
+        while (lo < hi && nums[hi + 1] === nums[hi]) hi--; // skip duplicates
+      }
+    }
+  }
+
+  return ans;
+}
+
+module.exports = threeSum;
+
+// Given an array find the unique triplets elements that sum zero.
+
+// Brute force: O(n^3)
+// Using twoSum: O(n^2)
+//
diff --git a/lab/exercises/10-mixed/3sum.spec.js b/lab/exercises/10-mixed/3sum.spec.js
new file mode 100644
index 00000000..862de0f2
--- /dev/null
+++ b/lab/exercises/10-mixed/3sum.spec.js
@@ -0,0 +1,44 @@
+const fn = require('./3sum');
+
+describe('3 Sum', () => {
+  it('should work', () => {
+    expect(fn([-1, 0, 1, 2, -1, 4])).toEqual(expect.arrayContaining([
+      expect.arrayContaining([-1, 0, 1]),
+      expect.arrayContaining([-1, 2, -1]),
+    ]));
+  });
+
+  it('should work', () => {
+    const actual = fn([-2, 0, 1, 1, 2]);
+    expect(actual.length).toEqual(2);
+    expect(actual).toEqual(expect.arrayContaining([
+      expect.arrayContaining([-2, 0, 2]),
+      expect.arrayContaining([-2, 1, 1]),
+    ]));
+  });
+
+  it('should work', () => {
+    const actual = fn([-2, -4, -2, -2, 0, 1, 2, 2, 2, 3, 3, 4, 4, 6, 6]);
+    expect(actual.length).toEqual(6);
+    expect(actual).toEqual(expect.arrayContaining([
+      expect.arrayContaining([-4, -2, 6]),
+      expect.arrayContaining([-4, 0, 4]),
+      expect.arrayContaining([-4, 1, 3]),
+      expect.arrayContaining([-4, 2, 2]),
+      expect.arrayContaining([-2, -2, 4]),
+      expect.arrayContaining([-2, 0, 2]),
+    ]));
+  });
+
+  it('should work with many zeros', () => {
+    const actual = fn(Array(5).fill(0));
+    expect(actual.length).toEqual(1);
+    expect(JSON.stringify(actual)).toEqual('[[0,0,0]]'); // jest negative zero workaround
+  });
+
+  it('should work with large arrays', () => {
+    const actual = fn(Array(3000).fill(0));
+    expect(actual.length).toEqual(1);
+    expect(JSON.stringify(actual)).toEqual('[[0,0,0]]');
+  });
+});
diff --git a/lab/exercises/10-mixed/alien-dictionary.js b/lab/exercises/10-mixed/alien-dictionary.js
new file mode 100644
index 00000000..07905d65
--- /dev/null
+++ b/lab/exercises/10-mixed/alien-dictionary.js
@@ -0,0 +1,109 @@
+
+/**
+ * Add nodes and edges into a graph using adjacency list
+ *
+ * @class Graph
+ */
+class Graph {
+  constructor() {
+    this.nodes = new Map();
+  }
+
+  // add node or directed edge
+  add(node1, node2) {
+    // console.log({node1, node2})
+    const adj = this.nodes.has(node1) ? this.nodes.get(node1) : new Set();
+    if (node2) adj.add(node2);
+    this.nodes.set(node1, adj);
+  }
+}
+
+/**
+ * DFS + tarjan for loop detection
+ *
+ * @param {Graph} g
+ * @param {Map} node
+ * @param {Set} set
+ * @param {Map} [parent=null]
+ * @param {Map} [grouping=new Map()]
+ * @param {number} [depth=0]
+ * @returns {boolean} true if has a loop, false otherwise
+ */
+function hasLoopOrAddToSet(g, node, set, parent = null, grouping = new Map(), depth = 0) {
+  if (set.has(node)) set.delete(node);
+  set.add(node);
+  grouping.set(node, depth);
+
+  // console.log({node, adjs: g.nodes.get(node)});
+
+  for (const adj of g.nodes.get(node)) {
+    // if (adj === parent) continue; // only for indirected graph
+
+    if (!grouping.has(adj)) {
+      if (hasLoopOrAddToSet(g, adj, set, node, grouping, depth + 1)) return true;
+    }
+
+    const minGroup = Math.min(grouping.get(adj), grouping.get(node));
+    grouping.set(node, minGroup);
+
+    if (grouping.get(adj) === grouping.get(node)) return true;
+  }
+}
+
+
+/**
+ * Find the order of the alien alphabet given a list of words on lexicographical order.
+ *
+ * @param {string[]} words
+ * @return {string} The alien alphabet order.
+ */
+function alienOrder(words) {
+  const g = new Graph();
+  if (!words || words.length < 2) return words && words.join('');
+
+  for (let i = 1; i < words.length; i++) { // O(n) * O(k)
+    const w1 = words[i - 1];
+    const w2 = words[i];
+    let j = 0;
+
+    while (j < w1.length && j < w2.length && w1[j] === w2[j]) { // O(k), k = max word length
+      g.add(w1[j++]);
+    }
+
+    if (j === w2.length && w1.length > w2.length) {
+      return ''; // shorter words should come first.
+    }
+
+    if (w1[j]) g.add(w1[j], w2[j]);
+    [...w1.slice(j)].forEach((n) => g.add(n));
+    [...w2.slice(j)].forEach((n) => g.add(n));
+  }
+
+  // console.log({ g: JSON.stringify(g) });
+  // console.log({ g: g.nodes });
+
+  const set = new Set();
+  for (const [node] of g.nodes) { // O(?)
+    if (hasLoopOrAddToSet(g, node, set)) { // DFS: O(E + V), V: total unique letters
+      return '';
+    }
+  }
+
+  return [...set].join('');
+}
+
+module.exports = alienOrder;
+
+// Find the order of the alien alphabet given a list of words on lexicographical order.
+
+// take words in pair, and build a dependency graph.
+  // skip while w1.char === w2.char
+  // add the first diff chars as a adj nodes
+  // add each letter as a node in the graph.
+
+// find the order of the alien alph
+  // iterate over each node
+  // dfs + tarjan to detect loops
+  // add each visited node to a set
+  // if there’s a loop return ‘’
+  // return set
diff --git a/lab/exercises/10-mixed/alien-dictionary.spec.js b/lab/exercises/10-mixed/alien-dictionary.spec.js
new file mode 100644
index 00000000..0507b0a7
--- /dev/null
+++ b/lab/exercises/10-mixed/alien-dictionary.spec.js
@@ -0,0 +1,60 @@
+// npx jest lab/exercises/10-mixed/alien-dictionary.spec.js --watch
+const alienOrder = require('./alien-dictionary');
+
+describe('Alien Dictionary', () => {
+  it('should work', () => {
+    const words = ['wrt', 'wrtkj', 'wrtkjd'];
+    expect(Array.from(alienOrder(words))).toEqual(expect.arrayContaining(Array.from('djkrtw')));
+  });
+
+  it('should work', () => {
+    const words = [
+      'wrt',
+      'wrf',
+      'er',
+      'ett',
+      'rftt',
+    ];
+    expect(alienOrder(words)).toEqual('wertf');
+  });
+
+  it('should work', () => {
+    const words = ['aac', 'aabb', 'aaba'];
+    expect(alienOrder(words)).toEqual('cba');
+  });
+
+  it('should work', () => {
+    const words = ['ab', 'cd', 'ef', 'f', 'x', 'xb', 'xc', 'zd', 'ze'];
+    expect((alienOrder(words))).toEqual('abcdefxz');
+  });
+
+  it('should work', () => {
+    const words = ['a', 'b', 'ca', 'cc'];
+    expect((alienOrder(words))).toEqual('abc');
+  });
+
+  it('should detect loops', () => {
+    const words = ['z', 'x', 'z'];
+    expect((alienOrder(words))).toEqual('');
+  });
+
+  it('should detect loops', () => {
+    const words = ['a', 'b', 'c', 'cc', 'ca'];
+    expect((alienOrder(words))).toEqual('');
+  });
+
+  it('detects length issues (prefixes are first)', () => {
+    const words = ['ab', 'a'];
+    expect((alienOrder(words))).toEqual('');
+  });
+
+  it('should work with one word and letter', () => {
+    const words = ['a'];
+    expect((alienOrder(words))).toEqual('a');
+  });
+
+  it('should work with one word', () => {
+    const words = ['abc'];
+    expect(Array.from(alienOrder(words))).toEqual(expect.arrayContaining(Array.from('abc')));
+  });
+});
diff --git a/lab/exercises/10-mixed/check-prerequisite.js b/lab/exercises/10-mixed/check-prerequisite.js
new file mode 100644
index 00000000..d80ad176
--- /dev/null
+++ b/lab/exercises/10-mixed/check-prerequisite.js
@@ -0,0 +1,33 @@
+const checkIfPrerequisite = function (n, prerequisites, queries) {
+  const graph = new Map();
+
+  Array(n).fill(0).forEach((v, i) => graph.set(i, {
+    children: [],
+    // connected: new Set(),
+  }));
+
+  prerequisites.forEach(([u, v]) => {
+    graph.get(u).children.push(v);
+    // graph.get(u).connected.add(v);
+  });
+
+
+  return queries.map(([u, v]) => isConnected(graph, u, v, new Set(), u));
+};
+
+function isConnected(graph, u, v, path = new Set(), p) {
+  // console.log({u, v, path}, graph)
+  // if (graph.get(u).connected.has(v)) return true;
+  // path.forEach(s => graph.get(p).connected.add(s));
+
+  for (const child of graph.get(u).children) {
+    if (child === v) return true;
+    if (path.has(child)) continue;
+    if (isConnected(graph, child, v, path.add(u), p)) return true;
+  }
+
+  return false;
+}
+
+
+module.exports = checkIfPrerequisite;
diff --git a/lab/exercises/10-mixed/check-prerequisite.spec.js b/lab/exercises/10-mixed/check-prerequisite.spec.js
new file mode 100644
index 00000000..4614161a
--- /dev/null
+++ b/lab/exercises/10-mixed/check-prerequisite.spec.js
@@ -0,0 +1,140 @@
+const checkIfPrerequisite = require('./check-prerequisite');
+
+describe('checkIfPrerequisite', () => {
+  it('should work with 2', () => {
+    const n = 2;
+    const prerequisites = [[1, 0]];
+    const queries = [
+      [0, 1],
+      [1, 0],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      false,
+      true,
+    ]);
+  });
+
+  it('example 4', () => {
+    const n = 3;
+    const prerequisites = [
+      [1, 0],
+      [2, 0],
+    ];
+    const queries = [
+      [0, 1],
+      [2, 0],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      false,
+      true,
+    ]);
+  });
+
+  it('example 5', () => {
+    const n = 5;
+    const prerequisites = [
+      [0, 1],
+      [1, 2],
+      [2, 3],
+      [3, 4],
+    ];
+    const queries = [
+      [0, 4],
+      [4, 0],
+      [1, 3],
+      [3, 0],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      true,
+      false,
+      true,
+      false,
+    ]);
+  });
+
+  it('no prereq', () => {
+    const n = 2;
+    const prerequisites = [];
+    const queries = [
+      [1, 0],
+      [0, 1],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      false,
+      false,
+    ]);
+  });
+
+  it('example 3', () => {
+    const n = 3;
+    const prerequisites = [
+      [1, 2],
+      [1, 0],
+      [2, 0],
+    ];
+    const queries = [
+      [1, 0],
+      [1, 2],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      true,
+      true,
+    ]);
+  });
+
+  it('should work with inferred', () => {
+    const n = 3;
+    const prerequisites = [
+      [0, 1],
+      [1, 2],
+    ];
+    const queries = [
+      [0, 1],
+      [1, 2],
+      [0, 2],
+      [2, 1],
+      [2, 0],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      true,
+      true,
+      true,
+      false,
+      false,
+    ]);
+  });
+
+  it('should work with 4', () => {
+    const n = 4;
+    const prerequisites = [
+      [2, 3],
+      [2, 1],
+      [0, 3],
+      [0, 1],
+    ];
+    // https://bit.ly/2ApKE3L
+    const queries = [
+      [0, 1],
+      [0, 3],
+      [2, 3],
+      [3, 0],
+      [2, 0],
+      [0, 2],
+    ];
+    expect(checkIfPrerequisite(n, prerequisites, queries)).toEqual([
+      true,
+      true,
+      true,
+      false,
+      false,
+      false,
+    ]);
+  });
+
+  it('stress testing', () => {
+    const n = 33;
+    const prerequisites = [[16,22],[16,15],[16,2],[16,3],[16,25],[16,21],[16,11],[16,23],[16,5],[16,13],[16,20],[16,29],[16,14],[16,9],[16,17],[16,6],[16,27],[16,8],[16,24],[16,1],[16,31],[16,7],[16,18],[16,26],[16,30],[16,28],[16,10],[16,12],[32,22],[32,15],[32,2],[32,25],[32,21],[32,11],[32,23],[32,5],[32,4],[32,20],[32,29],[32,14],[32,17],[32,19],[32,6],[32,27],[32,8],[32,24],[32,1],[32,31],[32,7],[32,18],[32,26],[32,30],[32,28],[32,10],[32,12],[32,0],[22,15],[22,2],[22,3],[22,21],[22,11],[22,23],[22,5],[22,4],[22,13],[22,20],[22,29],[22,9],[22,17],[22,19],[22,6],[22,8],[22,24],[22,31],[22,7],[22,18],[22,26],[22,30],[22,28],[22,10],[22,12],[22,0],[15,2],[15,3],[15,25],[15,21],[15,11],[15,23],[15,5],[15,4],[15,13],[15,29],[15,14],[15,9],[15,17],[15,19],[15,6],[15,27],[15,8],[15,24],[15,1],[15,31],[15,7],[15,18],[15,26],[15,30],[15,28],[15,10],[15,12],[15,0],[2,25],[2,21],[2,11],[2,23],[2,5],[2,4],[2,13],[2,29],[2,14],[2,9],[2,17],[2,19],[2,6],[2,27],[2,8],[2,24],[2,1],[2,31],[2,7],[2,18],[2,10],[2,12],[2,0],[3,25],[3,21],[3,11],[3,23],[3,5],[3,4],[3,13],[3,20],[3,29],[3,14],[3,9],[3,17],[3,6],[3,8],[3,1],[3,31],[3,7],[3,18],[3,26],[3,30],[3,10],[3,12],[3,0],[25,21],[25,11],[25,23],[25,5],[25,4],[25,20],[25,29],[25,14],[25,9],[25,17],[25,19],[25,6],[25,27],[25,8],[25,24],[25,1],[25,31],[25,7],[25,18],[25,26],[25,30],[25,28],[25,10],[25,12],[21,11],[21,23],[21,5],[21,4],[21,13],[21,20],[21,29],[21,14],[21,9],[21,19],[21,6],[21,27],[21,8],[21,24],[21,1],[21,31],[21,7],[21,18],[21,26],[21,30],[21,28],[21,10],[21,12],[21,0],[11,23],[11,5],[11,4],[11,13],[11,20],[11,29],[11,14],[11,9],[11,17],[11,19],[11,6],[11,27],[11,8],[11,24],[11,1],[11,31],[11,7],[11,18],[11,26],[11,30],[11,28],[11,10],[11,12],[11,0],[23,5],[23,4],[23,13],[23,20],[23,29],[23,14],[23,9],[23,17],[23,6],[23,8],[23,24],[23,1],[23,7],[23,18],[23,28],[23,10],[23,12],[5,13],[5,20],[5,9],[5,17],[5,6],[5,27],[5,24],[5,1],[5,31],[5,7],[5,18],[5,26],[5,30],[5,10],[5,12],[5,0],[4,13],[4,20],[4,29],[4,14],[4,17],[4,19],[4,6],[4,8],[4,24],[4,1],[4,31],[4,7],[4,18],[4,26],[4,30],[4,28],[4,10],[4,12],[4,0],[13,20],[13,14],[13,9],[13,17],[13,19],[13,8],[13,24],[13,1],[13,31],[13,7],[13,18],[13,26],[13,30],[13,28],[13,10],[13,12],[13,0],[20,29],[20,14],[20,9],[20,17],[20,19],[20,6],[20,27],[20,1],[20,31],[20,26],[20,30],[20,28],[20,12],[20,0],[29,14],[29,9],[29,17],[29,19],[29,6],[29,27],[29,8],[29,24],[29,1],[29,31],[29,7],[29,18],[29,30],[29,28],[29,10],[29,12],[29,0],[14,9],[14,17],[14,19],[14,6],[14,27],[14,24],[14,1],[14,31],[14,7],[14,18],[14,26],[14,30],[14,28],[14,12],[14,0],[9,17],[9,19],[9,6],[9,27],[9,8],[9,24],[9,1],[9,31],[9,7],[9,18],[9,26],[9,30],[9,28],[9,12],[9,0],[17,19],[17,6],[17,27],[17,8],[17,24],[17,1],[17,7],[17,18],[17,26],[17,30],[17,28],[17,10],[17,12],[17,0],[19,6],[19,8],[19,24],[19,1],[19,31],[19,7],[19,18],[19,26],[19,30],[19,28],[19,10],[19,0],[6,27],[6,8],[6,24],[6,1],[6,7],[6,26],[6,30],[6,28],[6,10],[6,12],[6,0],[27,8],[27,24],[27,1],[27,7],[27,26],[27,30],[27,28],[27,10],[27,12],[27,0],[8,24],[8,1],[8,31],[8,18],[8,26],[8,28],[8,12],[8,0],[24,1],[24,31],[24,7],[24,18],[24,26],[24,30],[24,28],[24,12],[24,0],[1,31],[1,7],[1,18],[1,26],[1,30],[1,28],[1,10],[1,12],[1,0],[31,7],[31,18],[31,26],[31,30],[31,28],[31,10],[31,12],[31,0],[7,18],[7,26],[7,30],[7,28],[7,10],[18,26],[18,28],[18,10],[26,30],[26,28],[26,10],[26,0],[30,28],[30,10],[30,12],[30,0],[28,12],[28,0],[10,0],[12,0]];
+    const queries = [[6,17],[31,23],[32,18],[31,18],[23,8],[30,20],[13,16],[25,31],[30,22],[17,0],[10,26],[32,20],[10,28],[31,32],[32,21],[24,27],[17,10],[27,3],[24,16],[31,29],[6,31],[23,22],[24,13],[7,21],[14,28],[28,30],[28,32],[16,7],[7,32],[10,24],[5,28],[20,32],[27,31],[31,32],[30,32],[4,10],[18,32],[18,22],[10,25],[32,22],[0,32],[32,27],[32,25],[18,12],[24,23],[30,31],[28,18],[18,26],[18,20],[30,16],[7,19],[29,31],[13,10],[18,7],[14,2],[32,30],[14,1],[27,29],[13,22],[25,1],[25,23],[8,29],[16,20],[10,24],[4,20],[0,13],[25,14],[29,27],[12,28],[24,31],[32,26],[29,3],[27,23],[22,31],[21,15],[29,11],[30,26],[18,25],[22,29],[10,26],[20,12],[6,1],[30,7],[4,31],[13,16],[30,18],[13,5],[16,25],[23,28],[28,25],[30,10],[16,21],[32,5],[19,16],[30,29],[2,16],[18,21],[20,24],[24,23],[13,7],[28,5],[32,31],[17,24],[6,29],[24,25],[21,24],[24,22],[26,29],[20,5],[22,29],[19,9],[16,21],[21,18],[2,11],[14,0],[14,7],[17,23],[31,1],[3,9],[22,4],[30,21],[32,11],[31,28],[26,29],[19,0],[22,13],[4,17],[1,2],[29,24],[14,15],[19,0],[6,25],[14,27],[20,31],[25,21],[32,31],[31,27],[6,14],[32,30],[22,20],[27,20],[23,22],[28,31],[15,26],[26,14],[28,7],[29,30],[32,28],[15,26],[14,27],[32,31],[6,23],[8,27],[5,7],[21,18],[14,11],[32,28],[29,8],[24,32],[14,28],[19,21],[15,13],[3,20],[30,27],[22,19],[32,29],[6,10],[26,30],[29,4],[16,3],[31,8],[20,24],[30,8],[17,28],[13,14],[4,10],[12,22],[26,3],[30,28],[3,8],[11,30],[20,4],[20,14],[15,27],[15,23],[27,25],[4,20],[7,31],[24,22],[28,29],[21,31],[32,11],[19,10],[30,25],[8,24],[26,17],[9,26],[17,14],[29,24],[30,31],[29,22],[10,21],[21,19],[25,28],[15,28],[32,29],[23,1],[26,29],[17,31],[25,21],[15,8],[29,28],[20,24],[19,24],[23,22],[2,15],[17,31],[31,32],[23,21],[27,31],[28,24],[2,21],[4,26],[27,20],[14,25],[4,3],[21,22],[28,22],[16,26],[12,22],[27,9],[8,30],[24,30],[23,28],[14,3],[1,7],[32,31],[23,29],[23,29],[11,20],[30,27],[20,7],[31,27],[2,30],[6,11],[18,10],[13,31],[19,31],[24,9],[7,10],[27,31],[3,4],[24,2],[8,21],[13,27],[23,20],[26,25],[18,30],[26,23],[20,29],[32,27],[29,14],[29,27],[6,27],[17,0],[29,30],[31,28],[24,29],[23,0],[31,23],[23,26],[21,14],[16,7],[28,12],[3,7],[3,30],[31,29],[15,0],[13,6],[32,12],[28,26],[27,30],[23,29],[5,13],[29,30],[14,22],[9,22],[16,13],[31,28],[30,13],[14,21],[32,31],[10,21],[27,24],[11,4],[13,1],[21,24],[31,32],[12,3],[29,32],[15,14],[9,26],[25,21],[32,31],[11,23],[18,11],[18,21],[24,1],[31,18],[26,20],[19,6],[25,10],[21,28],[23,32],[28,30],[31,30],[6,16],[19,29],[13,23],[31,30],[29,25],[32,19],[26,23],[2,3],[22,24],[32,29],[24,27],[30,8],[31,30],[24,13],[31,32],[26,30],[27,10],[26,5],[10,22],[27,32],[17,14],[26,18],[32,31],[10,5],[0,19],[28,30],[13,7],[9,28],[20,19],[4,23],[30,31],[26,21],[25,24],[23,22],[26,29],[22,17],[18,20],[18,31],[11,16],[0,23],[9,6],[17,19],[1,4],[18,27],[20,30],[16,19],[25,23],[32,29],[32,30],[18,3],[2,21],[0,5],[18,16],[11,1],[28,16],[31,10],[32,14],[13,32],[20,22],[25,23],[14,21],[14,26],[14,3],[18,5],[28,24],[4,19],[2,16],[24,16],[21,19],[32,25],[30,23],[12,8],[11,17],[13,5],[23,16],[28,22],[10,29],[10,15],[29,6],[18,10],[26,30],[31,21],[23,2],[27,20],[32,17],[1,16],[32,30],[22,12],[17,0],[4,6],[31,29],[30,32],[18,28],[32,28],[16,28],[19,25],[31,32],[25,31],[19,21],[12,9],[18,32],[17,10],[21,9],[0,16],[16,23],[10,30],[31,19],[30,19],[26,12],[24,25],[17,32],[23,22],[6,23],[14,28],[5,14],[25,29],[28,24],[8,3],[30,19],[26,7],[11,17],[32,29],[26,28],[14,23],[30,25],[25,31],[31,4],[11,9],[30,19],[19,2],[11,9],[21,28],[13,10],[19,6],[1,19],[24,15],[23,25],[16,30],[9,26],[28,0],[21,24],[19,3],[26,6],[11,21],[17,21],[16,21],[1,31],[15,16],[31,32],[30,8],[6,7],[23,14],[19,22],[31,32],[23,20],[17,30],[4,3],[17,1],[32,25],[10,31],[11,12],[26,28],[25,28],[31,23],[18,16],[16,14],[2,8],[1,14],[10,19],[26,8],[16,7],[25,18],[16,10],[19,27],[30,31],[29,21],[29,28],[17,30],[10,24],[16,0],[32,31],[32,31],[8,1],[23,11],[25,28],[20,14],[16,10],[10,0],[22,32],[15,9],[19,14],[27,25],[27,25],[31,32],[13,30],[9,31],[15,26],[15,28],[0,25],[30,22],[7,24],[10,28],[22,23],[16,28],[8,9],[31,17],[19,25],[23,6],[29,32],[4,5],[21,5],[27,23],[31,30],[29,30],[24,25],[20,30],[27,18],[21,9],[32,30],[23,5],[17,5],[20,18],[19,12],[32,28],[21,17],[32,20],[26,13],[28,25],[31,32],[19,29],[29,14],[24,15],[30,4],[14,20],[27,22],[4,16],[9,22],[18,5],[31,13],[26,29],[30,32],[27,30],[0,8],[17,1],[30,32],[18,19],[25,22],[23,17],[24,26],[8,10],[20,23],[32,31],[32,20],[22,9],[0,15],[19,18],[32,29],[22,1],[19,22],[13,23],[31,32],[20,3],[29,8],[27,29],[7,17],[16,20],[24,4],[30,32],[28,12],[32,31],[31,30],[6,18],[32,30],[11,6],[28,29],[21,31],[2,21],[9,30],[31,32],[13,21],[31,14],[22,30],[32,26],[11,30],[9,19],[10,20],[29,10],[1,20],[25,30],[13,20],[15,16],[27,28],[29,22],[13,3],[7,17],[12,14],[32,31],[18,15],[31,28],[25,31],[30,31],[29,2],[26,19],[31,24],[22,3],[32,31],[23,28],[2,24],[4,8],[19,5],[16,29],[7,13],[10,28],[12,9],[23,28],[14,27],[31,24],[14,3],[26,31],[23,32],[14,31],[14,18],[0,10],[23,13],[25,24],[17,7],[17,11],[19,2],[32,16],[9,15],[25,17],[12,3],[32,26],[12,20],[29,11],[29,32],[5,7],[23,25],[27,22],[31,32],[30,22],[30,31],[9,10],[28,29],[5,3],[8,24],[28,32],[14,22],[7,28],[30,20],[20,9],[32,31],[23,31],[31,29],[32,30],[24,32],[1,2],[20,4],[1,21],[31,15],[26,27],[10,24],[1,25],[23,20],[20,22],[9,29],[25,24],[17,9],[17,9],[13,32],[21,20],[29,20],[19,7],[28,30],[12,28],[21,28],[12,16],[14,11],[6,27],[31,23],[10,15],[28,7],[0,5],[19,17],[25,11],[7,16],[32,30],[32,30],[12,31],[11,12],[31,28],[27,32],[14,1],[17,5],[15,25],[8,19],[29,31],[14,27],[18,20],[7,3],[9,6],[20,24],[9,27],[1,11],[20,4],[12,30],[21,7],[28,3],[32,30],[11,0],[29,27],[31,32],[11,9],[24,0],[24,14],[21,3],[13,30],[25,32],[21,24],[7,8],[19,22],[21,20],[11,4],[5,4],[9,17],[31,26],[26,18],[25,21],[19,17],[7,4],[5,17],[2,4],[20,16],[13,20],[31,32],[29,24],[3,24],[10,23],[9,16],[18,17],[21,26],[27,29],[31,32],[24,2],[15,26],[1,8],[14,20],[31,32],[21,14],[32,23],[31,25],[31,17],[15,13],[22,32],[27,21],[32,10],[26,28],[27,29],[12,29],[6,16],[14,19],[17,21],[23,27],[1,12],[14,26],[22,21],[0,12],[10,9],[13,18],[20,23],[27,28],[11,15],[32,26],[23,4],[27,32],[19,10],[20,24],[9,12],[1,12],[16,12],[6,14],[11,21],[6,4],[11,22],[31,27],[21,20],[17,30],[31,32],[3,17],[27,22],[21,28],[25,5],[29,27],[31,18],[31,30],[29,27],[12,21],[29,10],[3,18],[21,31],[8,15],[16,22],[16,21],[32,31],[31,8],[5,13],[11,19],[23,4],[23,10],[5,26],[25,7],[8,27],[32,9],[30,28],[13,23],[32,28],[16,21],[31,32],[25,16],[32,28],[23,26],[11,22],[20,21],[28,12],[15,12],[14,13],[5,18],[15,14],[7,27],[11,0],[15,18],[32,17],[21,7],[27,26],[27,12],[27,32],[31,32],[19,30],[26,28],[32,31],[31,9],[30,26],[30,28],[27,24],[31,32],[14,20],[22,5],[5,26],[24,28],[29,30],[15,31],[11,30],[32,26],[31,13],[14,27],[32,18],[19,0],[28,24],[26,32],[17,15],[28,8],[26,10],[26,24],[21,10],[1,31],[14,29],[32,29],[9,16],[31,28],[25,31],[22,29],[32,22],[4,0],[26,5],[30,32],[8,24],[30,17],[32,30],[21,16],[17,24],[30,26],[18,32],[27,26],[16,31],[25,7],[30,18],[0,25],[32,31],[4,26],[31,27],[20,32],[9,24],[1,31],[22,30],[27,30],[23,32],[29,31],[31,30],[30,19],[6,5],[31,30],[22,1],[29,27],[24,31],[23,22],[15,20],[21,15],[8,14],[1,10],[32,23],[27,14],[15,30],[30,32],[28,30],[9,16],[20,14],[20,21],[32,25],[28,20],[29,16],[7,1],[7,28],[10,12],[20,27],[24,26],[6,26],[23,17],[20,6],[14,7],[30,15],[32,27],[30,27],[27,30],[24,1],[31,30],[30,28],[27,22],[22,27],[14,13],[28,17],[31,30],[23,29],[17,24],[29,32],[19,21],[29,21],[25,28],[16,12],[30,26],[10,19],[30,0],[20,12],[3,9],[7,32],[12,21],[24,8]];
+    checkIfPrerequisite(n, prerequisites, queries);
+  });
+});
diff --git a/lab/exercises/10-mixed/critical-connections-1.js b/lab/exercises/10-mixed/critical-connections-1.js
new file mode 100644
index 00000000..c7ca5c6c
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-connections-1.js
@@ -0,0 +1,74 @@
+/**
+ * @param {number} n
+ * @param {number[][]} connections
+ * @return {number[][]}
+ */
+function criticalConnections(n, connections) {
+  const critical = [];
+  const graph = buildGraph(n, connections);
+  // console.log({graph})
+
+  for (let i = 0; i < connections.length; i++) { // O(|E| * [|V|^3 + |V|^2|E|]) = O(|V|^3|E| + |V|^2|E|^2)
+    const link = connections[i];
+    if (isLinkCritical(link, graph)) {
+      critical.push(link);
+    }
+  }
+
+  return critical;
+}
+
+function buildGraph(n, connections) {
+  const graph = [...Array(n).keys()].reduce((map, i) => {
+    map.set(i, new Set());
+    return map;
+  }, new Map());
+
+  connections.forEach(([i, j]) => {
+    const iAdj = graph.get(i);
+    iAdj.add(j);
+    const jAdj = graph.get(j);
+    jAdj.add(i);
+  });
+
+  return graph;
+}
+
+function isLinkCritical(link, graph) { // DFS: O(|V|^2 * |E|+|V|) = O(|V|^3 + |V|^2|E|)
+  for (let i = 0; i < graph.size; i++) {
+    for (let j = 0; j < graph.size; j++) {
+      if (hasLink([i, j], link)) continue;
+      if (!isConnected(i, j, link, graph)) { // DFS: O(|E|+|V|)
+        // console.log({i, j, link});
+        return true;
+      }
+    }
+  }
+
+  return false;
+}
+
+function hasLink(a, b) {
+  return (a[0] === b[0] && a[1] === b[1]) || (a[0] === b[1] && a[1] === b[0]);
+}
+
+// DFS: O(|E|+|V|)
+function isConnected(i, j, ignoreLink, graph, seen = new Set()) {
+  if (i === j) return true;
+  if (graph.get(i).has(j)) return true;
+
+  for (const adj of graph.get(i)) {
+    if (hasLink([i, adj], ignoreLink)) continue;
+
+    if (seen.has(adj)) continue;
+    seen.add(adj);
+
+    if (isConnected(adj, j, ignoreLink, graph, seen)) {
+      return true;
+    }
+  }
+
+  return false;
+}
+
+module.exports = criticalConnections;
diff --git a/lab/exercises/10-mixed/critical-connections.data.js b/lab/exercises/10-mixed/critical-connections.data.js
new file mode 100644
index 00000000..c5e1631c
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-connections.data.js
@@ -0,0 +1,6 @@
+module.exports = {
+  test1000: {
+    n: 1000,
+    connections: [[1,0],[2,1],[3,1],[4,2],[5,4],[6,3],[7,4],[8,2],[9,4],[10,8],[11,5],[12,10],[13,0],[14,5],[15,5],[16,11],[17,1],[18,4],[19,10],[20,9],[21,18],[22,5],[23,18],[24,13],[25,8],[26,23],[27,1],[28,18],[29,1],[30,0],[31,13],[32,21],[33,27],[34,24],[35,24],[36,9],[37,16],[38,14],[39,15],[40,30],[41,8],[42,36],[43,36],[44,16],[45,3],[46,24],[47,9],[48,43],[49,46],[50,45],[51,17],[52,8],[53,19],[54,28],[55,14],[56,42],[57,25],[58,12],[59,43],[60,14],[61,50],[62,30],[63,37],[64,36],[65,53],[66,39],[67,64],[68,29],[69,9],[70,53],[71,29],[72,17],[73,17],[74,35],[75,65],[76,12],[77,56],[78,44],[79,5],[80,43],[81,14],[82,40],[83,60],[84,40],[85,41],[86,13],[87,46],[88,56],[89,85],[90,76],[91,8],[92,33],[93,33],[94,26],[95,54],[96,6],[97,7],[98,66],[99,33],[100,5],[101,90],[102,68],[103,12],[104,65],[105,31],[106,93],[107,81],[108,91],[109,56],[110,17],[111,54],[112,63],[113,47],[114,44],[115,50],[116,0],[117,6],[118,67],[119,37],[120,30],[121,24],[122,121],[123,2],[124,51],[125,85],[126,61],[127,54],[128,124],[129,90],[130,8],[131,73],[132,21],[133,127],[134,26],[135,50],[136,108],[137,0],[138,28],[139,4],[140,79],[141,121],[142,93],[143,59],[144,112],[145,119],[146,97],[147,46],[148,103],[149,121],[150,148],[151,81],[152,134],[153,93],[154,69],[155,139],[156,44],[157,153],[158,145],[159,1],[160,109],[161,138],[162,87],[163,4],[164,25],[165,48],[166,139],[167,68],[168,28],[169,148],[170,164],[171,65],[172,132],[173,39],[174,159],[175,126],[176,44],[177,171],[178,99],[179,48],[180,125],[181,37],[182,152],[183,164],[184,107],[185,42],[186,54],[187,50],[188,181],[189,29],[190,113],[191,67],[192,169],[193,190],[194,46],[195,20],[196,195],[197,79],[198,165],[199,177],[200,183],[201,13],[202,22],[203,103],[204,143],[205,142],[206,65],[207,68],[208,56],[209,113],[210,66],[211,34],[212,70],[213,73],[214,14],[215,98],[216,198],[217,201],[218,13],[219,81],[220,159],[221,144],[222,47],[223,23],[224,26],[225,102],[226,33],[227,99],[228,94],[229,10],[230,113],[231,59],[232,172],[233,111],[234,131],[235,126],[236,155],[237,163],[238,183],[239,100],[240,42],[241,226],[242,213],[243,236],[244,82],[245,171],[246,18],[247,11],[248,179],[249,110],[250,105],[251,189],[252,18],[253,120],[254,164],[255,99],[256,108],[257,249],[258,54],[259,80],[260,193],[261,74],[262,206],[263,231],[264,91],[265,115],[266,213],[267,142],[268,254],[269,239],[270,6],[271,74],[272,221],[273,22],[274,7],[275,218],[276,129],[277,1],[278,96],[279,176],[280,102],[281,174],[282,267],[283,15],[284,154],[285,94],[286,285],[287,83],[288,43],[289,4],[290,141],[291,241],[292,144],[293,229],[294,129],[295,262],[296,228],[297,218],[298,128],[299,147],[300,118],[301,123],[302,250],[303,268],[304,68],[305,213],[306,4],[307,91],[308,4],[309,23],[310,284],[311,139],[312,238],[313,136],[314,254],[315,262],[316,75],[317,216],[318,181],[319,67],[320,303],[321,302],[322,208],[323,287],[324,93],[325,234],[326,57],[327,322],[328,200],[329,19],[330,307],[331,97],[332,286],[333,294],[334,119],[335,117],[336,264],[337,158],[338,91],[339,254],[340,207],[341,274],[342,32],[343,209],[344,163],[345,258],[346,317],[347,1],[348,151],[349,55],[350,183],[351,324],[352,31],[353,115],[354,337],[355,117],[356,217],[357,337],[358,183],[359,233],[360,179],[361,188],[362,234],[363,111],[364,99],[365,0],[366,136],[367,334],[368,251],[369,308],[370,11],[371,295],[372,291],[373,6],[374,140],[375,230],[376,342],[377,274],[378,84],[379,111],[380,208],[381,138],[382,116],[383,273],[384,176],[385,383],[386,273],[387,231],[388,286],[389,4],[390,333],[391,384],[392,238],[393,333],[394,149],[395,164],[396,212],[397,391],[398,312],[399,374],[400,393],[401,394],[402,183],[403,223],[404,56],[405,331],[406,253],[407,334],[408,148],[409,136],[410,254],[411,57],[412,285],[413,352],[414,19],[415,44],[416,173],[417,251],[418,127],[419,190],[420,155],[421,319],[422,32],[423,2],[424,124],[425,325],[426,398],[427,261],[428,68],[429,225],[430,260],[431,289],[432,187],[433,155],[434,27],[435,351],[436,20],[437,218],[438,36],[439,4],[440,187],[441,96],[442,389],[443,67],[444,364],[445,235],[446,326],[447,181],[448,269],[449,333],[450,77],[451,190],[452,436],[453,399],[454,23],[455,368],[456,142],[457,14],[458,51],[459,275],[460,410],[461,252],[462,40],[463,159],[464,383],[465,284],[466,229],[467,92],[468,248],[469,276],[470,80],[471,291],[472,367],[473,379],[474,234],[475,243],[476,121],[477,182],[478,64],[479,22],[480,244],[481,306],[482,113],[483,391],[484,39],[485,247],[486,409],[487,473],[488,176],[489,287],[490,155],[491,289],[492,35],[493,342],[494,299],[495,1],[496,178],[497,341],[498,339],[499,374],[500,394],[501,332],[502,85],[503,352],[504,5],[505,203],[506,0],[507,60],[508,87],[509,348],[510,53],[511,197],[512,228],[513,159],[514,488],[515,158],[516,230],[517,489],[518,439],[519,278],[520,268],[521,380],[522,278],[523,213],[524,190],[525,80],[526,257],[527,391],[528,220],[529,272],[530,501],[531,381],[532,1],[533,32],[534,22],[535,35],[536,262],[537,475],[538,366],[539,132],[540,184],[541,213],[542,315],[543,328]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1,12],[725,390],[652,239],[523,8],[415,20],[949,838],[552,323],[772,163],[997,175],[933,231],[284,248],[592,139],[133,92],[481,187],[732,329],[821,784],[895,654],[882,345],[405,382],[669,579],[730,200],[516,473],[279,269],[956,679],[901,552],[925,560],[812,624],[705,588],[344,167],[479,18],[763,711],[851,263],[534,32],[462,443],[809,324],[446,131],[699,138],[553,444],[381,104],[726,718],[839,361],[920,272],[980,281],[581,315],[490,20],[381,330],[713,354],[816,43],[854,590],[629,378],[770,152],[712,393],[642,477],[628,368],[396,359],[581,510],[714,61],[652,375],[696,303],[627,530],[158,94],[341,157],[876,41],[941,494],[651,285],[950,106],[803,360],[586,458],[524,448],[870,348],[538,55],[703,31],[731,407],[620,157],[798,301],[479,394],[69,1],[867,786],[702,671],[421,267],[690,573],[578,249],[945,536],[635,524],[712,514],[311,150],[505,246],[877,398],[672,608],[350,170],[484,481],[671,606],[850,148],[830,316],[557,529],[706,72],[886,579],[634,492],[879,119],[797,596],[882,686],[116,46],[850,807],[855,530],[831,16],[677,461],[546,407],[978,583],[870,551],[549,535],[341,82],[864,668],[770,330],[223,2],[748,83],[908,410],[988,980],[173,105],[217,48],[830,594],[780,389],[958,770],[920,497],[626,17],[466,1],[448,190],[298,202],[242,55],[796,607],[986,290],[169,160],[543,35],[940,165],[938,418],[769,504],[329,325],[119,71],[270,27],[899,597],[977,143],[732,60],[784,284],[595,334],[927,865],[283,63],[785,661],[938,109],[737,717],[396,56],[771,31],[911,305],[570,259],[506,48],[986,819],[892,864],[141,130],[962,188],[622,26],[815,347],[741,62],[514,277],[886,755],[420,12],[197,136],[466,171],[442,103],[457,277],[678,18],[980,461],[629,177],[644,162],[826,152],[593,298],[537,180],[711,323],[113,103],[302,141],[928,634],[838,48],[498,279],[454,239],[473,214],[861,621],[902,263],[969,90],[477,132],[998,713],[828,559],[610,536],[433,70],[862,302],[663,479],[344,83],[611,107],[956,468],[749,628],[271,262],[557,476],[734,606],[661,306],[547,538],[801,488],[877,445],[342,22],[519,419],[910,524],[934,670],[985,197],[439,436],[819,770],[555,455],[789,267],[531,57],[980,175],[927,258],[832,104],[649,393],[486,427],[902,788],[897,823],[527,337],[600,31],[469,326],[769,286],[314,191],[422,354],[594,369],[104,88],[382,369],[825,321],[223,84],[728,379],[748,315],[650,354],[256,130],[903,428],[182,148],[719,707],[710,18],[648,131],[879,235],[913,163],[907,155],[440,104],[829,437],[524,178],[598,458],[20,5],[269,74],[963,114],[314,207],[659,445],[632,588],[280,115],[120,84],[853,252],[923,835],[922,98],[789,19],[835,471],[868,435],[855,824],[103,102],[883,347],[833,204],[836,351],[527,86],[695,481],[701,567],[759,383],[801,795],[834,417],[923,422],[920,508],[789,271],[794,480],[566,526],[950,64],[355,231],[346,193],[934,704],[883,854],[371,0],[686,114],[942,725],[523,266],[705,65],[647,522],[784,683],[593,44],[694,501],[348,59],[644,266],[646,14],[969,380],[592,135],[306,80],[761,652],[596,84],[999,795],[790,179],[749,365],[871,504],[809,3],[934,411],[600,20],[783,637],[555,380],[980,93],[396,251],[984,280],[381,111],[615,96],[507,389],[885,101],[724,30],[441,358],[692,7],[649,168],[942,751],[415,125],[850,768],[850,745],[719,90],[653,442],[556,555],[689,461],[171,11],[797,120],[485,245],[834,650],[859,732],[795,735],[863,98],[707,577],[707,592],[978,790],[222,18],[900,778],[799,663],[788,143],[911,268],[958,491],[530,27],[851,635],[662,366],[425,334],[107,11],[398,188],[888,436],[795,289],[875,406],[354,157],[869,819],[802,377],[579,260],[775,768],[921,276],[793,547],[890,532],[490,468],[724,330],[444,112],[960,857],[995,258],[893,153],[413,294],[615,490],[944,27],[953,223],[658,300],[426,388],[965,491],[762,127],[612,385],[886,692],[744,408],[500,265],[824,646],[945,818],[826,214],[936,700],[376,212],[823,375],[811,428],[346,343],[847,566],[410,276],[192,61],[281,15],[944,488],[575,246],[867,855],[785,343],[783,550],[938,422],[181,88],[545,36],[715,73],[499,473],[745,459],[913,412],[575,296],[867,216],[670,550],[736,622],[847,684],[382,238],[546,419],[985,973],[980,912],[945,330],[573,354],[808,787],[664,376],[894,461],[327,54],[466,57],[840,324],[986,376],[79,6],[317,247],[877,86],[256,112],[811,527],[740,678],[966,481],[731,696],[780,532],[894,869],[257,109],[286,180],[898,823],[908,838],[570,317],[941,911],[176,50],[488,364],[801,65],[789,435],[886,12],[997,292],[858,120],[217,167],[355,64],[944,273],[184,75],[565,328],[495,365],[670,446],[795,317],[262,247],[455,422],[633,236],[595,190],[863,131],[487,25],[810,402],[785,374],[557,306],[420,77],[994,916],[585,535],[682,646],[872,785],[922,916],[918,343],[918,109],[813,189],[832,535],[518,94],[993,2],[984,38],[576,256],[758,641],[675,269],[829,388],[862,216],[703,155],[729,548],[838,571],[830,325],[644,202],[459,257],[650,211],[280,234],[697,153],[870,290],[421,168],[982,466],[690,651],[775,667],[277,99],[875,853],[268,29],[627,464],[886,283],[558,421],[987,113],[34,6],[333,94],[439,222],[693,408],[710,607],[766,736],[865,594],[570,316],[897,603],[929,132],[932,245],[810,413],[111,89],[607,54],[825,502],[183,42],[306,222],[807,503],[744,313],[564,259],[330,93],[638,496],[482,469],[541,86],[663,85],[609,258],[921,275],[928,86],[943,161],[533,99],[773,96],[517,359],[560,465],[763,13],[749,232],[561,492],[412,360],[708,561],[405,95],[793,383],[600,124],[911,438],[913,693],[477,433],[938,630],[622,572],[237,190],[567,226],[28,3],[307,108],[584,260],[238,232],[420,355],[641,516],[866,524],[828,266]],
+  },
+};
diff --git a/lab/exercises/10-mixed/critical-connections.js b/lab/exercises/10-mixed/critical-connections.js
new file mode 100644
index 00000000..0544807d
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-connections.js
@@ -0,0 +1,54 @@
+/**
+ * @param {number} n
+ * @param {number[][]} connections
+ * @return {number[][]}
+ */
+function criticalConnections(n, connections) {
+  const graph = buildGraph(n, connections);
+  // console.log({graph})
+
+  return dfs(graph, 0);
+}
+
+
+function buildGraph(n, connections) {
+  const graph = [...Array(n).keys()].reduce((map, i) => {
+    map.set(i, new Set());
+    return map;
+  }, new Map());
+
+  connections.forEach(([i, j]) => {
+    const iAdj = graph.get(i);
+    iAdj.add(j);
+    const jAdj = graph.get(j);
+    jAdj.add(i);
+  });
+
+  return graph;
+}
+
+function dfs(graph, current, previous = null, rank = 1, group = []) {
+  let criticalLinks = [];
+  group[current] = rank;
+
+  for (const adj of graph.get(current)) {
+    if (adj === previous) continue;
+
+    if (!group[adj]) { // if not visited (and not in a group yet)
+      const links = dfs(graph, adj, current, rank + 1, group);
+      if (links.length) {
+        criticalLinks = criticalLinks.concat(links);
+      }
+    }
+
+    group[current] = Math.min(group[current], group[adj]);
+
+    if (group[adj] >= rank + 1) {
+      criticalLinks.push([current, adj]);
+    }
+  }
+
+  return criticalLinks;
+}
+
+module.exports = criticalConnections;
diff --git a/lab/exercises/10-mixed/critical-connections.runner.js b/lab/exercises/10-mixed/critical-connections.runner.js
new file mode 100644
index 00000000..9e682474
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-connections.runner.js
@@ -0,0 +1,17 @@
+const assert = require('assert');
+
+// const criticalConnections = require('./critical-connections');
+const criticalConnections = require('./critical-connections');
+const data = require('./critical-connections.data');
+
+assert.deepEqual(criticalConnections(4, [
+  [0, 1],
+  [1, 2],
+  [2, 0],
+  [1, 3],
+]), [[1, 3]]);
+
+const { n, connections } = data.test1000;
+assert.deepEqual(criticalConnections(n, connections), []);
+
+console.log('All tests passed!');
diff --git a/lab/exercises/10-mixed/critical-connections.spec.js b/lab/exercises/10-mixed/critical-connections.spec.js
new file mode 100644
index 00000000..c07f4f41
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-connections.spec.js
@@ -0,0 +1,42 @@
+// npx jest lab/exercises/10-mixed/critical-connections.spec.js --watch
+const criticalConnections = require('./critical-connections');
+
+describe('Critical Connections', () => {
+  it('should work with 4 nodes', () => {
+    const n = 4;
+    const connections = [
+      [0, 1],
+      [1, 2],
+      [2, 0],
+      [1, 3],
+    ];
+    expect(criticalConnections(n, connections)).toEqual([
+      [1, 3],
+    ]);
+  });
+
+  it('should work with nodes in line (all critical)', () => {
+    const n = 4;
+    const connections = [
+      [0, 1],
+      [1, 2],
+      [2, 3],
+    ];
+    expect(criticalConnections(n, connections)).toEqual(expect.arrayContaining([
+      [0, 1],
+      [1, 2],
+      [2, 3],
+    ]));
+  });
+
+  it('should work with nodes in daisy chain (no critical)', () => {
+    const n = 4;
+    const connections = [
+      [0, 1],
+      [1, 2],
+      [2, 3],
+      [3, 0],
+    ];
+    expect(criticalConnections(n, connections)).toEqual(expect.arrayContaining([]));
+  });
+});
diff --git a/lab/exercises/10-mixed/critical-routers.js b/lab/exercises/10-mixed/critical-routers.js
new file mode 100644
index 00000000..d112b1f9
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-routers.js
@@ -0,0 +1,77 @@
+/**
+ * Given a list of router links, find the routers that are critical to maintain communication.
+ * A router is considered critical if when they are removed,
+ * other routers lose connection to other routers different from the one removed.
+ *
+ * @runtime O(|E| * |V|^3)
+ * @space O(|E| + |V|)
+ * @pomodoro IIII (2h)
+ *
+ * @param {number} numRouters - The number of routers connected in a data center >= 3.
+ * @param {number} numLinks - The number of links.
+ * @param {[[number, number]]} links - The pair of routers connected by the link.
+ * @return {number[]} - The list of critical routers;
+ */
+function criticalRouters(numRouters, numLinks, links) {
+  const graph = buildGraph(numRouters, links);
+  const critical = [];
+
+  // console.log({graph});
+
+  for (let curr = 1; curr <= numRouters; curr++) {
+    if (isCritical(graph, curr)) {
+      critical.push(curr);
+    }
+  }
+
+  return critical;
+}
+
+function isCritical(graph, curr) {
+  for (let i = 1; i <= graph.size; i++) {
+    for (let j = 1; j <= graph.size; j++) {
+      if (curr === i || curr === j || i === j) { continue; }
+      if (!isConnected(graph, i, j, curr)) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+function addEdge(graph, from, to) {
+  // console.log('addEdge', {graph, from, to});
+  const adjacents = graph.get(from);
+  adjacents.add(to);
+}
+
+function buildGraph(numRouters, links) {
+  // console.log('buildGraph', { numRouters, links });
+  const graph = new Map();
+  const routers = [...Array(numRouters).keys()].map(r => r + 1);
+  routers.forEach(r => graph.set(r, new Set()));
+
+  links.forEach(([from, to]) => {
+    addEdge(graph, from, to);
+    addEdge(graph, to, from);
+  });
+
+  return graph;
+}
+
+function isConnected(graph, i, j, ignore, visited = new Set()) {
+  if (i === ignore || j === ignore) return false;
+  if (graph.get(i).has(j)) return true;
+
+  for (const adj of graph.get(i)) {
+    if (visited.has(adj)) { continue; }
+    visited.add(adj);
+    if (isConnected(graph, adj, j, ignore, visited)) {
+      return true;
+    }
+  }
+
+  return false;
+}
+
+module.exports = criticalRouters;
diff --git a/lab/exercises/10-mixed/critical-routers.spec.js b/lab/exercises/10-mixed/critical-routers.spec.js
new file mode 100644
index 00000000..c4ec5597
--- /dev/null
+++ b/lab/exercises/10-mixed/critical-routers.spec.js
@@ -0,0 +1,64 @@
+// npx jest lab/exercises/10-mixed/critical-routers.spec.js --watch
+const critialRouters = require('./critical-routers');
+
+describe('Critical Routers', () => {
+  it('should work with daisy chain nodes', () => {
+    const numRouters = 3;
+    const numLinks = 3;
+    const links = [
+      [1, 2],
+      [2, 3],
+      [3, 1],
+    ];
+    expect(critialRouters(numRouters, numLinks, links)).toEqual([]);
+  });
+
+  it('should work with 7 nodes', () => {
+    const numRouters = 7;
+    const numLinks = 7;
+    const links = [
+      [1, 2],
+      [1, 3],
+      [2, 4],
+      [3, 4],
+      [3, 6],
+      [6, 7],
+      [4, 5],
+    ];
+    expect(critialRouters(numRouters, numLinks, links)).toEqual([3, 4, 6]);
+  });
+
+  it('should work with 6 nodes', () => {
+    const numRouters = 6;
+    const numLinks = 5;
+    const links = [
+      [1, 2],
+      [2, 3],
+      [3, 4],
+      [4, 5],
+      [3, 6],
+    ];
+    expect(critialRouters(numRouters, numLinks, links)).toEqual([2, 3, 4]);
+  });
+
+  it('should work with 10 nodes', () => {
+    const numRouters = 10;
+    const numLinks = 13;
+    const links = [
+      [1, 2],
+      [1, 3],
+      [2, 3],
+      [3, 4],
+      [4, 5],
+      [4, 6],
+      [5, 6],
+      [5, 7],
+      [6, 7],
+      [7, 8],
+      [8, 9],
+      [8, 10],
+      [9, 10],
+    ];
+    expect(critialRouters(numRouters, numLinks, links)).toEqual([3, 4, 7, 8]);
+  });
+});
diff --git a/lab/exercises/10-mixed/document-distance.js b/lab/exercises/10-mixed/document-distance.js
new file mode 100644
index 00000000..4c0ca542
--- /dev/null
+++ b/lab/exercises/10-mixed/document-distance.js
@@ -0,0 +1,23 @@
+
+// npx jest lab/exercises/10-mixed/document-distance.spec.js  --watch -c 'jest-all.config.js'
+
+/**
+ * Find the distance between two documents.
+ *
+ * Convert files into vectors of words where the value is the frequency.
+ * Calculate the angle of the two vectors: cos α = v1 · v2 / |v1| * |v2|
+ * @param {string} file1 - String of words separated by whitespace
+ * @param {string} file2 - String of words separated by whitespace
+ */
+function documentDistance(file1, file2) {
+  // 0. slip words
+  // 1. calculate freq of each word per file
+  const byCounter = (map, w) => map.set(w, 1 + (map.get(w) || 0));
+  const f1 = file1.split(' ').reduce(byCounter, new Map());
+  const f2 = file2.split(' ').reduce(byCounter, new Map());
+  // 2. multiply each occurence and divide it
+  const dotProd = (m1, m2) => [...new Set([...m1.keys(), ...m2.keys()])].reduce((sum, w) => sum + (m1.get(w) || 0) * (m2.get(w) || 0), 0);
+  return Math.acos(dotProd(f1, f2) / Math.sqrt(dotProd(f1, f1) * dotProd(f2, f2)));
+}
+
+module.exports = { documentDistance };
diff --git a/lab/exercises/10-mixed/document-distance.spec.js b/lab/exercises/10-mixed/document-distance.spec.js
new file mode 100644
index 00000000..67adfb70
--- /dev/null
+++ b/lab/exercises/10-mixed/document-distance.spec.js
@@ -0,0 +1,20 @@
+const { documentDistance } = require('./document-distance');
+
+describe('documentDistance', () => {
+  it('should work with different files', () => {
+    const file1 = 'This is a cat.';
+    const file2 = 'This is a dog.';
+    expect(documentDistance(file1, file2)).toBeCloseTo(0.722);
+  });
+
+  it('should work with different files', () => {
+    const file1 = 'This is a cat.';
+    const file2 = 'Occaecat irure enim sint cupidatat id cillum cupidatat ipsum officia ea reprehenderit eiusmod voluptate. Est in laboris esse anim tempor sit in labore eiusmod consectetur aliqua. Quis nulla sunt incididunt magna velit in reprehenderit officia ut esse. Duis proident aute sint laborum consectetur eu reprehenderit amet et esse esse deserunt.';
+    expect(documentDistance(file1, file2)).toBeCloseTo(1.57);
+  });
+
+  it('should work with equal files', () => {
+    const file1 = 'This is a cat.';
+    expect(documentDistance(file1, file1)).toEqual(0);
+  });
+});
diff --git a/lab/exercises/10-mixed/even-first.js b/lab/exercises/10-mixed/even-first.js
new file mode 100644
index 00000000..ec08097c
--- /dev/null
+++ b/lab/exercises/10-mixed/even-first.js
@@ -0,0 +1,15 @@
+function sortArrayByParity(A) {
+  let lo = 0;
+  let hi = A.length - 1;
+  while (lo < hi) {
+    if (A[lo] % 2 === 0) {
+      lo++;
+    } else {
+      [A[hi], A[lo]] = [A[lo], A[hi]];
+      hi--;
+    }
+  }
+  return A;
+}
+
+module.exports = sortArrayByParity;
diff --git a/lab/exercises/10-mixed/even-first.spec.js b/lab/exercises/10-mixed/even-first.spec.js
new file mode 100644
index 00000000..e0fc5e2e
--- /dev/null
+++ b/lab/exercises/10-mixed/even-first.spec.js
@@ -0,0 +1,20 @@
+const fn = require('./even-first');
+
+describe('Sort even first and then odd', () => {
+  it('should work when start with even', () => {
+    const actual = fn([0, 1, 2]);
+    expect(actual.slice(0, 2)).toEqual(expect.arrayContaining([0, 2]));
+    expect(actual.slice(2)).toEqual(expect.arrayContaining([1]));
+  });
+
+  it('should work when start with odd', () => {
+    const actual = fn([5, 4, 3, 7, 8, 3, 2]);
+    expect(actual.slice(0, 3)).toEqual(expect.arrayContaining([4, 8, 2]));
+    expect(actual.slice(3)).toEqual(expect.arrayContaining([5, 3, 7, 3]));
+  });
+
+  it('should work with empty', () => {
+    const actual = fn([]);
+    expect(actual).toEqual([]);
+  });
+});
diff --git a/lab/exercises/10-mixed/get-temperature-api.js b/lab/exercises/10-mixed/get-temperature-api.js
new file mode 100644
index 00000000..c6be4cdf
--- /dev/null
+++ b/lab/exercises/10-mixed/get-temperature-api.js
@@ -0,0 +1,29 @@
+// simulation of window.fetch
+async function fetch(url) {
+  let temp = 4;
+  if (!url || !url.length) return Promise.reject(new Error(`Invalid URL ${url}.`));
+  if (/7836/.test(url)) temp = 42.12;
+  if (/783e/.test(url)) temp = 42.1;
+  if (/7833/.test(url)) temp = 42;
+  return {
+    json: () => ({ temp }),
+  };
+}
+
+async function getTemperature(urls) {
+  try {
+    const res = await fetch(urls[0]);
+    const json = await res.json();
+    return json.temp;
+  } catch (error) {
+    return Promise.reject(error);
+  }
+  // return new Promise((resolve, reject) => {
+  //   fetch(urls[0])
+  //     .then((res) => res.json())
+  //     .then((json) => resolve(json.temp))
+  //     .catch(reject);
+  // });
+}
+
+module.exports = getTemperature;
diff --git a/lab/exercises/10-mixed/get-temperature-api.spec.js b/lab/exercises/10-mixed/get-temperature-api.spec.js
new file mode 100644
index 00000000..bd57d37f
--- /dev/null
+++ b/lab/exercises/10-mixed/get-temperature-api.spec.js
@@ -0,0 +1,15 @@
+const fn = require('./get-temperature-api');
+
+describe('Get Temperature from flaky API', () => {
+  it('should work', (done) => {
+    const urls = [
+      '//www.mocky.io/v2/5e9ee4862d00000d00cb7836', // 42.12
+      '//www.mocky.io/v2/5e9ee4f52d00000d00cb783e', // 42.1
+      '//www.mocky.io/v2/5e9ee4702d00005a00cb7833', // 42
+    ];
+    fn(urls).then((res) => {
+      expect(res).toEqual(42.12);
+      done();
+    }).catch(done);
+  });
+});
diff --git a/lab/exercises/10-mixed/integer-to-words.js b/lab/exercises/10-mixed/integer-to-words.js
new file mode 100644
index 00000000..76cf8e87
--- /dev/null
+++ b/lab/exercises/10-mixed/integer-to-words.js
@@ -0,0 +1,48 @@
+// nodemon lab/exercises/10-mixed/integer-to-words.spec-assert.js
+// npx jest lab/exercises/10-mixed/integer-to-words.spec.js --watch
+const UPTO20 = ["Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Ten", "Eleven", "Twelve", "Thirteen", "Fourteen", "Fifteen", "Sixteen", "Seventeen", "Eighteen", "Nineteen", "Twenty"]
+const TENS = ["", "", "Twenty", "Thirty", "Forty", "Fifty", "Sixty", "Seventy", "Eighty", "Ninety"];
+const HUNDREDS = new Map([
+  [1_000_000_000, 'Billion'],
+  [1_000_000, 'Million'],
+  [1_000, 'Thousand'],
+  [100, 'Hundred'],
+]);
+
+/**
+ * You are creating a basic number-to-speech algorithms to use at search engine company.
+ * The first part is to convert a given number into its text representation.
+ * The 2nd part, is to take that text and synthetize the voice.
+ * We are going to focus on the first part for this exercise.
+ *
+ * Convert a positive integer into its English words representation.
+ *
+ * @param {number} num - The positive integer. Should be <= 2^31 - 1
+ * @return {string} - The English words for the given number
+ *
+ * @author Adrian Mejia <adrianmejia.com>
+ */
+function numberToWords(num) {
+  if (num < 21) return UPTO20[num];
+
+  let ans = '';
+
+  for (const [div, word] of HUNDREDS.entries()) {
+    if (Math.floor(num/div)) {
+      ans += `${ numberToWords(Math.floor(num/div)) } `;
+      ans += `${ word } `;
+      num %= div;
+    }
+  }
+
+  if (num && num < 21) {
+    ans += UPTO20[num]  + ' ';
+  } else {
+    if (Math.floor(num/10)) ans += `${ TENS[Math.floor(num/10)] } `;
+    if (Math.floor(num % 10)) ans += `${ UPTO20[Math.floor(num % 10)] } `;
+  }
+
+  return ans.trim();
+};
+
+module.exports = numberToWords;
diff --git a/lab/exercises/10-mixed/integer-to-words.spec-assert.js b/lab/exercises/10-mixed/integer-to-words.spec-assert.js
new file mode 100644
index 00000000..f6ec04e2
--- /dev/null
+++ b/lab/exercises/10-mixed/integer-to-words.spec-assert.js
@@ -0,0 +1,27 @@
+/*eslint-disable */
+const assert = require('assert');
+
+const numberToWords = require('./integer-to-words');
+const W9 = 'Nine Hundred Ninety Nine';
+
+assert.equal(numberToWords(0), 'Zero');
+assert.equal(numberToWords(1), 'One');
+assert.equal(numberToWords(20), 'Twenty');
+assert.equal(numberToWords(21), 'Twenty One');
+assert.equal(numberToWords(99), 'Ninety Nine');
+assert.equal(numberToWords(100), 'One Hundred');
+assert.equal(numberToWords(101), 'One Hundred One');
+assert.equal(numberToWords(110), 'One Hundred Ten');
+assert.equal(numberToWords(111), 'One Hundred Eleven');
+assert.equal(numberToWords(777), 'Seven Hundred Seventy Seven');
+assert.equal(numberToWords(999), W9);
+assert.equal(numberToWords(1_000), 'One Thousand');
+assert.equal(numberToWords(9999), `Nine Thousand ${W9}`);
+assert.equal(numberToWords(1_000_000), 'One Million');
+assert.equal(numberToWords(1_234_567), 'One Million Two Hundred Thirty Four Thousand Five Hundred Sixty Seven');
+assert.equal(numberToWords(999_999_999), `${W9} Million ${W9} Thousand ${W9}`);
+assert.equal(numberToWords(1_234_567_891), `One Billion Two Hundred Thirty Four Million Five Hundred Sixty Seven Thousand Eight Hundred Ninety One`);
+assert.equal(numberToWords(999_999_999_999), `${W9} Billion ${W9} Million ${W9} Thousand ${W9}`);
+assert.equal(numberToWords(2**31 - 1), `Two Billion One Hundred Forty Seven Million Four Hundred Eighty Three Thousand Six Hundred Forty Seven`);
+
+console.log('All tests passed!');
diff --git a/lab/exercises/10-mixed/integer-to-words.spec.js b/lab/exercises/10-mixed/integer-to-words.spec.js
new file mode 100644
index 00000000..922ec6e4
--- /dev/null
+++ b/lab/exercises/10-mixed/integer-to-words.spec.js
@@ -0,0 +1,9 @@
+// npx jest lab/exercises/10-mixed/integer-to-words.spec.js --watch
+
+const numberToWords = require('./integer-to-words');
+
+describe('Integer to English Words', () => {
+  it('should convert 0', () => {
+    expect(numberToWords(0)).toEqual('Zero');
+  });
+});
diff --git a/lab/exercises/10-mixed/linked-list-data-equality.js b/lab/exercises/10-mixed/linked-list-data-equality.js
new file mode 100644
index 00000000..7e61ffeb
--- /dev/null
+++ b/lab/exercises/10-mixed/linked-list-data-equality.js
@@ -0,0 +1,49 @@
+/**
+ * Given two linked list, determine if they have the same data
+
+    **Input:**
+    L1: he → ll → o
+
+    L2: hel → l → o
+
+    **Output:**
+
+    True
+
+    class Node {
+      constructor(value) {
+        this.value = value;
+        this.next = null;
+        this.previous = null;
+      }
+    }
+
+ * @param {Node} l1 - The list 1.
+ * @param {Node} l2 - The list 2.
+ */
+function hasSameData(l1, l2) {
+  if (l1 !== l2 && (!l1 || !l2)) return false;
+  let p1 = l1;
+  let p2 = l2;
+  let i1 = 0;
+  let i2 = 0;
+
+  while (p1 && p2) {
+    // console.log({v1: p1.value[i1], v2: p2.value[i2], i1, i2})
+    if (p1.value[i1] !== p2.value[i2]) return false;
+    i1++;
+    i2++;
+    if (i1 >= p1.value.length) {
+      p1 = p1.next;
+      i1 = 0;
+    }
+    if (i2 >= p2.value.length) {
+      p2 = p2.next;
+      i2 = 0;
+    }
+  }
+
+  return true;
+}
+
+module.exports = hasSameData;
diff --git a/lab/exercises/10-mixed/linked-list-data-equality.spec.js b/lab/exercises/10-mixed/linked-list-data-equality.spec.js
new file mode 100644
index 00000000..ff971732
--- /dev/null
+++ b/lab/exercises/10-mixed/linked-list-data-equality.spec.js
@@ -0,0 +1,40 @@
+const hasSameData = require('./linked-list-data-equality');
+const { LinkedList } = require('../../../src/index');
+
+describe('Linked List: Has Same Data', () => {
+  it('should work', () => {
+    const l1 = new LinkedList(['h', 'i']);
+    const l2 = new LinkedList(['hi']);
+    expect(hasSameData(l1.first, l2.first)).toEqual(true);
+  });
+
+  it('should work', () => {
+    const l1 = new LinkedList(['he', 'll', 'o']);
+    const l2 = new LinkedList(['hel', 'l', 'o']);
+    expect(hasSameData(l1.first, l2.first)).toEqual(true);
+  });
+
+  it('should work', () => {
+    const l1 = new LinkedList(['he', 'll', 'o']);
+    const l2 = new LinkedList(['ho', 'l', 'a']);
+    expect(hasSameData(l1.first, l2.first)).toEqual(false);
+  });
+
+  it('should work when one is empty', () => {
+    const l1 = new LinkedList();
+    const l2 = new LinkedList(['ho', 'l', 'a']);
+    expect(hasSameData(l1.first, l2.first)).toEqual(false);
+  });
+
+  it('should work when both are empty', () => {
+    const l1 = new LinkedList();
+    const l2 = new LinkedList();
+    expect(hasSameData(l1.first, l2.first)).toEqual(true);
+  });
+
+  it('should work when one is empty and other null', () => {
+    const l1 = new LinkedList(['']);
+    const l2 = new LinkedList();
+    expect(hasSameData(l1.first, l2.first)).toEqual(false);
+  });
+});
diff --git a/lab/exercises/10-mixed/maze.js b/lab/exercises/10-mixed/maze.js
new file mode 100644
index 00000000..1218e740
--- /dev/null
+++ b/lab/exercises/10-mixed/maze.js
@@ -0,0 +1,45 @@
+function hasPath(maze, start, destination) {
+  const queue = [start];
+  const directions = [[0, 1], [0, -1], [1, 0], [-1, 0]]; // right, left, up, down,
+
+  for (const curr of queue) {
+    let [currRow, currCol] = curr;
+    if (destination[0] === currRow && destination[1] === currCol) return true;
+    maze[currRow][currCol] = 2; // mark as visited.
+
+    // console.log({
+    //   currRow, currCol, maze, queue,
+    // });
+
+    for (const [nextRow, nextCol] of directions) {
+      [currRow, currCol] = curr; // reset to start point
+      while (currRow >= 0 && currRow < maze.length && currCol >= 0 && currCol < maze[currRow].length && maze[currRow][currCol] !== 1) {
+        currRow += nextRow;
+        currCol += nextCol;
+      }
+      // step back one position to go before the wall.
+      currRow -= nextRow;
+      currCol -= nextCol;
+      // check if it hasn't been visited
+      if (maze[currRow][currCol] === 0) queue.push([currRow, currCol]);
+      // console.log('*', { currRow, currCol, queue });
+    }
+  }
+
+  return false;
+}
+
+module.exports = hasPath;
+
+/* /
+const start = [0, 4];
+const end = [3, 2];
+const maze = [
+  [0, 0, 1, 0, 1],
+  [0, 0, 0, 0, 0],
+  [0, 0, 0, 1, 0],
+  [1, 1, 0, 1, 1],
+  [0, 0, 0, 0, 0],
+];
+hasPath(maze, start, end);
+// */
diff --git a/lab/exercises/10-mixed/maze.spec.js b/lab/exercises/10-mixed/maze.spec.js
new file mode 100644
index 00000000..5826dbfd
--- /dev/null
+++ b/lab/exercises/10-mixed/maze.spec.js
@@ -0,0 +1,24 @@
+// npx jest lab/exercises/10-mixed/maze.spec.js --collectCoverage
+const fn = require('./maze');
+
+describe('The Maze', () => {
+  const maze = [
+    [0, 0, 1, 0, 1],
+    [0, 0, 0, 0, 0],
+    [0, 0, 0, 1, 0],
+    [1, 1, 0, 1, 1],
+    [0, 0, 0, 0, 0],
+  ];
+
+  it('should have path', () => {
+    const start = [0, 4];
+    const end = [4, 4];
+    expect(fn(maze, start, end)).toEqual(true);
+  });
+
+  it('should NOT have path', () => {
+    const start = [0, 4];
+    const end = [3, 2];
+    expect(fn(maze, start, end)).toEqual(false);
+  });
+});
diff --git a/lab/exercises/10-mixed/min-permutations.js b/lab/exercises/10-mixed/min-permutations.js
new file mode 100644
index 00000000..d767f3bf
--- /dev/null
+++ b/lab/exercises/10-mixed/min-permutations.js
@@ -0,0 +1,34 @@
+/*eslint-disable */
+
+function reverse (arr, i, j) {
+  while (i < j) {
+    [arr[i], arr[j]] = [arr[j], arr[i]];
+    i++;
+    j--;
+  }
+}
+
+function backtracking(array, start = 0) {
+  let permutations = [array];
+
+  for (let i = start; i < array.length - 1; i++) {
+    for (let j = i + 1; j < array.length; j++) {
+      reverse(array, i, j);
+      permutations = permutations.concat(backtracking(array, i + 1));
+      reverse(array, i, j);
+    }
+  }
+
+  return permutations;
+}
+
+function minPermutations(arr) {
+  return backtracking(arr);
+}
+
+module.exports = minPermutations;
+
+//*/
+// console.log(backtracking([1,2,3]));
+// console.log(backtracking([1,2,3,4]));
+//*/
diff --git a/lab/exercises/10-mixed/min-permutations.spec.js b/lab/exercises/10-mixed/min-permutations.spec.js
new file mode 100644
index 00000000..f506e668
--- /dev/null
+++ b/lab/exercises/10-mixed/min-permutations.spec.js
@@ -0,0 +1,7 @@
+const fn = require('./min-permutations');
+
+describe('Min Permutations', () => {
+  it('should work', () => {
+    expect(fn([1,2,3])).toEqual();
+  });
+});
diff --git a/lab/exercises/10-mixed/network-delay-time-0.js b/lab/exercises/10-mixed/network-delay-time-0.js
new file mode 100644
index 00000000..1dc33de4
--- /dev/null
+++ b/lab/exercises/10-mixed/network-delay-time-0.js
@@ -0,0 +1,42 @@
+/**
+ * @param {number[][]} times
+ * @param {number} N
+ * @param {number} K
+ * @return {number}
+ */
+function networkDelayTime(times, N, K) {
+  const graph = new Map();
+  for (let i = 1; i <= N; i++) graph.set(i, []);
+  times.forEach(([u, v, w]) => {
+    graph.get(u).push([v, w]);
+  });
+
+
+  const distances = Array(N + 1).fill(Infinity);
+  distances[0] = 0;
+  distances[K] = 0;
+
+  const visited = [];
+
+  const queue = new Map([[K, 0]]);
+
+  while (queue.size) {
+    const [node, dist] = queue.entries().next().value;
+    queue.delete(node);
+
+    visited[node] = true;
+
+    // not working for some cases
+    graph.get(node).sort((a, b) => a[1] - b[1]).forEach(([n, w]) => {
+      distances[n] = Math.min(distances[n], dist + w);
+      if (queue.has(n) || !visited[n]) {
+        queue.set(n, distances[n]);
+      }
+    });
+  }
+
+  const max = Math.max(...distances);
+  return max === Infinity ? -1 : max;
+}
+
+module.exports = networkDelayTime;
diff --git a/lab/exercises/10-mixed/network-delay-time-1.js b/lab/exercises/10-mixed/network-delay-time-1.js
new file mode 100644
index 00000000..3669ed83
--- /dev/null
+++ b/lab/exercises/10-mixed/network-delay-time-1.js
@@ -0,0 +1,45 @@
+/**
+ * @param {number[][]} times
+ * @param {number} N
+ * @param {number} K
+ * @return {number}
+ */
+function networkDelayTime(times, N, K) {
+  const graph = new Map();
+  for (let i = 1; i <= N; i++) graph.set(i, []);
+  times.forEach(([u, v, w]) => {
+    graph.get(u).push([v, w]);
+  });
+
+
+  const distances = Array(N + 1).fill(Infinity);
+  distances[0] = 0;
+  distances[K] = 0;
+
+  const visited = [];
+
+  while (true) {
+    let node;
+    let dist = Infinity;
+    // From all the unseen nodes find the one with the min distance.
+    for (let i = 1; i <= N; i++) {
+      if (!visited[i] && distances[i] < dist) {
+        node = i;
+        dist = distances[i];
+      }
+    }
+
+    if (!node) break; // none found, so all node has been visited
+
+    visited[node] = true;
+
+    graph.get(node).forEach(([n, w]) => {
+      distances[n] = Math.min(distances[n], dist + w);
+    });
+  }
+
+  const max = Math.max(...distances);
+  return max === Infinity ? -1 : max;
+}
+
+module.exports = networkDelayTime;
diff --git a/lab/exercises/10-mixed/network-delay-time.js b/lab/exercises/10-mixed/network-delay-time.js
new file mode 100644
index 00000000..3669ed83
--- /dev/null
+++ b/lab/exercises/10-mixed/network-delay-time.js
@@ -0,0 +1,45 @@
+/**
+ * @param {number[][]} times
+ * @param {number} N
+ * @param {number} K
+ * @return {number}
+ */
+function networkDelayTime(times, N, K) {
+  const graph = new Map();
+  for (let i = 1; i <= N; i++) graph.set(i, []);
+  times.forEach(([u, v, w]) => {
+    graph.get(u).push([v, w]);
+  });
+
+
+  const distances = Array(N + 1).fill(Infinity);
+  distances[0] = 0;
+  distances[K] = 0;
+
+  const visited = [];
+
+  while (true) {
+    let node;
+    let dist = Infinity;
+    // From all the unseen nodes find the one with the min distance.
+    for (let i = 1; i <= N; i++) {
+      if (!visited[i] && distances[i] < dist) {
+        node = i;
+        dist = distances[i];
+      }
+    }
+
+    if (!node) break; // none found, so all node has been visited
+
+    visited[node] = true;
+
+    graph.get(node).forEach(([n, w]) => {
+      distances[n] = Math.min(distances[n], dist + w);
+    });
+  }
+
+  const max = Math.max(...distances);
+  return max === Infinity ? -1 : max;
+}
+
+module.exports = networkDelayTime;
diff --git a/lab/exercises/10-mixed/network-delay-time.spec.js b/lab/exercises/10-mixed/network-delay-time.spec.js
new file mode 100644
index 00000000..c1a77e91
--- /dev/null
+++ b/lab/exercises/10-mixed/network-delay-time.spec.js
@@ -0,0 +1,31 @@
+const fn = require('./network-delay-time');
+
+describe('Network Delay Time', () => {
+  it('should work', () => {
+    const times = [[2, 1, 1], [2, 3, 1], [3, 4, 1]];
+    const n = 4;
+    const k = 2;
+    expect(fn(times, n, k)).toEqual(2);
+  });
+
+  it('should work', () => {
+    const times = [[2, 1, 15], [2, 3, 85], [1, 3, 0], [1, 2, 91], [3, 2, 78], [3, 1, 36]];
+    const n = 3;
+    const k = 2;
+    expect(fn(times, n, k)).toEqual(15);
+  });
+
+  it('should work with highly connected networks', () => {
+    const times = [[14, 1, 8], [11, 2, 25], [14, 15, 37], [3, 7, 70], [11, 7, 60], [13, 11, 87], [15, 10, 67], [13, 10, 58], [5, 4, 56], [9, 3, 26], [5, 11, 51], [11, 4, 92], [7, 6, 8], [7, 10, 95], [14, 9, 0], [4, 13, 1], [7, 9, 89], [3, 14, 24], [11, 15, 30], [13, 2, 91], [15, 8, 60], [1, 4, 96], [8, 2, 71], [6, 8, 38], [14, 13, 46], [2, 12, 48], [10, 11, 92], [8, 12, 28], [8, 7, 12], [9, 13, 82], [8, 6, 27], [3, 2, 65], [4, 10, 62], [11, 13, 55], [1, 2, 52], [8, 3, 98], [7, 12, 85], [6, 12, 97], [9, 4, 90], [2, 4, 23], [9, 11, 20], [1, 14, 61], [8, 9, 77], [6, 5, 80], [14, 11, 33], [9, 8, 54], [13, 1, 42], [13, 8, 13], [10, 14, 40], [9, 7, 18], [14, 3, 50], [14, 6, 83], [14, 8, 14], [2, 1, 86], [9, 5, 54], [11, 5, 29], [9, 12, 43], [9, 2, 74], [14, 4, 87], [12, 7, 98], [7, 14, 13], [4, 12, 33], [5, 2, 60], [15, 11, 33], [8, 4, 99], [9, 6, 98], [4, 6, 57], [6, 11, 5], [9, 15, 37], [1, 3, 30], [9, 10, 60], [13, 12, 73], [13, 14, 56], [1, 11, 13], [14, 2, 8], [4, 15, 60], [11, 3, 90], [2, 5, 86], [11, 1, 1], [13, 4, 2], [15, 7, 91], [15, 4, 51], [11, 6, 70], [2, 7, 51], [11, 9, 37], [4, 2, 92], [10, 4, 4], [7, 2, 30], [13, 9, 79], [8, 15, 41], [11, 8, 18], [15, 2, 4], [12, 14, 88], [12, 6, 9], [12, 9, 44], [1, 6, 87], [15, 14, 42], [4, 9, 41], [7, 15, 90], [4, 1, 84], [7, 11, 9], [3, 11, 75], [5, 9, 2], [2, 11, 96], [12, 5, 89], [6, 15, 25], [5, 13, 7], [15, 5, 32], [13, 5, 84], [7, 5, 9], [15, 3, 14], [12, 13, 4], [5, 3, 73], [6, 9, 85], [6, 10, 29], [1, 8, 24], [12, 3, 85], [4, 3, 60], [1, 13, 6], [1, 5, 58], [2, 3, 29], [14, 5, 67], [13, 15, 70], [5, 14, 94], [15, 1, 95], [3, 1, 17], [10, 2, 6], [11, 10, 44], [9, 14, 62], [4, 11, 32], [15, 13, 48], [2, 10, 77], [3, 13, 90], [5, 7, 68], [10, 6, 78], [3, 6, 95], [10, 12, 68], [13, 6, 73], [10, 1, 8], [10, 7, 18], [10, 5, 64], [5, 1, 55], [13, 7, 90], [1, 9, 67], [3, 12, 76], [14, 10, 22], [12, 8, 83], [4, 7, 76], [8, 13, 25], [5, 6, 57], [13, 3, 90], [6, 2, 96], [11, 14, 61], [12, 1, 94], [12, 15, 12], [4, 8, 88], [4, 14, 27], [7, 4, 25], [3, 9, 57], [2, 15, 90], [1, 12, 85], [12, 11, 44], [5, 10, 13], [5, 12, 96], [14, 7, 24], [14, 12, 98], [10, 9, 36], [15, 6, 17], [8, 10, 11], [2, 13, 5], [10, 3, 78], [6, 13, 11], [5, 15, 34], [12, 10, 12], [9, 1, 68], [10, 13, 1], [7, 13, 86], [1, 7, 62], [2, 14, 53], [8, 14, 75], [2, 6, 49], [10, 15, 83], [7, 8, 88], [6, 1, 87], [8, 1, 38], [8, 11, 73], [3, 15, 1], [3, 8, 93], [2, 8, 26], [4, 5, 26], [3, 4, 58], [7, 1, 55], [7, 3, 84], [5, 8, 97], [12, 4, 42], [6, 3, 71], [6, 7, 48], [15, 12, 3], [1, 15, 30], [10, 8, 11], [2, 9, 49], [6, 14, 95], [3, 10, 68], [6, 4, 14], [11, 12, 29], [1, 10, 93], [8, 5, 55], [12, 2, 86], [3, 5, 26], [15, 9, 12]];
+    const n = 15;
+    const k = 11;
+    expect(fn(times, n, k)).toEqual(38);
+  });
+
+  it('should work with highly connected networks', () => {
+    const times = [[15,8,1],[7,10,41],[7,9,34],[9,4,31],[12,13,50],[14,3,52],[4,11,99],[4,7,86],[10,13,57],[9,6,10],[1,7,51],[7,15,38],[1,9,11],[12,7,94],[9,13,34],[11,7,79],[7,6,28],[5,3,34],[2,6,97],[14,1,97],[6,10,90],[12,10,37],[13,3,73],[11,14,7],[15,1,39],[6,5,90],[13,6,43],[6,9,32],[4,6,45],[11,10,2],[2,13,4],[14,15,29],[1,14,88],[14,6,19],[6,2,29],[3,14,72],[1,15,4],[11,5,2],[6,7,56],[8,7,88],[13,14,70],[14,12,58],[14,2,86],[11,3,57],[5,2,56],[3,10,26],[2,11,21],[14,5,54],[5,12,40],[14,4,81],[15,2,99],[5,7,57],[13,12,5],[4,9,60],[12,15,48],[6,14,1],[9,7,44],[13,7,69],[5,13,42],[4,1,7],[11,9,76],[8,1,76],[5,14,29],[2,3,69],[7,3,23],[12,14,28],[11,4,85],[10,1,10],[15,12,36],[1,11,69],[15,10,96],[11,13,69],[7,12,49],[1,2,95],[6,4,46],[8,12,94],[12,4,93],[13,5,31],[12,2,60],[6,1,87],[4,14,20],[5,11,89],[4,15,88],[4,10,21],[1,6,5],[10,8,26],[8,2,51],[3,15,23],[7,2,12],[11,1,47],[2,1,75],[3,8,63],[8,10,19],[6,8,18],[4,2,55],[14,11,80],[10,3,73],[3,5,22],[12,3,61],[1,13,33],[9,3,98],[9,12,69],[15,9,6],[7,13,76],[11,12,22],[11,15,51],[13,15,46],[5,10,58],[1,10,26],[13,4,85],[7,14,58],[5,8,46],[11,6,32],[10,9,41],[9,14,35],[14,13,60],[3,9,97],[2,5,39],[7,11,19],[1,12,27],[7,5,13],[8,4,34],[9,15,25],[5,1,93],[15,13,97],[14,9,35],[8,6,67],[9,5,39],[13,11,35],[7,4,21],[12,9,64],[14,8,8],[10,12,94],[8,9,76],[8,5,71],[2,9,64],[10,14,59],[1,4,74],[7,1,69],[15,5,55],[6,15,80],[13,8,84],[8,13,63],[8,3,91],[10,4,87],[1,5,39],[8,11,0],[1,3,79],[4,5,82],[4,12,87],[3,11,29],[7,8,92],[10,7,77],[6,12,42],[13,2,40],[9,10,13],[4,13,65],[2,4,34],[3,13,44],[2,14,69],[3,4,42],[5,15,98],[14,7,6],[15,3,94],[10,2,37],[15,11,7],[9,2,15],[13,9,66],[4,8,83],[8,15,23],[13,1,50],[6,13,57],[2,10,37],[10,6,38],[2,7,45],[9,8,8],[3,12,28],[3,2,83],[2,12,75],[1,8,91],[4,3,70],[12,6,48],[3,1,13],[5,6,42],[6,11,96],[3,6,22],[15,6,34],[11,8,43],[15,7,40],[9,11,57],[11,2,11],[2,8,22],[9,1,73],[2,15,40],[12,11,10],[15,4,78],[12,8,75],[10,15,37],[13,10,44],[8,14,33],[3,7,82],[5,4,46],[12,5,79],[15,14,43],[10,5,65],[5,9,34],[12,1,54],[6,3,16],[14,10,83],[10,11,67]];
+    const n = 15;
+    const k = 8;
+    expect(fn(times, n, k)).toEqual(34);
+  });
+});
diff --git a/lab/exercises/10-mixed/reorder-log-files.js b/lab/exercises/10-mixed/reorder-log-files.js
new file mode 100644
index 00000000..88e3f9e6
--- /dev/null
+++ b/lab/exercises/10-mixed/reorder-log-files.js
@@ -0,0 +1,24 @@
+function reorderLogFiles(logs) {
+  logs.sort((log1, log2) => {
+    const [id1, ...words1] = log1.split(' ');
+    const [id2, ...words2] = log2.split(' ');
+    const isNumber1 = !Number.isNaN(Number(words1[0]));
+    const isNumber2 = !Number.isNaN(Number(words2[0]));
+
+    // both letters
+    if (!isNumber1 && !isNumber2) {
+      const a = words1.join(' ');
+      const b = words2.join(' ');
+      if (a === b) return id1.localeCompare(id2);
+      return a.localeCompare(b);
+    }
+    // if one is number or both
+    if (isNumber1 && isNumber2) return 0;
+    if (isNumber1) return 1;
+    return -1;
+  });
+
+  return logs;
+}
+
+module.exports = reorderLogFiles;
diff --git a/lab/exercises/10-mixed/reorder-log-files.spec.js b/lab/exercises/10-mixed/reorder-log-files.spec.js
new file mode 100644
index 00000000..43c1f212
--- /dev/null
+++ b/lab/exercises/10-mixed/reorder-log-files.spec.js
@@ -0,0 +1,15 @@
+const fn = require('./reorder-log-files');
+
+describe('Reorder Log Files', () => {
+  it('should work', () => {
+    const actual = ['6p tzwmh ige mc', 'ns 566543603829', 'ubd cujg j d yf', 'ha6 1 938 376 5', '3yx 97 666 56 5', 'd 84 34353 2249', '0 tllgmf qp znc', 's 1088746413789', 'ys0 splqqxoflgx', 'uhb rfrwt qzx r', 'u lrvmdt ykmox', 'ah4 4209164350', 'rap 7729 8 125', '4 nivgc qo z i', 'apx 814023338 8'];
+    const expected = ['ubd cujg j d yf', 'u lrvmdt ykmox', '4 nivgc qo z i', 'uhb rfrwt qzx r', 'ys0 splqqxoflgx', '0 tllgmf qp znc', '6p tzwmh ige mc', 'ns 566543603829', 'ha6 1 938 376 5', '3yx 97 666 56 5', 'd 84 34353 2249', 's 1088746413789', 'ah4 4209164350', 'rap 7729 8 125', 'apx 814023338 8'];
+    expect(fn(actual)).toEqual(expected);
+  });
+
+  it('should work', () => {
+    const actual = ['dig1 8 1 5 1', 'let1 art can', 'dig2 3 6', 'let2 own kit dig', 'let3 art zero'];
+    const expected = ['let1 art can', 'let3 art zero', 'let2 own kit dig', 'dig1 8 1 5 1', 'dig2 3 6'];
+    expect(fn(actual)).toEqual(expected);
+  });
+});
diff --git a/lab/exercises/10-mixed/rotated-binary-search-1.js b/lab/exercises/10-mixed/rotated-binary-search-1.js
new file mode 100644
index 00000000..41f3bad4
--- /dev/null
+++ b/lab/exercises/10-mixed/rotated-binary-search-1.js
@@ -0,0 +1,24 @@
+/**
+ * Recursive binary search
+ * @param {*} array
+ * @param {*} target
+ * @param {*} l
+ * @param {*} r
+ */
+function search(array, target, l = 0, r = array.length - 1) {
+  if (!array.length) return -1;
+
+  const m = l + Math.floor((r - l) / 2);
+
+  if (array[m] === target) { return m; }
+  if (r === l || m < 0) { return -1; }
+
+  if (target > array[m]) {
+    // go right
+    return search(array, target, m + 1, r);
+  }
+  // go left
+  return search(array, target, l, m - 1);
+}
+
+module.exports = search;
diff --git a/lab/exercises/10-mixed/rotated-binary-search.js b/lab/exercises/10-mixed/rotated-binary-search.js
new file mode 100644
index 00000000..8ea4d8a2
--- /dev/null
+++ b/lab/exercises/10-mixed/rotated-binary-search.js
@@ -0,0 +1,23 @@
+function search(array, target, lo = 0, hi = array.length - 1) {
+  if (!array.length) return -1;
+  if (lo > hi) { return -1; }
+
+  const m = lo + Math.floor((hi - lo) / 2);
+  const M = array[m];
+  const L = array[lo];
+  const R = array[hi];
+
+  if (M === target) { return m; }
+  if (
+    (R > L && target > M) // sorted array with no rotation
+    || (R > M && (target > M && target <= R)) // sorted array with pivot on M or left
+    || (M > R && (target > M || target < L)) // sorted array with pivot to right of M
+  ) {
+    // go right
+    return search(array, target, m + 1, hi);
+  }
+  // go left
+  return search(array, target, lo, m - 1);
+}
+
+module.exports = search;
diff --git a/lab/exercises/10-mixed/rotated-binary-search.spec.js b/lab/exercises/10-mixed/rotated-binary-search.spec.js
new file mode 100644
index 00000000..2687354d
--- /dev/null
+++ b/lab/exercises/10-mixed/rotated-binary-search.spec.js
@@ -0,0 +1,72 @@
+const fn = require('./rotated-binary-search');
+
+describe('Search in Rotated Sorted Array', () => {
+  it('should work with empty', () => {
+    const array = [];
+    const target = 0;
+    const expected = -1;
+    expect(fn(array, target)).toEqual(expected);
+  });
+
+  it('should work with base case', () => {
+    const array = [0];
+    const target = 0;
+    const expected = 0;
+    expect(fn(array, target)).toEqual(expected);
+  });
+
+  it('should work with base case if not found', () => {
+    const array = [0];
+    const target = 1;
+    const expected = -1;
+    expect(fn(array, target)).toEqual(expected);
+  });
+
+  it('should work with 2 elements', () => {
+    const array = [0, 1];
+    expect(fn(array, 0)).toEqual(0);
+    expect(fn(array, 1)).toEqual(1);
+    expect(fn(array, 2)).toEqual(-1);
+    expect(fn(array, -1)).toEqual(-1);
+  });
+
+  it('should work with 2 elements "rotated"', () => {
+    const array = [1, 0];
+    expect(fn(array, 0)).toEqual(1);
+    expect(fn(array, 1)).toEqual(0);
+    expect(fn(array, 2)).toEqual(-1);
+    expect(fn(array, -1)).toEqual(-1);
+  });
+
+  it('should work with 3 elements', () => {
+    const array = [0, 1, 2];
+    expect(fn(array, 0)).toEqual(0);
+    expect(fn(array, 1)).toEqual(1);
+    expect(fn(array, 2)).toEqual(2);
+    expect(fn(array, 3)).toEqual(-1);
+    expect(fn(array, -1)).toEqual(-1);
+  });
+
+  it('should work with 3 elements "rotated"', () => {
+    const array = [2, 0, 1];
+    expect(fn(array, 0)).toEqual(1);
+    expect(fn(array, 1)).toEqual(2);
+    expect(fn(array, 2)).toEqual(0);
+    expect(fn(array, 3)).toEqual(-1);
+    expect(fn(array, -1)).toEqual(-1);
+  });
+
+  it('should work', () => {
+    const array = [4, 5, 6, 7, 0, 1, 2];
+    const target = 5;
+    const expected = 1;
+    expect(fn(array, target)).toEqual(expected);
+  });
+
+  it('should work', () => {
+    const array = [9, 0, 2, 7, 8];
+    const target = 3;
+    const expected = -1;
+    expect(fn(array, target)).toEqual(expected);
+  });
+});
diff --git a/lab/exercises/10-mixed/trapping-rain-water-1.js b/lab/exercises/10-mixed/trapping-rain-water-1.js
new file mode 100644
index 00000000..d79e90a2
--- /dev/null
+++ b/lab/exercises/10-mixed/trapping-rain-water-1.js
@@ -0,0 +1,28 @@
+/**
+ * Compute how much water it is able to trap after raining.
+ * @param {number[]} height Non-negative integers representing
+ *  an elevation map where the width of each bar is 1.
+ *
+ * @runtime O(n^2) - Brute force
+ * @space O(1)
+ */
+function trap(height) {
+  let ans = 0;
+
+  for (let i = 0; i < height.length; i++) {
+    let leftMax = 0;
+    let rightMax = 0;
+    for (let j = i; j >= 0; j--) {
+      leftMax = Math.max(leftMax, height[j]);
+    }
+    for (let j = i; j < height.length; j++) {
+      rightMax = Math.max(rightMax, height[j]);
+    }
+
+    ans += Math.min(leftMax, rightMax) - height[i];
+  }
+
+  return ans;
+}
+
+module.exports = trap;
diff --git a/lab/exercises/10-mixed/trapping-rain-water.js b/lab/exercises/10-mixed/trapping-rain-water.js
new file mode 100644
index 00000000..510e8b3a
--- /dev/null
+++ b/lab/exercises/10-mixed/trapping-rain-water.js
@@ -0,0 +1,28 @@
+/**
+ * Compute how much water it is able to trap after raining.
+ * @param {number[]} height Non-negative integers representing
+ *  an elevation map where the width of each bar is 1.
+ *
+ * @runtime O(n) - using DP
+ * @space O(n)
+ */
+function trap(height) {
+  let ans = 0;
+  const leftMax = [];
+  const rightMax = [];
+
+  for (let j = height.length - 1; j >= 0; j--) {
+    leftMax[j] = Math.max((leftMax[j + 1] || 0), height[j]);
+  }
+  for (let j = 0; j < height.length; j++) {
+    rightMax[j] = Math.max((rightMax[j - 1] || 0), height[j]);
+  }
+
+  for (let i = 0; i < height.length; i++) {
+    ans += Math.min(leftMax[i], rightMax[i]) - height[i];
+  }
+
+  return ans;
+}
+
+module.exports = trap;
diff --git a/lab/exercises/10-mixed/trapping-rain-water.spec.js b/lab/exercises/10-mixed/trapping-rain-water.spec.js
new file mode 100644
index 00000000..eeba5ab3
--- /dev/null
+++ b/lab/exercises/10-mixed/trapping-rain-water.spec.js
@@ -0,0 +1,19 @@
+const trap = require('./trapping-rain-water');
+
+describe('Trapping Rain Water', () => {
+  it('should trap', () => {
+    expect(trap([1, 0, 1])).toEqual(1);
+  });
+
+  it('should not trap', () => {
+    expect(trap([1, 1, 1])).toEqual(0);
+  });
+
+  it('should not trap', () => {
+    expect(trap([0, 0, 0])).toEqual(0);
+  });
+
+  it('should trap', () => {
+    expect(trap([0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1])).toEqual(6);
+  });
+});
diff --git a/lab/exercises/10-mixed/trie-wildcard-search.js b/lab/exercises/10-mixed/trie-wildcard-search.js
new file mode 100644
index 00000000..b2d403d1
--- /dev/null
+++ b/lab/exercises/10-mixed/trie-wildcard-search.js
@@ -0,0 +1,54 @@
+/**
+ * Your WordDictionary object will be instantiated and called as such:
+ * var obj = new WordDictionary()
+ * obj.addWord(word)
+ * var param_2 = obj.search(word)
+ */
+class WordDictionary {
+  children = {};
+  isWord = false;
+  /**
+   * Initialize your data structure here.
+   */
+  constructor() {
+  }
+
+  /**
+   * Adds a word into the data structure.
+   * @param {string} word
+   * @return {void}
+   */
+  addWord (word) {
+    let curr = this;
+
+    for (let char of word) {
+      if (!curr.children[char]) curr.children[char] = new WordDictionary();
+      curr = curr.children[char];
+    }
+
+    curr.isWord = true;
+  }
+
+  /**
+   * Returns if the word is in the data structure.
+   * A word could contain the dot character '.' to represent any one letter.
+   * @param {string} word
+   * @return {boolean}
+   */
+  search (word, curr = this, index = 0) {
+    if (index > word.length) return true; // e.g. final '.'
+    for (let [i, char] of [...word.slice(index)].entries()) {
+      if (char === '.') {
+        for (let child of Object.keys(curr.children)) {
+          if (this.search(word, curr.children[child], i + 1)) return true;
+        }
+      }
+      else if (!curr || !curr.children[char]) return false;
+      curr = curr.children[char];
+    }
+
+    return curr.isWord;
+  }
+}
+
+module.exports = WordDictionary;
diff --git a/lab/exercises/10-mixed/trie-wildcard-search.spec.js b/lab/exercises/10-mixed/trie-wildcard-search.spec.js
new file mode 100644
index 00000000..e1aabc90
--- /dev/null
+++ b/lab/exercises/10-mixed/trie-wildcard-search.spec.js
@@ -0,0 +1,47 @@
+const WordDictionary = require('./trie-wildcard-search');
+
+describe('WordDictionary', () => {
+  let wd;
+
+  beforeEach(() => {
+    wd = new WordDictionary();
+  });
+
+  describe('should find exact matches', () => {
+    beforeEach(() => {
+      wd.addWord('bad');
+      wd.addWord('mad');
+    });
+
+    it('should find match', () => {
+      expect(wd.search('bad')).toEqual(true);
+      expect(wd.search('mad')).toEqual(true);
+    });
+
+    it('should be false for partial match', () => {
+      expect(wd.search('ba')).toEqual(false);
+    });
+
+    it('should be false for NO match', () => {
+      expect(wd.search('dad')).toEqual(false);
+    });
+  });
+
+  describe('should find wildcard matches', () => {
+    beforeEach(() => {
+      wd.addWord('bad');
+    });
+
+    it('should work with 1 wildcard', () => {
+      expect(wd.search('.ad')).toEqual(true);
+    });
+
+    it('should work with 1 wildcard not match', () => {
+      expect(wd.search('.ax')).toEqual(false);
+    });
+
+    it('should work with 2 wildcard', () => {
+      expect(wd.search('..d')).toEqual(true);
+    });
+  });
+});
diff --git a/src/runtimes/linear/find-max.js b/lab/linear/find-max.js
similarity index 100%
rename from src/runtimes/linear/find-max.js
rename to lab/linear/find-max.js
diff --git a/src/runtimes/linear/get-min.js b/lab/linear/get-min.js
similarity index 100%
rename from src/runtimes/linear/get-min.js
rename to lab/linear/get-min.js
diff --git a/src/runtimes/linear/is-even.js b/lab/linear/is-even.js
similarity index 100%
rename from src/runtimes/linear/is-even.js
rename to lab/linear/is-even.js
diff --git a/src/runtimes/sortedArray1e1.txt b/lab/sortedArray1e1.txt
similarity index 100%
rename from src/runtimes/sortedArray1e1.txt
rename to lab/sortedArray1e1.txt
diff --git a/src/runtimes/sortedArray1e3.txt b/lab/sortedArray1e3.txt
similarity index 100%
rename from src/runtimes/sortedArray1e3.txt
rename to lab/sortedArray1e3.txt
diff --git a/src/runtimes/sortedArray1e4.txt b/lab/sortedArray1e4.txt
similarity index 100%
rename from src/runtimes/sortedArray1e4.txt
rename to lab/sortedArray1e4.txt
diff --git a/src/runtimes/test1e3.txt b/lab/test1e3.txt
similarity index 100%
rename from src/runtimes/test1e3.txt
rename to lab/test1e3.txt
diff --git a/lab/timsort.java b/lab/timsort.java
deleted file mode 100644
index 1d4e710a..00000000
--- a/lab/timsort.java
+++ /dev/null
@@ -1,928 +0,0 @@
-/*
- * Copyright 2009 Google Inc.  All Rights Reserved.
- * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
- *
- * This code is free software; you can redistribute it and/or modify it
- * under the terms of the GNU General Public License version 2 only, as
- * published by the Free Software Foundation.  Sun designates this
- * particular file as subject to the "Classpath" exception as provided
- * by Sun in the LICENSE file that accompanied this code.
- *
- * This code is distributed in the hope that it will be useful, but WITHOUT
- * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
- * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
- * version 2 for more details (a copy is included in the LICENSE file that
- * accompanied this code).
- *
- * You should have received a copy of the GNU General Public License version
- * 2 along with this work; if not, write to the Free Software Foundation,
- * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
- *
- * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
- * CA 95054 USA or visit www.sun.com if you need additional information or
- * have any questions.
- */
-
-package java.util;
-
-/**
- * A stable, adaptive, iterative mergesort that requires far fewer than
- * n lg(n) comparisons when running on partially sorted arrays, while
- * offering performance comparable to a traditional mergesort when run
- * on random arrays.  Like all proper mergesorts, this sort is stable and
- * runs O(n log n) time (worst case).  In the worst case, this sort requires
- * temporary storage space for n/2 object references; in the best case,
- * it requires only a small constant amount of space.
- *
- * This implementation was adapted from Tim Peters's list sort for
- * Python, which is described in detail here:
- *
- *   http://svn.python.org/projects/python/trunk/Objects/listsort.txt
- *
- * Tim's C code may be found here:
- *
- *   http://svn.python.org/projects/python/trunk/Objects/listobject.c
- *
- * The underlying techniques are described in this paper (and may have
- * even earlier origins):
- *
- *  "Optimistic Sorting and Information Theoretic Complexity"
- *  Peter McIlroy
- *  SODA (Fourth Annual ACM-SIAM Symposium on Discrete Algorithms),
- *  pp 467-474, Austin, Texas, 25-27 January 1993.
- *
- * While the API to this class consists solely of static methods, it is
- * (privately) instantiable; a TimSort instance holds the state of an ongoing
- * sort, assuming the input array is large enough to warrant the full-blown
- * TimSort. Small arrays are sorted in place, using a binary insertion sort.
- *
- * @author Josh Bloch
- */
-class TimSort<T> {
-    /**
-     * This is the minimum sized sequence that will be merged.  Shorter
-     * sequences will be lengthened by calling binarySort.  If the entire
-     * array is less than this length, no merges will be performed.
-     *
-     * This constant should be a power of two.  It was 64 in Tim Peter's C
-     * implementation, but 32 was empirically determined to work better in
-     * this implementation.  In the unlikely event that you set this constant
-     * to be a number that's not a power of two, you'll need to change the
-     * {@link #minRunLength} computation.
-     *
-     * If you decrease this constant, you must change the stackLen
-     * computation in the TimSort constructor, or you risk an
-     * ArrayOutOfBounds exception.  See listsort.txt for a discussion
-     * of the minimum stack length required as a function of the length
-     * of the array being sorted and the minimum merge sequence length.
-     */
-    private static final int MIN_MERGE = 32;
-
-    /**
-     * The array being sorted.
-     */
-    private final T[] a;
-
-    /**
-     * The comparator for this sort.
-     */
-    private final Comparator<? super T> c;
-
-    /**
-     * When we get into galloping mode, we stay there until both runs win less
-     * often than MIN_GALLOP consecutive times.
-     */
-    private static final int  MIN_GALLOP = 7;
-
-    /**
-     * This controls when we get *into* galloping mode.  It is initialized
-     * to MIN_GALLOP.  The mergeLo and mergeHi methods nudge it higher for
-     * random data, and lower for highly structured data.
-     */
-    private int minGallop = MIN_GALLOP;
-
-    /**
-     * Maximum initial size of tmp array, which is used for merging.  The array
-     * can grow to accommodate demand.
-     *
-     * Unlike Tim's original C version, we do not allocate this much storage
-     * when sorting smaller arrays.  This change was required for performance.
-     */
-    private static final int INITIAL_TMP_STORAGE_LENGTH = 256;
-
-    /**
-     * Temp storage for merges.
-     */
-    private T[] tmp; // Actual runtime type will be Object[], regardless of T
-
-    /**
-     * A stack of pending runs yet to be merged.  Run i starts at
-     * address base[i] and extends for len[i] elements.  It's always
-     * true (so long as the indices are in bounds) that:
-     *
-     *     runBase[i] + runLen[i] == runBase[i + 1]
-     *
-     * so we could cut the storage for this, but it's a minor amount,
-     * and keeping all the info explicit simplifies the code.
-     */
-    private int stackSize = 0;  // Number of pending runs on stack
-    private final int[] runBase;
-    private final int[] runLen;
-
-    /**
-     * Creates a TimSort instance to maintain the state of an ongoing sort.
-     *
-     * @param a the array to be sorted
-     * @param c the comparator to determine the order of the sort
-     */
-    private TimSort(T[] a, Comparator<? super T> c) {
-        this.a = a;
-        this.c = c;
-
-        // Allocate temp storage (which may be increased later if necessary)
-        int len = a.length;
-        @SuppressWarnings({"unchecked", "UnnecessaryLocalVariable"})
-        T[] newArray = (T[]) new Object[len < 2 * INITIAL_TMP_STORAGE_LENGTH ?
-                                        len >>> 1 : INITIAL_TMP_STORAGE_LENGTH];
-        tmp = newArray;
-
-        /*
-         * Allocate runs-to-be-merged stack (which cannot be expanded).  The
-         * stack length requirements are described in listsort.txt.  The C
-         * version always uses the same stack length (85), but this was
-         * measured to be too expensive when sorting "mid-sized" arrays (e.g.,
-         * 100 elements) in Java.  Therefore, we use smaller (but sufficiently
-         * large) stack lengths for smaller arrays.  The "magic numbers" in the
-         * computation below must be changed if MIN_MERGE is decreased.  See
-         * the MIN_MERGE declaration above for more information.
-         */
-        int stackLen = (len <    120  ?  5 :
-                        len <   1542  ? 10 :
-                        len < 119151  ? 19 : 40);
-        runBase = new int[stackLen];
-        runLen = new int[stackLen];
-    }
-
-    /*
-     * The next two methods (which are package private and static) constitute
-     * the entire API of this class.  Each of these methods obeys the contract
-     * of the public method with the same signature in java.util.Arrays.
-     */
-
-    static <T> void sort(T[] a, Comparator<? super T> c) {
-        sort(a, 0, a.length, c);
-    }
-
-    static <T> void sort(T[] a, int lo, int hi, Comparator<? super T> c) {
-        if (c == null) {
-            Arrays.sort(a, lo, hi);
-            return;
-        }
-
-        rangeCheck(a.length, lo, hi);
-        int nRemaining  = hi - lo;
-        if (nRemaining < 2)
-            return;  // Arrays of size 0 and 1 are always sorted
-
-        // If array is small, do a "mini-TimSort" with no merges
-        if (nRemaining < MIN_MERGE) {
-            int initRunLen = countRunAndMakeAscending(a, lo, hi, c);
-            binarySort(a, lo, hi, lo + initRunLen, c);
-            return;
-        }
-
-        /**
-         * March over the array once, left to right, finding natural runs,
-         * extending short natural runs to minRun elements, and merging runs
-         * to maintain stack invariant.
-         */
-        TimSort<T> ts = new TimSort<T>(a, c);
-        int minRun = minRunLength(nRemaining);
-        do {
-            // Identify next run
-            int runLen = countRunAndMakeAscending(a, lo, hi, c);
-
-            // If run is short, extend to min(minRun, nRemaining)
-            if (runLen < minRun) {
-                int force = nRemaining <= minRun ? nRemaining : minRun;
-                binarySort(a, lo, lo + force, lo + runLen, c);
-                runLen = force;
-            }
-
-            // Push run onto pending-run stack, and maybe merge
-            ts.pushRun(lo, runLen);
-            ts.mergeCollapse();
-
-            // Advance to find next run
-            lo += runLen;
-            nRemaining -= runLen;
-        } while (nRemaining != 0);
-
-        // Merge all remaining runs to complete sort
-        assert lo == hi;
-        ts.mergeForceCollapse();
-        assert ts.stackSize == 1;
-    }
-
-    /**
-     * Sorts the specified portion of the specified array using a binary
-     * insertion sort.  This is the best method for sorting small numbers
-     * of elements.  It requires O(n log n) compares, but O(n^2) data
-     * movement (worst case).
-     *
-     * If the initial part of the specified range is already sorted,
-     * this method can take advantage of it: the method assumes that the
-     * elements from index {@code lo}, inclusive, to {@code start},
-     * exclusive are already sorted.
-     *
-     * @param a the array in which a range is to be sorted
-     * @param lo the index of the first element in the range to be sorted
-     * @param hi the index after the last element in the range to be sorted
-     * @param start the index of the first element in the range that is
-     *        not already known to be sorted (@code lo <= start <= hi}
-     * @param c comparator to used for the sort
-     */
-    @SuppressWarnings("fallthrough")
-    private static <T> void binarySort(T[] a, int lo, int hi, int start,
-                                       Comparator<? super T> c) {
-        assert lo <= start && start <= hi;
-        if (start == lo)
-            start++;
-        for ( ; start < hi; start++) {
-            T pivot = a[start];
-
-            // Set left (and right) to the index where a[start] (pivot) belongs
-            int left = lo;
-            int right = start;
-            assert left <= right;
-            /*
-             * Invariants:
-             *   pivot >= all in [lo, left).
-             *   pivot <  all in [right, start).
-             */
-            while (left < right) {
-                int mid = (left + right) >>> 1;
-                if (c.compare(pivot, a[mid]) < 0)
-                    right = mid;
-                else
-                    left = mid + 1;
-            }
-            assert left == right;
-
-            /*
-             * The invariants still hold: pivot >= all in [lo, left) and
-             * pivot < all in [left, start), so pivot belongs at left.  Note
-             * that if there are elements equal to pivot, left points to the
-             * first slot after them -- that's why this sort is stable.
-             * Slide elements over to make room to make room for pivot.
-             */
-            int n = start - left;  // The number of elements to move
-            // Switch is just an optimization for arraycopy in default case
-            switch(n) {
-                case 2:  a[left + 2] = a[left + 1];
-                case 1:  a[left + 1] = a[left];
-                         break;
-                default: System.arraycopy(a, left, a, left + 1, n);
-            }
-            a[left] = pivot;
-        }
-    }
-
-    /**
-     * Returns the length of the run beginning at the specified position in
-     * the specified array and reverses the run if it is descending (ensuring
-     * that the run will always be ascending when the method returns).
-     *
-     * A run is the longest ascending sequence with:
-     *
-     *    a[lo] <= a[lo + 1] <= a[lo + 2] <= ...
-     *
-     * or the longest descending sequence with:
-     *
-     *    a[lo] >  a[lo + 1] >  a[lo + 2] >  ...
-     *
-     * For its intended use in a stable mergesort, the strictness of the
-     * definition of "descending" is needed so that the call can safely
-     * reverse a descending sequence without violating stability.
-     *
-     * @param a the array in which a run is to be counted and possibly reversed
-     * @param lo index of the first element in the run
-     * @param hi index after the last element that may be contained in the run.
-              It is required that @code{lo < hi}.
-     * @param c the comparator to used for the sort
-     * @return  the length of the run beginning at the specified position in
-     *          the specified array
-     */
-    private static <T> int countRunAndMakeAscending(T[] a, int lo, int hi,
-                                                    Comparator<? super T> c) {
-        assert lo < hi;
-        int runHi = lo + 1;
-        if (runHi == hi)
-            return 1;
-
-        // Find end of run, and reverse range if descending
-        if (c.compare(a[runHi++], a[lo]) < 0) { // Descending
-            while(runHi < hi && c.compare(a[runHi], a[runHi - 1]) < 0)
-                runHi++;
-            reverseRange(a, lo, runHi);
-        } else {                              // Ascending
-            while (runHi < hi && c.compare(a[runHi], a[runHi - 1]) >= 0)
-                runHi++;
-        }
-
-        return runHi - lo;
-    }
-
-    /**
-     * Reverse the specified range of the specified array.
-     *
-     * @param a the array in which a range is to be reversed
-     * @param lo the index of the first element in the range to be reversed
-     * @param hi the index after the last element in the range to be reversed
-     */
-    private static void reverseRange(Object[] a, int lo, int hi) {
-        hi--;
-        while (lo < hi) {
-            Object t = a[lo];
-            a[lo++] = a[hi];
-            a[hi--] = t;
-        }
-    }
-
-    /**
-     * Returns the minimum acceptable run length for an array of the specified
-     * length. Natural runs shorter than this will be extended with
-     * {@link #binarySort}.
-     *
-     * Roughly speaking, the computation is:
-     *
-     *  If n < MIN_MERGE, return n (it's too small to bother with fancy stuff).
-     *  Else if n is an exact power of 2, return MIN_MERGE/2.
-     *  Else return an int k, MIN_MERGE/2 <= k <= MIN_MERGE, such that n/k
-     *   is close to, but strictly less than, an exact power of 2.
-     *
-     * For the rationale, see listsort.txt.
-     *
-     * @param n the length of the array to be sorted
-     * @return the length of the minimum run to be merged
-     */
-    private static int minRunLength(int n) {
-        assert n >= 0;
-        int r = 0;      // Becomes 1 if any 1 bits are shifted off
-        while (n >= MIN_MERGE) {
-            r |= (n & 1);
-            n >>= 1;
-        }
-        return n + r;
-    }
-
-    /**
-     * Pushes the specified run onto the pending-run stack.
-     *
-     * @param runBase index of the first element in the run
-     * @param runLen  the number of elements in the run
-     */
-    private void pushRun(int runBase, int runLen) {
-        this.runBase[stackSize] = runBase;
-        this.runLen[stackSize] = runLen;
-        stackSize++;
-    }
-
-    /**
-     * Examines the stack of runs waiting to be merged and merges adjacent runs
-     * until the stack invariants are reestablished:
-     *
-     *     1. runLen[i - 3] > runLen[i - 2] + runLen[i - 1]
-     *     2. runLen[i - 2] > runLen[i - 1]
-     *
-     * This method is called each time a new run is pushed onto the stack,
-     * so the invariants are guaranteed to hold for i < stackSize upon
-     * entry to the method.
-     */
-    private void mergeCollapse() {
-        while (stackSize > 1) {
-            int n = stackSize - 2;
-            if (n > 0 && runLen[n-1] <= runLen[n] + runLen[n+1]) {
-                if (runLen[n - 1] < runLen[n + 1])
-                    n--;
-                mergeAt(n);
-            } else if (runLen[n] <= runLen[n + 1]) {
-                mergeAt(n);
-            } else {
-                break; // Invariant is established
-            }
-        }
-    }
-
-    /**
-     * Merges all runs on the stack until only one remains.  This method is
-     * called once, to complete the sort.
-     */
-    private void mergeForceCollapse() {
-        while (stackSize > 1) {
-            int n = stackSize - 2;
-            if (n > 0 && runLen[n - 1] < runLen[n + 1])
-                n--;
-            mergeAt(n);
-        }
-    }
-
-    /**
-     * Merges the two runs at stack indices i and i+1.  Run i must be
-     * the penultimate or antepenultimate run on the stack.  In other words,
-     * i must be equal to stackSize-2 or stackSize-3.
-     *
-     * @param i stack index of the first of the two runs to merge
-     */
-    private void mergeAt(int i) {
-        assert stackSize >= 2;
-        assert i >= 0;
-        assert i == stackSize - 2 || i == stackSize - 3;
-
-        int base1 = runBase[i];
-        int len1 = runLen[i];
-        int base2 = runBase[i + 1];
-        int len2 = runLen[i + 1];
-        assert len1 > 0 && len2 > 0;
-        assert base1 + len1 == base2;
-
-        /*
-         * Record the length of the combined runs; if i is the 3rd-last
-         * run now, also slide over the last run (which isn't involved
-         * in this merge).  The current run (i+1) goes away in any case.
-         */
-        runLen[i] = len1 + len2;
-        if (i == stackSize - 3) {
-            runBase[i + 1] = runBase[i + 2];
-            runLen[i + 1] = runLen[i + 2];
-        }
-        stackSize--;
-
-        /*
-         * Find where the first element of run2 goes in run1. Prior elements
-         * in run1 can be ignored (because they're already in place).
-         */
-        int k = gallopRight(a[base2], a, base1, len1, 0, c);
-        assert k >= 0;
-        base1 += k;
-        len1 -= k;
-        if (len1 == 0)
-            return;
-
-        /*
-         * Find where the last element of run1 goes in run2. Subsequent elements
-         * in run2 can be ignored (because they're already in place).
-         */
-        len2 = gallopLeft(a[base1 + len1 - 1], a, base2, len2, len2 - 1, c);
-        assert len2 >= 0;
-        if (len2 == 0)
-            return;
-
-        // Merge remaining runs, using tmp array with min(len1, len2) elements
-        if (len1 <= len2)
-            mergeLo(base1, len1, base2, len2);
-        else
-            mergeHi(base1, len1, base2, len2);
-    }
-
-    /**
-     * Locates the position at which to insert the specified key into the
-     * specified sorted range; if the range contains an element equal to key,
-     * returns the index of the leftmost equal element.
-     *
-     * @param key the key whose insertion point to search for
-     * @param a the array in which to search
-     * @param base the index of the first element in the range
-     * @param len the length of the range; must be > 0
-     * @param hint the index at which to begin the search, 0 <= hint < n.
-     *     The closer hint is to the result, the faster this method will run.
-     * @param c the comparator used to order the range, and to search
-     * @return the int k,  0 <= k <= n such that a[b + k - 1] < key <= a[b + k],
-     *    pretending that a[b - 1] is minus infinity and a[b + n] is infinity.
-     *    In other words, key belongs at index b + k; or in other words,
-     *    the first k elements of a should precede key, and the last n - k
-     *    should follow it.
-     */
-    private static <T> int gallopLeft(T key, T[] a, int base, int len, int hint,
-                                      Comparator<? super T> c) {
-        assert len > 0 && hint >= 0 && hint < len;
-        int lastOfs = 0;
-        int ofs = 1;
-        if (c.compare(key, a[base + hint]) > 0) {
-            // Gallop right until a[base+hint+lastOfs] < key <= a[base+hint+ofs]
-            int maxOfs = len - hint;
-            while (ofs < maxOfs && c.compare(key, a[base + hint + ofs]) > 0) {
-                lastOfs = ofs;
-                ofs = (ofs << 1) + 1;
-                if (ofs <= 0)   // int overflow
-                    ofs = maxOfs;
-            }
-            if (ofs > maxOfs)
-                ofs = maxOfs;
-
-            // Make offsets relative to base
-            lastOfs += hint;
-            ofs += hint;
-        } else { // key <= a[base + hint]
-            // Gallop left until a[base+hint-ofs] < key <= a[base+hint-lastOfs]
-            final int maxOfs = hint + 1;
-            while (ofs < maxOfs && c.compare(key, a[base + hint - ofs]) <= 0) {
-                lastOfs = ofs;
-                ofs = (ofs << 1) + 1;
-                if (ofs <= 0)   // int overflow
-                    ofs = maxOfs;
-            }
-            if (ofs > maxOfs)
-                ofs = maxOfs;
-
-            // Make offsets relative to base
-            int tmp = lastOfs;
-            lastOfs = hint - ofs;
-            ofs = hint - tmp;
-        }
-        assert -1 <= lastOfs && lastOfs < ofs && ofs <= len;
-
-        /*
-         * Now a[base+lastOfs] < key <= a[base+ofs], so key belongs somewhere
-         * to the right of lastOfs but no farther right than ofs.  Do a binary
-         * search, with invariant a[base + lastOfs - 1] < key <= a[base + ofs].
-         */
-        lastOfs++;
-        while (lastOfs < ofs) {
-            int m = lastOfs + ((ofs - lastOfs) >>> 1);
-
-            if (c.compare(key, a[base + m]) > 0)
-                lastOfs = m + 1;  // a[base + m] < key
-            else
-                ofs = m;          // key <= a[base + m]
-        }
-        assert lastOfs == ofs;    // so a[base + ofs - 1] < key <= a[base + ofs]
-        return ofs;
-    }
-
-    /**
-     * Like gallopLeft, except that if the range contains an element equal to
-     * key, gallopRight returns the index after the rightmost equal element.
-     *
-     * @param key the key whose insertion point to search for
-     * @param a the array in which to search
-     * @param base the index of the first element in the range
-     * @param len the length of the range; must be > 0
-     * @param hint the index at which to begin the search, 0 <= hint < n.
-     *     The closer hint is to the result, the faster this method will run.
-     * @param c the comparator used to order the range, and to search
-     * @return the int k,  0 <= k <= n such that a[b + k - 1] <= key < a[b + k]
-     */
-    private static <T> int gallopRight(T key, T[] a, int base, int len,
-                                       int hint, Comparator<? super T> c) {
-        assert len > 0 && hint >= 0 && hint < len;
-
-        int ofs = 1;
-        int lastOfs = 0;
-        if (c.compare(key, a[base + hint]) < 0) {
-            // Gallop left until a[b+hint - ofs] <= key < a[b+hint - lastOfs]
-            int maxOfs = hint + 1;
-            while (ofs < maxOfs && c.compare(key, a[base + hint - ofs]) < 0) {
-                lastOfs = ofs;
-                ofs = (ofs << 1) + 1;
-                if (ofs <= 0)   // int overflow
-                    ofs = maxOfs;
-            }
-            if (ofs > maxOfs)
-                ofs = maxOfs;
-
-            // Make offsets relative to b
-            int tmp = lastOfs;
-            lastOfs = hint - ofs;
-            ofs = hint - tmp;
-        } else { // a[b + hint] <= key
-            // Gallop right until a[b+hint + lastOfs] <= key < a[b+hint + ofs]
-            int maxOfs = len - hint;
-            while (ofs < maxOfs && c.compare(key, a[base + hint + ofs]) >= 0) {
-                lastOfs = ofs;
-                ofs = (ofs << 1) + 1;
-                if (ofs <= 0)   // int overflow
-                    ofs = maxOfs;
-            }
-            if (ofs > maxOfs)
-                ofs = maxOfs;
-
-            // Make offsets relative to b
-            lastOfs += hint;
-            ofs += hint;
-        }
-        assert -1 <= lastOfs && lastOfs < ofs && ofs <= len;
-
-        /*
-         * Now a[b + lastOfs] <= key < a[b + ofs], so key belongs somewhere to
-         * the right of lastOfs but no farther right than ofs.  Do a binary
-         * search, with invariant a[b + lastOfs - 1] <= key < a[b + ofs].
-         */
-        lastOfs++;
-        while (lastOfs < ofs) {
-            int m = lastOfs + ((ofs - lastOfs) >>> 1);
-
-            if (c.compare(key, a[base + m]) < 0)
-                ofs = m;          // key < a[b + m]
-            else
-                lastOfs = m + 1;  // a[b + m] <= key
-        }
-        assert lastOfs == ofs;    // so a[b + ofs - 1] <= key < a[b + ofs]
-        return ofs;
-    }
-
-    /**
-     * Merges two adjacent runs in place, in a stable fashion.  The first
-     * element of the first run must be greater than the first element of the
-     * second run (a[base1] > a[base2]), and the last element of the first run
-     * (a[base1 + len1-1]) must be greater than all elements of the second run.
-     *
-     * For performance, this method should be called only when len1 <= len2;
-     * its twin, mergeHi should be called if len1 >= len2.  (Either method
-     * may be called if len1 == len2.)
-     *
-     * @param base1 index of first element in first run to be merged
-     * @param len1  length of first run to be merged (must be > 0)
-     * @param base2 index of first element in second run to be merged
-     *        (must be aBase + aLen)
-     * @param len2  length of second run to be merged (must be > 0)
-     */
-    private void mergeLo(int base1, int len1, int base2, int len2) {
-        assert len1 > 0 && len2 > 0 && base1 + len1 == base2;
-
-        // Copy first run into temp array
-        T[] a = this.a; // For performance
-        T[] tmp = ensureCapacity(len1);
-        System.arraycopy(a, base1, tmp, 0, len1);
-
-        int cursor1 = 0;       // Indexes into tmp array
-        int cursor2 = base2;   // Indexes int a
-        int dest = base1;      // Indexes int a
-
-        // Move first element of second run and deal with degenerate cases
-        a[dest++] = a[cursor2++];
-        if (--len2 == 0) {
-            System.arraycopy(tmp, cursor1, a, dest, len1);
-            return;
-        }
-        if (len1 == 1) {
-            System.arraycopy(a, cursor2, a, dest, len2);
-            a[dest + len2] = tmp[cursor1]; // Last elt of run 1 to end of merge
-            return;
-        }
-
-        Comparator<? super T> c = this.c;  // Use local variable for performance
-        int minGallop = this.minGallop;    //  "    "       "     "      "
-    outer:
-        while (true) {
-            int count1 = 0; // Number of times in a row that first run won
-            int count2 = 0; // Number of times in a row that second run won
-
-            /*
-             * Do the straightforward thing until (if ever) one run starts
-             * winning consistently.
-             */
-            do {
-                assert len1 > 1 && len2 > 0;
-                if (c.compare(a[cursor2], tmp[cursor1]) < 0) {
-                    a[dest++] = a[cursor2++];
-                    count2++;
-                    count1 = 0;
-                    if (--len2 == 0)
-                        break outer;
-                } else {
-                    a[dest++] = tmp[cursor1++];
-                    count1++;
-                    count2 = 0;
-                    if (--len1 == 1)
-                        break outer;
-                }
-            } while ((count1 | count2) < minGallop);
-
-            /*
-             * One run is winning so consistently that galloping may be a
-             * huge win. So try that, and continue galloping until (if ever)
-             * neither run appears to be winning consistently anymore.
-             */
-            do {
-                assert len1 > 1 && len2 > 0;
-                count1 = gallopRight(a[cursor2], tmp, cursor1, len1, 0, c);
-                if (count1 != 0) {
-                    System.arraycopy(tmp, cursor1, a, dest, count1);
-                    dest += count1;
-                    cursor1 += count1;
-                    len1 -= count1;
-                    if (len1 <= 1) // len1 == 1 || len1 == 0
-                        break outer;
-                }
-                a[dest++] = a[cursor2++];
-                if (--len2 == 0)
-                    break outer;
-
-                count2 = gallopLeft(tmp[cursor1], a, cursor2, len2, 0, c);
-                if (count2 != 0) {
-                    System.arraycopy(a, cursor2, a, dest, count2);
-                    dest += count2;
-                    cursor2 += count2;
-                    len2 -= count2;
-                    if (len2 == 0)
-                        break outer;
-                }
-                a[dest++] = tmp[cursor1++];
-                if (--len1 == 1)
-                    break outer;
-                minGallop--;
-            } while (count1 >= MIN_GALLOP | count2 >= MIN_GALLOP);
-            if (minGallop < 0)
-                minGallop = 0;
-            minGallop += 2;  // Penalize for leaving gallop mode
-        }  // End of "outer" loop
-        this.minGallop = minGallop < 1 ? 1 : minGallop;  // Write back to field
-
-        if (len1 == 1) {
-            assert len2 > 0;
-            System.arraycopy(a, cursor2, a, dest, len2);
-            a[dest + len2] = tmp[cursor1]; //  Last elt of run 1 to end of merge
-        } else if (len1 == 0) {
-            throw new IllegalArgumentException(
-                "Comparison method violates its general contract!");
-        } else {
-            assert len2 == 0;
-            assert len1 > 1;
-            System.arraycopy(tmp, cursor1, a, dest, len1);
-        }
-    }
-
-    /**
-     * Like mergeLo, except that this method should be called only if
-     * len1 >= len2; mergeLo should be called if len1 <= len2.  (Either method
-     * may be called if len1 == len2.)
-     *
-     * @param base1 index of first element in first run to be merged
-     * @param len1  length of first run to be merged (must be > 0)
-     * @param base2 index of first element in second run to be merged
-     *        (must be aBase + aLen)
-     * @param len2  length of second run to be merged (must be > 0)
-     */
-    private void mergeHi(int base1, int len1, int base2, int len2) {
-        assert len1 > 0 && len2 > 0 && base1 + len1 == base2;
-
-        // Copy second run into temp array
-        T[] a = this.a; // For performance
-        T[] tmp = ensureCapacity(len2);
-        System.arraycopy(a, base2, tmp, 0, len2);
-
-        int cursor1 = base1 + len1 - 1;  // Indexes into a
-        int cursor2 = len2 - 1;          // Indexes into tmp array
-        int dest = base2 + len2 - 1;     // Indexes into a
-
-        // Move last element of first run and deal with degenerate cases
-        a[dest--] = a[cursor1--];
-        if (--len1 == 0) {
-            System.arraycopy(tmp, 0, a, dest - (len2 - 1), len2);
-            return;
-        }
-        if (len2 == 1) {
-            dest -= len1;
-            cursor1 -= len1;
-            System.arraycopy(a, cursor1 + 1, a, dest + 1, len1);
-            a[dest] = tmp[cursor2];
-            return;
-        }
-
-        Comparator<? super T> c = this.c;  // Use local variable for performance
-        int minGallop = this.minGallop;    //  "    "       "     "      "
-    outer:
-        while (true) {
-            int count1 = 0; // Number of times in a row that first run won
-            int count2 = 0; // Number of times in a row that second run won
-
-            /*
-             * Do the straightforward thing until (if ever) one run
-             * appears to win consistently.
-             */
-            do {
-                assert len1 > 0 && len2 > 1;
-                if (c.compare(tmp[cursor2], a[cursor1]) < 0) {
-                    a[dest--] = a[cursor1--];
-                    count1++;
-                    count2 = 0;
-                    if (--len1 == 0)
-                        break outer;
-                } else {
-                    a[dest--] = tmp[cursor2--];
-                    count2++;
-                    count1 = 0;
-                    if (--len2 == 1)
-                        break outer;
-                }
-            } while ((count1 | count2) < minGallop);
-
-            /*
-             * One run is winning so consistently that galloping may be a
-             * huge win. So try that, and continue galloping until (if ever)
-             * neither run appears to be winning consistently anymore.
-             */
-            do {
-                assert len1 > 0 && len2 > 1;
-                count1 = len1 - gallopRight(tmp[cursor2], a, base1, len1, len1 - 1, c);
-                if (count1 != 0) {
-                    dest -= count1;
-                    cursor1 -= count1;
-                    len1 -= count1;
-                    System.arraycopy(a, cursor1 + 1, a, dest + 1, count1);
-                    if (len1 == 0)
-                        break outer;
-                }
-                a[dest--] = tmp[cursor2--];
-                if (--len2 == 1)
-                    break outer;
-
-                count2 = len2 - gallopLeft(a[cursor1], tmp, 0, len2, len2 - 1, c);
-                if (count2 != 0) {
-                    dest -= count2;
-                    cursor2 -= count2;
-                    len2 -= count2;
-                    System.arraycopy(tmp, cursor2 + 1, a, dest + 1, count2);
-                    if (len2 <= 1)  // len2 == 1 || len2 == 0
-                        break outer;
-                }
-                a[dest--] = a[cursor1--];
-                if (--len1 == 0)
-                    break outer;
-                minGallop--;
-            } while (count1 >= MIN_GALLOP | count2 >= MIN_GALLOP);
-            if (minGallop < 0)
-                minGallop = 0;
-            minGallop += 2;  // Penalize for leaving gallop mode
-        }  // End of "outer" loop
-        this.minGallop = minGallop < 1 ? 1 : minGallop;  // Write back to field
-
-        if (len2 == 1) {
-            assert len1 > 0;
-            dest -= len1;
-            cursor1 -= len1;
-            System.arraycopy(a, cursor1 + 1, a, dest + 1, len1);
-            a[dest] = tmp[cursor2];  // Move first elt of run2 to front of merge
-        } else if (len2 == 0) {
-            throw new IllegalArgumentException(
-                "Comparison method violates its general contract!");
-        } else {
-            assert len1 == 0;
-            assert len2 > 0;
-            System.arraycopy(tmp, 0, a, dest - (len2 - 1), len2);
-        }
-    }
-
-    /**
-     * Ensures that the external array tmp has at least the specified
-     * number of elements, increasing its size if necessary.  The size
-     * increases exponentially to ensure amortized linear time complexity.
-     *
-     * @param minCapacity the minimum required capacity of the tmp array
-     * @return tmp, whether or not it grew
-     */
-    private T[] ensureCapacity(int minCapacity) {
-        if (tmp.length < minCapacity) {
-            // Compute smallest power of 2 > minCapacity
-            int newSize = minCapacity;
-            newSize |= newSize >> 1;
-            newSize |= newSize >> 2;
-            newSize |= newSize >> 4;
-            newSize |= newSize >> 8;
-            newSize |= newSize >> 16;
-            newSize++;
-
-            if (newSize < 0) // Not bloody likely!
-                newSize = minCapacity;
-            else
-                newSize = Math.min(newSize, a.length >>> 1);
-
-            @SuppressWarnings({"unchecked", "UnnecessaryLocalVariable"})
-            T[] newArray = (T[]) new Object[newSize];
-            tmp = newArray;
-        }
-        return tmp;
-    }
-
-    /**
-     * Checks that fromIndex and toIndex are in range, and throws an
-     * appropriate exception if they aren't.
-     *
-     * @param arrayLen the length of the array
-     * @param fromIndex the index of the first element of the range
-     * @param toIndex the index after the last element of the range
-     * @throws IllegalArgumentException if fromIndex > toIndex
-     * @throws ArrayIndexOutOfBoundsException if fromIndex < 0
-     *         or toIndex > arrayLen
-     */
-    private static void rangeCheck(int arrayLen, int fromIndex, int toIndex) {
-        if (fromIndex > toIndex)
-            throw new IllegalArgumentException("fromIndex(" + fromIndex +
-                       ") > toIndex(" + toIndex+")");
-        if (fromIndex < 0)
-            throw new ArrayIndexOutOfBoundsException(fromIndex);
-        if (toIndex > arrayLen)
-            throw new ArrayIndexOutOfBoundsException(toIndex);
-    }
-}
diff --git a/lab/timsort.py b/lab/timsort.py
deleted file mode 100644
index 6d6074e8..00000000
--- a/lab/timsort.py
+++ /dev/null
@@ -1,679 +0,0 @@
-"""
-Intro
------
-This describes an adaptive, stable, natural mergesort, modestly called
-timsort (hey, I earned it <wink>).  It has supernatural performance on many
-kinds of partially ordered arrays (less than lg(N!) comparisons needed, and
-as few as N-1), yet as fast as Python's previous highly tuned samplesort
-hybrid on random arrays.
-
-In a nutshell, the main routine marches over the array once, left to right,
-alternately identifying the next run, then merging it into the previous
-runs "intelligently".  Everything else is complication for speed, and some
-hard-won measure of memory efficiency.
-
-
-Comparison with Python's Samplesort Hybrid
-------------------------------------------
-+ timsort can require a temp array containing as many as N//2 pointers,
-  which means as many as 2*N extra bytes on 32-bit boxes.  It can be
-  expected to require a temp array this large when sorting random data; on
-  data with significant structure, it may get away without using any extra
-  heap memory.  This appears to be the strongest argument against it, but
-  compared to the size of an object, 2 temp bytes worst-case (also expected-
-  case for random data) doesn't scare me much.
-
-  It turns out that Perl is moving to a stable mergesort, and the code for
-  that appears always to require a temp array with room for at least N
-  pointers. (Note that I wouldn't want to do that even if space weren't an
-  issue; I believe its efforts at memory frugality also save timsort
-  significant pointer-copying costs, and allow it to have a smaller working
-  set.)
-
-+ Across about four hours of generating random arrays, and sorting them
-  under both methods, samplesort required about 1.5% more comparisons
-  (the program is at the end of this file).
-
-+ In real life, this may be faster or slower on random arrays than
-  samplesort was, depending on platform quirks.  Since it does fewer
-  comparisons on average, it can be expected to do better the more
-  expensive a comparison function is.  OTOH, it does more data movement
-  (pointer copying) than samplesort, and that may negate its small
-  comparison advantage (depending on platform quirks) unless comparison
-  is very expensive.
-
-+ On arrays with many kinds of pre-existing order, this blows samplesort out
-  of the water.  It's significantly faster than samplesort even on some
-  cases samplesort was special-casing the snot out of.  I believe that lists
-  very often do have exploitable partial order in real life, and this is the
-  strongest argument in favor of timsort (indeed, samplesort's special cases
-  for extreme partial order are appreciated by real users, and timsort goes
-  much deeper than those, in particular naturally covering every case where
-  someone has suggested "and it would be cool if list.sort() had a special
-  case for this too ... and for that ...").
-
-+ Here are exact comparison counts across all the tests in sortperf.py,
-  when run with arguments "15 20 1".
-
-  Column Key:
-      *sort: random data
-      \sort: descending data
-      /sort: ascending data
-      3sort: ascending, then 3 random exchanges
-      +sort: ascending, then 10 random at the end
-      ~sort: many duplicates
-      =sort: all equal
-      !sort: worst case scenario
-
-  First the trivial cases, trivial for samplesort because it special-cased
-  them, and trivial for timsort because it naturally works on runs.  Within
-  an "n" block, the first line gives the # of compares done by samplesort,
-  the second line by timsort, and the third line is the percentage by
-  which the samplesort count exceeds the timsort count:
-
-      n   \sort   /sort   =sort
--------  ------  ------  ------
-  32768   32768   32767   32767  samplesort
-          32767   32767   32767  timsort
-          0.00%   0.00%   0.00%  (samplesort - timsort) / timsort
-
-  65536   65536   65535   65535
-          65535   65535   65535
-          0.00%   0.00%   0.00%
-
- 131072  131072  131071  131071
-         131071  131071  131071
-          0.00%   0.00%   0.00%
-
- 262144  262144  262143  262143
-         262143  262143  262143
-          0.00%   0.00%   0.00%
-
- 524288  524288  524287  524287
-         524287  524287  524287
-          0.00%   0.00%   0.00%
-
-1048576 1048576 1048575 1048575
-        1048575 1048575 1048575
-          0.00%   0.00%   0.00%
-
-  The algorithms are effectively identical in these cases, except that
-  timsort does one less compare in \sort.
-
-  Now for the more interesting cases.  lg(n!) is the information-theoretic
-  limit for the best any comparison-based sorting algorithm can do on
-  average (across all permutations).  When a method gets significantly
-  below that, it's either astronomically lucky, or is finding exploitable
-  structure in the data.
-
-      n   lg(n!)    *sort    3sort     +sort   %sort    ~sort     !sort
--------  -------   ------   -------  -------  ------  -------  --------
-  32768   444255   453096   453614    32908   452871   130491    469141 old
-                   448885    33016    33007    50426   182083     65534 new
-                    0.94% 1273.92%   -0.30%  798.09%  -28.33%   615.87% %ch from new
-
-  65536   954037   972699   981940    65686   973104   260029   1004607
-                   962991    65821    65808   101667   364341    131070
-                    1.01% 1391.83%   -0.19%  857.15%  -28.63%   666.47%
-
- 131072  2039137  2101881  2091491   131232  2092894   554790   2161379
-                  2057533   131410   131361   206193   728871    262142
-                    2.16% 1491.58%   -0.10%  915.02%  -23.88%   724.51%
-
- 262144  4340409  4464460  4403233   262314  4445884  1107842   4584560
-                  4377402   262437   262459   416347  1457945    524286
-                    1.99% 1577.82%   -0.06%  967.83%  -24.01%   774.44%
-
- 524288  9205096  9453356  9408463   524468  9441930  2218577   9692015
-                  9278734   524580   524633   837947  2916107   1048574
-                   1.88%  1693.52%   -0.03% 1026.79%  -23.92%   824.30%
-
-1048576 19458756 19950272 19838588  1048766 19912134  4430649  20434212
-                 19606028  1048958  1048941  1694896  5832445   2097150
-                    1.76% 1791.27%   -0.02% 1074.83%  -24.03%   874.38%
-
-  Discussion of cases:
-
-  *sort:  There's no structure in random data to exploit, so the theoretical
-  limit is lg(n!).  Both methods get close to that, and timsort is hugging
-  it (indeed, in a *marginal* sense, it's a spectacular improvement --
-  there's only about 1% left before hitting the wall, and timsort knows
-  darned well it's doing compares that won't pay on random data -- but so
-  does the samplesort hybrid).  For contrast, Hoare's original random-pivot
-  quicksort does about 39% more compares than the limit, and the median-of-3
-  variant about 19% more.
-
-  3sort, %sort, and !sort:  No contest; there's structure in this data, but
-  not of the specific kinds samplesort special-cases.  Note that structure
-  in !sort wasn't put there on purpose -- it was crafted as a worst case for
-  a previous quicksort implementation.  That timsort nails it came as a
-  surprise to me (although it's obvious in retrospect).
-
-  +sort:  samplesort special-cases this data, and does a few less compares
-  than timsort.  However, timsort runs this case significantly faster on all
-  boxes we have timings for, because timsort is in the business of merging
-  runs efficiently, while samplesort does much more data movement in this
-  (for it) special case.
-
-  ~sort:  samplesort's special cases for large masses of equal elements are
-  extremely effective on ~sort's specific data pattern, and timsort just
-  isn't going to get close to that, despite that it's clearly getting a
-  great deal of benefit out of the duplicates (the # of compares is much less
-  than lg(n!)).  ~sort has a perfectly uniform distribution of just 4
-  distinct values, and as the distribution gets more skewed, samplesort's
-  equal-element gimmicks become less effective, while timsort's adaptive
-  strategies find more to exploit; in a database supplied by Kevin Altis, a
-  sort on its highly skewed "on which stock exchange does this company's
-  stock trade?" field ran over twice as fast under timsort.
-
-  However, despite that timsort does many more comparisons on ~sort, and
-  that on several platforms ~sort runs highly significantly slower under
-  timsort, on other platforms ~sort runs highly significantly faster under
-  timsort.  No other kind of data has shown this wild x-platform behavior,
-  and we don't have an explanation for it.  The only thing I can think of
-  that could transform what "should be" highly significant slowdowns into
-  highly significant speedups on some boxes are catastrophic cache effects
-  in samplesort.
-
-  But timsort "should be" slower than samplesort on ~sort, so it's hard
-  to count that it isn't on some boxes as a strike against it <wink>.
-
-+ Here's the highwater mark for the number of heap-based temp slots (4
-  bytes each on this box) needed by each test, again with arguments
-  "15 20 1":
-
-   2**i  *sort \sort /sort  3sort  +sort  %sort  ~sort  =sort  !sort
-  32768  16384     0     0   6256      0  10821  12288      0  16383
-  65536  32766     0     0  21652      0  31276  24576      0  32767
- 131072  65534     0     0  17258      0  58112  49152      0  65535
- 262144 131072     0     0  35660      0 123561  98304      0 131071
- 524288 262142     0     0  31302      0 212057 196608      0 262143
-1048576 524286     0     0 312438      0 484942 393216      0 524287
-
-  Discussion:  The tests that end up doing (close to) perfectly balanced
-  merges (*sort, !sort) need all N//2 temp slots (or almost all).  ~sort
-  also ends up doing balanced merges, but systematically benefits a lot from
-  the preliminary pre-merge searches described under "Merge Memory" later.
-  %sort approaches having a balanced merge at the end because the random
-  selection of elements to replace is expected to produce an out-of-order
-  element near the midpoint.  \sort, /sort, =sort are the trivial one-run
-  cases, needing no merging at all.  +sort ends up having one very long run
-  and one very short, and so gets all the temp space it needs from the small
-  temparray member of the MergeState struct (note that the same would be
-  true if the new random elements were prefixed to the sorted list instead,
-  but not if they appeared "in the middle").  3sort approaches N//3 temp
-  slots twice, but the run lengths that remain after 3 random exchanges
-  clearly has very high variance.
-
-
-A detailed description of timsort follows.
-
-Runs
-----
-count_run() returns the # of elements in the next run.  A run is either
-"ascending", which means non-decreasing:
-
-    a0 <= a1 <= a2 <= ...
-
-or "descending", which means strictly decreasing:
-
-    a0 > a1 > a2 > ...
-
-Note that a run is always at least 2 long, unless we start at the array's
-last element.
-
-The definition of descending is strict, because the main routine reverses
-a descending run in-place, transforming a descending run into an ascending
-run.  Reversal is done via the obvious fast "swap elements starting at each
-end, and converge at the middle" method, and that can violate stability if
-the slice contains any equal elements.  Using a strict definition of
-descending ensures that a descending run contains distinct elements.
-
-If an array is random, it's very unlikely we'll see long runs.  If a natural
-run contains less than minrun elements (see next section), the main loop
-artificially boosts it to minrun elements, via a stable binary insertion sort
-applied to the right number of array elements following the short natural
-run.  In a random array, *all* runs are likely to be minrun long as a
-result.  This has two primary good effects:
-
-1. Random data strongly tends then toward perfectly balanced (both runs have
-   the same length) merges, which is the most efficient way to proceed when
-   data is random.
-
-2. Because runs are never very short, the rest of the code doesn't make
-   heroic efforts to shave a few cycles off per-merge overheads.  For
-   example, reasonable use of function calls is made, rather than trying to
-   inline everything.  Since there are no more than N/minrun runs to begin
-   with, a few "extra" function calls per merge is barely measurable.
-
-
-Computing minrun
-----------------
-If N < 64, minrun is N.  IOW, binary insertion sort is used for the whole
-array then; it's hard to beat that given the overheads of trying something
-fancier.
-
-When N is a power of 2, testing on random data showed that minrun values of
-16, 32, 64 and 128 worked about equally well.  At 256 the data-movement cost
-in binary insertion sort clearly hurt, and at 8 the increase in the number
-of function calls clearly hurt.  Picking *some* power of 2 is important
-here, so that the merges end up perfectly balanced (see next section).  We
-pick 32 as a good value in the sweet range; picking a value at the low end
-allows the adaptive gimmicks more opportunity to exploit shorter natural
-runs.
-
-Because sortperf.py only tries powers of 2, it took a long time to notice
-that 32 isn't a good choice for the general case!  Consider N=2112:
-
->>> divmod(2112, 32)
-(66, 0)
->>>
-
-If the data is randomly ordered, we're very likely to end up with 66 runs
-each of length 32.  The first 64 of these trigger a sequence of perfectly
-balanced merges (see next section), leaving runs of lengths 2048 and 64 to
-merge at the end.  The adaptive gimmicks can do that with fewer than 2048+64
-compares, but it's still more compares than necessary, and-- mergesort's
-bugaboo relative to samplesort --a lot more data movement (O(N) copies just
-to get 64 elements into place).
-
-If we take minrun=33 in this case, then we're very likely to end up with 64
-runs each of length 33, and then all merges are perfectly balanced.  Better!
-
-What we want to avoid is picking minrun such that in
-
-    q, r = divmod(N, minrun)
-
-q is a power of 2 and r>0 (then the last merge only gets r elements into
-place, and r < minrun is small compared to N), or q a little larger than a
-power of 2 regardless of r (then we've got a case similar to "2112", again
-leaving too little work for the last merge to do).
-
-Instead we pick a minrun in range(32, 65) such that N/minrun is exactly a
-power of 2, or if that isn't possible, is close to, but strictly less than,
-a power of 2.  This is easier to do than it may sound:  take the first 6
-bits of N, and add 1 if any of the remaining bits are set.  In fact, that
-rule covers every case in this section, including small N and exact powers
-of 2; merge_compute_minrun() is a deceptively simple function.
-
-
-The Merge Pattern
------------------
-In order to exploit regularities in the data, we're merging on natural
-run lengths, and they can become wildly unbalanced.  That's a Good Thing
-for this sort!  It means we have to find a way to manage an assortment of
-potentially very different run lengths, though.
-
-Stability constrains permissible merging patterns.  For example, if we have
-3 consecutive runs of lengths
-
-    A:10000  B:20000  C:10000
-
-we dare not merge A with C first, because if A, B and C happen to contain
-a common element, it would get out of order wrt its occurrence(s) in B.  The
-merging must be done as (A+B)+C or A+(B+C) instead.
-
-So merging is always done on two consecutive runs at a time, and in-place,
-although this may require some temp memory (more on that later).
-
-When a run is identified, its base address and length are pushed on a stack
-in the MergeState struct.  merge_collapse() is then called to see whether it
-should merge it with preceding run(s).  We would like to delay merging as
-long as possible in order to exploit patterns that may come up later, but we
-like even more to do merging as soon as possible to exploit that the run just
-found is still high in the memory hierarchy.  We also can't delay merging
-"too long" because it consumes memory to remember the runs that are still
-unmerged, and the stack has a fixed size.
-
-What turned out to be a good compromise maintains two invariants on the
-stack entries, where A, B and C are the lengths of the three righmost not-yet
-merged slices:
-
-1.  A > B+C
-2.  B > C
-
-Note that, by induction, #2 implies the lengths of pending runs form a
-decreasing sequence.  #1 implies that, reading the lengths right to left,
-the pending-run lengths grow at least as fast as the Fibonacci numbers.
-Therefore the stack can never grow larger than about log_base_phi(N) entries,
-where phi = (1+sqrt(5))/2 ~= 1.618.  Thus a small # of stack slots suffice
-for very large arrays.
-
-If A <= B+C, the smaller of A and C is merged with B (ties favor C, for the
-freshness-in-cache reason), and the new run replaces the A,B or B,C entries;
-e.g., if the last 3 entries are
-
-    A:30  B:20  C:10
-
-then B is merged with C, leaving
-
-    A:30  BC:30
-
-on the stack.  Or if they were
-
-    A:500  B:400:  C:1000
-
-then A is merged with B, leaving
-
-    AB:900  C:1000
-
-on the stack.
-
-In both examples, the stack configuration after the merge still violates
-invariant #2, and merge_collapse() goes on to continue merging runs until
-both invariants are satisfied.  As an extreme case, suppose we didn't do the
-minrun gimmick, and natural runs were of lengths 128, 64, 32, 16, 8, 4, 2,
-and 2.  Nothing would get merged until the final 2 was seen, and that would
-trigger 7 perfectly balanced merges.
-
-The thrust of these rules when they trigger merging is to balance the run
-lengths as closely as possible, while keeping a low bound on the number of
-runs we have to remember.  This is maximally effective for random data,
-where all runs are likely to be of (artificially forced) length minrun, and
-then we get a sequence of perfectly balanced merges (with, perhaps, some
-oddballs at the end).
-
-OTOH, one reason this sort is so good for partly ordered data has to do
-with wildly unbalanced run lengths.
-
-
-Merge Memory
-------------
-Merging adjacent runs of lengths A and B in-place is very difficult.
-Theoretical constructions are known that can do it, but they're too difficult
-and slow for practical use.  But if we have temp memory equal to min(A, B),
-it's easy.
-
-If A is smaller (function merge_lo), copy A to a temp array, leave B alone,
-and then we can do the obvious merge algorithm left to right, from the temp
-area and B, starting the stores into where A used to live.  There's always a
-free area in the original area comprising a number of elements equal to the
-number not yet merged from the temp array (trivially true at the start;
-proceed by induction).  The only tricky bit is that if a comparison raises an
-exception, we have to remember to copy the remaining elements back in from
-the temp area, lest the array end up with duplicate entries from B.  But
-that's exactly the same thing we need to do if we reach the end of B first,
-so the exit code is pleasantly common to both the normal and error cases.
-
-If B is smaller (function merge_hi, which is merge_lo's "mirror image"),
-much the same, except that we need to merge right to left, copying B into a
-temp array and starting the stores at the right end of where B used to live.
-
-A refinement:  When we're about to merge adjacent runs A and B, we first do
-a form of binary search (more on that later) to see where B[0] should end up
-in A.  Elements in A preceding that point are already in their final
-positions, effectively shrinking the size of A.  Likewise we also search to
-see where A[-1] should end up in B, and elements of B after that point can
-also be ignored.  This cuts the amount of temp memory needed by the same
-amount.
-
-These preliminary searches may not pay off, and can be expected *not* to
-repay their cost if the data is random.  But they can win huge in all of
-time, copying, and memory savings when they do pay, so this is one of the
-"per-merge overheads" mentioned above that we're happy to endure because
-there is at most one very short run.  It's generally true in this algorithm
-that we're willing to gamble a little to win a lot, even though the net
-expectation is negative for random data.
-
-
-Merge Algorithms
-----------------
-merge_lo() and merge_hi() are where the bulk of the time is spent.  merge_lo
-deals with runs where A <= B, and merge_hi where A > B.  They don't know
-whether the data is clustered or uniform, but a lovely thing about merging
-is that many kinds of clustering "reveal themselves" by how many times in a
-row the winning merge element comes from the same run.  We'll only discuss
-merge_lo here; merge_hi is exactly analogous.
-
-Merging begins in the usual, obvious way, comparing the first element of A
-to the first of B, and moving B[0] to the merge area if it's less than A[0],
-else moving A[0] to the merge area.  Call that the "one pair at a time"
-mode.  The only twist here is keeping track of how many times in a row "the
-winner" comes from the same run.
-
-If that count reaches MIN_GALLOP, we switch to "galloping mode".  Here
-we *search* B for where A[0] belongs, and move over all the B's before
-that point in one chunk to the merge area, then move A[0] to the merge
-area.  Then we search A for where B[0] belongs, and similarly move a
-slice of A in one chunk.  Then back to searching B for where A[0] belongs,
-etc.  We stay in galloping mode until both searches find slices to copy
-less than MIN_GALLOP elements long, at which point we go back to one-pair-
-at-a-time mode.
-
-A refinement:  The MergeState struct contains the value of min_gallop that
-controls when we enter galloping mode, initialized to MIN_GALLOP.
-merge_lo() and merge_hi() adjust this higher when galloping isn't paying
-off, and lower when it is.
-
-
-Galloping
----------
-Still without loss of generality, assume A is the shorter run.  In galloping
-mode, we first look for A[0] in B.  We do this via "galloping", comparing
-A[0] in turn to B[0], B[1], B[3], B[7], ..., B[2**j - 1], ..., until finding
-the k such that B[2**(k-1) - 1] < A[0] <= B[2**k - 1].  This takes at most
-roughly lg(B) comparisons, and, unlike a straight binary search, favors
-finding the right spot early in B (more on that later).
-
-After finding such a k, the region of uncertainty is reduced to 2**(k-1) - 1
-consecutive elements, and a straight binary search requires exactly k-1
-additional comparisons to nail it.  Then we copy all the B's up to that
-point in one chunk, and then copy A[0].  Note that no matter where A[0]
-belongs in B, the combination of galloping + binary search finds it in no
-more than about 2*lg(B) comparisons.
-
-If we did a straight binary search, we could find it in no more than
-ceiling(lg(B+1)) comparisons -- but straight binary search takes that many
-comparisons no matter where A[0] belongs.  Straight binary search thus loses
-to galloping unless the run is quite long, and we simply can't guess
-whether it is in advance.
-
-If data is random and runs have the same length, A[0] belongs at B[0] half
-the time, at B[1] a quarter of the time, and so on:  a consecutive winning
-sub-run in B of length k occurs with probability 1/2**(k+1).  So long
-winning sub-runs are extremely unlikely in random data, and guessing that a
-winning sub-run is going to be long is a dangerous game.
-
-OTOH, if data is lopsided or lumpy or contains many duplicates, long
-stretches of winning sub-runs are very likely, and cutting the number of
-comparisons needed to find one from O(B) to O(log B) is a huge win.
-
-Galloping compromises by getting out fast if there isn't a long winning
-sub-run, yet finding such very efficiently when they exist.
-
-I first learned about the galloping strategy in a related context; see:
-
-    "Adaptive Set Intersections, Unions, and Differences" (2000)
-    Erik D. Demaine, Alejandro López-Ortiz, J. Ian Munro
-
-and its followup(s).  An earlier paper called the same strategy
-"exponential search":
-
-   "Optimistic Sorting and Information Theoretic Complexity"
-   Peter McIlroy
-   SODA (Fourth Annual ACM-SIAM Symposium on Discrete Algorithms), pp
-   467-474, Austin, Texas, 25-27 January 1993.
-
-and it probably dates back to an earlier paper by Bentley and Yao.  The
-McIlroy paper in particular has good analysis of a mergesort that's
-probably strongly related to this one in its galloping strategy.
-
-
-Galloping with a Broken Leg
----------------------------
-So why don't we always gallop?  Because it can lose, on two counts:
-
-1. While we're willing to endure small per-merge overheads, per-comparison
-   overheads are a different story.  Calling Yet Another Function per
-   comparison is expensive, and gallop_left() and gallop_right() are
-   too long-winded for sane inlining.
-
-2. Galloping can-- alas --require more comparisons than linear one-at-time
-   search, depending on the data.
-
-#2 requires details.  If A[0] belongs before B[0], galloping requires 1
-compare to determine that, same as linear search, except it costs more
-to call the gallop function.  If A[0] belongs right before B[1], galloping
-requires 2 compares, again same as linear search.  On the third compare,
-galloping checks A[0] against B[3], and if it's <=, requires one more
-compare to determine whether A[0] belongs at B[2] or B[3].  That's a total
-of 4 compares, but if A[0] does belong at B[2], linear search would have
-discovered that in only 3 compares, and that's a huge loss!  Really.  It's
-an increase of 33% in the number of compares needed, and comparisons are
-expensive in Python.
-
-index in B where    # compares linear  # gallop  # binary  gallop
-A[0] belongs        search needs       compares  compares  total
-----------------    -----------------  --------  --------  ------
-               0                    1         1         0       1
-
-               1                    2         2         0       2
-
-               2                    3         3         1       4
-               3                    4         3         1       4
-
-               4                    5         4         2       6
-               5                    6         4         2       6
-               6                    7         4         2       6
-               7                    8         4         2       6
-
-               8                    9         5         3       8
-               9                   10         5         3       8
-              10                   11         5         3       8
-              11                   12         5         3       8
-                                        ...
-
-In general, if A[0] belongs at B[i], linear search requires i+1 comparisons
-to determine that, and galloping a total of 2*floor(lg(i))+2 comparisons.
-The advantage of galloping is unbounded as i grows, but it doesn't win at
-all until i=6.  Before then, it loses twice (at i=2 and i=4), and ties
-at the other values.  At and after i=6, galloping always wins.
-
-We can't guess in advance when it's going to win, though, so we do one pair
-at a time until the evidence seems strong that galloping may pay.  MIN_GALLOP
-is 7, and that's pretty strong evidence.  However, if the data is random, it
-simply will trigger galloping mode purely by luck every now and again, and
-it's quite likely to hit one of the losing cases next.  On the other hand,
-in cases like ~sort, galloping always pays, and MIN_GALLOP is larger than it
-"should be" then.  So the MergeState struct keeps a min_gallop variable
-that merge_lo and merge_hi adjust:  the longer we stay in galloping mode,
-the smaller min_gallop gets, making it easier to transition back to
-galloping mode (if we ever leave it in the current merge, and at the
-start of the next merge).  But whenever the gallop loop doesn't pay,
-min_gallop is increased by one, making it harder to transition back
-to galloping mode (and again both within a merge and across merges).  For
-random data, this all but eliminates the gallop penalty:  min_gallop grows
-large enough that we almost never get into galloping mode.  And for cases
-like ~sort, min_gallop can fall to as low as 1.  This seems to work well,
-but in all it's a minor improvement over using a fixed MIN_GALLOP value.
-
-
-Galloping Complication
-----------------------
-The description above was for merge_lo.  merge_hi has to merge "from the
-other end", and really needs to gallop starting at the last element in a run
-instead of the first.  Galloping from the first still works, but does more
-comparisons than it should (this is significant -- I timed it both ways).
-For this reason, the gallop_left() and gallop_right() functions have a
-"hint" argument, which is the index at which galloping should begin.  So
-galloping can actually start at any index, and proceed at offsets of 1, 3,
-7, 15, ... or -1, -3, -7, -15, ... from the starting index.
-
-In the code as I type it's always called with either 0 or n-1 (where n is
-the # of elements in a run).  It's tempting to try to do something fancier,
-melding galloping with some form of interpolation search; for example, if
-we're merging a run of length 1 with a run of length 10000, index 5000 is
-probably a better guess at the final result than either 0 or 9999.  But
-it's unclear how to generalize that intuition usefully, and merging of
-wildly unbalanced runs already enjoys excellent performance.
-
-~sort is a good example of when balanced runs could benefit from a better
-hint value:  to the extent possible, this would like to use a starting
-offset equal to the previous value of acount/bcount.  Doing so saves about
-10% of the compares in ~sort.  However, doing so is also a mixed bag,
-hurting other cases.
-
-
-Comparing Average # of Compares on Random Arrays
-------------------------------------------------
-[NOTE:  This was done when the new algorithm used about 0.1% more compares
- on random data than does its current incarnation.]
-
-Here list.sort() is samplesort, and list.msort() this sort:
-
-"""
-import random
-from time import clock as now
-
-def fill(n):
-    from random import random
-    return [random() for i in xrange(n)]
-
-def mycmp(x, y):
-    global ncmp
-    ncmp += 1
-    return cmp(x, y)
-
-def timeit(values, method):
-    global ncmp
-    X = values[:]
-    bound = getattr(X, method)
-    ncmp = 0
-    t1 = now()
-    bound(mycmp)
-    t2 = now()
-    return t2-t1, ncmp
-
-format = "%5s  %9.2f  %11d"
-f2     = "%5s  %9.2f  %11.2f"
-
-def drive():
-    count = sst = sscmp = mst = mscmp = nelts = 0
-    while True:
-        n = random.randrange(100000)
-        nelts += n
-        x = fill(n)
-
-        t, c = timeit(x, 'sort')
-        sst += t
-        sscmp += c
-
-        t, c = timeit(x, 'msort')
-        mst += t
-        mscmp += c
-
-        count += 1
-        if count % 10:
-            continue
-
-        print "count", count, "nelts", nelts
-        print format % ("sort",  sst, sscmp)
-        print format % ("msort", mst, mscmp)
-        print f2     % ("", (sst-mst)*1e2/mst, (sscmp-mscmp)*1e2/mscmp)
-
-drive()
-"""
-
-I ran this on Windows and kept using the computer lightly while it was
-running.  time.clock() is wall-clock time on Windows, with better than
-microsecond resolution.  samplesort started with a 1.52% #-of-comparisons
-disadvantage, fell quickly to 1.48%, and then fluctuated within that small
-range.  Here's the last chunk of output before I killed the job:
-
-count 2630 nelts 130906543
- sort    6110.80   1937887573
-msort    6002.78   1909389381
-            1.80         1.49
-
-We've done nearly 2 billion comparisons apiece at Python speed there, and
-that's enough <wink>.
-
-For random arrays of size 2 (yes, there are only 2 interesting ones),
-samplesort has a 50%(!) comparison disadvantage.  This is a consequence of
-samplesort special-casing at most one ascending run at the start, then
-falling back to the general case if it doesn't find an ascending run
-immediately.  The consequence is that it ends up using two compares to sort
-[2, 1].  Gratifyingly, timsort doesn't do any special-casing, so had to be
-taught how to deal with mixtures of ascending and descending runs
-efficiently in all cases.
-"""
diff --git a/src/runtimes/wrapper.js b/lab/wrapper.js
similarity index 100%
rename from src/runtimes/wrapper.js
rename to lab/wrapper.js
diff --git a/notes.md b/notes.md
index 45e79f10..1321acf3 100644
--- a/notes.md
+++ b/notes.md
@@ -1,3 +1,57 @@
+# Semantic Versioning
+
+This project is using semantic versioning which means that versions has tree numbers:
+
+> Major.Minor.Patch, e.g. 1.3.7
+
+and the meaning the the following:
+
+- Major: breaking (remove functionality or change API)
+- Minor: Features (new functionality, adding new topics)
+- Patch: Fixes (bug fixes, typos, etc.)
+
+# Generating Changelog (manually) [deprecated]
+
+We use these three sections in changelog: new features, bug fixes, breaking changes.
+
+List of all subjects (first lines in commit message) since last release:
+
+```sh
+git log <last tag> HEAD --pretty=format:%s
+
+# example
+git log 1.1.0..HEAD --pretty=format:%s
+
+git log 1.3.8..HEAD --pretty=format:"- %s [commit](https://github.com/amejiarosario/dsa.js/commit/%H)" --grep "BREAKING CHANGE:"
+git log 1.3.8..HEAD --pretty=format:"- %s [commit](https://github.com/amejiarosario/dsa.js/commit/%H)" --grep "^feat.*:"
+git log 1.3.8..HEAD --pretty=format:"- %s [commit](https://github.com/amejiarosario/dsa.js/commit/%H)" --grep "^fix.*:"
+```
+
+New features in this release
+
+```sh
+git log <last release> HEAD --grep feat
+```
+
+# Generate TOC
+
+Install
+```
+npm install -g doctoc
+```
+
+Add to *.md:
+```
+<!-- START doctoc -->
+<!-- END doctoc -->
+```
+
+Run:
+```
+doctoc README.md
+```
+
+
 # Roadmap
 - [x] PDF: callouts and emojis are not showing correctly
 - [x] Writeup on balancing trees
@@ -64,3 +118,6 @@ console.log('bar');
 /* eslint-enable no-alert */
 ```
 
+# Asciidoctor Manual
+
+https://asciidoctor.org/docs/user-manual/
diff --git a/package-lock.json b/package-lock.json
index 75a7c5fd..961f44f2 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,4181 +1,33826 @@
 {
   "name": "dsa.js",
-  "version": "0.0.1",
-  "lockfileVersion": 1,
+  "version": "2.7.6",
+  "lockfileVersion": 2,
   "requires": true,
-  "dependencies": {
-    "@babel/code-frame": {
-      "version": "7.0.0",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.0.0.tgz",
-      "integrity": "sha512-OfC2uemaknXr87bdLUkWog7nYuliM9Ij5HUcajsVcMCpQrcLmtxRbVFTIqmcSkSeYRBFBRxs2FiUqFJDLdiebA==",
+  "packages": {
+    "": {
+      "name": "dsa.js",
+      "version": "2.7.5",
+      "license": "MIT",
+      "devDependencies": {
+        "@semantic-release/changelog": "^5.0.1",
+        "@semantic-release/git": "^9.0.0",
+        "benchmark": "2.1.4",
+        "braces": ">=2.3.1",
+        "commitizen": "4.1.2",
+        "conventional-changelog-cli": "2.0.34",
+        "cz-conventional-changelog": "3.2.0",
+        "eslint": "7.12.1",
+        "eslint-config-airbnb-base": "14.1.0",
+        "eslint-plugin-import": "2.20.2",
+        "eslint-plugin-jest": "24.1.0",
+        "handlebars": "4.7.6",
+        "husky": "4.2.5",
+        "jest": "26.0.1",
+        "js-yaml": ">=3.13.1",
+        "mem": "6.1.0",
+        "semantic-release": "17.0.7",
+        "textlint-plugin-asciidoctor": "1.0.3"
+      },
+      "engines": {
+        "node": ">=12.5.0"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.8.3",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.8.3.tgz",
+      "integrity": "sha512-a9gxpmdXtZEInkCSHUJDLHZVBgb1QS0jhss4cPP93EW7s+uC5bikET2twEF3KV+7rDblJcmNvTR7VJejqd2C2g==",
       "dev": true,
-      "requires": {
-        "@babel/highlight": "^7.0.0"
+      "dependencies": {
+        "@babel/highlight": "^7.8.3"
       }
     },
-    "@babel/highlight": {
-      "version": "7.0.0",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.0.0.tgz",
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+    "node_modules/@babel/core": {
+      "version": "7.9.6",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.9.6.tgz",
+      "integrity": "sha512-nD3deLvbsApbHAHttzIssYqgb883yU/d9roe4RZymBCDaZryMJDbptVpEpeQuRh4BJ+SYI8le9YGxKvFEvl1Wg==",
       "dev": true,
-      "requires": {
-        "chalk": "^2.0.0",
-        "esutils": "^2.0.2",
-        "js-tokens": "^4.0.0"
+      "dependencies": {
+        "@babel/code-frame": "^7.8.3",
+        "@babel/generator": "^7.9.6",
+        "@babel/helper-module-transforms": "^7.9.0",
+        "@babel/helpers": "^7.9.6",
+        "@babel/parser": "^7.9.6",
+        "@babel/template": "^7.8.6",
+        "@babel/traverse": "^7.9.6",
+        "@babel/types": "^7.9.6",
+        "convert-source-map": "^1.7.0",
+        "debug": "^4.1.0",
+        "gensync": "^1.0.0-beta.1",
+        "json5": "^2.1.2",
+        "lodash": "^4.17.13",
+        "resolve": "^1.3.2",
+        "semver": "^5.4.1",
+        "source-map": "^0.5.0"
       },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/core/node_modules/debug": {
+      "version": "4.1.1",
+      "resolved": "https://registry.npmjs.org/debug/-/debug-4.1.1.tgz",
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+      "dev": true,
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-          "dev": true
-        }
+        "ms": "^2.1.1"
       }
     },
-    "abab": {
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+    "node_modules/@babel/core/node_modules/json5": {
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+      "resolved": "https://registry.npmjs.org/json5/-/json5-2.1.3.tgz",
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+      "dev": true,
+      "dependencies": {
+        "minimist": "^1.2.5"
+      },
+      "bin": {
+        "json5": "lib/cli.js"
+      },
+      "engines": {
+        "node": ">=6"
+      }
     },
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+          "dev": true
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+          "dev": true,
+          "requires": {
+            "locate-path": "^5.0.0",
+            "path-exists": "^4.0.0"
+          }
+        },
+        "get-caller-file": {
+          "version": "2.0.5",
+          "resolved": "https://registry.npmjs.org/get-caller-file/-/get-caller-file-2.0.5.tgz",
+          "integrity": "sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg==",
+          "dev": true
+        },
+        "is-fullwidth-code-point": {
+          "version": "3.0.0",
+          "resolved": "https://registry.npmjs.org/is-fullwidth-code-point/-/is-fullwidth-code-point-3.0.0.tgz",
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+          "dev": true
+        },
+        "locate-path": {
+          "version": "5.0.0",
+          "resolved": "https://registry.npmjs.org/locate-path/-/locate-path-5.0.0.tgz",
+          "integrity": "sha512-t7hw9pI+WvuwNJXwk5zVHpyhIqzg2qTlklJOf0mVxGSbe3Fp2VieZcduNYjaLDoy6p9uGpQEGWG87WpMKlNq8g==",
+          "dev": true,
+          "requires": {
+            "p-locate": "^4.1.0"
+          }
+        },
+        "p-limit": {
+          "version": "2.3.0",
+          "resolved": "https://registry.npmjs.org/p-limit/-/p-limit-2.3.0.tgz",
+          "integrity": "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w==",
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+          "requires": {
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+          }
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+        "p-locate": {
+          "version": "4.1.0",
+          "resolved": "https://registry.npmjs.org/p-locate/-/p-locate-4.1.0.tgz",
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+          "requires": {
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+          }
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+          "resolved": "https://registry.npmjs.org/p-try/-/p-try-2.2.0.tgz",
+          "integrity": "sha512-R4nPAVTAU0B9D35/Gk3uJf/7XYbQcyohSKdvAxIRSNghFl4e71hVoGnBNQz9cWaXxO2I10KTC+3jMdvvoKw6dQ==",
+          "dev": true
+        },
+        "path-exists": {
+          "version": "4.0.0",
+          "resolved": "https://registry.npmjs.org/path-exists/-/path-exists-4.0.0.tgz",
+          "integrity": "sha512-ak9Qy5Q7jYb2Wwcey5Fpvg2KoAc/ZIhLSLOSBmRmygPsGwkVVt0fZa0qrtMz+m6tJTAHfZQ8FnmB4MG4LWy7/w==",
+          "dev": true
+        },
+        "require-main-filename": {
+          "version": "2.0.0",
+          "resolved": "https://registry.npmjs.org/require-main-filename/-/require-main-filename-2.0.0.tgz",
+          "integrity": "sha512-NKN5kMDylKuldxYLSUfrbo5Tuzh4hd+2E8NPPX02mZtn1VuREQToYe/ZdlJy+J3uCpfaiGF05e7B8W0iXbQHmg==",
+          "dev": true
+        },
+        "string-width": {
+          "version": "4.2.0",
+          "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.0.tgz",
+          "integrity": "sha512-zUz5JD+tgqtuDjMhwIg5uFVV3dtqZ9yQJlZVfq4I01/K5Paj5UHj7VyrQOJvzawSVlKpObApbfD0Ed6yJc+1eg==",
+          "dev": true,
+          "requires": {
+            "emoji-regex": "^8.0.0",
+            "is-fullwidth-code-point": "^3.0.0",
+            "strip-ansi": "^6.0.0"
+          }
+        },
+        "strip-ansi": {
+          "version": "6.0.0",
+          "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.0.tgz",
+          "integrity": "sha512-AuvKTrTfQNYNIctbR1K/YGTR1756GycPsg7b9bdV9Duqur4gv6aKqHXah67Z8ImS7WEz5QVcOtlfW2rZEugt6w==",
+          "dev": true,
+          "requires": {
+            "ansi-regex": "^5.0.0"
+          }
+        },
+        "wrap-ansi": {
+          "version": "6.2.0",
+          "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-6.2.0.tgz",
+          "integrity": "sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==",
+          "dev": true,
+          "requires": {
+            "ansi-styles": "^4.0.0",
+            "string-width": "^4.1.0",
+            "strip-ansi": "^6.0.0"
+          }
+        },
+        "y18n": {
+          "version": "4.0.0",
+          "resolved": "https://registry.npmjs.org/y18n/-/y18n-4.0.0.tgz",
+          "integrity": "sha512-r9S/ZyXu/Xu9q1tYlpsLIsa3EeLXXk0VwlxqTcFRfg9EhMW+17kbt9G0NrgCmhGb5vT2hyhJZLfDGx+7+5Uj/w==",
+          "dev": true
+        }
       }
     },
     "yargs-parser": {
-      "version": "9.0.2",
-      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-9.0.2.tgz",
-      "integrity": "sha1-nM9qQ0YP5O1Aqbto9I1DuKaMwHc=",
+      "version": "18.1.3",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-18.1.3.tgz",
+      "integrity": "sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==",
       "dev": true,
       "requires": {
-        "camelcase": "^4.1.0"
+        "camelcase": "^5.0.0",
+        "decamelize": "^1.2.0"
+      },
+      "dependencies": {
+        "camelcase": {
+          "version": "5.3.1",
+          "resolved": "https://registry.npmjs.org/camelcase/-/camelcase-5.3.1.tgz",
+          "integrity": "sha512-L28STB170nwWS63UjtlEOE3dldQApaJXZkOI1uMFfzf3rRuPegHaHesyee+YxQ+W6SvRDQV6UrdOdRiR153wJg==",
+          "dev": true
+        }
       }
     }
   }
diff --git a/package.json b/package.json
index 56bb9d2f..73ef1563 100644
--- a/package.json
+++ b/package.json
@@ -1,34 +1,100 @@
 {
   "name": "dsa.js",
-  "version": "0.0.1",
+  "version": "2.7.6",
   "description": "Data Structures & Algorithms in JS",
-  "main": "./src/data-structures/graphs/graph.js",
-  "dependencies": {
-    "lodash": "4.17.11"
-  },
-  "devDependencies": {
-    "benchmark": "2.1.4",
-    "eslint": "4.19.1",
-    "eslint-config-airbnb-base": "12.1.0",
-    "eslint-plugin-import": "2.12.0",
-    "eslint-plugin-jest": "21.17.0",
-    "jest": "23.6.0",
-    "textlint-plugin-asciidoctor": "1.0.2"
+  "author": "Adrian Mejia <hi+dsajs@adrianmejia.com> (https://adrianmejia.com)",
+  "homepage": "https://github.com/amejiarosario/dsa.js",
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/amejiarosario/dsa.js.git"
   },
+  "main": "./src/index.js",
+  "files": [
+    "src/**/*.js"
+  ],
   "scripts": {
-    "test": "jest src/ # jest # mocha src/**/*spec.js # jasmine JASMINE_CONFIG_PATH=jasmine.json # node jasmine-runner.js",
-    "watch": "jest src/ --watch --coverage",
-    "coverage": "jest src/ --coverage && open coverage/lcov-report/index.html",
-    "linter": "npx eslint --fix -f codeframe src/"
+    "test": "jest --verbose",
+    "watch": "jest --watch --coverage",
+    "lint:base": "npx eslint --fix '{src,book/interview-questions}/**/*.js'",
+    "lint": "npm run lint:base -- --format codeframe",
+    "ci": "npm run lint:base && jest --coverage",
+    "coverage": "jest --coverage && open coverage/lcov-report/index.html",
+    "coverage:win": "jest --coverage && cmd.exe /C start coverage/lcov-report/index.html",
+    "semantic-release": "semantic-release",
+    "release:check": "semantic-release --dry-run"
   },
   "keywords": [
     "algorithms",
     "data structures",
     "javascript",
-    "array",
+    "graphs",
     "linked lists",
     "binary search trees"
   ],
-  "author": "Adrian Mejia <hi+dsajs@adrianmejia.com>",
-  "license": "ISC"
+  "license": "MIT",
+  "dependencies": {},
+  "devDependencies": {
+    "@semantic-release/changelog": "^5.0.1",
+    "@semantic-release/git": "^9.0.0",
+    "benchmark": "2.1.4",
+    "braces": ">=2.3.1",
+    "commitizen": "4.1.2",
+    "conventional-changelog-cli": "2.0.34",
+    "cz-conventional-changelog": "3.2.0",
+    "eslint": "7.12.1",
+    "eslint-config-airbnb-base": "14.1.0",
+    "eslint-plugin-import": "2.20.2",
+    "eslint-plugin-jest": "24.1.0",
+    "handlebars": "4.7.6",
+    "husky": "4.2.5",
+    "jest": "26.0.1",
+    "js-yaml": ">=3.13.1",
+    "mem": "6.1.0",
+    "semantic-release": "17.0.7",
+    "textlint-plugin-asciidoctor": "1.0.3"
+  },
+  "engines": {
+    "node": ">=12.5.0"
+  },
+  "config": {
+    "commitizen": {
+      "path": "cz-conventional-changelog",
+      "types": {
+        "feat": {
+          "description": "Introducing new features on code and/or book.",
+          "title": "Features ✨"
+        },
+        "fix": {
+          "description": "Fixing a bug on code or book",
+          "title": "Bug Fixes 🐛"
+        },
+        "chore": {
+          "description": "Other changes that don't modify code or book files",
+          "title": "Chores 🔩"
+        }
+      }
+    }
+  },
+  "husky": {
+    "hooks": {
+      "pre-push": "npm run ci"
+    }
+  },
+  "release": {
+    "tagFormat": "${version}",
+    "branch": "master",
+    "plugins": [
+      "@semantic-release/commit-analyzer",
+      "@semantic-release/release-notes-generator",
+      "@semantic-release/changelog",
+      "@semantic-release/github",
+      "@semantic-release/npm",
+      [
+        "@semantic-release/git",
+        {
+          "message": ":bookmark: chore(release): ${nextRelease.version} \n\n${nextRelease.notes}"
+        }
+      ]
+    ]
+  }
 }
diff --git a/src/README.adoc b/src/README.adoc
new file mode 100644
index 00000000..3eed7bcc
--- /dev/null
+++ b/src/README.adoc
@@ -0,0 +1,11 @@
+= Welcome to DSA.js implementations!
+
+In the following file you can find all the exposed classes:
+
+include::index.js[]
+
+The implementations are divided in two main parts:
+
+- https://github.com/amejiarosario/dsa.js/tree/master/src/algorithms[Algorithms]
+- https://github.com/amejiarosario/dsa.js/tree/master/src/data-structures[Data Structures]
+
diff --git a/src/algorithms/README.adoc b/src/algorithms/README.adoc
new file mode 100644
index 00000000..83fd7c26
--- /dev/null
+++ b/src/algorithms/README.adoc
@@ -0,0 +1 @@
+include::../../book/part04-algorithmic-toolbox.asc[]
diff --git a/src/algorithms/fibanacci-dynamic-programming.js b/src/algorithms/fibonacci-dynamic-programming.js
similarity index 89%
rename from src/algorithms/fibanacci-dynamic-programming.js
rename to src/algorithms/fibonacci-dynamic-programming.js
index ed5a2850..9c20e082 100644
--- a/src/algorithms/fibanacci-dynamic-programming.js
+++ b/src/algorithms/fibonacci-dynamic-programming.js
@@ -2,6 +2,7 @@
 /**
  * Get Fibonacci number on the n-th position.
  * @param {integer} n position on the sequence
+ * @param {Map} memo cache of previous solutions
  * @returns {integer} n-th number
  */
 function fib(n, memo = new Map()) {
diff --git a/src/algorithms/fibonacci.spec.js b/src/algorithms/fibonacci.spec.js
index e9cae4cf..8be001f5 100644
--- a/src/algorithms/fibonacci.spec.js
+++ b/src/algorithms/fibonacci.spec.js
@@ -1,7 +1,7 @@
 const implementations = [
   'fibonacci',
   'fibonacci-recursive',
-  'fibanacci-dynamic-programming',
+  'fibonacci-dynamic-programming',
 ];
 
 implementations.forEach((fileName) => {
diff --git a/src/algorithms/get-min.js b/src/algorithms/get-min.js
new file mode 100644
index 00000000..6519ee2f
--- /dev/null
+++ b/src/algorithms/get-min.js
@@ -0,0 +1,16 @@
+/**
+ * Get the smallest number on an array of numbers
+ * @param {Array} n array of numbers
+ */
+function getMin(n = []) {
+  let min = n[0];
+
+  n.forEach((element) => {
+    if (element < min) {
+      min = element;
+    }
+  });
+  return min;
+}
+
+module.exports = getMin;
diff --git a/src/algorithms/get-min.spec.js b/src/algorithms/get-min.spec.js
new file mode 100644
index 00000000..a536e55d
--- /dev/null
+++ b/src/algorithms/get-min.spec.js
@@ -0,0 +1,19 @@
+const getMin = require('./get-min');
+
+describe('find smallest number', () => {
+  it('should get the min number of an array', () => {
+    expect(getMin([3, 10, 2])).toEqual(2);
+  });
+
+  it('should get the min number of an array with negatives', () => {
+    expect(getMin([3, 10, -2])).toEqual(-2);
+  });
+
+  it('should work with 0', () => {
+    expect(getMin([3, 0, 2])).toEqual(0);
+  });
+
+  it('should work with empty', () => {
+    expect(getMin([])).toEqual(undefined);
+  });
+});
diff --git a/src/algorithms/knapsack-fractional.js b/src/algorithms/knapsack-fractional.js
index 237dece2..daef2a41 100644
--- a/src/algorithms/knapsack-fractional.js
+++ b/src/algorithms/knapsack-fractional.js
@@ -1,3 +1,4 @@
+/* eslint-disable no-mixed-operators */
 // tag::snippet[]
 /**
  * Solves Bounded Knapsack Problem (BKP)
@@ -11,7 +12,7 @@ function solveFractionalKnapsack(input, max) {
   const items = [];
 
   // sort by value/weight ratio
-  input.sort((a, b) => a.value/a.weight - b.value/b.weight); // eslint-disable-line
+  input.sort((a, b) => a.value / a.weight - b.value / b.weight);
 
   while (input.length && weight < max) {
     const bestRatioItem = input.pop();
diff --git a/src/algorithms/sorting/merge-sort.js b/src/algorithms/sorting/merge-sort.js
index ad0f67a9..366f2590 100644
--- a/src/algorithms/sorting/merge-sort.js
+++ b/src/algorithms/sorting/merge-sort.js
@@ -2,8 +2,8 @@
 /**
  * Merge two arrays in ascending order
  *
- * @param {array} array1
- * @param {array} array2
+ * @param {array} array1 sorted array 1
+ * @param {array} array2 sorted array 2
  * @returns {array} merged arrays in asc order
  *
  * @example
@@ -15,9 +15,9 @@ function merge(array1, array2 = []) {
 
   // merge elements on a and b in asc order. Run-time O(a + b)
   for (let index = 0, i1 = 0, i2 = 0;
-    index < mergedLength; index += 1) { // <1>
-    if (i2 >= array2.length ||
-      (i1 < array1.length && array1[i1] <= array2[i2])) {
+    index < mergedLength; index++) { // <1>
+    if (i2 >= array2.length
+      || (i1 < array1.length && array1[i1] <= array2[i2])) {
       mergedArray[index] = array1[i1]; // <2>
       i1 += 1;
     } else {
@@ -45,7 +45,7 @@ function splitSort(array) {
   // base case
   if (size < 2) {
     return array;
-  } else if (size === 2) {
+  } if (size === 2) {
     return array[0] < array[1] ? array : [array[1], array[0]]; // <1>
   }
 
diff --git a/src/algorithms/sorting/quick-sort.js b/src/algorithms/sorting/quick-sort.js
index e84be147..2d6f9927 100644
--- a/src/algorithms/sorting/quick-sort.js
+++ b/src/algorithms/sorting/quick-sort.js
@@ -1,4 +1,4 @@
-const { swap } = require('./sorting-common');
+const { swap, shuffle } = require('./sorting-common');
 
 // tag::partition[]
 /**
@@ -14,18 +14,18 @@ const { swap } = require('./sorting-common');
  * @returns {integer} pivot index
  */
 function partition(array, low, high) {
-  const pivotInitialIndex = high; // <1>
-  let pivotIndex = low; // <2>
+  const pivotIndex = low; // <1>
+  let pivotFinalIndex = pivotIndex; // <2>
 
-  for (let current = low; current < high; current += 1) { // <3>
-    if (array[current] <= array[pivotInitialIndex]) { // <4>
-      swap(array, current, pivotIndex);
-      pivotIndex += 1;
+  for (let current = pivotIndex + 1; current <= high; current++) {
+    if (array[current] < array[pivotIndex]) { // <3>
+      pivotFinalIndex += 1; // <4>
+      swap(array, current, pivotFinalIndex); // <5>
     }
   }
 
-  swap(array, pivotInitialIndex, pivotIndex);
-  return pivotIndex;
+  swap(array, pivotIndex, pivotFinalIndex); // <6>
+  return pivotFinalIndex;
 }
 // end::partition[]
 
@@ -54,9 +54,11 @@ function quickSort(array, low = 0, high = array.length - 1) {
  * Quick sort
  * Runtime: O(n log n)
  * @param {Array|Set} collection elements to be sorted
+ * @returns {Array} sorted array
  */
 function quickSortWrapper(collection) {
   const array = Array.from(collection); // <1>
+  shuffle(array); // <2>
   return quickSort(array);
 }
 // end::sort[]
diff --git a/src/algorithms/sorting/selection-sort.js b/src/algorithms/sorting/selection-sort.js
index ac53960e..98e1238d 100644
--- a/src/algorithms/sorting/selection-sort.js
+++ b/src/algorithms/sorting/selection-sort.js
@@ -16,10 +16,10 @@ function selectionSort(collection) {
   const array = Array.from(collection); // <1>
 
   for (let left = 0; left < array.length; left++) { // <2>
-    let selection = left; // minimum value <3>
+    let selection = left; // <3>
 
-    for (let right = left + 1; right < array.length; right++) { // <4>
-      if (array[selection] > array[right]) {
+    for (let right = left + 1; right < array.length; right++) {
+      if (array[selection] > array[right]) { // <4>
         selection = right; // <5>
       }
     }
diff --git a/src/algorithms/sorting/sorting-common.js b/src/algorithms/sorting/sorting-common.js
index 819dda7b..ca0402b1 100644
--- a/src/algorithms/sorting/sorting-common.js
+++ b/src/algorithms/sorting/sorting-common.js
@@ -12,20 +12,23 @@ function swap(array, from, to) {
 }
 // end::swap[]
 
+// tag::shuffle[]
 /**
- * Move an element in an array *from* a postion *to* another.
+ * Shuffle items in an array in-place
  * Runtime: O(n)
- * @param {array} array
- * @param {integer} from index of the element to remove (source)
- * @param {integer} to index where the removed element would be move (destination)
+ * @param {*} array
  */
-function moveElement(array, from, to) {
-  if (from === to + 1) return;
-  const [elementToInsert] = array.splice(from, 1); // delete from position
-  array.splice(to + 1, 0, elementToInsert); // insert element in to the position.
+function shuffle(array) {
+  const { length } = array;
+  for (let index = 0; index < length; index++) {
+    const newIndex = Math.floor(Math.random() * length);
+    swap(array, index, newIndex);
+  }
+  return array;
 }
+// end::shuffle[]
 
 module.exports = {
   swap,
-  moveElement,
+  shuffle,
 };
diff --git a/src/algorithms/sorting/sorting.spec.js b/src/algorithms/sorting/sorting.spec.js
index 87408505..3810dd6c 100644
--- a/src/algorithms/sorting/sorting.spec.js
+++ b/src/algorithms/sorting/sorting.spec.js
@@ -1,10 +1,13 @@
-/*eslint-disable */
+const {
+  selectionSort, insertionSort, bubbleSort, mergeSort, quickSort,
+} = require('../../index');
+
 const sortingAlgorithms = [
-  require('./selection-sort'),
-  require('./insertion-sort'),
-  require('./bubble-sort'),
-  require('./merge-sort'),
-  require('./quick-sort'),
+  selectionSort,
+  insertionSort,
+  bubbleSort,
+  mergeSort,
+  quickSort,
 ];
 
 sortingAlgorithms.forEach((sort) => {
diff --git a/src/data-structures/README.adoc b/src/data-structures/README.adoc
new file mode 100644
index 00000000..c3285e95
--- /dev/null
+++ b/src/data-structures/README.adoc
@@ -0,0 +1,8 @@
+= Data Structures
+
+This directory contains all the data structures. Documentation can be found inline and also:
+
+include::../../book/chapters/part2.adoc[]
+
+include::../../book/chapters/part3.adoc[]
+
diff --git a/src/data-structures/arrays/README.adoc b/src/data-structures/arrays/README.adoc
new file mode 100644
index 00000000..87fe3e42
--- /dev/null
+++ b/src/data-structures/arrays/README.adoc
@@ -0,0 +1 @@
+include::../../../book/content/part02/array.asc[]
diff --git a/src/data-structures/arrays/array.js b/src/data-structures/arrays/array.js
index 2f36f940..909ed1da 100644
--- a/src/data-structures/arrays/array.js
+++ b/src/data-structures/arrays/array.js
@@ -1,6 +1,6 @@
 /* eslint-disable */
-const assert = require('assert');
 
+// see: book/chapters/array.adoc
 
 const array = [2, 5, 1, 9, 6, 7]; // JavaScript
 array[4]; //=> 6
@@ -31,45 +31,3 @@ array.splice(2, 1); // delete 1 element at position 2
 
 // Deleting last element from the array
 array.pop(); // => array: [2, 5, 1, 9, 6]
-
-
-// tag::searchByIndex[]
-/**
- * Search for array's element by index
- *
- * @example Given array = [2, 5, 1, 9, 6, 7];
- *    searchByIndex(array, 3); //↪️ 9
- *    searchByIndex(array, 13); //↪️ -1
- * @param {array} array
- * @param {number} index
- * @returns {any} value or -1 if not found
- */
-function searchByIndex(array, index) {
-  return array[index] || -1;
-}
-// end::searchByIndex[]
-
-assert.equal(searchByIndex(array, 3), 9);
-assert.equal(searchByIndex(array, 13), -1);
-
-// tag::searchByValue[]
-/**
- * Search for array's element by value
- *
- * @example Given array = [2, 5, 1, 9, 6, 7];
- *    searchByValue(array, 9); //↪️ 3
- *    searchByValue(array, 13); //↪️ -1
- * @param {array} array
- * @param {any} value
- */
-function searchByValue(array, value) {
-  for (let index = 0; index < array.length; index++) {
-    const element = array[index];
-    if (element === value) return index;
-  }
-  return -1;
-}
-// end::searchByValue[]
-
-assert.equal(searchByValue(array, 9), 3);
-assert.equal(searchByValue(array, 13), -1);
diff --git a/src/data-structures/custom/lru-cache-1.js b/src/data-structures/custom/lru-cache-1.js
new file mode 100644
index 00000000..521559a6
--- /dev/null
+++ b/src/data-structures/custom/lru-cache-1.js
@@ -0,0 +1,50 @@
+/**
+ * Least Recently Used (LRU) cache.
+ * Map + Array: O(n)
+ * @param {number} capacity - Number of items to hold.
+ */
+const LRUCache = function (capacity) {
+  this.map = new Map();
+  this.capacity = capacity;
+  this.cache = [];
+};
+
+/**
+* @param {number} key
+* @return {number}
+*/
+LRUCache.prototype.get = function (key) {
+  const value = this.map.get(key);
+  if (value) {
+    this.moveToTop(key);
+    return value;
+  }
+  return -1;
+};
+
+/**
+* @param {number} key
+* @param {number} value
+* @return {void}
+*/
+LRUCache.prototype.put = function (key, value) {
+  this.map.set(key, value);
+  this.rotate(key);
+};
+
+LRUCache.prototype.rotate = function (key) {
+  this.moveToTop(key);
+  while (this.cache.length > this.capacity) {
+    const keyToDelete = this.cache.shift();
+    this.map.delete(keyToDelete);
+  // console.log({keyToDelete})
+  }
+};
+
+LRUCache.prototype.moveToTop = function (key) {
+  const index = this.cache.indexOf(key);
+  if (index > -1) {
+    this.cache.splice(index, 1);
+  }
+  this.cache.push(key);
+};
diff --git a/src/data-structures/custom/lru-cache-2.js b/src/data-structures/custom/lru-cache-2.js
new file mode 100644
index 00000000..b07a4b6b
--- /dev/null
+++ b/src/data-structures/custom/lru-cache-2.js
@@ -0,0 +1,72 @@
+/**
+ * Least Recently Used (LRU) cache.
+ * Map + (Hash)Set: O(1)
+ * @param {number} capacity - Number of items to hold.
+ */
+var LRUCache = function(capacity) {
+  this.capacity = capacity || 2;
+  this.map = new Map();
+  this.set = new Set();
+  this.size = 0;
+};
+
+/**
+ * @param {number} key
+ * @return {number}
+ */
+LRUCache.prototype.get = function(key) {
+  if (!this.map.has(key)) return -1;
+  // move to top
+  this.set.delete(key);
+  this.set.add(key);
+
+  return this.map.get(key);
+};
+
+/**
+ * @param {number} key
+ * @param {number} value
+ * @return {void}
+ */
+LRUCache.prototype.put = function(key, value) {
+  this.map.set(key, value);
+  // move to top
+  this.set.delete(key);
+  this.set.add(key);
+
+  if (this.set.size > this.capacity) {
+    const leastUsedKey = this.set.values().next().value;
+    this.map.delete(leastUsedKey);
+    this.set.delete(leastUsedKey);
+  }
+
+  this.size = this.map.size;
+};
+
+/**
+ * Your LRUCache object will be instantiated and called as such:
+ * var obj = new LRUCache(capacity)
+ * var param_1 = obj.get(key)
+ * obj.put(key,value)
+ */
+
+
+/*
+  Implement a hashMap cache with a given capacity that once reach deletes the least used element and store the new one.
+
+  ---
+
+  c = new LRUCache(2);
+  c.put(1,1);
+  c.put(2,2);
+  c.put(3,3); // deletes key 1
+
+  c = new LRUCache(2);
+  c.put(1,1);
+  c.put(2,2);
+  c.get(1);
+  c.put(3,3); // deletes key 2
+
+*/
+
+module.exports = LRUCache;
diff --git a/src/data-structures/custom/lru-cache-3.js b/src/data-structures/custom/lru-cache-3.js
new file mode 100644
index 00000000..506ffcce
--- /dev/null
+++ b/src/data-structures/custom/lru-cache-3.js
@@ -0,0 +1,51 @@
+const DLinkedList = require('../linked-lists/linked-list');
+/**
+ * Least Recently Used (LRU) cache.
+ * Map + Double LinkedList: O(1)
+ * @param {number} capacity - Number of items to hold.
+ */
+class LRUCache extends Map {
+  constructor(capacity) {
+    super(); // initialize map
+    this.capacity = capacity;
+    this.list = new DLinkedList();
+  }
+
+  get(key) {
+    if (!super.has(key)) { return -1; }
+
+    // console.log('get', {key});
+    const node = super.get(key);
+    this.moveToHead(key, node);
+
+    return node.value.value;
+  }
+
+  put(key, value) {
+    // console.log('put', {key, value});
+    let node;
+    if (super.has(key)) {
+      node = super.get(key);
+      node.value.value = value;
+    } else {
+      node = this.list.addLast({key, value});
+    }
+    this.moveToHead(key, node);
+
+    if (this.list.size > this.capacity) {
+      const firstNode = this.list.removeFirst();
+      super.delete(firstNode.key);
+    }
+  }
+
+  moveToHead(key, node) {
+    // remove node and put it in front
+    this.list.removeByNode(node);
+    const newNode = this.list.addLast(node.value);
+    super.set(key, newNode);
+    // console.log('\tlist', Array.from(this.list).map(l => l.node.value));
+    // console.log('\tlist', Array.from(this.list).map(l => l.node.value.key));
+  }
+}
+
+module.exports = LRUCache;
diff --git a/src/data-structures/custom/lru-cache.js b/src/data-structures/custom/lru-cache.js
new file mode 100644
index 00000000..2891375f
--- /dev/null
+++ b/src/data-structures/custom/lru-cache.js
@@ -0,0 +1,45 @@
+/**
+ * Least Recently Used (LRU) cache.
+ * Key/Value storage with fixed max number of items.
+ * Least recently used items are discarded once the limit is reached.
+ * Reading and updating the values mark the items as recently used.
+ * @author Adrian Mejia <adrianmejia.com>
+ */
+class LRUCache extends Map {
+  /**
+   * @param {number} capacity - The max number of items on the cache
+   */
+  constructor(capacity) {
+    super();
+    this.capacity = capacity;
+  }
+
+  /**
+   * Get value associated with the key. Mark keys as recently used.
+   * @param {number} key
+   * @returns {number} value or if not found -1
+   */
+  get(key) {
+    if (!super.has(key)) return -1;
+    const value = super.get(key);
+    this.put(key, value); // re-insert at the top (most recent).
+    return value;
+  }
+
+  /**
+   * Upsert key/value pair. Updates mark keys are recently used.
+   * @param {number} key
+   * @param {number} value
+   * @returns {void}
+   */
+  put(key, value) {
+    if (super.has(key)) super.delete(key);
+    super.set(key, value);
+    if (super.size > this.capacity) {
+      const oldestKey = super.keys().next().value;
+      super.delete(oldestKey);
+    }
+  }
+}
+
+module.exports = LRUCache;
diff --git a/src/data-structures/custom/lru-cache.spec.js b/src/data-structures/custom/lru-cache.spec.js
new file mode 100644
index 00000000..6a00202f
--- /dev/null
+++ b/src/data-structures/custom/lru-cache.spec.js
@@ -0,0 +1,120 @@
+const LRUCache = require('./lru-cache-3');
+
+describe('LRU Cache', () => {
+  let c;
+
+  describe('#constructor', () => {
+    it('should initialize', () => {
+      c = new LRUCache();
+      expect(c).toBeDefined();
+    });
+
+    it('should initialize with capacity', () => {
+      c = new LRUCache(7);
+      expect(c.capacity).toEqual(7);
+    });
+  });
+
+  describe('when initialized', () => {
+    beforeEach(() => {
+      c = new LRUCache(2);
+    });
+
+    describe('#put', () => {
+      it('should insert new elements', () => {
+        c.put(1, 1);
+        expect(c.size).toEqual(1);
+      });
+
+      it('should update existing element', () => {
+        c.put(1, 1);
+        c.put(1, 2);
+        expect(c.size).toEqual(1);
+      });
+    });
+
+    describe('#get', () => {
+      it('should get element', () => {
+        c.put(1, 1);
+        expect(c.get(1)).toEqual(1);
+      });
+
+      it('should return -1 for non-existing elements', () => {
+        expect(c.get(1)).toEqual(-1);
+      });
+
+      it('should not add non-existing number to the top of the list', () => {
+        c.put(1, 1);
+        expect(c.get(8)).toEqual(-1);
+        c.put(2, 2);
+        expect(c.get(9)).toEqual(-1);
+        expect(c.get(1)).toEqual(1);
+        expect(c.get(2)).toEqual(2);
+      });
+
+      it('should return -1 for removed elements', () => {
+        c.put(1, 1);
+        c.put(2, 2);
+        c.put(3, 3);
+        expect(c.get(1)).toEqual(-1);
+      });
+
+      it('should not remove value if accessed recently', () => {
+        c.put(1, 1);
+        c.put(2, 2);
+        expect(c.get(1)).toEqual(1);
+        c.put(3, 3);
+        expect(c.get(1)).toEqual(1);
+        expect(c.get(2)).toEqual(-1);
+      });
+
+      it('should update a value', () => {
+        c.put(1, 1);
+        c.put(1, 2);
+        expect(c.get(1)).toEqual(2);
+      });
+    });
+
+    it('should work with updates', () => {
+      // ["LRUCache","put","put","put","put","get","get"]
+      // [[2],[2,1],[1,1],[2,3],[4,1],[1],[2]]
+      c = new LRUCache(2);
+      c.put(2, 1);
+      c.put(1, 1);
+      c.put(2, 3);
+      c.put(4, 1);
+      c.get(1);
+      c.get(2);
+    });
+
+    it('should work with size 10', () => {
+      c = new LRUCache(10);
+
+      c.put(10, 13);
+      c.put(3, 17);
+      c.put(6, 11);
+      c.put(10, 5);
+      c.put(9, 10);
+      expect(c.get(13)).toEqual(-1);
+      c.put(2, 19);
+      expect(c.get(2)).toEqual(19);
+      expect(c.get(3)).toEqual(17);
+      c.put(5, 25);
+      expect(c.get(8)).toEqual(-1);
+      c.put(9, 22);
+      c.put(5, 5);
+      c.put(1, 30);
+      expect(c.get(11)).toEqual(-1);
+      c.put(9, 12);
+      expect(c.get(7)).toEqual(-1);
+      expect(c.get(5)).toEqual(5);
+      expect(c.get(8)).toEqual(-1);
+      expect(c.get(9)).toEqual(12);
+      c.put(4, 30);
+      c.put(9, 3);
+      expect(c.get(9)).toEqual(3);
+      expect(c.get(10)).toEqual(5);
+      expect(c.get(10)).toEqual(5);
+    });
+  });
+});
diff --git a/src/data-structures/graphs/README.adoc b/src/data-structures/graphs/README.adoc
new file mode 100644
index 00000000..90a88bd5
--- /dev/null
+++ b/src/data-structures/graphs/README.adoc
@@ -0,0 +1 @@
+include::../../../book/content/part03/graph.asc[]
diff --git a/src/data-structures/graphs/graph.js b/src/data-structures/graphs/graph.js
index 5917799c..5342b6d3 100644
--- a/src/data-structures/graphs/graph.js
+++ b/src/data-structures/graphs/graph.js
@@ -41,13 +41,14 @@ class Graph {
    * Removes node from graph
    * It also removes the reference of the deleted node from
    *  anywhere it was adjacent to.
-   * Runtime: O(|V| + |E|)
+   * Runtime: O(|V|) because adjacency list is implemented with a HashSet.
+   * It were implemented with an array then it would be O(|V| + |E|).
    * @param {any} value node's value
    */
   removeVertex(value) {
     const current = this.nodes.get(value); // <1>
     if (current) {
-      Array.from(this.nodes.values()).forEach(node => node.removeAdjacent(current)); // <2>
+      Array.from(this.nodes.values()).forEach((node) => node.removeAdjacent(current)); // <2>
     }
     return this.nodes.delete(value); // <3>
   }
@@ -55,9 +56,9 @@ class Graph {
 
   // tag::addEdge[]
   /**
-   * Create a connection between source node and destination node.
-   * If the graph is undirected it will also create the conneciton from destination to destination.
-   * If the nodes doesn't exist then it will create them on the fly
+   * Create a connection between the source node and the destination node.
+   * If the graph is undirected, it will also create the link from destination to source.
+   * If the nodes don't exist, then it will make them on the fly.
    * Runtime: O(1)
    * @param {any} source
    * @param {any} destination
@@ -79,10 +80,11 @@ class Graph {
 
   // tag::removeEdge[]
   /**
-   * Remove connection between source node and destination.
-   * If the graph is undirected it will also remove the conneciton from destination to destination.
+   * Remove the connection between source node and destination.
+   * If the graph is undirected, it will also create the link from destination to source.
    *
-   * Runtime: O(|E|)
+   * Runtime: O(1): implemented with HashSet.
+   * If implemented with array, would be O(|E|).
    *
    * @param {any} source
    * @param {any} destination
@@ -105,7 +107,7 @@ class Graph {
 
   // tag::areAdjacents[]
   /**
-   * True if two nodes are adjacent to each other
+   * True if two nodes are adjacent.
    * @param {any} source node's value
    * @param {any} destination node's value
    */
@@ -132,7 +134,7 @@ class Graph {
   }
 
   /**
-   * Depth-first search
+   * Breadth-first search
    * Use a queue to visit nodes (FIFO)
    * @param {Node} first node to start the dfs
    */
@@ -156,7 +158,7 @@ class Graph {
       if (node && !visited.has(node)) {
         yield node;
         visited.set(node);
-        node.getAdjacents().forEach(adj => visitList.add(adj));
+        node.getAdjacents().forEach((adj) => visitList.add(adj));
       }
     }
   }
@@ -243,21 +245,14 @@ class Graph {
     sourceNode.getAdjacents().forEach((node) => {
       if (!newPath.has(node)) {
         const nextPaths = this.findAllPaths(node.value, destination, newPath);
-        nextPaths.forEach(nextPath => paths.push(nextPath));
+        nextPaths.forEach((nextPath) => paths.push(nextPath));
       }
     });
     return paths;
   }
-// tag::constructor[]
 }
 
-Graph.UNDIRECTED = Symbol('directed graph'); // one-way edges
-Graph.DIRECTED = Symbol('undirected graph'); // two-ways edges
-// end::constructor[]
+Graph.UNDIRECTED = Symbol('undirected graph'); // two-way edges
+Graph.DIRECTED = Symbol('directed graph'); // one-way edges
 
 module.exports = Graph;
-
-/*
- * https://repl.it/@amejiarosario/graphpy
- * http://www.pythontutor.com/visualize.html#mode=edit - https://goo.gl/Xp7Zpm
- */
diff --git a/src/data-structures/graphs/graph.spec.js b/src/data-structures/graphs/graph.spec.js
index 47d154f2..4ff1fb3b 100644
--- a/src/data-structures/graphs/graph.spec.js
+++ b/src/data-structures/graphs/graph.spec.js
@@ -1,8 +1,8 @@
-const Graph = require('./graph');
+const { Graph } = require('../../index');
 
 describe('Graph', () => {
   let graph;
-  const getValues = node => (Array.isArray(node) ? node.map(a => getValues(a)) : node.value);
+  const getValues = (node) => (Array.isArray(node) ? node.map((a) => getValues(a)) : node.value);
 
   beforeEach(() => {
     graph = new Graph();
@@ -252,11 +252,11 @@ describe('Graph', () => {
         expect(graph.areConnected('you', 'barbara')).toBe(true);
       });
 
-      it('should return true if two nodes are connected', () => {
+      it('should return true if two nodes are connected to itself', () => {
         expect(graph.areConnected('you', 'you')).toBe(true);
       });
 
-      it('should return true if two nodes are connected', () => {
+      it('should return true if two nodes are connected to other', () => {
         expect(graph.areConnected('you', 'John')).toBe(false);
       });
     });
diff --git a/src/data-structures/graphs/node.spec.js b/src/data-structures/graphs/node.spec.js
index 16d07d13..360480be 100644
--- a/src/data-structures/graphs/node.spec.js
+++ b/src/data-structures/graphs/node.spec.js
@@ -10,7 +10,7 @@ describe('Node (Graph)', () => {
   describe('#addAdjacent', () => {
     it('should add node to adjacent list', () => {
       node.addAdjacent(new Node(2));
-      expect(node.getAdjacents().map(n => n.value)).toEqual([2]);
+      expect(node.getAdjacents().map((n) => n.value)).toEqual([2]);
     });
   });
 
@@ -32,26 +32,26 @@ describe('Node (Graph)', () => {
 
     it('should remove node to adjacent list', () => {
       expect(a.removeAdjacent(c)).toBe(true);
-      expect(node.getAdjacents().map(n => n.value)).toEqual(['b']);
+      expect(node.getAdjacents().map((n) => n.value)).toEqual(['b']);
 
       expect(a.removeAdjacent(b)).toBe(true);
-      expect(node.getAdjacents().map(n => n.value)).toEqual([]);
+      expect(node.getAdjacents().map((n) => n.value)).toEqual([]);
     });
 
     it('should return undefined if not found', () => {
       expect(a.removeAdjacent(d)).toBe(false);
-      expect(node.getAdjacents().map(n => n.value)).toEqual(['b', 'c']);
+      expect(node.getAdjacents().map((n) => n.value)).toEqual(['b', 'c']);
     });
   });
 
   describe('#getAdjacents', () => {
     it('should get adjacents', () => {
       node.addAdjacent(new Node('b'));
-      expect(node.getAdjacents().map(n => n.value)).toEqual(['b']);
+      expect(node.getAdjacents().map((n) => n.value)).toEqual(['b']);
     });
 
     it('should get adjacents when empty', () => {
-      expect(node.getAdjacents().map(n => n.value)).toEqual([]);
+      expect(node.getAdjacents().map((n) => n.value)).toEqual([]);
     });
   });
 
@@ -62,7 +62,7 @@ describe('Node (Graph)', () => {
       expect(node.isAdjacent(b)).toBe(true);
     });
 
-    it('should return true if they are adjacent', () => {
+    it('should return true if they are adjacent on c', () => {
       const c = new Node('c');
       expect(node.isAdjacent(c)).toBe(false);
     });
diff --git a/src/data-structures/heaps/heap.js b/src/data-structures/heaps/heap.js
new file mode 100644
index 00000000..537894ee
--- /dev/null
+++ b/src/data-structures/heaps/heap.js
@@ -0,0 +1,103 @@
+/**
+ * Heap data structure a.k.a Priority Queue
+ *
+ * Used to get min or max values from a collection in constant time.
+ *
+ * @author Adrian Mejia <adrian@adrianmejia.com>
+ */
+class Heap {
+  constructor(comparator = (a, b) => a - b) {
+    this.array = [];
+    this.comparator = (i1, i2) => {
+      const value = comparator(this.array[i1], this.array[i2]);
+      if (Number.isNaN(value)) { throw new Error(`Comparator should evaluate to a number. Got ${value} when comparing ${this.array[i1]} with ${this.array[i2]}`); }
+      return value;
+    };
+  }
+
+  /**
+   * Insert element
+   * @runtime O(log n)
+   * @param {any} value
+   */
+  add(value) {
+    this.array.push(value);
+    this.bubbleUp();
+  }
+
+  /**
+   * Retrieves, but does not remove, the head of this heap
+   * @runtime O(1)
+   */
+  peek() {
+    return this.array[0];
+  }
+
+  /**
+   * Retrieves and removes the head of this heap, or returns null if this heap is empty.
+   * @runtime O(log n)
+   */
+  remove(index = 0) {
+    if (!this.size) return null;
+    this.swap(index, this.size - 1); // swap with last
+    const value = this.array.pop(); // remove element
+    this.bubbleDown(index);
+    return value;
+  }
+
+  /**
+   * Returns the number of elements in this collection.
+   * @runtime O(1)
+   */
+  get size() {
+    return this.array.length;
+  }
+
+  /**
+   * Move new element upwards on the heap, if it's out of order
+   * @runtime O(log n)
+   */
+  bubbleUp() {
+    let index = this.size - 1;
+    const parent = (i) => Math.ceil(i / 2) - 1;
+    while (parent(index) >= 0 && this.comparator(parent(index), index) > 0) {
+      this.swap(parent(index), index);
+      index = parent(index);
+    }
+  }
+
+  /**
+   * After removal, moves element downwards on the heap, if it's out of order
+   * @runtime O(log n)
+   */
+  bubbleDown(index = 0) {
+    let curr = index;
+    const left = (i) => 2 * i + 1;
+    const right = (i) => 2 * i + 2;
+    const getTopChild = (i) => (right(i) < this.size
+      && this.comparator(left(i), right(i)) > 0 ? right(i) : left(i));
+
+    while (left(curr) < this.size && this.comparator(curr, getTopChild(curr)) > 0) {
+      const next = getTopChild(curr);
+      this.swap(curr, next);
+      curr = next;
+    }
+  }
+
+  /**
+   * Swap elements on the heap
+   * @runtime O(1)
+   * @param {number} i1 index 1
+   * @param {number} i2 index 2
+   */
+  swap(i1, i2) {
+    [this.array[i1], this.array[i2]] = [this.array[i2], this.array[i1]];
+  }
+}
+
+// aliases
+Heap.prototype.poll = Heap.prototype.remove;
+Heap.prototype.offer = Heap.prototype.add;
+Heap.prototype.element = Heap.prototype.peek;
+
+module.exports = Heap;
diff --git a/src/data-structures/heaps/heap.spec.js b/src/data-structures/heaps/heap.spec.js
new file mode 100644
index 00000000..f1d6bb09
--- /dev/null
+++ b/src/data-structures/heaps/heap.spec.js
@@ -0,0 +1,172 @@
+const Heap = require('./heap');
+const PriorityQueue = require('./priority-queue');
+const MaxHeap = require('./max-heap');
+const MinHeap = require('./min-heap');
+
+[
+  [Heap],
+  [PriorityQueue, [], (a, b) => a - b],
+  [MinHeap],
+].forEach(([DS, ...arg]) => {
+  describe('Min-Heap and Priority Queue', () => {
+    let heap;
+
+    beforeEach(() => {
+      heap = new DS(...arg);
+    });
+
+    describe('#contructor', () => {
+      it('should initialize', () => {
+        expect(heap).not.toBe(undefined);
+      });
+    });
+
+    describe('#add', () => {
+      it('should add an element', () => {
+        expect(heap.add(1)).toBe(undefined);
+        expect(heap.array).toEqual([1]);
+        expect(heap.size).toBe(1);
+      });
+
+      it('should keep things in order', () => {
+        heap.add(3);
+        expect(heap.array[0]).toEqual(3);
+        heap.add(2);
+        expect(heap.array[0]).toEqual(2);
+        heap.add(1);
+        expect(heap.array[0]).toEqual(1);
+        expect(heap.size).toEqual(3);
+      });
+    });
+
+    describe('#remove', () => {
+      it('should work', () => {
+        heap.add(1);
+        heap.add(0);
+        expect(heap.remove()).toBe(0);
+        expect(heap.size).toBe(1);
+        expect(heap.array).toEqual([1]);
+      });
+
+      it('should return null if empty', () => {
+        heap = new Heap();
+        expect(heap.remove()).toBe(null);
+      });
+    });
+
+    describe('when has elements', () => {
+      beforeEach(() => {
+        heap.add(1);
+        heap.add(2);
+        heap.add(3);
+        heap.add(0);
+      });
+
+      describe('#peek', () => {
+        it('should get min', () => {
+          expect(heap.peek()).toEqual(0);
+        });
+      });
+
+      describe('#remove', () => {
+        it('should get min', () => {
+          expect(heap.remove()).toEqual(0);
+          expect(heap.remove()).toEqual(1);
+          expect(heap.remove()).toEqual(2);
+          expect(heap.remove()).toEqual(3);
+          expect(heap.size).toBe(0);
+        });
+      });
+    });
+  });
+});
+
+[
+  [Heap, (a, b) => b - a],
+  [PriorityQueue, [], (a, b) => b - a],
+  [MaxHeap],
+].forEach(([DS, ...arg]) => {
+  describe('Max-Heap (Priority Queue)', () => {
+    let heap;
+
+    beforeEach(() => {
+      heap = new DS(...arg);
+    });
+
+    describe('#contructor', () => {
+      it('should initialize', () => {
+        expect(heap).not.toBe(undefined);
+      });
+    });
+
+    describe('#add', () => {
+      it('should add an element', () => {
+        expect(heap.add(1)).toBe(undefined);
+        expect(heap.array).toEqual([1]);
+        expect(heap.size).toBe(1);
+      });
+
+      it('should keep things in order', () => {
+        heap.add(1);
+        expect(heap.array[0]).toEqual(1);
+        heap.add(2);
+        expect(heap.array[0]).toEqual(2);
+        heap.add(3);
+        expect(heap.array[0]).toEqual(3);
+        expect(heap.size).toEqual(3);
+      });
+    });
+
+    describe('#remove', () => {
+      it('should work', () => {
+        heap.add(1);
+        heap.add(0);
+        expect(heap.remove()).toBe(1);
+        expect(heap.size).toBe(1);
+        expect(heap.array).toEqual([0]);
+      });
+
+      it('should work with duplicates', () => {
+        heap.add(3);
+        heap.add(2);
+        heap.add(3);
+        heap.add(1);
+        heap.add(2);
+        heap.add(4);
+        heap.add(5);
+        heap.add(5);
+        heap.add(6);
+
+        expect(heap.remove()).toEqual(6);
+        expect(heap.remove()).toEqual(5);
+        expect(heap.remove()).toEqual(5);
+        expect(heap.remove()).toEqual(4);
+      });
+    });
+
+    describe('when has elements', () => {
+      beforeEach(() => {
+        heap.add(1);
+        heap.add(2);
+        heap.add(3);
+        heap.add(0);
+      });
+
+      describe('#peek', () => {
+        it('should get min', () => {
+          expect(heap.peek()).toEqual(3);
+        });
+      });
+
+      describe('#remove', () => {
+        it('should get min when duplicates', () => {
+          expect(heap.remove()).toEqual(3);
+          expect(heap.remove()).toEqual(2);
+          expect(heap.remove()).toEqual(1);
+          expect(heap.remove()).toEqual(0);
+          expect(heap.size).toBe(0);
+        });
+      });
+    });
+  });
+});
diff --git a/src/data-structures/heaps/max-heap.js b/src/data-structures/heaps/max-heap.js
new file mode 100644
index 00000000..46dbb89e
--- /dev/null
+++ b/src/data-structures/heaps/max-heap.js
@@ -0,0 +1,8 @@
+const Heap = require('./heap');
+
+class MaxHeap extends Heap {
+  constructor() {
+    super((a, b) => b - a);
+  }
+}
+module.exports = MaxHeap;
diff --git a/src/data-structures/heaps/median-heap.js b/src/data-structures/heaps/median-heap.js
new file mode 100644
index 00000000..e8295963
--- /dev/null
+++ b/src/data-structures/heaps/median-heap.js
@@ -0,0 +1,75 @@
+const Heap = require('./heap');
+
+/**
+ * Median Heap using one MaxHeap and one MinHeap.
+ *
+ * Each heap contains about one half of the data.
+ * Every element in the min-heap is greater or equal to the median,
+ * and every element in the max-heap is less or equal to the median.
+ *
+ * @author Adrian Mejia <adrian@adrianmejia.com>
+ */
+class MedianHeap {
+  constructor() {
+    this.min = new Heap((a, b) => a - b);
+    this.max = new Heap((a, b) => b - a);
+  }
+
+  /**
+   * Add a value to the heap
+   * @runtime O(log n)
+   * @param {any} value
+   */
+  add(value) {
+    if (value > this.findMedian()) {
+      // If the new element is greater than the current median, it goes to the min-heap.
+      this.min.add(value);
+    } else {
+      // If it is less than the current median, it goes to the max heap.
+      this.max.add(value);
+    }
+
+    // rebalance if the sizes of the heaps differ by more than one element
+    if (Math.abs(this.min.size - this.max.size) > 1) {
+      // extract the min/max from the heap with more elements and insert it into the other heap.
+      if (this.min.size > this.max.size) {
+        this.max.add(this.min.remove());
+      } else {
+        this.min.add(this.max.remove());
+      }
+    }
+  }
+
+  /**
+   * Find median
+   * @runtime O(1)
+   */
+  findMedian() {
+    let median;
+
+    if (this.max.size === this.min.size) {
+      // When both heaps contain the same number of elements,
+      // the total number of elements is even.
+      // The median is the mean of the two middle elements.
+      median = (this.max.peek() + this.min.peek()) / 2;
+    } else if (this.max.size > this.min.size) {
+      // when the max-heap contains one more element than the min-heap,
+      //  the median is in the top of the max-heap.
+      median = this.max.peek();
+    } else {
+      // When the min-heap contains one more element than the max-heap,
+      // the median is in the top of the min-heap.
+      median = this.min.peek();
+    }
+    return median;
+  }
+
+  /**
+   * Return size of the heap.
+   */
+  get size() {
+    return this.min.size + this.max.size;
+  }
+}
+
+module.exports = MedianHeap;
diff --git a/src/data-structures/heaps/median-heap.spec.js b/src/data-structures/heaps/median-heap.spec.js
new file mode 100644
index 00000000..4f7a8a6b
--- /dev/null
+++ b/src/data-structures/heaps/median-heap.spec.js
@@ -0,0 +1,61 @@
+const MedianHeap = require('./median-heap');
+
+describe('Median Heap', () => {
+  let medianHeap;
+
+  beforeEach(() => {
+    medianHeap = new MedianHeap();
+  });
+
+  describe('#add', () => {
+    it('should work', () => {
+      expect(medianHeap.add(1)).toEqual(undefined);
+      expect(medianHeap.size).toEqual(1);
+    });
+
+    it('should work with 2 additions', () => {
+      expect(medianHeap.add(1)).toEqual(undefined);
+      expect(medianHeap.add(1)).toEqual(undefined);
+      expect(medianHeap.size).toEqual(2);
+    });
+  });
+
+  describe('#findMedian', () => {
+    it('should work', () => {
+      medianHeap.add(5);
+      expect(medianHeap.findMedian()).toEqual(5);
+      medianHeap.add(15);
+      expect(medianHeap.findMedian()).toEqual(10);
+      medianHeap.add(10);
+      expect(medianHeap.findMedian()).toEqual(10);
+    });
+
+    it('should work with even numbers', () => {
+      const values = [5, 15, 1, 3];
+      const medians = values.map((v) => {
+        medianHeap.add(v);
+        return medianHeap.findMedian();
+      });
+      expect(medians).toEqual([5, 10, 5, 4]);
+    });
+
+    it('should work with odd numbers', () => {
+      const values = [2, 4, 7, 1, 5, 3];
+      const medians = values.map((v) => {
+        medianHeap.add(v);
+        return medianHeap.findMedian();
+      });
+      expect(medians).toEqual([2, 3, 4, 3, 4, 3.5]);
+    });
+
+    it('should work with negative numbers', () => {
+      const values = [-1, -2, -3];
+      const expected = [-1, -1.5, -2];
+      const medians = values.map((v) => {
+        medianHeap.add(v);
+        return medianHeap.findMedian();
+      });
+      expect(medians).toEqual(expected);
+    });
+  });
+});
diff --git a/src/data-structures/heaps/min-heap.js b/src/data-structures/heaps/min-heap.js
new file mode 100644
index 00000000..0b77c760
--- /dev/null
+++ b/src/data-structures/heaps/min-heap.js
@@ -0,0 +1,8 @@
+const Heap = require('./heap');
+
+class MinHeap extends Heap {
+  constructor() {
+    super((a, b) => a - b);
+  }
+}
+module.exports = MinHeap;
diff --git a/src/data-structures/heaps/priority-queue.js b/src/data-structures/heaps/priority-queue.js
new file mode 100644
index 00000000..b81772e8
--- /dev/null
+++ b/src/data-structures/heaps/priority-queue.js
@@ -0,0 +1,28 @@
+const Heap = require('./heap');
+
+class PriorityQueue extends Heap {
+  constructor(iterable = [], comparator = (a, b) => a[0] - b[0]) {
+    super(comparator);
+    Array.from(iterable).forEach((el) => this.add(el));
+  }
+
+  /**
+   * Add data to the Queue with Priority
+   * @param {[number, any]|any} value - Pair with [priority, value]
+   *  any object as value is also possible if a custom comparator is passed in.
+   * @returns {void}
+   */
+  enqueue(value) {
+    super.add(value);
+  }
+
+  /**
+   * Remove from the queue the element with the highest priority.
+   * @returns {[number, any]|any}
+   */
+  dequeue() {
+    return super.remove();
+  }
+}
+
+module.exports = PriorityQueue;
diff --git a/src/data-structures/heaps/priority-queue.spec.js b/src/data-structures/heaps/priority-queue.spec.js
new file mode 100644
index 00000000..860480da
--- /dev/null
+++ b/src/data-structures/heaps/priority-queue.spec.js
@@ -0,0 +1,75 @@
+const { PriorityQueue } = require('../..');
+
+describe('Priorty Queue (as MinHeap default)', () => {
+  const num = 1;
+  const obj = { a: 1, b: 2 };
+  let pq;
+
+  describe('with default contructor', () => {
+    beforeEach(() => {
+      pq = new PriorityQueue();
+    });
+
+    describe('.enqueue', () => {
+      it('should enqueue [priority, element]', () => {
+        pq.enqueue([Infinity, 2]);
+        pq.enqueue([0, 1]);
+        pq.enqueue([100, { a: 1, b: 2 }]);
+        expect(pq.size).toEqual(3);
+        expect(pq.peek()).toEqual([0, 1]);
+      });
+    });
+
+    describe('.dequeue', () => {
+      it('should enqueue and dequeue elements on priority order', () => {
+        pq.enqueue([100, obj]);
+        pq.enqueue([Infinity, 2]);
+        pq.enqueue([0, num]);
+
+        expect(pq.dequeue()).toEqual([0, num]);
+        expect(pq.size).toEqual(2);
+        expect(pq.dequeue()).toEqual([100, obj]);
+        expect(pq.dequeue()).toEqual([Infinity, 2]);
+        expect(pq.size).toEqual(0);
+      });
+
+      it('should handle case when priorty was forgotten', () => {
+        expect(() => pq.enqueue({ a: 100 })).not.toThrow();
+        expect(() => pq.enqueue({ b: 200 })).toThrow();
+      });
+    });
+  });
+
+  describe('with default values', () => {
+    it('should add values on creation', () => {
+      pq = new PriorityQueue([[100, obj], [Infinity, 2], [0, num]]);
+      expect(pq.size).toEqual(3);
+      expect(pq.peek()).toEqual([0, num]);
+      expect(pq.dequeue()).toEqual([0, num]);
+      expect(pq.size).toEqual(2);
+    });
+  });
+
+  describe('with custom comparator', () => {
+    const alice = { name: 'Alice', grade: 80, assistance: 1 };
+    const bob = { name: 'Bob', grade: 93, assistance: 0.7 };
+    const ana = { name: 'Ana', grade: 98, assistance: 0.8 };
+
+    it('should become MaxPriortyQueue and compare objects', () => {
+      pq = new PriorityQueue([], (a, b) => b.grade * b.assistance - a.grade * a.assistance);
+      pq.enqueue(alice);
+      pq.enqueue(ana);
+      pq.enqueue(bob);
+      expect(pq.size).toEqual(3);
+      expect(pq.dequeue()).toEqual(alice);
+      expect(pq.dequeue()).toEqual(ana);
+      expect(pq.dequeue()).toEqual(bob);
+    });
+
+    it('should handle errors', () => {
+      pq = new PriorityQueue([], (a, b) => b.grade - a.grade);
+      expect(() => pq.enqueue(alice)).not.toThrow();
+      expect(() => pq.enqueue({ name: 'Oops', error: 98 })).toThrow();
+    });
+  });
+});
diff --git a/src/data-structures/linked-lists/README.adoc b/src/data-structures/linked-lists/README.adoc
new file mode 100644
index 00000000..62290fdd
--- /dev/null
+++ b/src/data-structures/linked-lists/README.adoc
@@ -0,0 +1 @@
+include::../../../book/content/part02/linked-list.asc[]
diff --git a/src/data-structures/linked-lists/linked-list.js b/src/data-structures/linked-lists/linked-list.js
index 3197b68b..987069ca 100644
--- a/src/data-structures/linked-lists/linked-list.js
+++ b/src/data-structures/linked-lists/linked-list.js
@@ -1,5 +1,5 @@
-const Node = require('./node');
 const util = require('util');
+const Node = require('./node'); // Doubly
 
 // tag::constructor[]
 /**
@@ -7,34 +7,37 @@ const util = require('util');
  * the last and first element
  */
 class LinkedList {
-  constructor() {
+  constructor(
+    iterable = [],
+    // end::constructor[]
+    ListNode = Node, // Node class (e.g. singly, doubly, multilevel)
+    // tag::constructor[]
+  ) {
     this.first = null; // head/root element
     this.last = null; // last element of the list
     this.size = 0; // total number of elements in the list
-  }
-  // end::constructor[]
+    // end::constructor[]
+    this.ListNode = ListNode; // ListNode class
+    // tag::constructor[]
 
-  /**
-   * Alias for size
-   */
-  get length() {
-    return this.size;
+    Array.from(iterable, (i) => this.addLast(i));
   }
+  // end::constructor[]
 
   // tag::addFirst[]
   /**
    * Adds element to the begining of the list. Similar to Array.unshift
    * Runtime: O(1)
-   * @param {any} value
+   * @param {Node} value
    */
   addFirst(value) {
-    const newNode = new Node(value);
+    const newNode = new this.ListNode(value);
 
     newNode.next = this.first;
 
-    if (this.first) {
-      this.first.previous = newNode;
-    } else {
+    if (this.first) { // check if first node exists (list not empty)
+      this.first.previous = newNode; // <1>
+    } else { // if list is empty, first & last will point to newNode.
       this.last = newNode;
     }
 
@@ -57,11 +60,11 @@ class LinkedList {
   addLast(value) {
     const newNode = new Node(value);
 
-    if (this.first) {
+    if (this.first) { // check if first node exists (list not empty)
       newNode.previous = this.last;
       this.last.next = newNode;
       this.last = newNode;
-    } else {
+    } else { // if list is empty, first & last will point to newNode.
       this.first = newNode;
       this.last = newNode;
     }
@@ -76,95 +79,90 @@ class LinkedList {
   /**
    * Insert new element at the given position (index)
    *
+   * Runtime: O(n)
+   *
    * @param {any} value new node's value
    * @param {Number} position position to insert element
-   * @returns {Node} new node or 'undefined' if the index is out of bound.
+   * @returns {Node|undefined} new node or 'undefined' if the index is out of bound.
    */
-  add(value, position = 0) {
-    if (position === 0) { // <1>
-      return this.addFirst(value);
-    }
+  addAt(value, position = 0) {
+    if (position === 0) return this.addFirst(value); // <1>
+    if (position === this.size) return this.addLast(value); // <2>
 
-    if (position === this.size) { // <2>
-      return this.addLast(value);
-    }
     // Adding element in the middle
-    const current = this.get(position);
-    if (current) {
-      const newNode = new Node(value); // <3>
-      newNode.previous = current.previous; // <4>
-      newNode.next = current; // <5>
-
-      current.previous.next = newNode; // <6>
-      if (current.next) { current.next.previous = newNode; } // <7>
-      this.size += 1;
-      return newNode;
-    }
-
-    return undefined; // out of bound index
+    const current = this.findBy({ index: position }).node;
+    if (!current) return undefined; // out of bound index
+
+    const newNode = new Node(value); // <3>
+    newNode.previous = current.previous; // <4>
+    newNode.next = current; // <5>
+    current.previous.next = newNode; // <6>
+    current.previous = newNode; // <7>
+    this.size += 1;
+    return newNode;
   }
   // end::addMiddle[]
 
   // tag::searchByValue[]
   /**
+   * @deprecated use findBy
    * Search by value. It finds first occurrence  of
-   * the element matching the value.
+   * the position of element matching the value.
+   * Similar to Array.indexOf.
+   *
    * Runtime: O(n)
+   *
    * @example: assuming a linked list with: a -> b -> c
-   *  linkedList.indexOf('b') // ↪️ 1
-   *  linkedList.indexOf('z') // ↪️ undefined
+   *  linkedList.getIndexByValue('b') // ↪️ 1
+   *  linkedList.getIndexByValue('z') // ↪️ undefined
    * @param {any} value
    * @returns {number} return index or undefined
    */
-  indexOf(value) {
-    return this.find((current, position) => {
-      if (current.value === value) {
-        return position;
-      }
-      return undefined;
-    });
+  getIndexByValue(value) {
+    return this.findBy({ value }).index;
   }
   // end::searchByValue[]
 
   // tag::searchByIndex[]
   /**
+   * @deprecated use findBy directly
    * Search by index
    * Runtime: O(n)
    * @example: assuming a linked list with: a -> b -> c
    *  linkedList.get(1) // ↪️ 'b'
    *  linkedList.get(40) // ↪️ undefined
    * @param {Number} index position of the element
-   * @returns {Node} element at the specified position in this list.
+   * @returns {Node|undefined} element at the specified position in
+   *   this list or undefined if was not found.
    */
   get(index = 0) {
-    return this.find((current, position) => {
-      if (position === index) {
-        return current;
-      }
-      return undefined;
-    });
+    return this.findBy({ index }).node;
   }
   // end::searchByIndex[]
 
   // tag::find[]
   /**
-   * Iterate through the list until callback returns thruthy
-   * @example see #get and  #indexOf
-   * @param {Function} callback evaluates current node and index.
-   *  If any value other than undefined it's returned it will stop the search.
-   * @returns {any} callbacks's return value or undefined
+   * Find by index or by value, whichever happens first.
+   * Runtime: O(n)
+   * @example
+   *  this.findBy({ index: 10 }).node; // node at index 10.
+   *  this.findBy({ value: 10 }).node; // node with value 10.
+   *  this.findBy({ value: 10 }).index; // node's index with value 10.
+   *
+   * @param {Object} params - The search params
+   * @param {number} params.index - The index/position to search for.
+   * @param {any} params.value - The value to search for.
+   * @returns {{node: any, index: number}}
    */
-  find(callback) {
+  findBy({ value, index = Infinity } = {}) {
     for (let current = this.first, position = 0; // <1>
-      current; // <2>
+      current && position <= index; // <2>
       position += 1, current = current.next) { // <3>
-      const result = callback(current, position); // <4>
-
-      if (result !== undefined) {
-        return result; // <5>
+      if (position === index || value === current.value) { // <4>
+        return { node: current, index: position }; // <5>
       }
     }
-    return undefined; // not found
+    return {}; // not found
   }
   // end::find[]
 
@@ -172,74 +170,130 @@ class LinkedList {
   // tag::removeFirst[]
   /**
    * Removes element from the start of the list (head/root).
-   * Similar to Array.shift
+   * Similar to Array.shift().
    * Runtime: O(1)
    * @returns {any} the first element's value which was removed.
    */
   removeFirst() {
+    if (!this.first) return null; // Check if list is already empty.
     const head = this.first;
 
-    if (head) {
-      this.first = head.next;
-      if (this.first) {
-        this.first.previous = null;
-      }
-      this.size -= 1;
-    } else {
+    this.first = head.next; // move first pointer to the next element.
+    if (this.first) {
+      this.first.previous = null;
+    } else { // if list has size zero, then we need to null out last.
       this.last = null;
     }
-    return head && head.value;
+    this.size -= 1;
+    return head.value;
   }
   // end::removeFirst[]
 
   // tag::removeLast[]
   /**
-   * Removes element to the end of the list. Similar to Array.pop
-   * Using the `last.previous` we can reduce the runtime from O(n) to O(1)
+   * Removes element to the end of the list.
+   * Similar to Array.pop().
    * Runtime: O(1)
-   * @returns {value} the last element's value which was removed
+   * @returns {any} the last element's value which was removed
    */
   removeLast() {
+    if (!this.last) return null; // Check if list is already empty.
     const tail = this.last;
 
-    if (tail) {
-      this.last = tail.previous;
-      if (this.last) {
-        this.last.next = null;
-      } else {
-        this.first = null;
-      }
-      this.size -= 1;
+    this.last = tail.previous;
+    if (this.last) {
+      this.last.next = null;
+    } else { // if list has size zero, then we need to null out first.
+      this.first = null;
     }
-    return tail && tail.value;
+    this.size -= 1;
+    return tail.value;
   }
   // end::removeLast[]
 
   // tag::removeByPosition[]
   /**
-   * Removes the element at the specified position in this list.
+   * Removes the element at the given position (index) in this list.
    * Runtime: O(n)
    * @param {any} position
    * @returns {any} the element's value at the specified position that was removed.
    */
   removeByPosition(position = 0) {
-    const current = this.get(position);
-
-    if (position === 0) {
-      this.removeFirst();
-    } else if (position === this.size - 1) {
-      this.removeLast();
-    } else if (current) {
-      current.previous.next = current.next;
-      current.next.previous = current.previous;
-      this.size -= 1;
-    }
-
+    if (position === 0) return this.removeFirst();
+    if (position === this.size - 1) return this.removeLast();
+    const current = this.findBy({ index: position }).node;
+    if (!current) return null;
+    current.previous.next = current.next;
+    current.next.previous = current.previous;
+    this.size -= 1;
     return current && current.value;
   }
   // end::removeByPosition[]
 
   /**
+   * Remove element by Node
+   * O(1)
+   */
+  removeByNode(node) {
+    if (!node) { return null; }
+    if (node === this.first) {
+      return this.removeFirst();
+    }
+    if (node === this.last) {
+      return this.removeLast();
+    }
+    node.previous.next = node.next;
+    node.next.previous = node.previous;
+    this.size -= 1;
+
+    return node.value;
+  }
+
+  /**
+   * Iterate through the list yield on each node
+   * @see https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#User-defined_iterables
+   */
+  * [Symbol.iterator]() {
+    for (let node = this.first; node; node = node.next) {
+      yield node;
+    }
+  }
+
+  toString() {
+    const parts = [...this]; // see [Symbol.iterator]()
+    return parts.map((n) => util.inspect(n.value)).join(' -> ');
+  }
+
+  /**
+   * Alias for size
+   */
+  get length() {
+    return this.size;
+  }
+
+  /**
+   * @deprecated use findBy
+   * Iterate through the list until callback returns a truthy value
+   * @example see #get and  #getIndexByValue
+   * @param {Function} callback evaluates current node and index.
+   *  If any value other than undefined it's returned it will stop the search.
+   * @returns {any} callbacks's return value or undefined
+   */
+  find(callback) {
+    for (let current = this.first, position = 0; // <1>
+      current; // <2>
+      position += 1, current = current.next) { // <3>
+      const result = callback(current, position); // <4>
+
+      if (result !== undefined) {
+        return result; // <5>
+      }
+    }
+    return undefined; // not found
+  }
+
+  /**
+   * @deprecated use removeByNode or removeByPosition
    * Removes the first occurrence of the specified elementt
    * from this list, if it is present.
    * Runtime: O(n)
@@ -264,23 +318,6 @@ class LinkedList {
 
     return false;
   }
-
-  /**
-   * Iterate through the list yield on each node
-   * @see https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#User-defined_iterables
-   */
-  * [Symbol.iterator]() {
-    for (let node = this.first, position = 0;
-      node;
-      position += 1, node = node.next) {
-      yield { node, position };
-    }
-  }
-
-  toString() {
-    const parts = [...this]; // see [Symbol.iterator]()
-    return parts.map(n => util.inspect(n.node.value)).join(' -> ');
-  }
 }
 
 // Aliases
diff --git a/src/data-structures/linked-lists/linked-list.spec.js b/src/data-structures/linked-lists/linked-list.spec.js
index 9ceb0682..8285e316 100644
--- a/src/data-structures/linked-lists/linked-list.spec.js
+++ b/src/data-structures/linked-lists/linked-list.spec.js
@@ -1,4 +1,4 @@
-const LinkedList = require('./linked-list');
+const { LinkedList } = require('../../index');
 
 describe('LinkedList Test', () => {
   let linkedList;
@@ -32,6 +32,17 @@ describe('LinkedList Test', () => {
       linkedList.addLast('b');
     });
 
+    it('should remove with only one element', () => {
+      linkedList = new LinkedList();
+      linkedList.addLast('a');
+
+      expect(linkedList.removeFirst()).toBe('a');
+
+      expect(linkedList.size).toBe(0);
+      expect(linkedList.first).toBe(null);
+      expect(linkedList.last).toBe(null);
+    });
+
     it('should remove first item: a', () => {
       expect(linkedList.removeFirst()).toBe('a');
       expect(linkedList.first.value).toBe('b');
@@ -44,17 +55,18 @@ describe('LinkedList Test', () => {
 
       expect(linkedList.first).toBe(null);
       expect(linkedList.last).toBe(null);
+      expect(linkedList.size).toBe(0);
     });
   });
 
   describe('#addFirst', () => {
-    it('add element to the head/root of the list', () => {
+    it('add 1 element to the head/root of the list', () => {
       linkedList.addFirst('a');
       expect(linkedList.first.value).toBe('a');
       expect(linkedList.last.value).toBe('a');
     });
 
-    it('add element to the head/root of the list', () => {
+    it('add 2 elements to the head/root of the list', () => {
       linkedList.addFirst('a');
       linkedList.addFirst('b');
       expect(linkedList.first.value).toBe('b');
@@ -104,14 +116,20 @@ describe('LinkedList Test', () => {
       linkedList.addLast('found');
     });
 
-    describe('#indexOf', () => {
+    describe('#length', () => {
+      it('should have length property', () => {
+        expect(linkedList.length).toBe(2);
+      });
+    });
+
+    describe('#getIndexByValue', () => {
       it('should find element index', () => {
-        expect(linkedList.indexOf(0)).toBe(0);
-        expect(linkedList.indexOf('found')).toBe(1);
+        expect(linkedList.getIndexByValue(0)).toBe(0);
+        expect(linkedList.getIndexByValue('found')).toBe(1);
       });
 
       it('should return undefined', () => {
-        expect(linkedList.indexOf('hola')).toBe(undefined);
+        expect(linkedList.getIndexByValue('hola')).toBe(undefined);
       });
     });
 
@@ -137,8 +155,8 @@ describe('LinkedList Test', () => {
       });
 
       it('should return undefined if not found', () => {
-        expect(linkedList.remove(2)).toBe(undefined);
-        expect(linkedList.remove(-2)).toBe(undefined);
+        expect(linkedList.remove(2)).toBe(null);
+        expect(linkedList.remove(-2)).toBe(null);
       });
 
       it('should update size, last and first', () => {
@@ -146,7 +164,7 @@ describe('LinkedList Test', () => {
         expect(linkedList.size).toBe(1);
         expect(linkedList.remove(0)).toBe('found');
         expect(linkedList.size).toBe(0);
-        expect(linkedList.remove(0)).toBe(undefined);
+        expect(linkedList.remove(0)).toBe(null);
         expect(linkedList.size).toBe(0);
         expect(linkedList.first).toBe(null);
         expect(linkedList.last).toBe(null);
@@ -155,7 +173,7 @@ describe('LinkedList Test', () => {
 
     describe('#addAt', () => {
       it('should insert at the beginning', () => {
-        const newNode = linkedList.add('first', 0);
+        const newNode = linkedList.addAt('first', 0);
         expect(newNode.value).toBe('first');
         expect(newNode.next.value).toBe(0);
         expect(linkedList.size).toBe(3);
@@ -163,16 +181,20 @@ describe('LinkedList Test', () => {
       });
 
       it('should insert at the middle', () => {
-        const newNode = linkedList.add('middle', 1);
+        const newNode = linkedList.addAt('middle', 1);
         expect(newNode.value).toBe('middle');
+        // checking the 4 surrounding links were updated
         expect(newNode.next.value).toBe('found');
         expect(newNode.previous.value).toBe(0);
+        expect(linkedList.get(0).next.value).toBe('middle');
+        expect(linkedList.get(2).previous.value).toBe('middle');
+
         expect(linkedList.size).toBe(3);
         expect(linkedList.first.value).toBe(0);
       });
 
       it('should insert at the end', () => {
-        const newNode = linkedList.add('end', 2);
+        const newNode = linkedList.addAt('end', 2);
         expect(newNode.value).toBe('end');
         expect(newNode.next).toBe(null);
         expect(newNode.previous.value).toBe('found');
@@ -181,12 +203,53 @@ describe('LinkedList Test', () => {
       });
 
       it('should not insert out of bound', () => {
-        const newNode = linkedList.add('out-of-bound', 3);
+        const newNode = linkedList.addAt('out-of-bound', 3);
         expect(newNode).toBe(undefined);
         expect(linkedList.last.value).toBe('found');
         expect(linkedList.size).toBe(2);
       });
     });
+
+    describe('#removeByPosition', () => {
+      it('should remove last element', () => {
+        expect(linkedList.length).toBe(2);
+        expect(linkedList.removeByPosition(1)).toBe('found');
+        expect(linkedList.length).toBe(1);
+      });
+
+      it('should remove first element', () => {
+        expect(linkedList.length).toBe(2);
+        expect(linkedList.removeByPosition(0)).toBe(0);
+        expect(linkedList.length).toBe(1);
+      });
+    });
+
+    describe('#removeByNode', () => {
+      it('should remove first node', () => {
+        const node = linkedList.first;
+        linkedList.removeByNode(node);
+        expect(linkedList.first.value).toEqual('found');
+        expect(linkedList.first.previous).toEqual(null);
+        expect(linkedList.size).toEqual(1);
+      });
+
+      it('should remove last node', () => {
+        const node = linkedList.last;
+        linkedList.removeByNode(node);
+        expect(linkedList.first.value).toEqual(0);
+        expect(linkedList.first.next).toEqual(null);
+        expect(linkedList.size).toEqual(1);
+      });
+
+      it('should remove from the middle', () => {
+        const node = linkedList.first;
+        linkedList.addLast('last');
+        linkedList.removeByNode(node);
+        expect(linkedList.first.next).toEqual(linkedList.last);
+        expect(linkedList.last.previous).toEqual(linkedList.first);
+        expect(linkedList.size).toEqual(2);
+      });
+    });
   });
 
   describe('Doubly Linked List and aliases', () => {
@@ -366,5 +429,24 @@ describe('LinkedList Test', () => {
         expect(linkedList.toString()).toBe("'a' -> 2 -> 'c' -> { k: 4, v: 'd' }");
       });
     });
+
+
+    describe('iterator', () => {
+      let a;
+      let b;
+      let c;
+      let d;
+      beforeEach(() => {
+        a = linkedList.addLast('a');
+        b = linkedList.addLast('b');
+        c = linkedList.addLast('c');
+        d = linkedList.addLast('d');
+      });
+
+      it('should convert to array of nodes', () => {
+        expect([...linkedList]).toEqual([a, b, c, d]);
+        expect(Array.from(linkedList)).toEqual([a, b, c, d]);
+      });
+    });
   });
 });
diff --git a/src/data-structures/linked-lists/node.js b/src/data-structures/linked-lists/node.js
index 1547a953..0d3147f5 100644
--- a/src/data-structures/linked-lists/node.js
+++ b/src/data-structures/linked-lists/node.js
@@ -1,14 +1,18 @@
 // tag::snippet[]
 /**
- * Node with reference to next and previous element
+ * Linked List Node
  */
+// tag::singly[]
 class Node {
-  constructor(value) {
+  constructor(value = null) {
     this.value = value;
     this.next = null;
-    this.previous = null;
+    // end::singly[]
+    this.previous = null; // if doubly linked list
+    // tag::singly[]
   }
 }
+// end::singly[]
 // end::snippet[]
 
 module.exports = Node;
diff --git a/src/data-structures/maps/README.adoc b/src/data-structures/maps/README.adoc
new file mode 100644
index 00000000..b5eaa146
--- /dev/null
+++ b/src/data-structures/maps/README.adoc
@@ -0,0 +1,7 @@
+include::../../../book/content/part03/map.asc[]
+
+<<<
+include::../../../book/content/part02/hash-map.asc[]
+
+<<<
+include::../../../book/content/part03/tree-map.asc[]
diff --git a/src/data-structures/maps/hash-maps/hash-map-1.js b/src/data-structures/maps/hash-maps/hash-map-1.js
index c66109df..4deec02c 100644
--- a/src/data-structures/maps/hash-maps/hash-map-1.js
+++ b/src/data-structures/maps/hash-maps/hash-map-1.js
@@ -74,4 +74,3 @@ module.exports = HashMap;
 // assert.equal(hashMap.get('cat'), 8); // got overwritten by art 😱
 // assert.equal(hashMap.get('rat'), 8); // got overwritten by art 😱
 // assert.equal(hashMap.get('dog'), 8); // got overwritten by art 😱
-
diff --git a/src/data-structures/maps/hash-maps/hash-map.js b/src/data-structures/maps/hash-maps/hash-map.js
index 2fdd5324..00a26bea 100644
--- a/src/data-structures/maps/hash-maps/hash-map.js
+++ b/src/data-structures/maps/hash-maps/hash-map.js
@@ -1,7 +1,11 @@
 /* eslint-disable no-bitwise, no-iterator, no-restricted-syntax */
+const { TextEncoder } = require('util');
 const LinkedList = require('../../linked-lists/linked-list');
 const { nextPrime } = require('./primes');
 
+// Text encoding
+const encoding = new TextEncoder();
+
 /**
  * The map holds key-value pairs.
  * Any value (both objects and primitive values) may be used as either a key or a value.
@@ -26,6 +30,17 @@ class HashMap {
     this.reset();
   }
 
+  /**
+   * Reset or reinitialize all values on the hashmap.
+   *
+   * Used for rehashing, clear and initializing the map.
+   *
+   * @param {array} buckets - New bucket.
+   * @param {number} size - The new size of the hashmap.
+   * @param {number} collisions - The number of collisions.
+   * @param {array} keysTrackerArray - The array of keys in insertion order
+   * @param {number} keysTrackerIndex - The last index of keysTrackerArray
+   */
   reset(
     buckets = new Array(this.initialCapacity),
     size = 0,
@@ -50,12 +65,16 @@ class HashMap {
    * @return {integer} bucket index
    */
   hashFunction(key) {
-    const str = String(key);
+    const bytes = encoding.encode(key);
+    const { length } = bytes;
+
     let hash = 2166136261; // FNV_offset_basis (32 bit)
-    for (let i = 0; i < str.length; i += 1) {
-      hash ^= str.codePointAt(i); // XOR
+
+    for (let i = 0; i < length; i++) {
+      hash ^= bytes[i]; // XOR
       hash *= 16777619; // 32 bit FNV_prime
     }
+
     return (hash >>> 0) % this.buckets.length;
   }
   // end::hashFunction[]
@@ -252,7 +271,7 @@ class HashMap {
   }
 
   /**
-   * the same function object as the initial value of the `entries` method.
+   * The same function object as the initial value of the `entries` method.
    * Contains the [key, value] pairs for each element in the Map.
    */
   * [Symbol.iterator]() {
@@ -265,6 +284,13 @@ class HashMap {
   get length() {
     return this.size;
   }
+
+  /**
+   * Removes all key/value pairs from the Map object.
+   */
+  clear() {
+    this.reset();
+  }
 }
 
 // Aliases
diff --git a/src/data-structures/maps/hash-maps/hash-map.spec.js b/src/data-structures/maps/hash-maps/hash-map.spec.js
index 5523e1a7..daaf93b7 100644
--- a/src/data-structures/maps/hash-maps/hash-map.spec.js
+++ b/src/data-structures/maps/hash-maps/hash-map.spec.js
@@ -1,5 +1,4 @@
-const HashMap = require('./hash-map');
-// import HashMap from './hash-map';
+const { HashMap } = require('../../../index');
 
 
 describe('HashMap Tests', () => {
@@ -266,7 +265,7 @@ describe('HashMap Tests', () => {
       hashMap.delete('All About That Bass');
       hashMap.set('All About That Bass', 'Meghan Trainor');
       expect(hashMap.keysTrackerIndex).toBe(12);
-      // should hava a hole
+      // should have a hole
       expect(hashMap.keysTrackerArray).toEqual(['Pineapple', 'Despacito', 'Bailando', 'Dura', 'Lean On', 'Hello',
         undefined,
         'Wake Me Up', 'Brother', 'Faded', 'The Spectre', 'All About That Bass']);
diff --git a/src/data-structures/maps/hash-maps/hashing.js b/src/data-structures/maps/hash-maps/hashing.js
index cc4d0647..d2d104ea 100644
--- a/src/data-structures/maps/hash-maps/hashing.js
+++ b/src/data-structures/maps/hash-maps/hashing.js
@@ -1,3 +1,5 @@
+/* eslint-disable */
+
 // tag::naiveHashCode[]
 /**
  * Naïve implementation of a non-cryptographic hashing function
diff --git a/src/data-structures/maps/hash-maps/readme.asc b/src/data-structures/maps/hash-maps/readme.asc
new file mode 100644
index 00000000..d06231ea
--- /dev/null
+++ b/src/data-structures/maps/hash-maps/readme.asc
@@ -0,0 +1 @@
+include::../../../../book/content/part02/hash-map.asc[]
diff --git a/src/data-structures/maps/map.spec.js b/src/data-structures/maps/map.spec.js
index dea0cdf8..a69c70e3 100644
--- a/src/data-structures/maps/map.spec.js
+++ b/src/data-structures/maps/map.spec.js
@@ -1,5 +1,4 @@
-const HashMap = require('./hash-maps/hash-map');
-const TreeMap = require('./tree-maps/tree-map');
+const { HashMap, TreeMap } = require('../../index');
 
 const mapImplementations = [
   Map,
@@ -139,5 +138,19 @@ mapImplementations.forEach((MapImplementation) => {
         ]);
       });
     });
+
+    describe('#clear', () => {
+      beforeEach(() => {
+        map.set(1, 2);
+        map.set(2, 'dos');
+        map.set(3, 3);
+      });
+
+      it('should work', () => {
+        expect(map.size).toBe(3);
+        expect(map.clear()).toEqual();
+        expect(map.size).toBe(0);
+      });
+    });
   });
 });
diff --git a/src/data-structures/maps/tree-maps/readme.asc b/src/data-structures/maps/tree-maps/readme.asc
new file mode 100644
index 00000000..15d5c234
--- /dev/null
+++ b/src/data-structures/maps/tree-maps/readme.asc
@@ -0,0 +1 @@
+include::../../../../book/content/part03/tree-map.asc[]
diff --git a/src/data-structures/maps/tree-maps/tree-map.js b/src/data-structures/maps/tree-maps/tree-map.js
index 68562da5..10957aed 100644
--- a/src/data-structures/maps/tree-maps/tree-map.js
+++ b/src/data-structures/maps/tree-maps/tree-map.js
@@ -18,6 +18,11 @@ const Tree = require('../../trees/red-black-tree'); // fast insertion
  *        allocate memory beforehand (e.g. HashMap’s initial capacity)
  *        nor you have to rehash when is getting full.
  *
+ * Implementations in other languages:
+ * Java: https://docs.oracle.com/en/java/javase/15/docs/api/java.base/java/util/TreeMap.html
+ * C++: https://en.cppreference.com/w/cpp/container/map
+ * Python: none
+ *
  */
 class TreeMap {
   // tag::constructor[]
@@ -92,6 +97,22 @@ class TreeMap {
   }
   // end::delete[]
 
+  /**
+   * Get the last key/value pair (node with largest key)
+   */
+  lastEntry() {
+    const node = this.tree.getRightmost();
+    return node ? [node.value, node.data()] : [];
+  }
+
+  /**
+   * Get the first key/value pair (node with smallest key)
+   */
+  firstEntry() {
+    const node = this.tree.getLeftmost();
+    return node ? [node.value, node.data()] : [];
+  }
+
   // tag::iterators[]
   /**
    * Default iterator for this map
@@ -133,6 +154,13 @@ class TreeMap {
       yield [node.value, node.data()];
     }
   }
+
+  /**
+   * Removes all key/value pairs from the Map object.
+   */
+  clear() {
+    this.tree = new Tree();
+  }
 }
 
 // Aliases
diff --git a/src/data-structures/maps/tree-maps/tree-map.spec.js b/src/data-structures/maps/tree-maps/tree-map.spec.js
new file mode 100644
index 00000000..8661207a
--- /dev/null
+++ b/src/data-structures/maps/tree-maps/tree-map.spec.js
@@ -0,0 +1,43 @@
+// some parts tested on src/data-structures/maps/map.spec.js
+
+const TreeMap = require('./tree-map');
+
+describe('TreeMap: keep values sorted', () => {
+  let map;
+
+  beforeEach(() => {
+    map = new TreeMap();
+  });
+
+  describe('when map is empty', () => {
+    describe('.lastEntry and .firstEntry', () => {
+      it('should get last/first entry', () => {
+        expect(map.lastEntry()).toEqual([]);
+        expect(map.firstEntry()).toEqual([]);
+      });
+    });
+  });
+
+  describe('when map has entries', () => {
+    beforeEach(() => {
+      map.set(20, { title: '3sum', passed: true });
+      map.set(30, { title: '3sum', passed: false });
+      map.set(10, { title: '2sum', passed: true });
+      map.set(5, { title: '4sum', passed: false });
+    });
+
+    describe('.lastEntry and .firstEntry', () => {
+      it('should get last/first entry', () => {
+        expect(map.lastEntry()).toEqual([
+          30,
+          { title: '3sum', passed: false },
+        ]);
+
+        expect(map.firstEntry()).toEqual([
+          5,
+          { title: '4sum', passed: false },
+        ]);
+      });
+    });
+  });
+});
diff --git a/src/data-structures/queues/README.adoc b/src/data-structures/queues/README.adoc
new file mode 100644
index 00000000..68aa475e
--- /dev/null
+++ b/src/data-structures/queues/README.adoc
@@ -0,0 +1 @@
+include::../../../book/content/part02/queue.asc[]
diff --git a/src/data-structures/queues/queue-1.js b/src/data-structures/queues/queue-1.js
index aa7082b3..b82c9721 100644
--- a/src/data-structures/queues/queue-1.js
+++ b/src/data-structures/queues/queue-1.js
@@ -8,6 +8,7 @@ class Queue {
 
   /**
    * Add element to the queue
+   * Insert to the end of the array
    * Runtime: O(1)
    * @param {any} element
    */
@@ -17,7 +18,8 @@ class Queue {
 
   /**
    * Add element to the queue
-   * Runtime: O(1)
+   * Removing from the beginning of the array
+   * Runtime: O(n)
    * @param {any} element
    */
   remove() {
diff --git a/src/data-structures/queues/queue.js b/src/data-structures/queues/queue.js
index 60d38178..b62c57ae 100644
--- a/src/data-structures/queues/queue.js
+++ b/src/data-structures/queues/queue.js
@@ -1,18 +1,31 @@
 const LinkedList = require('../linked-lists/linked-list');
 
+/* Usage Example:
+// tag::snippet[]
+const queue = new Queue();
+
+queue.enqueue('a');
+queue.enqueue('b');
+queue.dequeue(); //↪️ a
+queue.enqueue('c');
+queue.dequeue(); //↪️ b
+queue.dequeue(); //↪️ c
+// end::snippet[]
+// */
+
 // tag::constructor[]
 /**
- * Data structure where add and remove elements in a first-in, first-out (FIFO)
+ * Data structure where we add and remove elements in a first-in, first-out (FIFO) fashion
  */
 class Queue {
-  constructor() {
-    this.items = new LinkedList();
+  constructor(iterable = []) {
+    this.items = new LinkedList(iterable);
   }
   // end::constructor[]
 
   // tag::enqueue[]
   /**
-   * Add element to the queue
+   * Add element to the back of the queue.
    * Runtime: O(1)
    * @param {any} item
    * @returns {queue} instance to allow chaining.
@@ -25,13 +38,14 @@ class Queue {
 
   // tag::dequeue[]
   /**
-   * Remove element from the queue
+   * Remove element from the front of the queue.
    * Runtime: O(1)
    * @returns {any} removed value.
    */
   dequeue() {
     return this.items.removeFirst();
   }
+
   // end::dequeue[]
   /**
    * Size of the queue
@@ -46,24 +60,30 @@ class Queue {
   isEmpty() {
     return !this.items.size;
   }
+
+  /**
+   * Return the most recent value or null if empty.
+   */
+  back() {
+    if (this.isEmpty()) return null;
+    return this.items.last.value;
+  }
+
+  /**
+   * Return oldest value from the queue or null if empty.
+   * (Peek at the next value to be dequeue)
+   */
+  front() {
+    if (this.isEmpty()) return null;
+    return this.items.first.value;
+  }
 }
 
 // Aliases
+Queue.prototype.peek = Queue.prototype.front;
 Queue.prototype.add = Queue.prototype.enqueue;
+Queue.prototype.push = Queue.prototype.enqueue;
 Queue.prototype.remove = Queue.prototype.dequeue;
+Queue.prototype.pop = Queue.prototype.dequeue;
 
 module.exports = Queue;
-
-/* Usage Example:
-// tag::snippet[]
-const queue = new Queue();
-
-queue.enqueue('a');
-queue.enqueue('b');
-queue.dequeue(); //↪️ a
-queue.enqueue('c');
-queue.dequeue(); //↪️ b
-queue.dequeue(); //↪️ c
-// end::snippet[]
-// */
-
diff --git a/src/data-structures/queues/queue.spec.js b/src/data-structures/queues/queue.spec.js
index 0aa44723..289e0e39 100644
--- a/src/data-structures/queues/queue.spec.js
+++ b/src/data-structures/queues/queue.spec.js
@@ -1,4 +1,4 @@
-const Queue = require('./queue.js');
+const { Queue } = require('../../index');
 
 describe('Queue', () => {
   let queue;
@@ -46,4 +46,28 @@ describe('Queue', () => {
       expect(queue.isEmpty()).toBe(false);
     });
   });
+
+  describe('#back', () => {
+    it('should return null if empty', () => {
+      expect(queue.back()).toEqual(null);
+    });
+
+    it('should return newest element', () => {
+      queue.enqueue('oldest');
+      queue.enqueue('newest');
+      expect(queue.back()).toEqual('newest');
+    });
+  });
+
+  describe('#front', () => {
+    it('should return null if empty', () => {
+      expect(queue.front()).toEqual(null);
+    });
+
+    it('should return oldest element', () => {
+      queue.enqueue('oldest');
+      queue.enqueue('newest');
+      expect(queue.front()).toEqual('oldest');
+    });
+  });
 });
diff --git a/src/data-structures/sets/README.adoc b/src/data-structures/sets/README.adoc
new file mode 100644
index 00000000..ce08b551
--- /dev/null
+++ b/src/data-structures/sets/README.adoc
@@ -0,0 +1 @@
+include::../../../book/content/part02/hash-set.asc[]
diff --git a/src/data-structures/sets/array-set.js b/src/data-structures/sets/array-set.js
index 8c11d069..180c5223 100644
--- a/src/data-structures/sets/array-set.js
+++ b/src/data-structures/sets/array-set.js
@@ -9,7 +9,7 @@ class ArraySet {
    */
   constructor(iterable = []) {
     this.array = [];
-    Array.from(iterable).forEach(element => this.add(element));
+    Array.from(iterable).forEach((element) => this.add(element));
   }
 
   /**
@@ -85,4 +85,3 @@ class ArraySet {
 }
 
 module.exports = ArraySet;
-
diff --git a/src/data-structures/sets/hash-set.js b/src/data-structures/sets/hash-set.js
index 3327745b..0817a560 100644
--- a/src/data-structures/sets/hash-set.js
+++ b/src/data-structures/sets/hash-set.js
@@ -12,7 +12,7 @@ class HashMapSet {
    */
   constructor(iterable = []) {
     this.hashMap = new HashMap();
-    Array.from(iterable).forEach(element => this.add(element));
+    Array.from(iterable).forEach((element) => this.add(element));
   }
 
   /**
@@ -87,4 +87,3 @@ class HashMapSet {
 }
 
 module.exports = HashMapSet;
-
diff --git a/src/data-structures/sets/map-set.js b/src/data-structures/sets/map-set.js
index b1c53485..fdb42391 100644
--- a/src/data-structures/sets/map-set.js
+++ b/src/data-structures/sets/map-set.js
@@ -9,7 +9,7 @@ class MapSet {
    */
   constructor(iterable = []) {
     this.map = new Map();
-    Array.from(iterable).forEach(element => this.add(element));
+    Array.from(iterable).forEach((element) => this.add(element));
   }
 
   /**
@@ -72,4 +72,3 @@ class MapSet {
 }
 
 module.exports = MapSet;
-
diff --git a/src/data-structures/sets/set.spec.js b/src/data-structures/sets/set.spec.js
index 6ed0c49b..9a9e6200 100644
--- a/src/data-structures/sets/set.spec.js
+++ b/src/data-structures/sets/set.spec.js
@@ -1,7 +1,6 @@
-const ArraySet = require('./array-set');
-const HashSet = require('./hash-set');
-const MapSet = require('./map-set');
-const TreeSet = require('./tree-set');
+const {
+  ArraySet, HashSet, MapSet, TreeSet,
+} = require('../../index');
 
 const setImplementations = [Set, HashSet, TreeSet, MapSet, ArraySet];
 
diff --git a/src/data-structures/sets/tree-set.js b/src/data-structures/sets/tree-set.js
index 3eecb3e2..1483e63f 100644
--- a/src/data-structures/sets/tree-set.js
+++ b/src/data-structures/sets/tree-set.js
@@ -16,7 +16,7 @@ class TreeSet {
    */
   constructor(iterable = []) {
     this.tree = new Tree();
-    Array.from(iterable).forEach(value => this.add(value)); // <1>
+    Array.from(iterable).forEach((value) => this.add(value)); // <1>
   }
 
   /**
diff --git a/src/data-structures/stacks/README.adoc b/src/data-structures/stacks/README.adoc
new file mode 100644
index 00000000..7cdcbe0e
--- /dev/null
+++ b/src/data-structures/stacks/README.adoc
@@ -0,0 +1 @@
+include::../../../book/content/part02/stack.asc[]
diff --git a/src/data-structures/stacks/stack-1.js b/src/data-structures/stacks/stack-1.js
index c9022de8..51169097 100644
--- a/src/data-structures/stacks/stack-1.js
+++ b/src/data-structures/stacks/stack-1.js
@@ -37,4 +37,3 @@ Stack.prototype.push = Stack.prototype.add;
 Stack.prototype.pop = Stack.prototype.remove;
 
 module.exports = Stack;
-
diff --git a/src/data-structures/stacks/stack.js b/src/data-structures/stacks/stack.js
index 9ab5a357..e96a6807 100644
--- a/src/data-structures/stacks/stack.js
+++ b/src/data-structures/stacks/stack.js
@@ -67,4 +67,3 @@ stack.remove(); //↪️ c
 stack.remove(); //↪️ a
 // end::snippet[]
 // */
-
diff --git a/src/data-structures/stacks/stack.spec.js b/src/data-structures/stacks/stack.spec.js
index 0cba79f8..6fa472f3 100644
--- a/src/data-structures/stacks/stack.spec.js
+++ b/src/data-structures/stacks/stack.spec.js
@@ -1,4 +1,4 @@
-const Stack = require('./stack.js');
+const { Stack } = require('../../index');
 
 describe('Stack', () => {
   let stack;
diff --git a/src/data-structures/trees/README.adoc b/src/data-structures/trees/README.adoc
new file mode 100644
index 00000000..2a652738
--- /dev/null
+++ b/src/data-structures/trees/README.adoc
@@ -0,0 +1,16 @@
+include::../../../book/content/part03/tree-intro.asc[]
+
+<<<
+include::../../../book/content/part03/binary-search-tree.asc[]
+
+<<<
+include::../../../book/content/part03/tree-search-traversal.asc[]
+
+<<<
+include::../../../book/content/part03/binary-search-tree-traversal.asc[]
+
+<<<
+include::../../../book/B-self-balancing-binary-search-trees.asc[]
+
+<<<
+include::../../../book/C-AVL-tree.asc[]
diff --git a/src/data-structures/trees/avl-tree.js b/src/data-structures/trees/avl-tree.js
index c3b013d2..272a16e1 100644
--- a/src/data-structures/trees/avl-tree.js
+++ b/src/data-structures/trees/avl-tree.js
@@ -17,35 +17,33 @@ const {
  * - LR rotations: double rotation left-right
  * - RL rotations: double rotation right-left
  *
- * @param {TreeNode} node
+ * @param {BinaryTreeNode} node
  */
 function balance(node) {
   if (node.balanceFactor > 1) {
     // left subtree is higher than right subtree
-    if (node.left.balanceFactor > 0) {
-      return rightRotation(node);
-    } else if (node.left.balanceFactor < 0) {
+    if (node.left.balanceFactor < 0) {
       return leftRightRotation(node);
     }
-  } else if (node.balanceFactor < -1) {
+    return rightRotation(node);
+  } if (node.balanceFactor < -1) {
     // right subtree is higher than left subtree
-    if (node.right.balanceFactor < 0) {
-      return leftRotation(node);
-    } else if (node.right.balanceFactor > 0) {
+    if (node.right.balanceFactor > 0) {
       return rightLeftRotation(node);
     }
+    return leftRotation(node);
   }
   return node;
 }
 // end::balance[]
 
-// tag::balanceUptream[]
+// tag::balanceUpstream[]
 /**
  * Bubbles up balancing nodes a their parents
  *
- * @param {TreeNode} node
+ * @param {BinaryTreeNode} node
  */
-function balanceUptream(node) {
+function balanceUpstream(node) {
   let current = node;
   let newParent;
   while (current) {
@@ -54,7 +52,7 @@ function balanceUptream(node) {
   }
   return newParent;
 }
-// end::balanceUptream[]
+// end::balanceUpstream[]
 
 // tag::AvlTree[]
 /**
@@ -68,7 +66,7 @@ class AvlTree extends BinarySearchTree {
    */
   add(value) {
     const node = super.add(value);
-    this.root = balanceUptream(node);
+    this.root = balanceUpstream(node);
     return node;
   }
 
@@ -80,7 +78,7 @@ class AvlTree extends BinarySearchTree {
     const node = super.find(value);
     if (node) {
       const found = super.remove(value);
-      this.root = balanceUptream(node.parent);
+      this.root = balanceUpstream(node.parent);
       return found;
     }
 
diff --git a/src/data-structures/trees/avl-tree.spec.js b/src/data-structures/trees/avl-tree.spec.js
index de25a0b1..c45ff94e 100644
--- a/src/data-structures/trees/avl-tree.spec.js
+++ b/src/data-structures/trees/avl-tree.spec.js
@@ -69,7 +69,7 @@ describe('AvlTree', () => {
     });
 
     it('should not balance', () => {
-      [30, 20, 40, 10, 50].forEach(v => tree.add(v));
+      [30, 20, 40, 10, 50].forEach((v) => tree.add(v));
 
       expect(tree.toArray()).toEqual([
         30,
@@ -144,4 +144,86 @@ describe('AvlTree', () => {
       expect(tree.root).toBe(n);
     });
   });
+
+  describe('balance without loosing nodes', () => {
+    beforeEach(() => {
+      tree.add(16);
+      tree.add(4);
+      tree.add(32);
+      tree.add(8);
+      tree.add(2);
+    });
+
+    it('should have all nodes', () => {
+      expect(tree.toArray()).toEqual([16, 4, 32, 2, 8,
+        null, null, null, null, null, null]);
+    });
+
+    it('should rebalance and keep all nodes', () => {
+      tree.add(1);
+      expect(tree.toArray()).toEqual([4, 2, 16, 1, null, 8, 32,
+        null, null, null, null, null, null]);
+    });
+  });
+
+  describe('balancing to the left', () => {
+    let n32;
+    beforeEach(() => {
+      n32 = tree.add(32);
+      tree.add(8);
+      tree.add(64);
+      tree.add(4);
+      tree.add(16);
+      tree.add(48);
+      tree.add(128);
+      tree.add(2);
+      tree.add(6);
+      tree.add(10);
+      tree.add(20);
+    });
+
+    it('should have all nodes', () => {
+      expect(tree.toArray()).toEqual([32, 8, 64, 4, 16, 48, 128, 2, 6, 10, 20,
+        null, null, null, null, null, null, null, null, null, null, null, null]);
+    });
+
+    it('should rebalance when removing', () => {
+      tree.remove(64);
+      expect(tree.toArray()).toEqual([32, 8, 128, 4, 16, 48, null, 2, 6, 10, 20,
+        null, null, null, null, null, null, null, null, null, null]);
+      expect(n32.balanceFactor).toBe(1);
+      expect(n32.right.balanceFactor).toBe(1);
+      expect(n32.left.balanceFactor).toBe(0);
+
+      tree.remove(48);
+      expect(tree.toArray()).toEqual([8, 4, 32, 2, 6, 16, 128, null, null, null, null, 10, 20,
+        null, null, null, null, null, null]);
+    });
+  });
+
+  describe('balancing to the right', () => {
+    beforeEach(() => {
+      tree.add(8);
+      tree.add(4);
+      tree.add(32);
+      tree.add(2);
+      tree.add(16);
+      tree.add(64);
+      tree.add(10);
+      tree.add(20);
+      tree.add(60);
+      tree.add(70);
+    });
+
+    it('should build the tree', () => {
+      expect(tree.toArray()).toEqual([8, 4, 32, 2, null, 16, 64, null, null, 10, 20, 60, 70,
+        null, null, null, null, null, null, null, null]);
+    });
+
+    it('should rebalance right side', () => {
+      tree.remove(2);
+      expect(tree.toArray()).toEqual([32, 8, 64, 4, 16, 60, 70, null, null, 10, 20,
+        null, null, null, null, null, null, null, null]);
+    });
+  });
 });
diff --git a/src/data-structures/trees/binary-search-tree.js b/src/data-structures/trees/binary-search-tree.js
index 12b562bc..e7a5cc49 100644
--- a/src/data-structures/trees/binary-search-tree.js
+++ b/src/data-structures/trees/binary-search-tree.js
@@ -19,23 +19,24 @@ class BinarySearchTree {
    * @returns {BinaryTreeNode} newly added node
    */
   add(value) {
-    const newNode = new BinaryTreeNode(value);
+    let node = new BinaryTreeNode(value);
 
     if (this.root) {
       const { found, parent } = this.findNodeAndParent(value); // <1>
       if (found) { // duplicated: value already exist on the tree
         found.meta.multiplicity = (found.meta.multiplicity || 1) + 1; // <2>
+        node = found;
       } else if (value < parent.value) {
-        parent.setLeftAndUpdateParent(newNode);
+        parent.setLeftAndUpdateParent(node);
       } else {
-        parent.setRightAndUpdateParent(newNode);
+        parent.setRightAndUpdateParent(node);
       }
     } else {
-      this.root = newNode;
+      this.root = node;
     }
 
     this.size += 1;
-    return newNode;
+    return node;
   }
   // end::add[]
 
@@ -69,7 +70,7 @@ class BinarySearchTree {
   findNodeAndParent(value, node = this.root, parent = null) {
     if (!node || node.value === value) {
       return { found: node, parent };
-    } else if (value < node.value) {
+    } if (value < node.value) {
       return this.findNodeAndParent(value, node.left, node);
     }
     return this.findNodeAndParent(value, node.right, node);
@@ -249,14 +250,14 @@ class BinarySearchTree {
   /**
    * Represent Binary Tree as an array.
    *
-   * Leaf nodes will have two `undefined` descendents.
+   * Leaf nodes will have two `undefined` descendants.
    *
    * The array representation of the binary tree is as follows:
    *
    * First element (index=0) is the root.
-   * The following two elements (index=1,2) are descendents of the root: left (a) and right (b).
-   * The next two elements (index=3,4) are the descendents of a
-   * The next two elements (index=5,6) are the descendents of b and so on.
+   * The following two elements (index=1,2) are descendants of the root: left (a) and right (b).
+   * The next two elements (index=3,4) are the descendants of a
+   * The next two elements (index=5,6) are the descendants of b and so on.
    *
    *  0     1            2             3       4        5       6        n
    * [root, a=root.left, b=root.right, a.left, a.right, b.left, b.right, ...]
diff --git a/src/data-structures/trees/binary-search-tree.spec.js b/src/data-structures/trees/binary-search-tree.spec.js
index 21d84e67..22508eb2 100644
--- a/src/data-structures/trees/binary-search-tree.spec.js
+++ b/src/data-structures/trees/binary-search-tree.spec.js
@@ -1,8 +1,8 @@
-const BinarySearchTree = require('./binary-search-tree');
+const { BinarySearchTree } = require('../../index');
 
 describe('Binary Search Tree', () => {
   let bst;
-  const getValues = treeGenerator => Array.from(treeGenerator).map(node => node.value);
+  const getValues = (treeGenerator) => Array.from(treeGenerator).map((node) => node.value);
 
   beforeEach(() => {
     bst = new BinarySearchTree();
@@ -66,7 +66,7 @@ describe('Binary Search Tree', () => {
       it('should deal with duplicates', () => {
         const root = bst.add(1);
         expect(root.meta.multiplicity).toBe(undefined);
-        bst.add(1);
+        expect(bst.add(1)).toBe(root); // should return existing
         expect(bst.size).toBe(2);
         expect(root.toValues()).toMatchObject({
           value: 1, parent: null, left: null, right: null,
@@ -105,7 +105,7 @@ describe('Binary Search Tree', () => {
         expect(parent).toMatchObject({ value: 5 });
       });
 
-      it('should find future parent of a node that doesnt exist yet', () => {
+      it('should find future parent of a node that doesnt exist yet with -1', () => {
         bst.add(5);
         bst.add(1);
         const { found, parent } = bst.findNodeAndParent(-1);
@@ -134,7 +134,7 @@ describe('Binary Search Tree', () => {
         // get keys
         expect(getValues(bst.inOrderTraversal())).toEqual([1, {}, 'dos']);
         // get data
-        expect(Array.from(bst.inOrderTraversal()).map(n => n.data())).toEqual([
+        expect(Array.from(bst.inOrderTraversal()).map((n) => n.data())).toEqual([
           1,
           fn,
           2,
@@ -262,7 +262,7 @@ describe('Binary Search Tree', () => {
       });
 
       it('should remove duplicates', () => {
-        bst.add(40); // add duplicate
+        expect(bst.add(40)).toBe(n40); // add duplicate
         expect(n40.meta.multiplicity).toBe(2);
 
         expect(bst.remove(40)).toBe(true);
@@ -312,7 +312,7 @@ describe('Binary Search Tree', () => {
 
       it('should generate an array from dfs', () => {
         const nodes = Array.from(bst.dfs());
-        const values = nodes.map(node => node.value);
+        const values = nodes.map((node) => node.value);
         expect(values).toEqual([10, 5, 4, 3, 30, 15, 40]);
       });
     });
@@ -326,7 +326,7 @@ describe('Binary Search Tree', () => {
     describe('#preOrderTraversal', () => {
       it('should generate an array from preOrderTraversal', () => {
         const nodes = Array.from(bst.preOrderTraversal());
-        const values = nodes.map(node => node.value);
+        const values = nodes.map((node) => node.value);
         expect(values).toEqual([10, 5, 4, 3, 30, 15, 40]);
       });
     });
@@ -334,7 +334,7 @@ describe('Binary Search Tree', () => {
     describe('#postOrderTraversal', () => {
       it('should generate an array from postOrderTraversal', () => {
         const nodes = Array.from(bst.postOrderTraversal());
-        const values = nodes.map(node => node.value);
+        const values = nodes.map((node) => node.value);
         expect(values).toEqual([3, 4, 5, 15, 40, 30, 10]);
       });
     });
diff --git a/src/data-structures/trees/binary-tree-node.js b/src/data-structures/trees/binary-tree-node.js
index 5008dcee..781b080d 100644
--- a/src/data-structures/trees/binary-tree-node.js
+++ b/src/data-structures/trees/binary-tree-node.js
@@ -19,7 +19,7 @@ class BinaryTreeNode {
 
   // tag::setAndUpdateParent[]
   /**
-   * Set a left node descendents.
+   * Set a left node descendants.
    * Also, children get associated to parent.
    */
   setLeftAndUpdateParent(node) {
@@ -31,7 +31,7 @@ class BinaryTreeNode {
   }
 
   /**
-   * Set a right node descendents.
+   * Set a right node descendants.
    * Also, children get associated to parent.
    */
   setRightAndUpdateParent(node) {
@@ -71,7 +71,7 @@ class BinaryTreeNode {
   }
 
   /**
-   * Node is leaf is it has no descendents
+   * Node is leaf is it has no descendants
    */
   get isLeaf() {
     return !this.left && !this.right;
@@ -172,6 +172,21 @@ class BinaryTreeNode {
     this.meta.data = value;
     return this;
   }
+
+  /**
+   * Convert Binary tree from an iterable (e.g. array)
+   * @param {array|string} iterable - The iterable
+   */
+  static from(iterable = []) {
+    const toBinaryTree = (array, index = 0) => {
+      if (index >= array.length) return null;
+      const node = new BinaryTreeNode(array[index]);
+      node.setLeftAndUpdateParent(toBinaryTree(array, index * 2 + 1));
+      node.setRightAndUpdateParent(toBinaryTree(array, index * 2 + 2));
+      return node;
+    };
+    return toBinaryTree(Array.from(iterable));
+  }
 }
 
 BinaryTreeNode.RIGHT = RIGHT;
diff --git a/src/data-structures/trees/binary-tree-node.spec.js b/src/data-structures/trees/binary-tree-node.spec.js
index 4a44b899..675e6a01 100644
--- a/src/data-structures/trees/binary-tree-node.spec.js
+++ b/src/data-structures/trees/binary-tree-node.spec.js
@@ -1,114 +1,167 @@
-const BinaryTreeNode = require('./binary-tree-node');
+const { BinaryTreeNode } = require('../../index');
+
+const { LEFT, RIGHT } = BinaryTreeNode;
 
 describe('Binary Tree Node', () => {
   let treeNode;
 
-  beforeEach(() => {
-    treeNode = new BinaryTreeNode('hola');
-  });
-
-  it('should start with null parent', () => {
-    expect(treeNode.parent).toBe(null);
-  });
-
-  it('should start with empty metadata', () => {
-    expect(treeNode.meta).toEqual({});
-  });
-
-  it('should hold a value', () => {
-    expect(treeNode.value).toBe('hola');
-  });
-
-  it('should have a height 0', () => {
-    expect(treeNode.height).toBe(0);
-  });
-
-  it('should set/get left node', () => {
-    expect(treeNode.left).toBe(null);
-    const newNode = new BinaryTreeNode(1);
-    treeNode.setLeftAndUpdateParent(newNode);
-    expect(treeNode.left.value).toBe(1);
-
-    expect(newNode.parent).toBe(treeNode);
-    expect(treeNode.height).toBe(1);
-    expect(treeNode.balanceFactor).toBe(1);
-  });
-
-  it('should set/get right node', () => {
-    expect(treeNode.right).toBe(null);
-    const newNode = new BinaryTreeNode(1);
-    treeNode.setRightAndUpdateParent(newNode);
-
-    expect(treeNode.right.value).toBe(1);
-    expect(newNode.parent).toBe(treeNode);
-    expect(treeNode.height).toBe(1);
-    expect(treeNode.balanceFactor).toBe(-1);
-  });
-
-  describe('Family operations', () => {
-    let g;
-    let p;
-    let u;
-    let c;
-    let s;
-
+  describe('with instance', () => {
     beforeEach(() => {
-      g = new BinaryTreeNode('grandparent');
-      p = new BinaryTreeNode('parent');
-      u = new BinaryTreeNode('uncle');
-      c = new BinaryTreeNode('child');
-      s = new BinaryTreeNode('sibling');
-
-      g.setRightAndUpdateParent(p);
-      g.setLeftAndUpdateParent(u);
-      p.setRightAndUpdateParent(c);
-      p.setLeftAndUpdateParent(s);
+      treeNode = new BinaryTreeNode('hola');
     });
 
-    it('should set heights', () => {
-      expect(g.height).toBe(2);
-      expect(g.balanceFactor).toBe(-1);
-
-      expect(p.height).toBe(1);
-      expect(p.balanceFactor).toBe(0);
+    it('should start with null parent', () => {
+      expect(treeNode.parent).toBe(null);
+    });
 
-      expect(u.height).toBe(0);
-      expect(u.balanceFactor).toBe(0);
+    it('should start with empty metadata', () => {
+      expect(treeNode.meta).toEqual({});
     });
 
-    it('should get the sibling', () => {
-      expect(c.sibling).toBe(s);
-      expect(p.sibling).toBe(u);
+    it('should hold a value', () => {
+      expect(treeNode.value).toBe('hola');
     });
 
-    it('should set leaf correctly', () => {
-      expect(c.isLeaf).toBe(true);
-      expect(u.isLeaf).toBe(true);
-      expect(p.isLeaf).toBe(false);
-      expect(g.isLeaf).toBe(false);
+    it('should have a height 0', () => {
+      expect(treeNode.height).toBe(0);
     });
 
-    it('should get null if no sibling', () => {
-      expect(g.sibling).toBe(null);
+    it('should set/get left node', () => {
+      expect(treeNode.left).toBe(null);
+      const newNode = new BinaryTreeNode(1);
+      treeNode.setLeftAndUpdateParent(newNode);
+      expect(treeNode.left.value).toBe(1);
+
+      expect(newNode.parent).toBe(treeNode);
+      expect(treeNode.height).toBe(1);
+      expect(treeNode.balanceFactor).toBe(1);
     });
 
-    it('should get the uncle', () => {
-      expect(c.uncle).toBe(u);
+    it('should set/get right node', () => {
+      expect(treeNode.right).toBe(null);
+      const newNode = new BinaryTreeNode(1);
+      treeNode.setRightAndUpdateParent(newNode);
+
+      expect(treeNode.right.value).toBe(1);
+      expect(newNode.parent).toBe(treeNode);
+      expect(treeNode.height).toBe(1);
+      expect(treeNode.balanceFactor).toBe(-1);
     });
 
-    it('should get null if no uncle', () => {
-      expect(g.uncle).toBe(null);
-      expect(p.uncle).toBe(null);
+    describe('Family operations', () => {
+      let g;
+      let p;
+      let u;
+      let c;
+      let s;
+
+      beforeEach(() => {
+        g = new BinaryTreeNode('grandparent');
+        p = new BinaryTreeNode('parent');
+        u = new BinaryTreeNode('uncle');
+        c = new BinaryTreeNode('child');
+        s = new BinaryTreeNode('sibling');
+
+        g.setRightAndUpdateParent(p);
+        g.setLeftAndUpdateParent(u);
+        p.setRightAndUpdateParent(c);
+        p.setLeftAndUpdateParent(s);
+      });
+
+      it('should set heights', () => {
+        expect(g.height).toBe(2);
+        expect(g.balanceFactor).toBe(-1);
+
+        expect(p.height).toBe(1);
+        expect(p.balanceFactor).toBe(0);
+
+        expect(u.height).toBe(0);
+        expect(u.balanceFactor).toBe(0);
+      });
+
+      it('should get the sibling', () => {
+        expect(c.sibling).toBe(s);
+        expect(p.sibling).toBe(u);
+      });
+
+      it('should set leaf correctly', () => {
+        expect(c.isLeaf).toBe(true);
+        expect(u.isLeaf).toBe(true);
+        expect(p.isLeaf).toBe(false);
+        expect(g.isLeaf).toBe(false);
+      });
+
+      it('should get null if no sibling', () => {
+        expect(g.sibling).toBe(null);
+      });
+
+      it('should get the uncle', () => {
+        expect(c.uncle).toBe(u);
+      });
+
+      it('should get null if no uncle', () => {
+        expect(g.uncle).toBe(null);
+        expect(p.uncle).toBe(null);
+      });
+
+      it('true if is parent left child for sibling', () => {
+        expect(s.isParentLeftChild).toBe(true);
+        expect(s.isParentRightChild).toBe(false);
+      });
+
+      it('true if is parent left child for child', () => {
+        expect(c.isParentLeftChild).toBe(false);
+        expect(c.isParentRightChild).toBe(true);
+      });
     });
+  });
 
-    it('true if is parent left child', () => {
-      expect(s.isParentLeftChild).toBe(true);
-      expect(s.isParentRightChild).toBe(false);
+  describe('with static methods', () => {
+    it('should work with null', () => {
+      const tree = BinaryTreeNode.from();
+      expect(tree).toEqual(null);
     });
 
-    it('true if is parent left child', () => {
-      expect(c.isParentLeftChild).toBe(false);
-      expect(c.isParentRightChild).toBe(true);
+    it('should build from array', () => {
+      /*
+                        0
+                       / \
+                      1   2
+                     / \   \
+                    3   5   4
+      */
+      const tree = BinaryTreeNode.from([0, 1, 2, 3, 5, null, 4]);
+      expect(tree.toValues()).toEqual({
+        value: 0,
+        left: 1,
+        right: 2,
+        parent: null,
+        parentSide: null,
+      });
+
+      expect(tree.left.toValues()).toEqual({
+        value: 1,
+        left: 3,
+        right: 5,
+        parent: 0,
+        parentSide: LEFT,
+      });
+
+      expect(tree.right.toValues()).toEqual({
+        value: 2,
+        left: null,
+        right: 4,
+        parent: 0,
+        parentSide: RIGHT,
+      });
+
+      expect(tree.right.right.toValues()).toEqual({
+        value: 4,
+        left: null,
+        right: null,
+        parent: 2,
+        parentSide: RIGHT,
+      });
     });
   });
 });
diff --git a/src/data-structures/trees/red-black-tree.spec.js b/src/data-structures/trees/red-black-tree.spec.js
index a49e389d..52d7f843 100644
--- a/src/data-structures/trees/red-black-tree.spec.js
+++ b/src/data-structures/trees/red-black-tree.spec.js
@@ -1,4 +1,4 @@
-const RedBlackTree = require('./red-black-tree.js');
+const { RedBlackTree } = require('../../index');
 
 const { RED, BLACK } = RedBlackTree;
 
@@ -27,12 +27,9 @@ describe('RedBlackTree', () => {
     });
 
     it('should balance tree by rotating left', () => {
-      const n1 = tree.add(1);
-      const n2 = tree.add(2);
-      const n3 = tree.add(3);
-
-      // console.log(n3)
-
+      tree.add(1);
+      tree.add(2);
+      tree.add(3);
       expect(tree.size).toBe(3);
 
       expect(tree.toArray()).toEqual([
diff --git a/src/data-structures/trees/tree-rotations.js b/src/data-structures/trees/tree-rotations.js
index 8f2c6338..235c3691 100644
--- a/src/data-structures/trees/tree-rotations.js
+++ b/src/data-structures/trees/tree-rotations.js
@@ -2,7 +2,6 @@
 /**
  * Swap parent's child
  *
- *
  * @example Child on the left side (it also work for the right side)
  *
  * p = parent
@@ -34,7 +33,7 @@ function swapParentChild(oldChild, newChild, parent) {
 /**
  * Single Left Rotation (LL Rotation)
  *
- * @example: tree with values 1-2-3-4
+ * @example: #1 tree with values 1-2-3-4
  *
  * 1                                 1
  *  \                                 \
@@ -43,21 +42,34 @@ function swapParentChild(oldChild, newChild, parent) {
  *     3                           2*    4
  *      \
  *       4
- * @param {TreeNode} node
- * @returns {TreeNode} new parent after the rotation
+ *
+ * @example: #2 left rotation
+ *
+ *     1                                          1
+ *      \                                          \
+ *       4*                                         16
+ *      / \                                        / \
+ *    2    16    -- left-rotation(4) ->          4   32
+ *       /  \                                   / \    \
+ *      8   32                                2   8    64
+ *            \
+ *            64
+ * @param {TreeNode} node current node to rotate (e.g. 4)
+ * @returns {TreeNode} new parent after the rotation (e.g. 16)
  */
 function leftRotation(node) {
-  const newParent = node.right; // E.g., node 3
+  const newParent = node.right; // E.g., node 16
   const grandparent = node.parent; // E.g., node 1
+  const previousLeft = newParent.left; // E.g., node 8
 
-  // swap node 1 left child from 2 to 3.
+  // swap parent of node 4 from node 1 to node 16
   swapParentChild(node, newParent, grandparent);
 
-  // Update node 3 left child to be 2, and
-  // updates node 2 parent to be node 3 (instead of 1).
+  // Update node 16 left child to be 4, and
+  // updates node 4 parent to be node 16 (instead of 1).
   newParent.setLeftAndUpdateParent(node);
-  // remove node 2 left child (previouly was node 3)
-  node.setRightAndUpdateParent(null);
+  // set node4 right child to be previousLeft (node 8)
+  node.setRightAndUpdateParent(previousLeft);
 
   return newParent;
 }
@@ -66,8 +78,7 @@ function leftRotation(node) {
 // tag::rightRotation[]
 /**
  * Single Right Rotation (RR Rotation)
- *
- * @example rotate node 3 to the right
+* @example: #1 rotate node 3 to the right
  *
  *       4                                  4
  *      /                                  /
@@ -77,22 +88,34 @@ function leftRotation(node) {
  *  /
  * 1
  *
+ * @example: #2 rotate 16 to the right and preserve nodes
+ *       64                                       64
+ *       /                                        /
+ *      16*                                      4
+ *      / \                                     / \
+ *     4  32    -- right-rotation(16) -->      2   16
+ *    / \                                     /    / \
+ *   2   8                                   1    8   32
+ *  /
+ * 1
+ *
  * @param {TreeNode} node
- *    this is the node we want to rotate to the right. (E.g., node 3)
- * @returns {TreeNode} new parent after the rotation (E.g., node 2)
+ *    this is the node we want to rotate to the right. (E.g., node 16)
+ * @returns {TreeNode} new parent after the rotation (E.g., node 4)
  */
 function rightRotation(node) {
-  const newParent = node.left; // E.g., node 2
-  const grandparent = node.parent; // E.g., node 4
+  const newParent = node.left; // E.g., node 4
+  const grandparent = node.parent; // E.g., node 64
+  const previousRight = newParent.right; // E.g., node 8
 
-  // swap node 4 left children (node 3) with node 2.
+  // swap node 64's left children (node 16) with node 4 (newParent).
   swapParentChild(node, newParent, grandparent);
 
-  // update right child on node 2 to be node 3,
-  // also make node 2 the new parent of node 3.
+  // update node 4's right child to be node 16,
+  // also make node 4 the new parent of node 16.
   newParent.setRightAndUpdateParent(node);
-  // remove node 3 left child (so it doesn't point to node 2)
-  node.setLeftAndUpdateParent(null);
+  // Update 16's left child to be the `previousRight` node.
+  node.setLeftAndUpdateParent(previousRight);
 
   return newParent;
 }
diff --git a/src/data-structures/trees/tree-rotations.spec.js b/src/data-structures/trees/tree-rotations.spec.js
index bb02039f..3a826532 100644
--- a/src/data-structures/trees/tree-rotations.spec.js
+++ b/src/data-structures/trees/tree-rotations.spec.js
@@ -96,6 +96,39 @@ describe('Tree rotations', () => {
         value: 3, left: 2, right: null, parent: 4,
       });
     });
+
+    it('should not lose nodes on LL', () => {
+      //  1                                          1
+      //   \                                          \
+      //    4*                                         16
+      //   / \                                        / \
+      // 2    16    -- left-rotation(4) ->          4   32
+      //    /  \                                   / \    \
+      //   8   32                                2   8    64
+      //         \
+      //         64
+      const n8 = new BinaryTreeNode(8);
+      const n16 = new BinaryTreeNode(16);
+      const n32 = new BinaryTreeNode(32);
+      const n64 = new BinaryTreeNode(64);
+
+      n1.setRightAndUpdateParent(n4);
+      n4.setLeftAndUpdateParent(n2);
+      n4.setRightAndUpdateParent(n16);
+      n16.setLeftAndUpdateParent(n8);
+      n16.setRightAndUpdateParent(n32);
+      n32.setLeftAndUpdateParent(n64);
+
+      const newParent = leftRotation(n4);
+
+      expect(newParent).toBe(n16);
+      expect(n8.toValues()).toMatchObject({
+        value: 8, left: null, right: null, parent: 4,
+      });
+      expect(n4.toValues()).toMatchObject({
+        value: 4, left: 2, right: 8, parent: 16,
+      });
+    });
   });
 
   describe('#rightRotation (RR Rotation)', () => {
@@ -145,7 +178,7 @@ describe('Tree rotations', () => {
       });
     });
 
-    it('should last two', () => {
+    it('should RR last two', () => {
       //  1
       //   \
       //     3*
@@ -167,6 +200,34 @@ describe('Tree rotations', () => {
         value: 3, left: null, right: null, parent: 2,
       });
     });
+
+    it('should not lose nodes on RR', () => {
+      //      16*                                      4
+      //      / \                                     / \
+      //     4  32    -- right-rotation(16) -->      2   16
+      //    / \                                     /    / \
+      //   2   8                                   1    8   32
+      //  /
+      // 1
+      const n8 = new BinaryTreeNode(8);
+      const n16 = new BinaryTreeNode(16);
+      const n32 = new BinaryTreeNode(32);
+      n16.setLeftAndUpdateParent(n4);
+      n16.setRightAndUpdateParent(n32);
+      n4.setLeftAndUpdateParent(n2);
+      n4.setRightAndUpdateParent(n8);
+      n2.setLeftAndUpdateParent(n1);
+
+      const newParent = rightRotation(n16);
+
+      expect(newParent).toBe(n4);
+      expect(n8.toValues()).toMatchObject({
+        value: 8, left: null, right: null, parent: 16,
+      });
+      expect(n16.toValues()).toMatchObject({
+        value: 16, left: 8, right: 32, parent: 4,
+      });
+    });
   });
 
   describe('#leftRightRotation (LR Rotation)', () => {
diff --git a/src/data-structures/trees/trie-1.js b/src/data-structures/trees/trie-1.js
new file mode 100644
index 00000000..fb6ed1bf
--- /dev/null
+++ b/src/data-structures/trees/trie-1.js
@@ -0,0 +1,123 @@
+class Trie {
+  constructor(val) {
+    this.val = val;
+    this.children = {};
+    this.isWord = false;
+  }
+
+  /**
+   * Insert word into trie and mark last element as such.
+   * @param {string} word
+   * @return {undefined}
+   */
+  insert(word) {
+    let curr = this;
+
+    for (const char of word) {
+      curr.children[char] = curr.children[char] || new Trie(char);
+      curr = curr.children[char];
+    }
+
+    curr.isWord = true;
+  }
+
+  /**
+   * Search for complete word (by default) or partial if flag is set.
+   * @param {string} word - Word to search.
+   * @param {boolean} options.partial - Whether or not match partial matches.
+   * @return {boolean}
+   */
+  search(word, { partial } = {}) {
+    let curr = this;
+
+    for (const char of word) {
+      if (!curr.children[char]) { return false; }
+      curr = curr.children[char];
+    }
+
+    return partial ? true : curr.isWord;
+  }
+
+  /**
+   * Return true if any word on the trie starts with the given prefix
+   * @param {string} prefix - Partial word to search.
+   * @return {boolean}
+   */
+  startsWith(prefix) {
+    return this.search(prefix, { partial: true });
+  }
+
+  /**
+   * Returns all the words from the current `node`.
+   * Uses backtracking.
+   *
+   * @param {string} prefix - The prefix to append to each word.
+   * @param {string} node - Current node to start backtracking.
+   * @param {string[]} words - Accumulated words.
+   * @param {string} string - Current string.
+   */
+  getAllWords(prefix = '', node = this, words = [], string = '') {
+    if (node.isWord) {
+      words.push(`${prefix}${string}`);
+    }
+
+    for (const char of Object.keys(node.children)) {
+      this.getAllWords(prefix, node.children[char], words, `${string}${char}`);
+    }
+
+    return words;
+  }
+
+  /**
+   * Return true if found the word to be removed, otherwise false.
+   * Iterative approach
+   * @param {string} word - The word to remove
+   * @returns {boolean}
+   */
+  remove(word) {
+    const stack = [];
+    let curr = this;
+
+    for (const char of word) {
+      if (!curr.children[char]) { return false; }
+      stack.push(curr);
+      curr = curr.children[char];
+    }
+
+    if (!curr.isWord) { return false; }
+    let node = stack.pop();
+
+    do {
+      node.children = {};
+      node = stack.pop();
+    } while (node && !node.isWord);
+
+    return true;
+  }
+
+  /**
+   * Return true if found the word to be removed, otherwise false.
+   * recursive approach
+   * @param {string} word - The word to remove
+   * @returns {boolean}
+   */
+  remove2(word, i = 0, parent = this) {
+    if (i === word.length - 1) {
+      return true;
+    }
+    const child = parent.children[word.charAt(i)];
+    if (!child) return false;
+
+    const found = this.remove(word, i + 1, child);
+
+    if (found) {
+      delete parent.children[word.charAt(i)];
+    }
+    return true;
+  }
+}
+
+// Aliases
+Trie.prototype.add = Trie.prototype.insert;
+
+module.exports = Trie;
diff --git a/src/data-structures/trees/trie-2.js b/src/data-structures/trees/trie-2.js
new file mode 100644
index 00000000..1a03c241
--- /dev/null
+++ b/src/data-structures/trees/trie-2.js
@@ -0,0 +1,133 @@
+class Trie {
+  constructor(val) {
+    this.val = val;
+    this.children = {};
+    this.isWord = false;
+  }
+
+  /**
+   * Insert word into trie and mark last element as such.
+   * @param {string} word
+   * @return {undefined}
+   */
+  insert(word) {
+    let curr = this;
+
+    for (const char of word) {
+      curr.children[char] = curr.children[char] || new Trie(char);
+      curr = curr.children[char];
+    }
+
+    curr.isWord = true;
+  }
+
+  /**
+   * Return true if found the word to be removed, otherwise false.
+   * @param {string} word - The word to remove
+   * @returns {boolean}
+   */
+  remove(word) {
+    return this.removeHelper(word);
+  }
+
+  /**
+   * Remove word from trie, return true if found, otherwise false.
+   * @param {string} word - The word to remove.
+   * @param {Trie} parent - The parent node.
+   * @param {number} index - The index.
+   * @param {number} meta.stop - Keeps track of the last letter that won't be removed.
+   * @returns {boolean}
+   */
+  removeHelper(word, parent = this, index = 0, meta = { stop: 0 }) {
+    if (index === word.length) {
+      parent.isWord = false;
+      if (Object.keys(parent.children)) { meta.stop = index; }
+      return true;
+    }
+    const child = parent.children[word.charAt(index)];
+    if (!child) { return false; }
+    if (parent.isWord) { meta.stop = index; }
+    const found = this.removeHelper(word, child, index + 1, meta);
+    // deletes all the nodes beyond `meta.stop`.
+    if (found && index >= meta.stop) {
+      delete parent.children[word.charAt(index)];
+    }
+    return found;
+  }
+
+  /**
+   * Retun last node that matches word or prefix or false if not found.
+   * @param {string} word - Word to search.
+   * @param {boolean} options.partial - Whether or not match partial matches.
+   * @return {Trie|false}
+   */
+  searchNode(word) {
+    let curr = this;
+
+    for (const char of word) {
+      if (!curr.children[char]) { return false; }
+      curr = curr.children[char];
+    }
+
+    return curr;
+  }
+
+  /**
+   * Search for complete word (by default) or partial if flag is set.
+   * @param {string} word - Word to search.
+   * @param {boolean} options.partial - Whether or not match partial matches.
+   * @return {boolean}
+   */
+  search(word, { partial } = {}) {
+    const curr = this.searchNode(word);
+    if (!curr) { return false; }
+    return partial ? true : curr.isWord;
+  }
+
+  /**
+   * Return true if any word on the trie starts with the given prefix
+   * @param {string} prefix - Partial word to search.
+   * @return {boolean}
+   */
+  startsWith(prefix) {
+    return this.search(prefix, { partial: true });
+  }
+
+  /**
+   * Returns all the words from the current `node`.
+   * Uses backtracking.
+   *
+   * @param {string} prefix - The prefix to append to each word.
+   * @param {string} node - Current node to start backtracking.
+   */
+  getAllWords(prefix = '', node = this) {
+    let words = [];
+
+    if (!node) { return words; }
+    if (node.isWord) {
+      words.push(prefix);
+    }
+
+    for (const char of Object.keys(node.children)) {
+      const newWords = this.getAllWords(`${prefix}${char}`, node.children[char]);
+      words = words.concat(newWords);
+    }
+
+    return words;
+  }
+
+  /**
+   * Return a list of words matching the prefix
+   * @param {*} prefix - The prefix to match.
+   * @returns {string[]}
+   */
+  autocomplete(prefix = '') {
+    const curr = this.searchNode(prefix);
+    return this.getAllWords(prefix, curr);
+  }
+}
+
+// Aliases
+Trie.prototype.add = Trie.prototype.insert;
+
+module.exports = Trie;
diff --git a/src/data-structures/trees/trie.js b/src/data-structures/trees/trie.js
new file mode 100644
index 00000000..77fa5b57
--- /dev/null
+++ b/src/data-structures/trees/trie.js
@@ -0,0 +1,132 @@
+class Trie {
+  constructor(val) {
+    this.val = val;
+    this.children = {};
+    this.isWord = false;
+  }
+
+  /**
+   * Insert word into trie and mark last element as such.
+   * @param {string} word
+   * @return {undefined}
+   */
+  insert(word) {
+    let curr = this;
+
+    for (const char of word) {
+      curr.children[char] = curr.children[char] || new Trie(char);
+      curr = curr.children[char];
+    }
+
+    curr.isWord = true;
+  }
+
+  /**
+   * Return true if found the word to be removed, otherwise false.
+   * @param {string} word - The word to remove
+   * @returns {boolean}
+   */
+  remove(word) {
+    let curr = this;
+    // let lastWordToKeep = 0;
+    const stack = [curr];
+
+    // find word and stack path
+    for (const char of word) {
+      if (!curr.children[char]) { return false; }
+      // lastWordToKeep += 1;
+      curr = curr.children[char];
+      stack.push(curr);
+    }
+
+    let child = stack.pop();
+    child.isWord = false;
+
+    // remove non words without children
+    while (stack.length) {
+      const parent = stack.pop();
+      if (!child.isWord && !Object.keys(child.children).length) {
+        delete parent.children[child.val];
+      }
+      child = parent;
+    }
+
+    return true;
+  }
+
+  /**
+   * Retun last node that matches word or prefix or false if not found.
+   * @param {string} word - Word to search.
+   * @param {boolean} options.partial - Whether or not match partial matches.
+   * @return {Trie|false}
+   */
+  searchNode(word) {
+    let curr = this;
+
+    for (const char of word) {
+      if (!curr.children[char]) { return false; }
+      curr = curr.children[char];
+    }
+
+    return curr;
+  }
+
+  /**
+   * Search for complete word (by default) or partial if flag is set.
+   * @param {string} word - Word to search.
+   * @param {boolean} options.partial - Whether or not match partial matches.
+   * @return {boolean}
+   */
+  search(word, { partial } = {}) {
+    const curr = this.searchNode(word);
+    if (!curr) { return false; }
+    return partial ? true : curr.isWord;
+  }
+
+  /**
+   * Return true if any word on the trie starts with the given prefix
+   * @param {string} prefix - Partial word to search.
+   * @return {boolean}
+   */
+  startsWith(prefix) {
+    return this.search(prefix, { partial: true });
+  }
+
+  /**
+   * Returns all the words from the current `node`.
+   * Uses backtracking.
+   *
+   * @param {string} prefix - The prefix to append to each word.
+   * @param {string} node - Current node to start backtracking.
+   */
+  getAllWords(prefix = '', node = this) {
+    let words = [];
+
+    if (!node) { return words; }
+    if (node.isWord) {
+      words.push(prefix);
+    }
+
+    for (const char of Object.keys(node.children)) {
+      const newWords = this.getAllWords(`${prefix}${char}`, node.children[char]);
+      words = words.concat(newWords);
+    }
+
+    return words;
+  }
+
+  /**
+   * Return a list of words matching the prefix
+   * @param {*} prefix - The prefix to match.
+   * @returns {string[]}
+   */
+  autocomplete(prefix = '') {
+    const curr = this.searchNode(prefix);
+    return this.getAllWords(prefix, curr);
+  }
+}
+
+// Aliases
+Trie.prototype.add = Trie.prototype.insert;
+
+module.exports = Trie;
diff --git a/src/data-structures/trees/trie.spec.js b/src/data-structures/trees/trie.spec.js
new file mode 100644
index 00000000..fca6588e
--- /dev/null
+++ b/src/data-structures/trees/trie.spec.js
@@ -0,0 +1,188 @@
+const Trie = require('./trie');
+
+describe('Trie', () => {
+  let trie;
+
+  beforeEach(() => {
+    trie = new Trie();
+  });
+
+  describe('construtor', () => {
+    it('should initialize trie', () => {
+      expect(trie).toBeDefined();
+    });
+
+    it('should set default value to undefined', () => {
+      expect(trie.val).toEqual(undefined);
+    });
+
+    it('should initialization value', () => {
+      trie = new Trie(1);
+      expect(trie.val).toEqual(1);
+    });
+
+    it('should initialize children as empty map', () => {
+      expect(trie.children).toEqual({});
+    });
+
+    it('should not be a word by default', () => {
+      expect(trie.isWord).toEqual(false);
+    });
+  });
+
+  describe('insert', () => {
+    it('should insert a word', () => {
+      trie.insert('ab');
+      expect(trie.children.a).toBeDefined();
+      expect(trie.children.a.children.b).toBeDefined();
+      expect(trie.children.a.isWord).toEqual(false);
+      expect(trie.children.a.children.b.isWord).toEqual(true);
+    });
+
+    it('should insert multiple words with the same root', () => {
+      trie.insert('a');
+      trie.insert('ab');
+      expect(trie.children.a.isWord).toEqual(true);
+      expect(trie.children.a.children.b.isWord).toEqual(true);
+    });
+  });
+
+  describe('search & startsWith', () => {
+    beforeEach(() => {
+      trie.insert('dog');
+      trie.insert('dogs');
+      trie.insert('door');
+    });
+
+    it('should search for words', () => {
+      expect(trie.search('dog')).toEqual(true);
+    });
+
+    it('should not match incomplete words by default', () => {
+      expect(trie.search('do')).toEqual(false);
+    });
+
+    it('should match partial words if partial is set', () => {
+      expect(trie.search('do', {
+        partial: true,
+      })).toEqual(true);
+      expect(trie.startsWith('do')).toEqual(true);
+    });
+
+    it('should match full words if partial is set', () => {
+      expect(trie.search('dogs', {
+        partial: true,
+      })).toEqual(true);
+      expect(trie.startsWith('dogs')).toEqual(true);
+    });
+
+    it('should not match non existing words', () => {
+      expect(trie.search('doors')).toEqual(false);
+    });
+
+    it('should not match non existing words with partials', () => {
+      expect(trie.search('doors', {
+        partial: true,
+      })).toEqual(false);
+      expect(trie.startsWith('doors')).toEqual(false);
+    });
+  });
+
+  describe('when multiple words are inserted', () => {
+    beforeEach(() => {
+      trie.insert('dog');
+      trie.insert('dogs');
+      trie.insert('door');
+      trie.insert('day');
+      trie.insert('cat');
+    });
+
+    describe('getAllWords', () => {
+      it('should get all words', () => {
+        const words = trie.getAllWords();
+        expect(words.length).toEqual(5);
+        expect(words).toEqual(['dog', 'dogs', 'door', 'day', 'cat']);
+      });
+
+      it('should use prefix', () => {
+        const words = trie.getAllWords("Adrian's ");
+        expect(words.length).toEqual(5);
+        expect(words).toEqual([
+          "Adrian's dog",
+          "Adrian's dogs",
+          "Adrian's door",
+          "Adrian's day",
+          "Adrian's cat",
+        ]);
+      });
+    });
+
+    describe('autocomplete', () => {
+      it('should return all words if not prefix is given', () => {
+        const words = trie.autocomplete();
+        expect(words.length).toBe(5);
+        expect(words).toEqual(['dog', 'dogs', 'door', 'day', 'cat']);
+      });
+
+      it('should auto complete words given a prefix', () => {
+        const words = trie.autocomplete('do');
+        expect(words.length).toBe(3);
+        expect(words).toEqual(['dog', 'dogs', 'door']);
+      });
+
+      it('should handle non-existing words prefixes', () => {
+        const words = trie.autocomplete('co');
+        expect(words.length).toBe(0);
+        expect(words).toEqual([]);
+      });
+    });
+
+    describe('remove', () => {
+      it('should remove a word', () => {
+        trie = new Trie();
+        trie.insert('a');
+        expect(trie.remove('a')).toEqual(true);
+        expect(trie.getAllWords()).toEqual([]);
+      });
+
+      it('should remove word and keep other words', () => {
+        trie = new Trie();
+        trie.insert('a');
+        trie.insert('ab');
+        expect(trie.remove('a')).toEqual(true);
+        expect(trie.getAllWords()).toEqual(['ab']);
+      });
+
+      it('should remove surrounding word', () => {
+        trie = new Trie();
+        trie.insert('a');
+        trie.insert('ab');
+        expect(trie.remove('ab')).toEqual(true);
+        expect(trie.getAllWords()).toEqual(['a']);
+      });
+
+      it('should return false when word is not found', () => {
+        expect(trie.remove('not there')).toBe(false);
+      });
+
+      it('should remove words in between and still match', () => {
+        expect(trie.remove('dog')).toBe(true);
+        expect(trie.search('dogs')).toBe(true);
+        expect(trie.startsWith('dog')).toBe(true);
+        expect(trie.getAllWords()).toEqual([
+          'dogs', 'door', 'day', 'cat',
+        ]);
+      });
+
+      it('should remove word and no longer match partials', () => {
+        expect(trie.remove('dogs')).toBe(true);
+        expect(trie.search('dogs')).toBe(false);
+        expect(trie.search('dog')).toBe(true);
+        expect(trie.startsWith('dog')).toBe(true);
+        expect(trie.getAllWords()).toEqual([
+          'dog', 'door', 'day', 'cat',
+        ]);
+      });
+    });
+  });
+});
diff --git a/src/data-structures/zz-mixed-ds/lru-cache-1.js b/src/data-structures/zz-mixed-ds/lru-cache-1.js
deleted file mode 100644
index d6453b5d..00000000
--- a/src/data-structures/zz-mixed-ds/lru-cache-1.js
+++ /dev/null
@@ -1,73 +0,0 @@
-
-/**
- * Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.
-
-    get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
-    put(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.
-
-    Follow up:
-    Could you do both operations in O(1) time complexity?
-
-    Example:
-
-    LRUCache cache = new LRUCache( 2);
-
-    cache.put(1, 1);
-    cache.put(2, 2);
-    cache.get(1);       // returns 1
-    cache.put(3, 3);    // evicts key 2
-    cache.get(2);       // returns -1 (not found)
-    cache.put(4, 4);    // evicts key 1
-    cache.get(1);       // returns -1 (not found)
-    cache.get(3);       // returns 3
-    cache.get(4);       // returns 4
-
- *  https://leetcode.com/problems/lru-cache/description/
- *
- * @param {number} capacity
- */
-const LRUCache = function (capacity) {
-  this.map = new Map();
-  this.capacity = capacity;
-  this.cache = [];
-};
-
-/**
-* @param {number} key
-* @return {number}
-*/
-LRUCache.prototype.get = function (key) {
-  const value = this.map.get(key);
-  if (value) {
-    this.moveToTop(key);
-    return value;
-  }
-  return -1;
-};
-
-/**
-* @param {number} key
-* @param {number} value
-* @return {void}
-*/
-LRUCache.prototype.put = function (key, value) {
-  this.map.set(key, value);
-  this.rotate(key);
-};
-
-LRUCache.prototype.rotate = function (key) {
-  this.moveToTop(key);
-  while (this.cache.length > this.capacity) {
-    const keyToDelete = this.cache.shift();
-    this.map.delete(keyToDelete);
-  // console.log({keyToDelete})
-  }
-};
-
-LRUCache.prototype.moveToTop = function (key) {
-  const index = this.cache.indexOf(key);
-  if (index > -1) {
-    this.cache.splice(index, 1);
-  }
-  this.cache.push(key);
-};
diff --git a/src/data-structures/zz-mixed-ds/lru-cache.js b/src/data-structures/zz-mixed-ds/lru-cache.js
deleted file mode 100644
index 817f3c1b..00000000
--- a/src/data-structures/zz-mixed-ds/lru-cache.js
+++ /dev/null
@@ -1,72 +0,0 @@
-
-/**
- * Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.
-
-    get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
-    put(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.
-
-    Follow up:
-    Could you do both operations in O(1) time complexity?
-
-    Example:
-
-    LRUCache cache = new LRUCache( 2);
-
-    cache.put(1, 1);
-    cache.put(2, 2);
-    cache.get(1);       // returns 1
-    cache.put(3, 3);    // evicts key 2
-    cache.get(2);       // returns -1 (not found)
-    cache.put(4, 4);    // evicts key 1
-    cache.get(1);       // returns -1 (not found)
-    cache.get(3);       // returns 3
-    cache.get(4);       // returns 4
-
- *  https://leetcode.com/problems/lru-cache/description/
- *  https://leetcode.com/submissions/detail/178329173/
- *
- * @param {number} capacity
- */
-const LRUCache = function (capacity) {
-  this.map = new Map();
-  this.capacity = capacity;
-};
-
-/**
-* @param {number} key
-* @return {number}
-*/
-LRUCache.prototype.get = function (key) {
-  const value = this.map.get(key);
-  if (value) {
-    this.moveToTop(key);
-    return value;
-  }
-  return -1;
-};
-
-/**
-* @param {number} key
-* @param {number} value
-* @return {void}
-*/
-LRUCache.prototype.put = function (key, value) {
-  this.map.set(key, value);
-  this.rotate(key);
-};
-
-LRUCache.prototype.rotate = function (key) {
-  this.moveToTop(key);
-  while (this.map.size > this.capacity) {
-    const it = this.map.keys();
-    this.map.delete(it.next().value);
-  }
-};
-
-LRUCache.prototype.moveToTop = function (key) {
-  if (this.map.has(key)) {
-    const value = this.map.get(key);
-    this.map.delete(key);
-    this.map.set(key, value);
-  }
-};
diff --git a/src/index.js b/src/index.js
new file mode 100644
index 00000000..449b034f
--- /dev/null
+++ b/src/index.js
@@ -0,0 +1,58 @@
+// data structures
+const LinkedList = require('./data-structures/linked-lists/linked-list');
+const ListNode = require('./data-structures/linked-lists/node');
+const Queue = require('./data-structures/queues/queue');
+const Stack = require('./data-structures/stacks/stack');
+const Graph = require('./data-structures/graphs/graph');
+const HashMap = require('./data-structures/maps/hash-maps/hash-map');
+const TreeMap = require('./data-structures/maps/tree-maps/tree-map');
+const HashSet = require('./data-structures/sets/hash-set');
+const TreeSet = require('./data-structures/sets/tree-set');
+const ArraySet = require('./data-structures/sets/array-set');
+const MapSet = require('./data-structures/sets/map-set');
+const BinarySearchTree = require('./data-structures/trees/binary-search-tree');
+const BinaryTreeNode = require('./data-structures/trees/binary-tree-node');
+const AvlTree = require('./data-structures/trees/avl-tree');
+const RedBlackTree = require('./data-structures/trees/red-black-tree');
+const LRUCache = require('./data-structures/custom/lru-cache');
+const Trie = require('./data-structures/trees/trie');
+const Heap = require('./data-structures/heaps/heap');
+const MinHeap = require('./data-structures/heaps/min-heap');
+const MaxHeap = require('./data-structures/heaps/max-heap');
+const PriorityQueue = require('./data-structures/heaps/priority-queue');
+
+// algorithms
+const bubbleSort = require('./algorithms/sorting/bubble-sort');
+const insertionSort = require('./algorithms/sorting/insertion-sort');
+const selectionSort = require('./algorithms/sorting/selection-sort');
+const quickSort = require('./algorithms/sorting/quick-sort');
+const mergeSort = require('./algorithms/sorting/merge-sort');
+
+module.exports = {
+  LinkedList,
+  ListNode,
+  Queue,
+  Stack,
+  Graph,
+  HashMap,
+  TreeMap,
+  HashSet,
+  TreeSet,
+  ArraySet,
+  MapSet,
+  BinarySearchTree,
+  BinaryTreeNode,
+  AvlTree,
+  RedBlackTree,
+  LRUCache,
+  Trie,
+  Heap,
+  MinHeap,
+  MaxHeap,
+  PriorityQueue,
+  bubbleSort,
+  insertionSort,
+  selectionSort,
+  quickSort,
+  mergeSort,
+};
diff --git a/src/runtimes/01-is-empty.js b/src/runtimes/01-is-empty.js
index f15d9ef8..5c095ff0 100644
--- a/src/runtimes/01-is-empty.js
+++ b/src/runtimes/01-is-empty.js
@@ -1,5 +1,6 @@
 /**
  * Return true if an array is empty and false otherwise
+ * Check for objects keys as well
  * @param {array|string|object} thing
  * @example
  *      isEmpty() // => true
@@ -30,4 +31,7 @@ function isEmpty(thing) {
 }
 // end::isEmpty[]
 
-module.exports = isEmpty;
+module.exports = {
+  isEmpty,
+  isEmpty2,
+};
diff --git a/src/runtimes/02-binary-search.js b/src/runtimes/02-binary-search.js
index 4a72c859..c8118160 100644
--- a/src/runtimes/02-binary-search.js
+++ b/src/runtimes/02-binary-search.js
@@ -18,9 +18,11 @@ function binarySearchRecursive(array, search, offset = 0) {
 
   if (current === search) {
     return offset + half;
-  } else if (array.length < 2) {
+  }
+  if (array.length === 1) {
     return -1;
-  } else if (search > current) {
+  }
+  if (search > current) {
     const right = array.slice(half);
     return binarySearchRecursive(right, search, offset + half);
   }
@@ -38,48 +40,24 @@ function binarySearchRecursive(array, search, offset = 0) {
  * @param {string|number} search value to search for
  */
 function binarySearchIterative(array, search) {
-  // console.log('binarySearchIterative', {array, search});
   let start = 0;
-  let end = array.length;
-  const half = () => parseInt((end - start) / 2, 10) + start;
+  let end = array.length - 1;
+  const half = () => start + parseInt((end - start) / 2, 10);
 
-  while (end - start > 0) {
+  while (start <= end) {
     const currentIndex = half();
     const current = array[currentIndex];
 
-    if (current === search) {
-      return currentIndex;
-    } else if (search > current) {
-      start = currentIndex;
+    if (current === search) return currentIndex;
+
+    if (search > current) {
+      start = currentIndex + 1;
     } else if (search < current) {
-      end = currentIndex;
+      end = currentIndex - 1;
     }
   }
 
   return -1;
 }
 
-// const binarySearch = binarySearchRecursive;
-const binarySearch = binarySearchIterative;
-
-function test() {
-  const directory = ['Adrian', 'Bella', 'Charlotte', 'Daniel', 'Emma', 'Hanna', 'Isabella', 'Jayden', 'Kaylee', 'Luke', 'Mia', 'Nora', 'Olivia', 'Paisley', 'Riley', 'Thomas', 'Wyatt', 'Xander', 'Zoe'];
-
-  const assert = require('assert');
-  assert.equal(binarySearch([], 'not found'), -1);
-  assert.equal(binarySearch([1], 2), -1);
-  assert.equal(binarySearch([1], 1), 0);
-  assert.equal(binarySearch([1, 2, 3], 1), 0);
-  assert.equal(binarySearch([1, 2, 3], 2), 1);
-  assert.equal(binarySearch([1, 2, 3], 3), 2);
-  assert.equal(binarySearch([1, 2, 3], 31), -1);
-  assert.equal(binarySearch(directory, 'Adrian'), 0);
-  assert.equal(binarySearch(directory, 'Hanna'), 5);
-  assert.equal(binarySearch(directory, 'Zoe'), 18);
-  assert.equal(binarySearch(directory, 'not found'), -1);
-}
-
-test();
-
-
-module.exports = binarySearch;
+module.exports = { binarySearchIterative, binarySearchRecursive };
diff --git a/src/runtimes/02-binary-search.spec.js b/src/runtimes/02-binary-search.spec.js
new file mode 100644
index 00000000..1e8f8f56
--- /dev/null
+++ b/src/runtimes/02-binary-search.spec.js
@@ -0,0 +1,39 @@
+const {
+  binarySearchRecursive,
+  binarySearchIterative,
+} = require("./02-binary-search");
+
+const binarySearchImplementations = [
+  binarySearchRecursive,
+  binarySearchIterative,
+];
+
+binarySearchImplementations.forEach((binarySearchImpl) => {
+  describe(binarySearchImpl.name, () => {
+    let array;
+
+    beforeEach(() => {
+      array = [7, 9, 13, 23];
+    });
+
+    it("should find a middle element", () => {
+      expect(binarySearchImpl(array, 9)).toEqual(1);
+    });
+
+    it("should find an first element", () => {
+      expect(binarySearchImpl(array, 7)).toEqual(0);
+    });
+
+    it("should find the last element", () => {
+      expect(binarySearchImpl(array, 23)).toEqual(3);
+    });
+
+    it("should not find an bigger element", () => {
+      expect(binarySearchImpl(array, 9000)).toEqual(-1);
+    });
+
+    it("should find a smaller element", () => {
+      expect(binarySearchImpl(array, -9)).toEqual(-1);
+    });
+  });
+});
diff --git a/src/runtimes/04-merge-sort.js b/src/runtimes/04-merge-sort.js
index 0279161f..fe7c87a9 100644
--- a/src/runtimes/04-merge-sort.js
+++ b/src/runtimes/04-merge-sort.js
@@ -5,22 +5,18 @@ const assert = require('assert');
  * Merge two arrays in asc order
  * @example
  *    merge([2,5,9], [1,6,7]) => [1, 2, 5, 6, 7, 9]
- * @param {array} array1
- * @param {array} array2
+ * @param {array} a
+ * @param {array} b
  * @returns {array} merged arrays in asc order
  */
-function merge(array1 = [], array2 = []) {
+function merge(a = [], b = []) {
   const merged = [];
-  let array1Index = 0;
-  let array2Index = 0;
   // merge elements on a and b in asc order. Run-time O(a + b)
-  while (array1Index < array1.length || array2Index < array2.length) {
-    if (array1Index >= array1.length || array1[array1Index] > array2[array2Index]) {
-      merged.push(array2[array2Index]);
-      array2Index += 1;
+  for (let ai = 0, bi = 0; ai < a.length || bi < b.length;) {
+    if (ai >= a.length || a[ai] > b[bi]) {
+      merged.push(b[bi++]);
     } else {
-      merged.push(array1[array1Index]);
-      array1Index += 1;
+      merged.push(a[ai++]);
     }
   }
   return merged;
@@ -46,7 +42,7 @@ function sort(array = []) {
     return array[0] > array[1] ? [array[1], array[0]] : array;
   }
   // slit and merge
-  const mid = size / 2;
+  const mid = parseInt(size / 2, 10);
   return merge(sort(array.slice(0, mid)), sort(array.slice(mid)));
 }
 // end::sort[]
@@ -59,3 +55,4 @@ assert.deepStrictEqual(sort(), []);
 
 assert.deepStrictEqual(merge([2, 5, 9], [1, 6, 7]), [1, 2, 5, 6, 7, 9]);
 assert.deepStrictEqual(merge(), []);
+assert.deepStrictEqual(merge([3, 5, 7], [2, 4]), [2, 3, 4, 5, 7]);
diff --git a/src/runtimes/06-multi-variable-equation-solver.js b/src/runtimes/06-multi-variable-equation-solver.js
index 4470eb50..a2373ea1 100644
--- a/src/runtimes/06-multi-variable-equation-solver.js
+++ b/src/runtimes/06-multi-variable-equation-solver.js
@@ -19,7 +19,7 @@ function findXYZ({ start = 0, end = 10 } = {}) {
   for (let x = start; x < end; x++) {
     for (let y = start; y < end; y++) {
       for (let z = start; z < end; z++) {
-        if (3 * x + 9 * y + 8 * z === 79) {
+        if (3 * x + 9 * y + 8 * z === 79) { // eslint-disable-line
           solutions.push({ x, y, z });
         }
       }
diff --git a/src/runtimes/07-sub-sets.js b/src/runtimes/07-sub-sets.js
index f5c24262..f8da0e5b 100644
--- a/src/runtimes/07-sub-sets.js
+++ b/src/runtimes/07-sub-sets.js
@@ -18,7 +18,7 @@ function findSubsets(n = '') {
   const base = ['']; // <1>
 
   const results = array.reduce((previous, element) => {
-    const previousPlusElement = previous.map(el => `${el}${element}`); // <2>
+    const previousPlusElement = previous.map((el) => `${el}${element}`); // <2>
     return previous.concat(previousPlusElement); // <3>
   }, base);