MongoDB support

The MongoDB support contains a wide range of features which are summarized below.

Spring configuration support using Java based @Configuration classes or an XML namespace for a Mongo driver instance and replica sets
MongoTemplate helper class that increases productivity performing common Mongo operations. Includes integrated object mapping between documents and POJOs.
Exception translation into Spring’s portable Data Access Exception hierarchy
Feature Rich Object Mapping integrated with Spring’s Conversion Service
Annotation based mapping metadata but extensible to support other metadata formats
Persistence and mapping lifecycle events
Java based Query, Criteria, and Update DSLs
Automatic implementation of Repository interfaces including support for custom finder methods.
QueryDSL integration to support type-safe queries.
Cross-store persistence - support for JPA Entities with fields transparently persisted/retrieved using MongoDB (deprecated - will be removed without replacement)
GeoSpatial integration

For most tasks you will find yourself using MongoTemplate or the Repository support that both leverage the rich mapping functionality. MongoTemplate is the place to look for accessing functionality such as incrementing counters or ad-hoc CRUD operations. MongoTemplate also provides callback methods so that it is easy for you to get a hold of the low level API artifacts such as com.mongo.DB to communicate directly with MongoDB. The goal with naming conventions on various API artifacts is to copy those in the base MongoDB Java driver so you can easily map your existing knowledge onto the Spring APIs.

Getting Started

Spring MongoDB support requires MongoDB 2.6 or higher and Java SE 8 or higher. An easy way to bootstrap setting up a working environment is to create a Spring based project in STS.

First you need to set up a running Mongodb server. Refer to the Mongodb Quick Start guide for an explanation on how to startup a MongoDB instance. Once installed starting MongoDB is typically a matter of executing the following command: MONGO_HOME/bin/mongod

To create a Spring project in STS go to File → New → Spring Template Project → Simple Spring Utility Project → press Yes when prompted. Then enter a project and a package name such as org.spring.mongodb.example.

Then add the following to pom.xml dependencies section.

<dependencies>

  <!-- other dependency elements omitted -->

  <dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-mongodb</artifactId>
    <version>{version}</version>
  </dependency>

</dependencies>

Also change the version of Spring in the pom.xml to be

<spring.framework.version>{springVersion}</spring.framework.version>

You will also need to add the location of the Spring Milestone repository for maven to your pom.xml which is at the same level of your <dependencies/> element

<repositories>
  <repository>
    <id>spring-milestone</id>
    <name>Spring Maven MILESTONE Repository</name>
    <url>http://repo.spring.io/libs-milestone</url>
  </repository>
</repositories>

The repository is also browseable here.

You may also want to set the logging level to DEBUG to see some additional information, edit the log4j.properties file to have

log4j.category.org.springframework.data.mongodb=DEBUG
log4j.appender.stdout.layout.ConversionPattern=%d{ABSOLUTE} %5p %40.40c:%4L - %m%n

Create a simple Person class to persist:

package org.spring.mongodb.example;

public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String name, int age) {
    this.name = name;
    this.age = age;
  }

  public String getId() {
    return id;
  }
  public String getName() {
    return name;
  }
  public int getAge() {
    return age;
  }

  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
  }
}

And a main application to run

package org.spring.mongodb.example;

import static org.springframework.data.mongodb.core.query.Criteria.where;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.springframework.data.mongodb.core.MongoOperations;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.query.Query;

import com.mongodb.MongoClient;

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) throws Exception {

    MongoOperations mongoOps = new MongoTemplate(new MongoClient(), "database");
    mongoOps.insert(new Person("Joe", 34));

    log.info(mongoOps.findOne(new Query(where("name").is("Joe")), Person.class));

    mongoOps.dropCollection("person");
  }
}

This will produce the following output

10:01:32,062 DEBUG apping.MongoPersistentEntityIndexCreator:  80 - Analyzing class class org.spring.example.Person for index information.
10:01:32,265 DEBUG ramework.data.mongodb.core.MongoTemplate: 631 - insert Document containing fields: [_class, age, name] in collection: Person
10:01:32,765 DEBUG ramework.data.mongodb.core.MongoTemplate:1243 - findOne using query: { "name" : "Joe"} in db.collection: database.Person
10:01:32,953  INFO      org.spring.mongodb.example.MongoApp:  25 - Person [id=4ddbba3c0be56b7e1b210166, name=Joe, age=34]
10:01:32,984 DEBUG ramework.data.mongodb.core.MongoTemplate: 375 - Dropped collection [database.person]

Even in this simple example, there are few things to take notice of

You can instantiate the central helper class of Spring Mongo, MongoTemplate, using the standard com.mongodb.MongoClient object and the name of the database to use.
The mapper works against standard POJO objects without the need for any additional metadata (though you can optionally provide that information. See here.).
Conventions are used for handling the id field, converting it to be a ObjectId when stored in the database.
Mapping conventions can use field access. Notice the Person class has only getters.
If the constructor argument names match the field names of the stored document, they will be used to instantiate the object

Examples Repository

There is an github repository with several examples that you can download and play around with to get a feel for how the library works.

Connecting to MongoDB with Spring

One of the first tasks when using MongoDB and Spring is to create a com.mongodb.MongoClient object using the IoC container. There are two main ways to do this, either using Java based bean metadata or XML based bean metadata. These are discussed in the following sections.

Note	For those not familiar with how to configure the Spring container using Java based bean metadata instead of XML based metadata see the high level introduction in the reference docs here as well as the detailed documentation here.

Registering a Mongo instance using Java based metadata

An example of using Java based bean metadata to register an instance of a com.mongodb.MongoClient is shown below

Example 1. Registering a com.mongodb.MongoClient object using Java based bean metadata

@Configuration
public class AppConfig {

  /*
   * Use the standard Mongo driver API to create a com.mongodb.MongoClient instance.
   */
   public @Bean MongoClient mongoClient() {
       return new MongoClient("localhost");
   }
}

This approach allows you to use the standard com.mongodb.MongoClient instance with the container using Spring’s MongoClientFactoryBean. As compared to instantiating a com.mongodb.MongoClient instance directly, the FactoryBean has the added advantage of also providing the container with an ExceptionTranslator implementation that translates MongoDB exceptions to exceptions in Spring’s portable DataAccessException hierarchy for data access classes annotated with the @Repository annotation. This hierarchy and use of @Repository is described in Spring’s DAO support features.

An example of a Java based bean metadata that supports exception translation on @Repository annotated classes is shown below:

Example 2. Registering a com.mongodb.MongoClient object using Spring’s MongoClientFactoryBean and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

    /*
     * Factory bean that creates the com.mongodb.MongoClient instance
     */
     public @Bean MongoClientFactoryBean mongo() {
          MongoClientFactoryBean mongo = new MongoClientFactoryBean();
          mongo.setHost("localhost");
          return mongo;
     }
}

To access the com.mongodb.MongoClient object created by the MongoClientFactoryBean in other @Configuration or your own classes, use a “private @Autowired Mongo mongo;” field.

Registering a Mongo instance using XML based metadata

While you can use Spring’s traditional <beans/> XML namespace to register an instance of com.mongodb.MongoClient with the container, the XML can be quite verbose as it is general purpose. XML namespaces are a better alternative to configuring commonly used objects such as the Mongo instance. The mongo namespace allows you to create a Mongo instance server location, replica-sets, and options.

To use the Mongo namespace elements you will need to reference the Mongo schema:

Example 3. XML schema to configure MongoDB

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xmlns:context="http://www.springframework.org/schema/context"
          xmlns:mongo="http://www.springframework.org/schema/data/mongo"
          xsi:schemaLocation=
          "http://www.springframework.org/schema/context
          http://www.springframework.org/schema/context/spring-context-3.0.xsd
          *http://www.springframework.org/schema/data/mongo http://www.springframework.org/schema/data/mongo/spring-mongo-1.0.xsd*
          http://www.springframework.org/schema/beans
          http://www.springframework.org/schema/beans/spring-beans-3.0.xsd">

    <!-- Default bean name is 'mongo' -->
    *<mongo:mongo-client host="localhost" port="27017"/>*

</beans>

A more advanced configuration with MongoClientOptions is shown below (note these are not recommended values)

Example 4. XML schema to configure a com.mongodb.MongoClient object with MongoClientOptions

<beans>

  <mongo:mongo-client host="localhost" port="27017">
    <mongo:client-options connections-per-host="8"
                   threads-allowed-to-block-for-connection-multiplier="4"
                   connect-timeout="1000"
                   max-wait-time="1500}"
                   auto-connect-retry="true"
                   socket-keep-alive="true"
                   socket-timeout="1500"
                   slave-ok="true"
                   write-number="1"
                   write-timeout="0"
                   write-fsync="true"/>
  </mongo:mongo-client>

</beans>

A configuration using replica sets is shown below.

Example 5. XML schema to configure com.mongodb.MongoClient object with Replica Sets

<mongo:mongo-client id="replicaSetMongo" replica-set="127.0.0.1:27017,localhost:27018"/>

The MongoDbFactory interface

While com.mongodb.MongoClient is the entry point to the MongoDB driver API, connecting to a specific MongoDB database instance requires additional information such as the database name and an optional username and password. With that information you can obtain a com.mongodb.DB object and access all the functionality of a specific MongoDB database instance. Spring provides the org.springframework.data.mongodb.core.MongoDbFactory interface shown below to bootstrap connectivity to the database.

public interface MongoDbFactory {

  MongoDatabase getDb() throws DataAccessException;

  MongoDatabase getDb(String dbName) throws DataAccessException;
}

The following sections show how you can use the container with either Java or the XML based metadata to configure an instance of the MongoDbFactory interface. In turn, you can use the MongoDbFactory instance to configure MongoTemplate.

Instead of using the IoC container to create an instance of MongoTemplate, you can just use them in standard Java code as shown below.

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) throws Exception {

    MongoOperations mongoOps = new MongoTemplate(*new SimpleMongoDbFactory(new MongoClient(), "database")*);

    mongoOps.insert(new Person("Joe", 34));

    log.info(mongoOps.findOne(new Query(where("name").is("Joe")), Person.class));

    mongoOps.dropCollection("person");
  }
}

The code in bold highlights the use of SimpleMongoDbFactory and is the only difference between the listing shown in the getting started section.

Registering a MongoDbFactory instance using Java based metadata

To register a MongoDbFactory instance with the container, you write code much like what was highlighted in the previous code listing. A simple example is shown below

@Configuration
public class MongoConfiguration {

  public @Bean MongoDbFactory mongoDbFactory() {
    return new SimpleMongoDbFactory(new MongoClient(), "database");
  }
}

MongoDB Server generation 3 changed the authentication model when connecting to the DB. Therefore some of the configuration options available for authentication are no longer valid. Please use the MongoClient specific options for setting credentials via MongoCredential to provide authentication data.

@Configuration
public class ApplicationContextEventTestsAppConfig extends AbstractMongoConfiguration {

  @Override
  public String getDatabaseName() {
    return "database";
  }

  @Override
  @Bean
  public MongoClient mongoClient() {
    return new MongoClient(singletonList(new ServerAddress("127.0.0.1", 27017)),
      singletonList(MongoCredential.createCredential("name", "db", "pwd".toCharArray())));
  }
}

In order to use authentication with XML configuration use the credentials attribue on <mongo-client>.

Note

Username/password credentials used in XML configuration must be URL encoded when these contain reserved characters such as :, %, @, ,. Example: m0ng0@dmin:mo_res:bw6},Qsdxx@admin@database → m0ng0%40dmin:mo_res%3Abw6%7D%2CQsdxx%40admin@database See section 2.2 of RFC 3986 for further details.

Registering a MongoDbFactory instance using XML based metadata

The mongo namespace provides a convenient way to create a SimpleMongoDbFactory as compared to using the <beans/> namespace. Simple usage is shown below

<mongo:db-factory dbname="database">

If you need to configure additional options on the com.mongodb.MongoClient instance that is used to create a SimpleMongoDbFactory you can refer to an existing bean using the mongo-ref attribute as shown below. To show another common usage pattern, this listing shows the use of a property placeholder to parametrise the configuration and creating MongoTemplate.

<context:property-placeholder location="classpath:/com/myapp/mongodb/config/mongo.properties"/>

<mongo:mongo-client host="${mongo.host}" port="${mongo.port}">
  <mongo:client-options
     connections-per-host="${mongo.connectionsPerHost}"
     threads-allowed-to-block-for-connection-multiplier="${mongo.threadsAllowedToBlockForConnectionMultiplier}"
     connect-timeout="${mongo.connectTimeout}"
     max-wait-time="${mongo.maxWaitTime}"
     auto-connect-retry="${mongo.autoConnectRetry}"
     socket-keep-alive="${mongo.socketKeepAlive}"
     socket-timeout="${mongo.socketTimeout}"
     slave-ok="${mongo.slaveOk}"
     write-number="1"
     write-timeout="0"
     write-fsync="true"/>
</mongo:mongo-client>

<mongo:db-factory dbname="database" mongo-ref="mongoClient"/>

<bean id="anotherMongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
  <constructor-arg name="mongoDbFactory" ref="mongoDbFactory"/>
</bean>

Introduction to MongoTemplate

The class MongoTemplate, located in the package org.springframework.data.mongodb.core, is the central class of the Spring’s MongoDB support providing a rich feature set to interact with the database. The template offers convenience operations to create, update, delete and query for MongoDB documents and provides a mapping between your domain objects and MongoDB documents.

Note	Once configured, `MongoTemplate` is thread-safe and can be reused across multiple instances.

The mapping between MongoDB documents and domain classes is done by delegating to an implementation of the interface MongoConverter. Spring provides the MappingMongoConverter, but you can also write your own converter. Please refer to the section on MongoConverters for more detailed information.

The MongoTemplate class implements the interface MongoOperations. In as much as possible, the methods on MongoOperations are named after methods available on the MongoDB driver Collection object to make the API familiar to existing MongoDB developers who are used to the driver API. For example, you will find methods such as "find", "findAndModify", "findOne", "insert", "remove", "save", "update" and "updateMulti". The design goal was to make it as easy as possible to transition between the use of the base MongoDB driver and MongoOperations. A major difference in between the two APIs is that MongoOperations can be passed domain objects instead of Document and there are fluent APIs for Query, Criteria, and Update operations instead of populating a Document to specify the parameters for those operations.

Note	The preferred way to reference the operations on `MongoTemplate` instance is via its interface `MongoOperations`.

The default converter implementation used by MongoTemplate is MappingMongoConverter. While the MappingMongoConverter can make use of additional metadata to specify the mapping of objects to documents it is also capable of converting objects that contain no additional metadata by using some conventions for the mapping of IDs and collection names. These conventions as well as the use of mapping annotations is explained in the Mapping chapter.

Another central feature of MongoTemplate is exception translation of exceptions thrown in the MongoDB Java driver into Spring’s portable Data Access Exception hierarchy. Refer to the section on exception translation for more information.

While there are many convenience methods on MongoTemplate to help you easily perform common tasks if you should need to access the MongoDB driver API directly to access functionality not explicitly exposed by the MongoTemplate you can use one of several Execute callback methods to access underlying driver APIs. The execute callbacks will give you a reference to either a com.mongodb.Collection or a com.mongodb.DB object. Please see the section mongo.executioncallback[Execution Callbacks] for more information.

Now let’s look at an example of how to work with the MongoTemplate in the context of the Spring container.

Instantiating MongoTemplate

You can use Java to create and register an instance of MongoTemplate as shown below.

Example 6. Registering a com.mongodb.MongoClient object and enabling Spring’s exception translation support

@Configuration
public class AppConfig {

  public @Bean MongoClient mongoClient() {
      return new MongoClient("localhost");
  }

  public @Bean MongoTemplate mongoTemplate() {
      return new MongoTemplate(mongoClient(), "mydatabase");
  }
}

There are several overloaded constructors of MongoTemplate. These are

MongoTemplate(MongoClient mongo, String databaseName) - takes the com.mongodb.MongoClient object and the default database name to operate against.
MongoTemplate(MongoDbFactory mongoDbFactory) - takes a MongoDbFactory object that encapsulated the com.mongodb.MongoClient object, database name, and username and password.
MongoTemplate(MongoDbFactory mongoDbFactory, MongoConverter mongoConverter) - adds a MongoConverter to use for mapping.

You can also configure a MongoTemplate using Spring’s XML <beans/> schema.

<mongo:mongo-client host="localhost" port="27017"/>

<bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
  <constructor-arg ref="mongoClient"/>
  <constructor-arg name="databaseName" value="geospatial"/>
</bean>

Other optional properties that you might like to set when creating a MongoTemplate are the default WriteResultCheckingPolicy, WriteConcern, and ReadPreference.

Note	The preferred way to reference the operations on `MongoTemplate` instance is via its interface `MongoOperations`.

WriteResultChecking Policy

When in development it is very handy to either log or throw an exception if the com.mongodb.WriteResult returned from any MongoDB operation contains an error. It is quite common to forget to do this during development and then end up with an application that looks like it runs successfully but in fact the database was not modified according to your expectations. Set MongoTemplate’s property to an enum with the following values, EXCEPTION, or NONE to either throw an Exception or do nothing. The default is to use a WriteResultChecking value of NONE.

WriteConcern

You can set the com.mongodb.WriteConcern property that the MongoTemplate will use for write operations if it has not yet been specified via the driver at a higher level such as com.mongodb.MongoClient. If MongoTemplate’s WriteConcern property is not set it will default to the one set in the MongoDB driver’s DB or Collection setting.

WriteConcernResolver

For more advanced cases where you want to set different WriteConcern values on a per-operation basis (for remove, update, insert and save operations), a strategy interface called WriteConcernResolver can be configured on MongoTemplate. Since MongoTemplate is used to persist POJOs, the WriteConcernResolver lets you create a policy that can map a specific POJO class to a WriteConcern value. The WriteConcernResolver interface is shown below.

public interface WriteConcernResolver {
  WriteConcern resolve(MongoAction action);
}

The passed in argument, MongoAction, is what you use to determine the WriteConcern value to be used or to use the value of the Template itself as a default. MongoAction contains the collection name being written to, the java.lang.Class of the POJO, the converted Document, as well as the operation as an enumeration (MongoActionOperation: REMOVE, UPDATE, INSERT, INSERT_LIST, SAVE) and a few other pieces of contextual information. For example,

private class MyAppWriteConcernResolver implements WriteConcernResolver {

  public WriteConcern resolve(MongoAction action) {
    if (action.getEntityClass().getSimpleName().contains("Audit")) {
      return WriteConcern.NONE;
    } else if (action.getEntityClass().getSimpleName().contains("Metadata")) {
      return WriteConcern.JOURNAL_SAFE;
    }
    return action.getDefaultWriteConcern();
  }
}

Saving, Updating, and Removing Documents

MongoTemplate provides a simple way for you to save, update, and delete your domain objects and map those objects to documents stored in MongoDB.

Given a simple class such as Person

public class Person {

  private String id;
  private String name;
  private int age;

  public Person(String name, int age) {
    this.name = name;
    this.age = age;
  }

  public String getId() {
    return id;
  }
  public String getName() {
    return name;
  }
  public int getAge() {
    return age;
  }

  @Override
  public String toString() {
    return "Person [id=" + id + ", name=" + name + ", age=" + age + "]";
  }

}

You can save, update and delete the object as shown below.

Note	`MongoOperations` is the interface that `MongoTemplate` implements.

package org.spring.example;

import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Update.update;
import static org.springframework.data.mongodb.core.query.Query.query;

import java.util.List;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.springframework.data.mongodb.core.MongoOperations;
import org.springframework.data.mongodb.core.MongoTemplate;
import org.springframework.data.mongodb.core.SimpleMongoDbFactory;

import com.mongodb.MongoClient;

public class MongoApp {

  private static final Log log = LogFactory.getLog(MongoApp.class);

  public static void main(String[] args) {

    MongoOperations mongoOps = new MongoTemplate(new SimpleMongoDbFactory(new MongoClient(), "database"));

    Person p = new Person("Joe", 34);

    // Insert is used to initially store the object into the database.
    mongoOps.insert(p);
    log.info("Insert: " + p);

    // Find
    p = mongoOps.findById(p.getId(), Person.class);
    log.info("Found: " + p);

    // Update
    mongoOps.updateFirst(query(where("name").is("Joe")), update("age", 35), Person.class);
    p = mongoOps.findOne(query(where("name").is("Joe")), Person.class);
    log.info("Updated: " + p);

    // Delete
    mongoOps.remove(p);

    // Check that deletion worked
    List<Person> people =  mongoOps.findAll(Person.class);
    log.info("Number of people = : " + people.size());


    mongoOps.dropCollection(Person.class);
  }
}

This would produce the following log output (including debug messages from MongoTemplate itself)

DEBUG apping.MongoPersistentEntityIndexCreator:  80 - Analyzing class class org.spring.example.Person for index information.
DEBUG work.data.mongodb.core.MongoTemplate: 632 - insert Document containing fields: [_class, age, name] in collection: person
INFO               org.spring.example.MongoApp:  30 - Insert: Person [id=4ddc6e784ce5b1eba3ceaf5c, name=Joe, age=34]
DEBUG work.data.mongodb.core.MongoTemplate:1246 - findOne using query: { "_id" : { "$oid" : "4ddc6e784ce5b1eba3ceaf5c"}} in db.collection: database.person
INFO               org.spring.example.MongoApp:  34 - Found: Person [id=4ddc6e784ce5b1eba3ceaf5c, name=Joe, age=34]
DEBUG work.data.mongodb.core.MongoTemplate: 778 - calling update using query: { "name" : "Joe"} and update: { "$set" : { "age" : 35}} in collection: person
DEBUG work.data.mongodb.core.MongoTemplate:1246 - findOne using query: { "name" : "Joe"} in db.collection: database.person
INFO               org.spring.example.MongoApp:  39 - Updated: Person [id=4ddc6e784ce5b1eba3ceaf5c, name=Joe, age=35]
DEBUG work.data.mongodb.core.MongoTemplate: 823 - remove using query: { "id" : "4ddc6e784ce5b1eba3ceaf5c"} in collection: person
INFO               org.spring.example.MongoApp:  46 - Number of people = : 0
DEBUG work.data.mongodb.core.MongoTemplate: 376 - Dropped collection [database.person]

There was implicit conversion using the MongoConverter between a String and ObjectId as stored in the database and recognizing a convention of the property "Id" name.

Note	This example is meant to show the use of save, update and remove operations on MongoTemplate and not to show complex mapping functionality

The query syntax used in the example is explained in more detail in the section Querying Documents.

How the `_id` field is handled in the mapping layer

MongoDB requires that you have an _id field for all documents. If you don’t provide one the driver will assign a ObjectId with a generated value. When using the MappingMongoConverter there are certain rules that govern how properties from the Java class is mapped to this _id field.

The following outlines what property will be mapped to the _id document field:

A property or field annotated with @Id (org.springframework.data.annotation.Id) will be mapped to the _id field.
A property or field without an annotation but named id will be mapped to the _id field.

The following outlines what type conversion, if any, will be done on the property mapped to the _id document field when using the MappingMongoConverter, the default for MongoTemplate.

An id property or field declared as a String in the Java class will be converted to and stored as an ObjectId if possible using a Spring Converter<String, ObjectId>. Valid conversion rules are delegated to the MongoDB Java driver. If it cannot be converted to an ObjectId, then the value will be stored as a string in the database.
An id property or field declared as BigInteger in the Java class will be converted to and stored as an ObjectId using a Spring Converter<BigInteger, ObjectId>.

If no field or property specified above is present in the Java class then an implicit _id file will be generated by the driver but not mapped to a property or field of the Java class.

When querying and updating MongoTemplate will use the converter to handle conversions of the Query and Update objects that correspond to the above rules for saving documents so field names and types used in your queries will be able to match what is in your domain classes.

Type mapping

As MongoDB collections can contain documents that represent instances of a variety of types. A great example here is if you store a hierarchy of classes or simply have a class with a property of type Object. In the latter case the values held inside that property have to be read in correctly when retrieving the object. Thus we need a mechanism to store type information alongside the actual document.

To achieve that the MappingMongoConverter uses a MongoTypeMapper abstraction with DefaultMongoTypeMapper as it’s main implementation. Its default behavior is storing the fully qualified classname under _class inside the document. Type hints are written for top-level documents as well as for every value if it’s a complex type and a subtype of the property type declared.

Example 7. Type mapping

public class Sample {
  Contact value;
}

public abstract class Contact { … }

public class Person extends Contact { … }

Sample sample = new Sample();
sample.value = new Person();

mongoTemplate.save(sample);

{
  "value" : { "_class" : "com.acme.Person" },
  "_class" : "com.acme.Sample"
}

As you can see we store the type information as last field for the actual root class as well as for the nested type as it is complex and a subtype of Contact. So if you’re now using mongoTemplate.findAll(Object.class, "sample") we are able to find out that the document stored shall be a Sample instance. We are also able to find out that the value property shall be a Person actually.

Customizing type mapping

In case you want to avoid writing the entire Java class name as type information but rather like to use some key you can use the @TypeAlias annotation at the entity class being persisted. If you need to customize the mapping even more have a look at the TypeInformationMapper interface. An instance of that interface can be configured at the DefaultMongoTypeMapper which can be configured in turn on MappingMongoConverter.

Example 8. Defining a TypeAlias for an Entity

@TypeAlias("pers")
class Person {

}

Note that the resulting document will contain "pers" as the value in the _class Field.

Configuring custom type mapping

The following example demonstrates how to configure a custom MongoTypeMapper in MappingMongoConverter.

Example 9. Configuring a custom MongoTypeMapper via Spring Java Config

class CustomMongoTypeMapper extends DefaultMongoTypeMapper {
  //implement custom type mapping here
}

@Configuration
class SampleMongoConfiguration extends AbstractMongoConfiguration {

  @Override
  protected String getDatabaseName() {
    return "database";
  }

  @Override
  public MongoClient mongoClient() {
    return new MongoClient();
  }

  @Bean
  @Override
  public MappingMongoConverter mappingMongoConverter() throws Exception {
    MappingMongoConverter mmc = super.mappingMongoConverter();
    mmc.setTypeMapper(customTypeMapper());
    return mmc;
  }

  @Bean
  public MongoTypeMapper customTypeMapper() {
    return new CustomMongoTypeMapper();
  }
}

Note that we are extending the AbstractMongoConfiguration class and override the bean definition of the MappingMongoConverter where we configure our custom MongoTypeMapper.

Example 10. Configuring a custom MongoTypeMapper via XML

<mongo:mapping-converter type-mapper-ref="customMongoTypeMapper"/>

<bean name="customMongoTypeMapper" class="com.bubu.mongo.CustomMongoTypeMapper"/>

Methods for saving and inserting documents

There are several convenient methods on MongoTemplate for saving and inserting your objects. To have more fine-grained control over the conversion process you can register Spring converters with the MappingMongoConverter, for example Converter<Person, Document> and Converter<Document, Person>.

Note	The difference between insert and save operations is that a save operation will perform an insert if the object is not already present.

The simple case of using the save operation is to save a POJO. In this case the collection name will be determined by name (not fully qualified) of the class. You may also call the save operation with a specific collection name. The collection to store the object can be overridden using mapping metadata.

When inserting or saving, if the Id property is not set, the assumption is that its value will be auto-generated by the database. As such, for auto-generation of an ObjectId to succeed the type of the Id property/field in your class must be either a String, ObjectId, or BigInteger.

Here is a basic example of using the save operation and retrieving its contents.

Example 11. Inserting and retrieving documents using the MongoTemplate

import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Criteria.query;
…

Person p = new Person("Bob", 33);
mongoTemplate.insert(p);

Person qp = mongoTemplate.findOne(query(where("age").is(33)), Person.class);

The insert/save operations available to you are listed below.

void save (Object objectToSave) Save the object to the default collection.
void save (Object objectToSave, String collectionName) Save the object to the specified collection.

A similar set of insert operations is listed below

void insert (Object objectToSave) Insert the object to the default collection.
void insert (Object objectToSave, String collectionName) Insert the object to the specified collection.

Which collection will my documents be saved into?

There are two ways to manage the collection name that is used for operating on the documents. The default collection name that is used is the class name changed to start with a lower-case letter. So a com.test.Person class would be stored in the "person" collection. You can customize this by providing a different collection name using the @Document annotation. You can also override the collection name by providing your own collection name as the last parameter for the selected MongoTemplate method calls.

Inserting or saving individual objects

The MongoDB driver supports inserting a collection of documents in one operation. The methods in the MongoOperations interface that support this functionality are listed below

insert inserts an object. If there is an existing document with the same id then an error is generated.
insertAll takes a Collection of objects as the first parameter. This method inspects each object and inserts it to the appropriate collection based on the rules specified above.
save saves the object overwriting any object that might exist with the same id.

Inserting several objects in a batch

The MongoDB driver supports inserting a collection of documents in one operation. The methods in the MongoOperations interface that support this functionality are listed below

insert methods that take a Collection as the first argument. This inserts a list of objects in a single batch write to the database.

Updating documents in a collection

For updates we can elect to update the first document found using MongoOperation 's method updateFirst or we can update all documents that were found to match the query using the method updateMulti. Here is an example of an update of all SAVINGS accounts where we are adding a one-time $50.00 bonus to the balance using the $inc operator.

Example 12. Updating documents using the MongoTemplate

import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query;
import static org.springframework.data.mongodb.core.query.Update;

...

WriteResult wr = mongoTemplate.updateMulti(new Query(where("accounts.accountType").is(Account.Type.SAVINGS)),
  new Update().inc("accounts.$.balance", 50.00), Account.class);

In addition to the Query discussed above we provide the update definition using an Update object. The Update class has methods that match the update modifiers available for MongoDB.

As you can see most methods return the Update object to provide a fluent style for the API.

Methods for executing updates for documents

updateFirst Updates the first document that matches the query document criteria with the provided updated document.
updateMulti Updates all objects that match the query document criteria with the provided updated document.

Methods for the Update class

The Update class can be used with a little 'syntax sugar' as its methods are meant to be chained together and you can kick-start the creation of a new Update instance via the static method public static Update update(String key, Object value) and using static imports.

Here is a listing of methods on the Update class

Update addToSet (String key, Object value) Update using the $addToSet update modifier
Update currentDate (String key) Update using the $currentDate update modifier
Update currentTimestamp (String key) Update using the $currentDate update modifier with $type timestamp
Update inc (String key, Number inc) Update using the $inc update modifier
Update max (String key, Object max) Update using the $max update modifier
Update min (String key, Object min) Update using the $min update modifier
Update multiply (String key, Number multiplier) Update using the $mul update modifier
Update pop (String key, Update.Position pos) Update using the $pop update modifier
Update pull (String key, Object value) Update using the $pull update modifier
Update pullAll (String key, Object[] values) Update using the $pullAll update modifier
Update push (String key, Object value) Update using the $push update modifier
Update pushAll (String key, Object[] values) Update using the $pushAll update modifier
Update rename (String oldName, String newName) Update using the $rename update modifier
Update set (String key, Object value) Update using the $set update modifier
Update setOnInsert (String key, Object value) Update using the $setOnInsert update modifier
Update unset (String key) Update using the $unset update modifier

Some update modifiers like $push and $addToSet allow nesting of additional operators.

// { $push : { "category" : { "$each" : [ "spring" , "data" ] } } }
new Update().push("category").each("spring", "data")

// { $push : { "key" : { "$position" : 0 , "$each" : [ "Arya" , "Arry" , "Weasel" ] } } }
new Update().push("key").atPosition(Position.FIRST).each(Arrays.asList("Arya", "Arry", "Weasel"));

// { $push : { "key" : { "$slice" : 5 , "$each" : [ "Arya" , "Arry" , "Weasel" ] } } }
new Update().push("key").slice(5).each(Arrays.asList("Arya", "Arry", "Weasel"));

// { $addToSet : { "values" : { "$each" : [ "spring" , "data" , "mongodb" ] } } }
new Update().addToSet("values").each("spring", "data", "mongodb");

Upserting documents in a collection

Related to performing an updateFirst operations, you can also perform an upsert operation which will perform an insert if no document is found that matches the query. The document that is inserted is a combination of the query document and the update document. Here is an example

template.upsert(query(where("ssn").is(1111).and("firstName").is("Joe").and("Fraizer").is("Update")), update("address", addr), Person.class);

Finding and Upserting documents in a collection

The findAndModify(…) method on DBCollection can update a document and return either the old or newly updated document in a single operation. MongoTemplate provides a findAndModify method that takes Query and Update classes and converts from Document to your POJOs. Here are the methods

<T> T findAndModify(Query query, Update update, Class<T> entityClass);

<T> T findAndModify(Query query, Update update, Class<T> entityClass, String collectionName);

<T> T findAndModify(Query query, Update update, FindAndModifyOptions options, Class<T> entityClass);

<T> T findAndModify(Query query, Update update, FindAndModifyOptions options, Class<T> entityClass, String collectionName);

As an example usage, we will insert of few Person objects into the container and perform a simple findAndUpdate operation

mongoTemplate.insert(new Person("Tom", 21));
mongoTemplate.insert(new Person("Dick", 22));
mongoTemplate.insert(new Person("Harry", 23));

Query query = new Query(Criteria.where("firstName").is("Harry"));
Update update = new Update().inc("age", 1);
Person p = mongoTemplate.findAndModify(query, update, Person.class); // return's old person object

assertThat(p.getFirstName(), is("Harry"));
assertThat(p.getAge(), is(23));
p = mongoTemplate.findOne(query, Person.class);
assertThat(p.getAge(), is(24));

// Now return the newly updated document when updating
p = template.findAndModify(query, update, new FindAndModifyOptions().returnNew(true), Person.class);
assertThat(p.getAge(), is(25));

The FindAndModifyOptions lets you set the options of returnNew, upsert, and remove. An example extending off the previous code snippet is shown below

Query query2 = new Query(Criteria.where("firstName").is("Mary"));
p = mongoTemplate.findAndModify(query2, update, new FindAndModifyOptions().returnNew(true).upsert(true), Person.class);
assertThat(p.getFirstName(), is("Mary"));
assertThat(p.getAge(), is(1));

Methods for removing documents

You can use several overloaded methods to remove an object from the database.

template.remove(tywin, "GOT");                                              (1)

template.remove(query(where("lastname").is("lannister")), "GOT");           (2)

template.remove(new Query().limit(3), "GOT");                               (3)

template.findAllAndRemove(query(where("lastname").is("lannister"), "GOT");  (4)

template.findAllAndRemove(new Query().limit(3), "GOT");                     (5)

Remove a single entity via its id from the associated collection.
Remove all documents matching the criteria of the query from the GOT collection.
Rewmove the first 3 documents in the GOT collection. Unlike <2> the documents to remove are identified via their id using the given query applying sort, limit and skip options and then removed all at once in a seperate step.
Remove all documents matching the criteria of the query from the GOT collection. Unlike <3> documents do not get deleted in a batch but one by one.
Remove the first 3 documents in the GOT collection. Unlike <3> documents do not get deleted in a batch but one by one.

Optimistic locking

The @Version annotation provides a JPA similar semantic in the context of MongoDB and makes sure updates are only applied to documents with matching version. Therefore the actual value of the version property is added to the update query in a way that the update won’t have any effect if another operation altered the document in between. In that case an OptimisticLockingFailureException is thrown.

@Document
class Person {

  @Id String id;
  String firstname;
  String lastname;
  @Version Long version;
}

Person daenerys = template.insert(new Person("Daenerys"));                           (1)

Person tmp = teplate.findOne(query(where("id").is(daenerys.getId())), Person.class); (2)

daenerys.setLastname("Targaryen");
template.save(daenerys);                                                             (3)

template.save(tmp); // throws OptimisticLockingFailureException                      (4)

Intially insert document. version is set to 0.
Load the just inserted document version is still 0.
Update document with version = 0. Set the lastname and bump version to 1.
Try to update previously loaded document sill having version = 0 fails with OptimisticLockingFailureException as the current version is 1.

Important

Using MongoDB driver version 3 requires to set the WriteConcern to ACKNOWLEDGED. Otherwise OptimisticLockingFailureException can be silently swallowed.

Querying Documents

You can express your queries using the Query and Criteria classes which have method names that mirror the native MongoDB operator names such as lt, lte, is, and others. The Query and Criteria classes follow a fluent API style so that you can easily chain together multiple method criteria and queries while having easy to understand the code. Static imports in Java are used to help remove the need to see the 'new' keyword for creating Query and Criteria instances so as to improve readability. If you like to create Query instances from a plain JSON String use BasicQuery.

Example 13. Creating a Query instance from a plain JSON String

BasicQuery query = new BasicQuery("{ age : { $lt : 50 }, accounts.balance : { $gt : 1000.00 }}");
List<Person> result = mongoTemplate.find(query, Person.class);

GeoSpatial queries are also supported and are described more in the section GeoSpatial Queries.

Map-Reduce operations are also supported and are described more in the section Map-Reduce.

Querying documents in a collection

We saw how to retrieve a single document using the findOne and findById methods on MongoTemplate in previous sections which return a single domain object. We can also query for a collection of documents to be returned as a list of domain objects. Assuming that we have a number of Person objects with name and age stored as documents in a collection and that each person has an embedded account document with a balance. We can now run a query using the following code.

Example 14. Querying for documents using the MongoTemplate

import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query.query;

…

List<Person> result = mongoTemplate.find(query(where("age").lt(50)
  .and("accounts.balance").gt(1000.00d)), Person.class);

All find methods take a Query object as a parameter. This object defines the criteria and options used to perform the query. The criteria is specified using a Criteria object that has a static factory method named where used to instantiate a new Criteria object. We recommend using a static import for org.springframework.data.mongodb.core.query.Criteria.where and Query.query to make the query more readable.

This query should return a list of Person objects that meet the specified criteria. The Criteria class has the following methods that correspond to the operators provided in MongoDB.

As you can see most methods return the Criteria object to provide a fluent style for the API.

Methods for the Criteria class

Criteria all (Object o) Creates a criterion using the $all operator
Criteria and (String key) Adds a chained Criteria with the specified key to the current Criteria and returns the newly created one
Criteria andOperator (Criteria… criteria) Creates an and query using the $and operator for all of the provided criteria (requires MongoDB 2.0 or later)
Criteria elemMatch (Criteria c) Creates a criterion using the $elemMatch operator
Criteria exists (boolean b) Creates a criterion using the $exists operator
Criteria gt (Object o) Creates a criterion using the $gt operator
Criteria gte (Object o) Creates a criterion using the $gte operator
Criteria in (Object… o) Creates a criterion using the $in operator for a varargs argument.
Criteria in (Collection<?> collection) Creates a criterion using the $in operator using a collection
Criteria is (Object o) Creates a criterion using field matching ({ key:value }). If the specified value is a document, the order of the fields and exact equality in the document matters.
Criteria lt (Object o) Creates a criterion using the $lt operator
Criteria lte (Object o) Creates a criterion using the $lte operator
Criteria mod (Number value, Number remainder) Creates a criterion using the $mod operator
Criteria ne (Object o) Creates a criterion using the $ne operator
Criteria nin (Object… o) Creates a criterion using the $nin operator
Criteria norOperator (Criteria… criteria) Creates an nor query using the $nor operator for all of the provided criteria
Criteria not () Creates a criterion using the $not meta operator which affects the clause directly following
Criteria orOperator (Criteria… criteria) Creates an or query using the $or operator for all of the provided criteria
Criteria regex (String re) Creates a criterion using a $regex
Criteria size (int s) Creates a criterion using the $size operator
Criteria type (int t) Creates a criterion using the $type operator

There are also methods on the Criteria class for geospatial queries. Here is a listing but look at the section on GeoSpatial Queries to see them in action.

Criteria within (Circle circle) Creates a geospatial criterion using $geoWithin $center operators.
Criteria within (Box box) Creates a geospatial criterion using a $geoWithin $box operation.
Criteria withinSphere (Circle circle) Creates a geospatial criterion using $geoWithin $center operators.
Criteria near (Point point) Creates a geospatial criterion using a $near operation
Criteria nearSphere (Point point) Creates a geospatial criterion using $nearSphere$center operations. This is only available for MongoDB 1.7 and higher.
Criteria minDistance (double minDistance) Creates a geospatial criterion using the $minDistance operation, for use with $near.
Criteria maxDistance (double maxDistance) Creates a geospatial criterion using the $maxDistance operation, for use with $near.

The Query class has some additional methods used to provide options for the query.

Methods for the Query class

Query addCriteria (Criteria criteria) used to add additional criteria to the query
Field fields () used to define fields to be included in the query results
Query limit (int limit) used to limit the size of the returned results to the provided limit (used for paging)
Query skip (int skip) used to skip the provided number of documents in the results (used for paging)
Query with (Sort sort) used to provide sort definition for the results

Methods for querying for documents

The query methods need to specify the target type T that will be returned and they are also overloaded with an explicit collection name for queries that should operate on a collection other than the one indicated by the return type.

findAll Query for a list of objects of type T from the collection.
findOne Map the results of an ad-hoc query on the collection to a single instance of an object of the specified type.
findById Return an object of the given id and target class.
find Map the results of an ad-hoc query on the collection to a List of the specified type.
findAndRemove Map the results of an ad-hoc query on the collection to a single instance of an object of the specified type. The first document that matches the query is returned and also removed from the collection in the database.

GeoSpatial Queries

MongoDB supports GeoSpatial queries through the use of operators such as $near, $within, geoWithin and $nearSphere. Methods specific to geospatial queries are available on the Criteria class. There are also a few shape classes, Box, Circle, and Point that are used in conjunction with geospatial related Criteria methods.

To understand how to perform GeoSpatial queries we will use the following Venue class taken from the integration tests which relies on using the rich MappingMongoConverter.

@Document(collection="newyork")
public class Venue {

  @Id
  private String id;
  private String name;
  private double[] location;

  @PersistenceConstructor
  Venue(String name, double[] location) {
    super();
    this.name = name;
    this.location = location;
  }

  public Venue(String name, double x, double y) {
    super();
    this.name = name;
    this.location = new double[] { x, y };
  }

  public String getName() {
    return name;
  }

  public double[] getLocation() {
    return location;
  }

  @Override
  public String toString() {
    return "Venue [id=" + id + ", name=" + name + ", location="
        + Arrays.toString(location) + "]";
  }
}

To find locations within a Circle, the following query can be used.

Circle circle = new Circle(-73.99171, 40.738868, 0.01);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(circle)), Venue.class);

To find venues within a Circle using spherical coordinates the following query can be used

Circle circle = new Circle(-73.99171, 40.738868, 0.003712240453784);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").withinSphere(circle)), Venue.class);

To find venues within a Box the following query can be used

//lower-left then upper-right
Box box = new Box(new Point(-73.99756, 40.73083), new Point(-73.988135, 40.741404));
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(box)), Venue.class);

To find venues near a Point, the following queries can be used

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).maxDistance(0.01)), Venue.class);

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).minDistance(0.01).maxDistance(100)), Venue.class);

To find venues near a Point using spherical coordinates the following query can be used

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(
        Criteria.where("location").nearSphere(point).maxDistance(0.003712240453784)),
        Venue.class);

Geo near queries

MongoDB supports querying the database for geo locations and calculation the distance from a given origin at the very same time. With geo-near queries it’s possible to express queries like: "find all restaurants in the surrounding 10 miles". To do so MongoOperations provides geoNear(…) methods taking a NearQuery as argument as well as the already familiar entity type and collection

Point location = new Point(-73.99171, 40.738868);
NearQuery query = NearQuery.near(location).maxDistance(new Distance(10, Metrics.MILES));

GeoResults<Restaurant> = operations.geoNear(query, Restaurant.class);

As you can see we use the NearQuery builder API to set up a query to return all Restaurant instances surrounding the given Point by 10 miles maximum. The Metrics enum used here actually implements an interface so that other metrics could be plugged into a distance as well. A Metric is backed by a multiplier to transform the distance value of the given metric into native distances. The sample shown here would consider the 10 to be miles. Using one of the pre-built in metrics (miles and kilometers) will automatically trigger the spherical flag to be set on the query. If you want to avoid that, simply hand in plain double values into maxDistance(…). For more information see the JavaDoc of NearQuery and Distance.

The geo near operations return a GeoResults wrapper object that encapsulates GeoResult instances. The wrapping GeoResults allows accessing the average distance of all results. A single GeoResult object simply carries the entity found plus its distance from the origin.

GeoJSON Support

MongoDB supports GeoJSON and simple (legacy) coordinate pairs for geospatial data. Those formats can both be used for storing as well as querying data.

Note	Please refer to the MongoDB manual on GeoJSON support to learn about requirements and restrictions.

GeoJSON types in domain classes

Usage of GeoJSON types in domain classes is straight forward. The org.springframework.data.mongodb.core.geo package contains types like GeoJsonPoint, GeoJsonPolygon and others. Those are extensions to the existing org.springframework.data.geo types.

public class Store {

	String id;

	/**
	 * location is stored in GeoJSON format.
	 * {
	 *   "type" : "Point",
	 *   "coordinates" : [ x, y ]
	 * }
	 */
	GeoJsonPoint location;
}

GeoJSON types in repository query methods

Using GeoJSON types as repository query parameters forces usage of the $geometry operator when creating the query.

public interface StoreRepository extends CrudRepository<Store, String> {

	List<Store> findByLocationWithin(Polygon polygon);  (1)

}

/*
 * {
 *   "location": {
 *     "$geoWithin": {
 *       "$geometry": {
 *         "type": "Polygon",
 *         "coordinates": [
 *           [
 *             [-73.992514,40.758934],
 *             [-73.961138,40.760348],
 *             [-73.991658,40.730006],
 *             [-73.992514,40.758934]
 *           ]
 *         ]
 *       }
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (2)
  new GeoJsonPolygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006),
    new Point(-73.992514, 40.758934)));                 (3)

/*
 * {
 *   "location" : {
 *     "$geoWithin" : {
 *        "$polygon" : [ [-73.992514,40.758934] , [-73.961138,40.760348] , [-73.991658,40.730006] ]
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (4)
  new Polygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006));

Repository method definition using the commons type allows calling it with both GeoJSON and legacy format.
Use GeoJSON type the make use of $geometry operator.
Plase note that GeoJSON polygons need the define a closed ring.
Use legacy format $polygon operator.

Full Text Queries

Since MongoDB 2.6 full text queries can be executed using the $text operator. Methods and operations specific for full text queries are available in TextQuery and TextCriteria. When doing full text search please refer to the MongoDB reference for its behavior and limitations.

Full Text Search

Before we are actually able to use full text search we have to ensure to set up the search index correctly. Please refer to section Text Index for creating index structures.

db.foo.createIndex(
{
  title : "text",
  content : "text"
},
{
  weights : {
              title : 3
            }
}
)

A query searching for coffee cake, sorted by relevance according to the weights can be defined and executed as:

Query query = TextQuery.searching(new TextCriteria().matchingAny("coffee", "cake")).sortByScore();
List<Document> page = template.find(query, Document.class);

Exclusion of search terms can directly be done by prefixing the term with - or using notMatching

// search for 'coffee' and not 'cake'
TextQuery.searching(new TextCriteria().matching("coffee").matching("-cake"));
TextQuery.searching(new TextCriteria().matching("coffee").notMatching("cake"));

As TextCriteria.matching takes the provided term as is. Therefore phrases can be defined by putting them between double quotes (eg. \"coffee cake\") or using TextCriteria.phrase.

// search for phrase 'coffee cake'
TextQuery.searching(new TextCriteria().matching("\"coffee cake\""));
TextQuery.searching(new TextCriteria().phrase("coffee cake"));

The flags for $caseSensitive and $diacriticSensitive can be set via the according methods on TextCriteria. Please note that these two optional flags have been introduced in MongoDB 3.2 and will not be included in the query unless explicitly set.

Collations

MongoDB supports since 3.4 collations for collection and index creation and various query operations. Collations define string comparison rules based on the ICU collations. A collation document consists of various properties that are encapsulated in Collation:

Collation collation = Collation.of("fr")         (1)

  .strength(ComparisonLevel.secondary()          (2)
    .includeCase())

  .numericOrderingEnabled()                      (3)

  .alternate(Alternate.shifted().punct())        (4)

  .forwardDiacriticSort()                        (5)

  .normalizationEnabled();                       (6)

Collation requires a locale for creation. This can be either a string representation of the locale, a Locale (considering language, country and variant) or a CollationLocale. The locale is mandatory for creation.
Collation strength defines comparison levels denoting differences between characters. You can configure various options (case-sensitivity, case-ordering) depending on the selected strength.
Specify whether to compare numeric strings as numbers or as strings.
Specify whether the collation should consider whitespace and punctuation as base characters for purposes of comparison.
Specify whether strings with diacritics sort from back of the string, such as with some French dictionary ordering.
Specify whether to check if text requires normalization and to perform normalization.

Collations can be used to create collections and indexes. If you create a collection specifying a collation, the collation is applied to index creation and queries unless you specify a different collation. A collation is valid for a whole operation and cannot be specified on a per-field basis.

Collation french = Collation.of("fr");
Collation german = Collation.of("de");

template.createCollection(Person.class, CollectionOptions.just(collation));

template.indexOps(Person.class).ensureIndex(new Index("name", Direction.ASC).collation(german));

Note	MongoDB uses simple binary comparison if no collation is specified (`Collation.simple()`).

Using collations with collection operations is a matter of specifying a Collation instance in your query or operation options.

Example 15. Using collation with find

Collation collation = Collation.of("de");

Query query = new Query(Criteria.where("firstName").is("Amél")).collation(collation);

List<Person> results = template.find(query, Person.class);

Example 16. Using collation with aggregate

Collation collation = Collation.of("de");

AggregationOptions options = new AggregationOptions.Builder().collation(collation).build();

Aggregation aggregation = newAggregation(
  project("tags"),
  unwind("tags"),
  group("tags")
    .count().as("count")
).withOptions(options);

AggregationResults<TagCount> results = template.aggregate(aggregation, "tags", TagCount.class);

Warning

Indexes are only used if the collation used for the operation and the index collation matches.

Fluent Template API

The MongoOperations interface is one of the central components when it comes to more low level interaction with MongoDB. It offers a wide range of methods covering needs from collection / index creation and CRUD operations to more advanced functionality like map-reduce and aggregations. One can find multiple overloads for each and every method. Most of them just cover optional / nullable parts of the API.

FluentMongoOperations provide a more narrow interface for common methods of MongoOperations providing a more readable, fluent API. The entry points insert(…), find(…), update(…), etc. follow a natural naming schema based on the operation to execute. Moving on from the entry point the API is designed to only offer context dependent methods guiding towards a terminating method that invokes the actual MongoOperations counterpart.

List<SWCharacter> all = ops.find(SWCharacter.class)
  .inCollection("star-wars")                        (1)
  .all();

Skip this step if SWCharacter defines the collection via @Document or if using the class name as the collection name is just fine.

Sometimes a collection in MongoDB holds entities of different types. Like a Jedi within a collection of SWCharacters. To use different types for Query and return value mapping one can use as(Class<?> targetType) map results differently.

List<Jedi> all = ops.find(SWCharacter.class)    (1)
  .as(Jedi.class)                               (2)
  .matching(query(where("jedi").is(true)))
  .all();

The query fields are mapped against the SWCharacter type.
Resulting documents are mapped into Jedi.

Tip	It is possible to directly apply [projections] to resulting documents by providing just the `interface` type via `as(Class<?>)`.

Switching between retrieving a single entity, multiple ones as List or Stream like is done via the terminating methods first(), one(), all() or stream().

When writing a geo-spatial query via near(NearQuery) the number of terminating methods is altered to just the ones valid for executing a geoNear command in MongoDB fetching entities as GeoResult within GeoResults.

GeoResults<Jedi> results = mongoOps.query(SWCharacter.class)
  .as(Jedi.class)
  .near(alderaan) // NearQuery.near(-73.9667, 40.78).maxDis…
  .all();

../{spring-data-commons-docs}/query-by-example.adoc query-by-example.adoc

Map-Reduce Operations

You can query MongoDB using Map-Reduce which is useful for batch processing, data aggregation, and for when the query language doesn’t fulfill your needs.

Spring provides integration with MongoDB’s map reduce by providing methods on MongoOperations to simplify the creation and execution of Map-Reduce operations. It can convert the results of a Map-Reduce operation to a POJO also integrates with Spring’s Resource abstraction abstraction. This will let you place your JavaScript files on the file system, classpath, http server or any other Spring Resource implementation and then reference the JavaScript resources via an easy URI style syntax, e.g. 'classpath:reduce.js;. Externalizing JavaScript code in files is often preferable to embedding them as Java strings in your code. Note that you can still pass JavaScript code as Java strings if you prefer.

Example Usage

To understand how to perform Map-Reduce operations an example from the book 'MongoDB - The definitive guide' is used. In this example we will create three documents that have the values [a,b], [b,c], and [c,d] respectfully. The values in each document are associated with the key 'x' as shown below. For this example assume these documents are in the collection named "jmr1".

{ "_id" : ObjectId("4e5ff893c0277826074ec533"), "x" : [ "a", "b" ] }
{ "_id" : ObjectId("4e5ff893c0277826074ec534"), "x" : [ "b", "c" ] }
{ "_id" : ObjectId("4e5ff893c0277826074ec535"), "x" : [ "c", "d" ] }

A map function that will count the occurrence of each letter in the array for each document is shown below

function () {
    for (var i = 0; i < this.x.length; i++) {
        emit(this.x[i], 1);
    }
}

The reduce function that will sum up the occurrence of each letter across all the documents is shown below

function (key, values) {
    var sum = 0;
    for (var i = 0; i < values.length; i++)
        sum += values[i];
    return sum;
}

Executing this will result in a collection as shown below.

{ "_id" : "a", "value" : 1 }
{ "_id" : "b", "value" : 2 }
{ "_id" : "c", "value" : 2 }
{ "_id" : "d", "value" : 1 }

Assuming that the map and reduce functions are located in map.js and reduce.js and bundled in your jar so they are available on the classpath, you can execute a map-reduce operation and obtain the results as shown below

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js", ValueObject.class);
for (ValueObject valueObject : results) {
  System.out.println(valueObject);
}

The output of the above code is

ValueObject [id=a, value=1.0]
ValueObject [id=b, value=2.0]
ValueObject [id=c, value=2.0]
ValueObject [id=d, value=1.0]

The MapReduceResults class implements Iterable and provides access to the raw output, as well as timing and count statistics. The ValueObject class is simply

public class ValueObject {

  private String id;
  private float value;

  public String getId() {
    return id;
  }

  public float getValue() {
    return value;
  }

  public void setValue(float value) {
    this.value = value;
  }

  @Override
  public String toString() {
    return "ValueObject [id=" + id + ", value=" + value + "]";
  }
}

By default the output type of INLINE is used so you don’t have to specify an output collection. To specify additional map-reduce options use an overloaded method that takes an additional MapReduceOptions argument. The class MapReduceOptions has a fluent API so adding additional options can be done in a very compact syntax. Here an example that sets the output collection to "jmr1_out". Note that setting only the output collection assumes a default output type of REPLACE.

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     new MapReduceOptions().outputCollection("jmr1_out"), ValueObject.class);

There is also a static import import static org.springframework.data.mongodb.core.mapreduce.MapReduceOptions.options; that can be used to make the syntax slightly more compact

MapReduceResults<ValueObject> results = mongoOperations.mapReduce("jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     options().outputCollection("jmr1_out"), ValueObject.class);

You can also specify a query to reduce the set of data that will be used to feed into the map-reduce operation. This will remove the document that contains [a,b] from consideration for map-reduce operations.

Query query = new Query(where("x").ne(new String[] { "a", "b" }));
MapReduceResults<ValueObject> results = mongoOperations.mapReduce(query, "jmr1", "classpath:map.js", "classpath:reduce.js",
                                                                     options().outputCollection("jmr1_out"), ValueObject.class);

Note that you can specify additional limit and sort values as well on the query but not skip values.

Script Operations

MongoDB allows executing JavaScript functions on the server by either directly sending the script or calling a stored one. ScriptOperations can be accessed via MongoTemplate and provides basic abstraction for JavaScript usage.

Example Usage

ScriptOperations scriptOps = template.scriptOps();

ExecutableMongoScript echoScript = new ExecutableMongoScript("function(x) { return x; }");
scriptOps.execute(echoScript, "directly execute script");     (1)

scriptOps.register(new NamedMongoScript("echo", echoScript)); (2)
scriptOps.call("echo", "execute script via name");            (3)

Execute the script directly without storing the function on server side.
Store the script using 'echo' as its name. The given name identifies the script and allows calling it later.
Execute the script with name 'echo' using the provided parameters.

Group Operations

As an alternative to using Map-Reduce to perform data aggregation, you can use the group operation which feels similar to using SQL’s group by query style, so it may feel more approachable vs. using Map-Reduce. Using the group operations does have some limitations, for example it is not supported in a shared environment and it returns the full result set in a single BSON object, so the result should be small, less than 10,000 keys.

Spring provides integration with MongoDB’s group operation by providing methods on MongoOperations to simplify the creation and execution of group operations. It can convert the results of the group operation to a POJO and also integrates with Spring’s Resource abstraction abstraction. This will let you place your JavaScript files on the file system, classpath, http server or any other Spring Resource implementation and then reference the JavaScript resources via an easy URI style syntax, e.g. 'classpath:reduce.js;. Externalizing JavaScript code in files if often preferable to embedding them as Java strings in your code. Note that you can still pass JavaScript code as Java strings if you prefer.

Example Usage

In order to understand how group operations work the following example is used, which is somewhat artificial. For a more realistic example consult the book 'MongoDB - The definitive guide'. A collection named group_test_collection created with the following rows.

{ "_id" : ObjectId("4ec1d25d41421e2015da64f1"), "x" : 1 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f2"), "x" : 1 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f3"), "x" : 2 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f4"), "x" : 3 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f5"), "x" : 3 }
{ "_id" : ObjectId("4ec1d25d41421e2015da64f6"), "x" : 3 }

We would like to group by the only field in each row, the x field and aggregate the number of times each specific value of x occurs. To do this we need to create an initial document that contains our count variable and also a reduce function which will increment it each time it is encountered. The Java code to execute the group operation is shown below

GroupByResults<XObject> results = mongoTemplate.group("group_test_collection",
                                                      GroupBy.key("x").initialDocument("{ count: 0 }").reduceFunction("function(doc, prev) { prev.count += 1 }"),
                                                      XObject.class);

The first argument is the name of the collection to run the group operation over, the second is a fluent API that specifies properties of the group operation via a GroupBy class. In this example we are using just the intialDocument and reduceFunction methods. You can also specify a key-function, as well as a finalizer as part of the fluent API. If you have multiple keys to group by, you can pass in a comma separated list of keys.

The raw results of the group operation is a JSON document that looks like this

{
  "retval" : [ { "x" : 1.0 , "count" : 2.0} ,
               { "x" : 2.0 , "count" : 1.0} ,
               { "x" : 3.0 , "count" : 3.0} ] ,
  "count" : 6.0 ,
  "keys" : 3 ,
  "ok" : 1.0
}

The document under the "retval" field is mapped onto the third argument in the group method, in this case XObject which is shown below.

public class XObject {

  private float x;

  private float count;


  public float getX() {
    return x;
  }

  public void setX(float x) {
    this.x = x;
  }

  public float getCount() {
    return count;
  }

  public void setCount(float count) {
    this.count = count;
  }

  @Override
  public String toString() {
    return "XObject [x=" + x + " count = " + count + "]";
  }
}

You can also obtain the raw result as a Document by calling the method getRawResults on the GroupByResults class.

There is an additional method overload of the group method on MongoOperations which lets you specify a Criteria object for selecting a subset of the rows. An example which uses a Criteria object, with some syntax sugar using static imports, as well as referencing a key-function and reduce function javascript files via a Spring Resource string is shown below.

import static org.springframework.data.mongodb.core.mapreduce.GroupBy.keyFunction;
import static org.springframework.data.mongodb.core.query.Criteria.where;

GroupByResults<XObject> results = mongoTemplate.group(where("x").gt(0),
                                        "group_test_collection",
                                        keyFunction("classpath:keyFunction.js").initialDocument("{ count: 0 }").reduceFunction("classpath:groupReduce.js"), XObject.class);

Aggregation Framework Support

Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2.

The MongoDB Documentation describes the Aggregation Framework as follows:

For further information see the full reference documentation of the aggregation framework and other data aggregation tools for MongoDB.

Basic Concepts

The Aggregation Framework support in Spring Data MongoDB is based on the following key abstractions Aggregation, AggregationOperation and AggregationResults.

Aggregation

An Aggregation represents a MongoDB aggregate operation and holds the description of the aggregation pipeline instructions. Aggregations are created by invoking the appropriate newAggregation(…) static factory Method of the Aggregation class which takes the list of AggregateOperation as a parameter next to the optional input class.

The actual aggregate operation is executed by the aggregate method of the MongoTemplate which also takes the desired output class as parameter.
AggregationOperation

An AggregationOperation represents a MongoDB aggregation pipeline operation and describes the processing that should be performed in this aggregation step. Although one could manually create an AggregationOperation the recommended way to construct an AggregateOperation is to use the static factory methods provided by the Aggregate class.
AggregationResults

AggregationResults is the container for the result of an aggregate operation. It provides access to the raw aggregation result in the form of an Document, to the mapped objects and information which performed the aggregation.

The canonical example for using the Spring Data MongoDB support for the MongoDB Aggregation Framework looks as follows:

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

Aggregation agg = newAggregation(
    pipelineOP1(),
    pipelineOP2(),
    pipelineOPn()
);

AggregationResults<OutputType> results = mongoTemplate.aggregate(agg, "INPUT_COLLECTION_NAME", OutputType.class);
List<OutputType> mappedResult = results.getMappedResults();

Note that if you provide an input class as the first parameter to the newAggregation method the MongoTemplate will derive the name of the input collection from this class. Otherwise if you don’t not specify an input class you must provide the name of the input collection explicitly. If an input-class and an input-collection is provided the latter takes precedence.

Supported Aggregation Operations

The MongoDB Aggregation Framework provides the following types of Aggregation Operations:

Pipeline Aggregation Operators
Group Aggregation Operators
Boolean Aggregation Operators
Comparison Aggregation Operators
Arithmetic Aggregation Operators
String Aggregation Operators
Date Aggregation Operators
Array Aggregation Operators
Conditional Aggregation Operators
Lookup Aggregation Operators

At the time of this writing we provide support for the following Aggregation Operations in Spring Data MongoDB.

Table 1. Aggregation Operations currently supported by Spring Data MongoDB

Pipeline Aggregation Operators	bucket, bucketAuto, count, facet, geoNear, graphLookup, group, limit, lookup, match, project, replaceRoot, skip, sort, unwind
Set Aggregation Operators	setEquals, setIntersection, setUnion, setDifference, setIsSubset, anyElementTrue, allElementsTrue
Group Aggregation Operators	addToSet, first, last, max, min, avg, push, sum, (*count), stdDevPop, stdDevSamp
Arithmetic Aggregation Operators	abs, add (via plus), ceil, divide, exp, floor, ln, log, log10, mod, multiply, pow, sqrt, subtract (via minus), trunc
String Aggregation Operators	concat, substr, toLower, toUpper, stcasecmp, indexOfBytes, indexOfCP, split, strLenBytes, strLenCP, substrCP,
Comparison Aggregation Operators	eq (*via: is), gt, gte, lt, lte, ne
Array Aggregation Operators	arrayElementAt, concatArrays, filter, in, indexOfArray, isArray, range, reverseArray, reduce, size, slice, zip
Literal Operators	literal
Date Aggregation Operators	dayOfYear, dayOfMonth, dayOfWeek, year, month, week, hour, minute, second, millisecond, dateToString, isoDayOfWeek, isoWeek, isoWeekYear
Variable Operators	map
Conditional Aggregation Operators	cond, ifNull, switch
Type Aggregation Operators	type

Note that the aggregation operations not listed here are currently not supported by Spring Data MongoDB. Comparison aggregation operators are expressed as Criteria expressions.

*) The operation is mapped or added by Spring Data MongoDB.

Projection Expressions

Projection expressions are used to define the fields that are the outcome of a particular aggregation step. Projection expressions can be defined via the project method of the Aggregation class either by passing a list of String's or an aggregation framework Fields object. The projection can be extended with additional fields through a fluent API via the and(String) method and aliased via the as(String) method. Note that one can also define fields with aliases via the static factory method Fields.field of the aggregation framework that can then be used to construct a new Fields instance. References to projected fields in later aggregation stages are only valid by using the field name of included fields or their alias of aliased or newly defined fields. Fields not included in the projection cannot be referenced in later aggregation stages.

Example 17. Projection expression examples

// will generate {$project: {name: 1, netPrice: 1}}
project("name", "netPrice")

// will generate {$project: {bar: $foo}}
project().and("foo").as("bar")

// will generate {$project: {a: 1, b: 1, bar: $foo}}
project("a","b").and("foo").as("bar")

Example 18. Multi-Stage Aggregation using Projection and Sorting

// will generate {$project: {name: 1, netPrice: 1}}, {$sort: {name: 1}}
project("name", "netPrice"), sort(ASC, "name")

// will generate {$project: {bar: $foo}}, {$sort: {bar: 1}}
project().and("foo").as("bar"), sort(ASC, "bar")

// this will not work
project().and("foo").as("bar"), sort(ASC, "foo")

More examples for project operations can be found in the AggregationTests class. Note that further details regarding the projection expressions can be found in the corresponding section of the MongoDB Aggregation Framework reference documentation.

Faceted classification

MongoDB supports as of Version 3.4 faceted classification using the Aggregation Framework. A faceted classification uses semantic categories, either general or subject-specific, that are combined to create the full classification entry. Documents flowing through the aggregation pipeline are classificated into buckets. A multi-faceted classification enables various aggregations on the same set of input documents, without needing to retrieve the input documents multiple times.

Buckets

Bucket operations categorize incoming documents into groups, called buckets, based on a specified expression and bucket boundaries. Bucket operations require a grouping field or grouping expression. They can be defined via the bucket()/bucketAuto() methods of the Aggregate class. BucketOperation and BucketAutoOperation can expose accumulations based on aggregation expressions for input documents. The bucket operation can be extended with additional parameters through a fluent API via the with…() methods, the andOutput(String) method and aliased via the as(String) method. Each bucket is represented as a document in the output.

BucketOperation takes a defined set of boundaries to group incoming documents into these categories. Boundaries are required to be sorted.

Example 19. Bucket operation examples

// will generate {$bucket: {groupBy: $price, boundaries: [0, 100, 400]}}
bucket("price").withBoundaries(0, 100, 400);

// will generate {$bucket: {groupBy: $price, default: "Other" boundaries: [0, 100]}}
bucket("price").withBoundaries(0, 100).withDefault("Other");

// will generate {$bucket: {groupBy: $price, boundaries: [0, 100], output: { count: { $sum: 1}}}}
bucket("price").withBoundaries(0, 100).andOutputCount().as("count");

// will generate {$bucket: {groupBy: $price, boundaries: [0, 100], 5, output: { titles: { $push: "$title"}}}
bucket("price").withBoundaries(0, 100).andOutput("title").push().as("titles");

BucketAutoOperation determines boundaries itself in an attempt to evenly distribute documents into a specified number of buckets. BucketAutoOperation optionally takes a granularity specifies the preferred number series to use to ensure that the calculated boundary edges end on preferred round numbers or their powers of 10.

Example 20. Bucket operation examples

// will generate {$bucketAuto: {groupBy: $price, buckets: 5}}
bucketAuto("price", 5)

// will generate {$bucketAuto: {groupBy: $price, buckets: 5, granularity: "E24"}}
bucketAuto("price", 5).withGranularity(Granularities.E24).withDefault("Other");

// will generate {$bucketAuto: {groupBy: $price, buckets: 5, output: { titles: { $push: "$title"}}}
bucketAuto("price", 5).andOutput("title").push().as("titles");

Bucket operations can use AggregationExpression via andOutput() and SpEL expressions via andOutputExpression() to create output fields in buckets.

Note that further details regarding bucket expressions can be found in the $bucket section and $bucketAuto section of the MongoDB Aggregation Framework reference documentation.

Multi-faceted aggregation

Multiple aggregation pipelines can be used to create multi-faceted aggregations which characterize data across multiple dimensions, or facets, within a single aggregation stage. Multi-faceted aggregations provide multiple filters and categorizations to guide data browsing and analysis. A common implementation of faceting is how many online retailers provide ways to narrow down search results by applying filters on product price, manufacturer, size, etc.

A FacetOperation can be defined via the facet() method of the Aggregation class. It can be customized with multiple aggregation pipelines via the and() method. Each sub-pipeline has its own field in the output document where its results are stored as an array of documents.

Sub-pipelines can project and filter input documents prior grouping. Common cases are extraction of date parts or calculations before categorization.

Example 21. Facet operation examples

// will generate {$facet: {categorizedByPrice: [ { $match: { price: {$exists : true}}}, { $bucketAuto: {groupBy: $price, buckets: 5}}]}}
facet(match(Criteria.where("price").exists(true)), bucketAuto("price", 5)).as("categorizedByPrice"))

// will generate {$facet: {categorizedByYear: [
//                            { $project: { title: 1, publicationYear: { $year: "publicationDate"}}},
//                            { $bucketAuto: {groupBy: $price, buckets: 5, output: { titles: {$push:"$title"}}}
//                         ]}}
facet(project("title").and("publicationDate").extractYear().as("publicationYear"),
      bucketAuto("publicationYear", 5).andOutput("title").push().as("titles"))
  .as("categorizedByYear"))

Note that further details regarding facet operation can be found in the $facet section of the MongoDB Aggregation Framework reference documentation.

Spring Expression Support in Projection Expressions

We support the use of SpEL expression in projection expressions via the andExpression method of the ProjectionOperation and BucketOperation classes. This allows you to define the desired expression as a SpEL expression which is translated into a corresponding MongoDB projection expression part on query execution. This makes it much easier to express complex calculations.

Complex calculations with SpEL expressions

The following SpEL expression:

1 + (q + 1) / (q - 1)

will be translated into the following projection expression part:

{ "$add" : [ 1, {
    "$divide" : [ {
        "$add":["$q", 1]}, {
        "$subtract":[ "$q", 1]}
    ]
}]}

Have a look at an example in more context in Aggregation Framework Example 5 and Aggregation Framework Example 6. You can find more usage examples for supported SpEL expression constructs in SpelExpressionTransformerUnitTests.

Table 2. Supported SpEL transformations

a == b	{ $eq : [$a, $b] }
a != b	{ $ne : [$a , $b] }
a > b	{ $gt : [$a, $b] }
a >= b	{ $gte : [$a, $b] }
a < b	{ $lt : [$a, $b] }
a ⇐ b	{ $lte : [$a, $b] }
a + b	{ $add : [$a, $b] }
a - b	{ $subtract : [$a, $b] }
a * b	{ $multiply : [$a, $b] }
a / b	{ $divide : [$a, $b] }
a^b	{ $pow : [$a, $b] }
a % b	{ $mod : [$a, $b] }
a && b	{ $and : [$a, $b] }
a \|\| b	{ $or : [$a, $b] }
!a	{ $not : [$a] }

Next to the transformations shown in Supported SpEL transformations it is possible to use standard SpEL operations like new to eg. create arrays and reference expressions via their name followed by the arguments to use in brackets.

// { $setEquals : [$a, [5, 8, 13] ] }
.andExpression("setEquals(a, new int[]{5, 8, 13})");

Aggregation Framework Examples

The following examples demonstrate the usage patterns for the MongoDB Aggregation Framework with Spring Data MongoDB.

Aggregation Framework Example 1

In this introductory example we want to aggregate a list of tags to get the occurrence count of a particular tag from a MongoDB collection called "tags" sorted by the occurrence count in descending order. This example demonstrates the usage of grouping, sorting, projections (selection) and unwinding (result splitting).

class TagCount {
 String tag;
 int n;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

Aggregation agg = newAggregation(
    project("tags"),
    unwind("tags"),
    group("tags").count().as("n"),
    project("n").and("tag").previousOperation(),
    sort(DESC, "n")
);

AggregationResults<TagCount> results = mongoTemplate.aggregate(agg, "tags", TagCount.class);
List<TagCount> tagCount = results.getMappedResults();

In order to do this we first create a new aggregation via the newAggregation static factory method to which we pass a list of aggregation operations. These aggregate operations define the aggregation pipeline of our Aggregation.
As a second step we select the "tags" field (which is an array of strings) from the input collection with the project operation.
In a third step we use the unwind operation to generate a new document for each tag within the "tags" array.
In the forth step we use the group operation to define a group for each "tags"-value for which we aggregate the occurrence count via the count aggregation operator and collect the result in a new field called "n".
As a fifth step we select the field "n" and create an alias for the id-field generated from the previous group operation (hence the call to previousOperation()) with the name "tag".
As the sixth step we sort the resulting list of tags by their occurrence count in descending order via the sort operation.
Finally we call the aggregate Method on the MongoTemplate in order to let MongoDB perform the actual aggregation operation with the created Aggregation as an argument.

Note that the input collection is explicitly specified as the "tags" parameter to the aggregate Method. If the name of the input collection is not specified explicitly, it is derived from the input-class passed as first parameter to the newAggreation Method.

Aggregation Framework Example 2

This example is based on the Largest and Smallest Cities by State example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return the smallest and largest cities by population for each state, using the aggregation framework. This example demonstrates the usage of grouping, sorting and projections (selection).

class ZipInfo {
   String id;
   String city;
   String state;
   @Field("pop") int population;
   @Field("loc") double[] location;
}

class City {
   String name;
   int population;
}

class ZipInfoStats {
   String id;
   String state;
   City biggestCity;
   City smallestCity;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> aggregation = newAggregation(ZipInfo.class,
    group("state", "city")
       .sum("population").as("pop"),
    sort(ASC, "pop", "state", "city"),
    group("state")
       .last("city").as("biggestCity")
       .last("pop").as("biggestPop")
       .first("city").as("smallestCity")
       .first("pop").as("smallestPop"),
    project()
       .and("state").previousOperation()
       .and("biggestCity")
          .nested(bind("name", "biggestCity").and("population", "biggestPop"))
       .and("smallestCity")
          .nested(bind("name", "smallestCity").and("population", "smallestPop")),
    sort(ASC, "state")
);

AggregationResults<ZipInfoStats> result = mongoTemplate.aggregate(aggregation, ZipInfoStats.class);
ZipInfoStats firstZipInfoStats = result.getMappedResults().get(0);

The class ZipInfo maps the structure of the given input-collection. The class ZipInfoStats defines the structure in the desired output format.
As a first step we use the group operation to define a group from the input-collection. The grouping criteria is the combination of the fields "state" and "city" which forms the id structure of the group. We aggregate the value of the "population" property from the grouped elements with by using the sum operator saving the result in the field "pop".
In a second step we use the sort operation to sort the intermediate-result by the fields "pop", "state" and "city" in ascending order, such that the smallest city is at the top and the biggest city is at the bottom of the result. Note that the sorting on "state" and "city" is implicitly performed against the group id fields which Spring Data MongoDB took care of.
In the third step we use a group operation again to group the intermediate result by "state". Note that "state" again implicitly references an group-id field. We select the name and the population count of the biggest and smallest city with calls to the last(…) and first(…) operator respectively via the project operation.
As the forth step we select the "state" field from the previous group operation. Note that "state" again implicitly references an group-id field. As we do not want an implicitly generated id to appear, we exclude the id from the previous operation via and(previousOperation()).exclude(). As we want to populate the nested City structures in our output-class accordingly we have to emit appropriate sub-documents with the nested method.
Finally as the fifth step we sort the resulting list of StateStats by their state name in ascending order via the sort operation.

Note that we derive the name of the input-collection from the ZipInfo-class passed as first parameter to the newAggregation-Method.

Aggregation Framework Example 3

This example is based on the States with Populations Over 10 Million example from the MongoDB Aggregation Framework documentation. We added additional sorting to produce stable results with different MongoDB versions. Here we want to return all states with a population greater than 10 million, using the aggregation framework. This example demonstrates the usage of grouping, sorting and matching (filtering).

class StateStats {
   @Id String id;
   String state;
   @Field("totalPop") int totalPopulation;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<ZipInfo> agg = newAggregation(ZipInfo.class,
    group("state").sum("population").as("totalPop"),
    sort(ASC, previousOperation(), "totalPop"),
    match(where("totalPop").gte(10 * 1000 * 1000))
);

AggregationResults<StateStats> result = mongoTemplate.aggregate(agg, StateStats.class);
List<StateStats> stateStatsList = result.getMappedResults();

As a first step we group the input collection by the "state" field and calculate the sum of the "population" field and store the result in the new field "totalPop".
In the second step we sort the intermediate result by the id-reference of the previous group operation in addition to the "totalPop" field in ascending order.
Finally in the third step we filter the intermediate result by using a match operation which accepts a Criteria query as an argument.

Note that we derive the name of the input-collection from the ZipInfo-class passed as first parameter to the newAggregation-Method.

Aggregation Framework Example 4

This example demonstrates the use of simple arithmetic operations in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .and("netPrice").plus(1).as("netPricePlus1")
        .and("netPrice").minus(1).as("netPriceMinus1")
        .and("netPrice").multiply(1.19).as("grossPrice")
        .and("netPrice").divide(2).as("netPriceDiv2")
        .and("spaceUnits").mod(2).as("spaceUnitsMod2")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we derive the name of the input-collection from the Product-class passed as first parameter to the newAggregation-Method.

Aggregation Framework Example 5

This example demonstrates the use of simple arithmetic operations derived from SpEL Expressions in the projection operation.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("netPrice + 1").as("netPricePlus1")
        .andExpression("netPrice - 1").as("netPriceMinus1")
        .andExpression("netPrice / 2").as("netPriceDiv2")
        .andExpression("netPrice * 1.19").as("grossPrice")
        .andExpression("spaceUnits % 2").as("spaceUnitsMod2")
        .andExpression("(netPrice * 0.8  + 1.2) * 1.19").as("grossPriceIncludingDiscountAndCharge")

);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Aggregation Framework Example 6

This example demonstrates the use of complex arithmetic operations derived from SpEL Expressions in the projection operation.

Note: The additional parameters passed to the addExpression Method can be referenced via indexer expressions according to their position. In this example we reference the parameter which is the first parameter of the parameters array via [0]. External parameter expressions are replaced with their respective values when the SpEL expression is transformed into a MongoDB aggregation framework expression.

class Product {
    String id;
    String name;
    double netPrice;
    int spaceUnits;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

double shippingCosts = 1.2;

TypedAggregation<Product> agg = newAggregation(Product.class,
    project("name", "netPrice")
        .andExpression("(netPrice * (1-discountRate)  + [0]) * (1+taxRate)", shippingCosts).as("salesPrice")
);

AggregationResults<Document> result = mongoTemplate.aggregate(agg, Document.class);
List<Document> resultList = result.getMappedResults();

Note that we can also refer to other fields of the document within the SpEL expression.

Aggregation Framework Example 7

This example uses conditional projection. It’s derived from the $cond reference documentation.

public class InventoryItem {

  @Id int id;
  String item;
  String description;
  int qty;
}

public class InventoryItemProjection {

  @Id int id;
  String item;
  String description;
  int qty;
  int discount
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;

TypedAggregation<InventoryItem> agg = newAggregation(InventoryItem.class,
  project("item").and("discount")
    .applyCondition(ConditionalOperator.newBuilder().when(Criteria.where("qty").gte(250))
      .then(30)
      .otherwise(20))
    .and(ifNull("description", "Unspecified")).as("description")
);

AggregationResults<InventoryItemProjection> result = mongoTemplate.aggregate(agg, "inventory", InventoryItemProjection.class);
List<InventoryItemProjection> stateStatsList = result.getMappedResults();

This one-step aggregation uses a projection operation with the inventory collection. We project the discount field using a conditional operation for all inventory items that have a qty greater or equal to 250. A second conditional projection is performed for the description field. We apply the description Unspecified to all items that either do not have a description field of items that have a null description.

Overriding default mapping with custom converters

In order to have more fine-grained control over the mapping process you can register Spring converters with the MongoConverter implementations such as the MappingMongoConverter.

The MappingMongoConverter checks to see if there are any Spring converters that can handle a specific class before attempting to map the object itself. To 'hijack' the normal mapping strategies of the MappingMongoConverter, perhaps for increased performance or other custom mapping needs, you first need to create an implementation of the Spring Converter interface and then register it with the MappingConverter.

Note	For more information on the Spring type conversion service see the reference docs here.

Saving using a registered Spring Converter

An example implementation of the Converter that converts from a Person object to a org.bson.Document is shown below

import org.springframework.core.convert.converter.Converter;

import org.bson.Document;

public class PersonWriteConverter implements Converter<Person, Document> {

  public Document convert(Person source) {
    Document document = new Document();
    document.put("_id", source.getId());
    document.put("name", source.getFirstName());
    document.put("age", source.getAge());
    return document;
  }
}

Reading using a Spring Converter

An example implementation of a Converter that converts from a Document to a Person object is shown below.

public class PersonReadConverter implements Converter<Document, Person> {

  public Person convert(Document source) {
    Person p = new Person((ObjectId) source.get("_id"), (String) source.get("name"));
    p.setAge((Integer) source.get("age"));
    return p;
  }
}

Registering Spring Converters with the MongoConverter

The Mongo Spring namespace provides a convenience way to register Spring Converter s with the MappingMongoConverter. The configuration snippet below shows how to manually register converter beans as well as configuring the wrapping MappingMongoConverter into a MongoTemplate.

<mongo:db-factory dbname="database"/>

<mongo:mapping-converter>
  <mongo:custom-converters>
    <mongo:converter ref="readConverter"/>
    <mongo:converter>
      <bean class="org.springframework.data.mongodb.test.PersonWriteConverter"/>
    </mongo:converter>
  </mongo:custom-converters>
</mongo:mapping-converter>

<bean id="readConverter" class="org.springframework.data.mongodb.test.PersonReadConverter"/>

<bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
  <constructor-arg name="mongoDbFactory" ref="mongoDbFactory"/>
  <constructor-arg name="mongoConverter" ref="mappingConverter"/>
</bean>

You can also use the base-package attribute of the custom-converters element to enable classpath scanning for all Converter and GenericConverter implementations below the given package.

<mongo:mapping-converter>
  <mongo:custom-converters base-package="com.acme.**.converters" />
</mongo:mapping-converter>

Converter disambiguation

Generally we inspect the Converter implementations for the source and target types they convert from and to. Depending on whether one of those is a type MongoDB can handle natively we will register the converter instance as reading or writing one. Have a look at the following samples:

// Write converter as only the target type is one Mongo can handle natively
class MyConverter implements Converter<Person, String> { … }

// Read converter as only the source type is one Mongo can handle natively
class MyConverter implements Converter<String, Person> { … }

In case you write a Converter whose source and target type are native Mongo types there’s no way for us to determine whether we should consider it as reading or writing converter. Registering the converter instance as both might lead to unwanted results then. E.g. a Converter<String, Long> is ambiguous although it probably does not make sense to try to convert all String instances into Long instances when writing. To be generally able to force the infrastructure to register a converter for one way only we provide @ReadingConverter as well as @WritingConverter to be used in the converter implementation.

Index and Collection management

MongoTemplate provides a few methods for managing indexes and collections. These are collected into a helper interface called IndexOperations. You access these operations by calling the method indexOps and pass in either the collection name or the java.lang.Class of your entity (the collection name will be derived from the .class either by name or via annotation metadata).

The IndexOperations interface is shown below

public interface IndexOperations {

  void ensureIndex(IndexDefinition indexDefinition);

  void dropIndex(String name);

  void dropAllIndexes();

  void resetIndexCache();

  List<IndexInfo> getIndexInfo();
}

Methods for creating an Index

We can create an index on a collection to improve query performance.

Creating an index using the MongoTemplate

mongoTemplate.indexOps(Person.class).ensureIndex(new Index().on("name",Order.ASCENDING));

ensureIndex Ensure that an index for the provided IndexDefinition exists for the collection.

You can create standard, geospatial and text indexes using the classes IndexDefinition, GeoSpatialIndex and TextIndexDefinition. For example, given the Venue class defined in a previous section, you would declare a geospatial query as shown below.

mongoTemplate.indexOps(Venue.class).ensureIndex(new GeospatialIndex("location"));

Note	`Index` and `GeospatialIndex` support configuration of collations.

Accessing index information

The IndexOperations interface has the method getIndexInfo that returns a list of IndexInfo objects. This contains all the indexes defined on the collection. Here is an example that defines an index on the Person class that has age property.

template.indexOps(Person.class).ensureIndex(new Index().on("age", Order.DESCENDING).unique(Duplicates.DROP));

List<IndexInfo> indexInfoList = template.indexOps(Person.class).getIndexInfo();

// Contains
// [IndexInfo [fieldSpec={_id=ASCENDING}, name=_id_, unique=false, dropDuplicates=false, sparse=false],
//  IndexInfo [fieldSpec={age=DESCENDING}, name=age_-1, unique=true, dropDuplicates=true, sparse=false]]

Methods for working with a Collection

It’s time to look at some code examples showing how to use the MongoTemplate. First we look at creating our first collection.

Example 22. Working with collections using the MongoTemplate

DBCollection collection = null;
if (!mongoTemplate.getCollectionNames().contains("MyNewCollection")) {
    collection = mongoTemplate.createCollection("MyNewCollection");
}

mongoTemplate.dropCollection("MyNewCollection");

getCollectionNames Returns a set of collection names.
collectionExists Check to see if a collection with a given name exists.
createCollection Create an uncapped collection
dropCollection Drop the collection
getCollection Get a collection by name, creating it if it doesn’t exist.

Note	Collection creation allows customization via `CollectionOptions` and supports collations.

Executing Commands

You can also get at the MongoDB driver’s MongoDatabase.runCommand( ) method using the executeCommand(…) methods on MongoTemplate. These will also perform exception translation into Spring’s DataAccessException hierarchy.

Methods for executing commands

Document executeCommand (Document command) Execute a MongoDB command.
Document executeCommand (Document command, ReadPreference readPreference) Execute a MongoDB command using the given nullable MongoDB ReadPreference.
Document executeCommand (String jsonCommand) Execute the a MongoDB command expressed as a JSON string.

Lifecycle Events

Built into the MongoDB mapping framework are several org.springframework.context.ApplicationEvent events that your application can respond to by registering special beans in the ApplicationContext. By being based off Spring’s ApplicationContext event infrastructure this enables other products, such as Spring Integration, to easily receive these events as they are a well known eventing mechanism in Spring based applications.

To intercept an object before it goes through the conversion process (which turns your domain object into a org.bson.Document), you’d register a subclass of AbstractMongoEventListener that overrides the onBeforeConvert method. When the event is dispatched, your listener will be called and passed the domain object before it goes into the converter.

public class BeforeConvertListener extends AbstractMongoEventListener<Person> {
  @Override
  public void onBeforeConvert(BeforeConvertEvent<Person> event) {
    ... does some auditing manipulation, set timestamps, whatever ...
  }
}

To intercept an object before it goes into the database, you’d register a subclass of org.springframework.data.mongodb.core.mapping.event.AbstractMongoEventListener that overrides the onBeforeSave method. When the event is dispatched, your listener will be called and passed the domain object and the converted com.mongodb.Document.

public class BeforeSaveListener extends AbstractMongoEventListener<Person> {
  @Override
  public void onBeforeSave(BeforeSaveEvent<Person> event) {
    … change values, delete them, whatever …
  }
}

Simply declaring these beans in your Spring ApplicationContext will cause them to be invoked whenever the event is dispatched.

The list of callback methods that are present in AbstractMappingEventListener are

onBeforeConvert - called in MongoTemplate insert, insertList and save operations before the object is converted to a Document using a MongoConveter.
onBeforeSave - called in MongoTemplate insert, insertList and save operations before inserting/saving the Document in the database.
onAfterSave - called in MongoTemplate insert, insertList and save operations after inserting/saving the Document in the database.
onAfterLoad - called in MongoTemplate find, findAndRemove, findOne and getCollection methods after the Document is retrieved from the database.
onAfterConvert - called in MongoTemplate find, findAndRemove, findOne and getCollection methods after the Document retrieved from the database was converted to a POJO.

Note	Lifecycle events are only emitted for root level types. Complex types used as properties within a document root are not subject of event publication unless they are document references annotated with `@DBRef`.

Exception Translation

The Spring framework provides exception translation for a wide variety of database and mapping technologies. This has traditionally been for JDBC and JPA. The Spring support for MongoDB extends this feature to the MongoDB Database by providing an implementation of the org.springframework.dao.support.PersistenceExceptionTranslator interface.

The motivation behind mapping to Spring’s consistent data access exception hierarchy is that you are then able to write portable and descriptive exception handling code without resorting to coding against MongoDB error codes. All of Spring’s data access exceptions are inherited from the root DataAccessException class so you can be sure that you will be able to catch all database related exception within a single try-catch block. Note, that not all exceptions thrown by the MongoDB driver inherit from the MongoException class. The inner exception and message are preserved so no information is lost.

Some of the mappings performed by the MongoExceptionTranslator are: com.mongodb.Network to DataAccessResourceFailureException and MongoException error codes 1003, 12001, 12010, 12011, 12012 to InvalidDataAccessApiUsageException. Look into the implementation for more details on the mapping.

Execution callbacks

One common design feature of all Spring template classes is that all functionality is routed into one of the templates execute callback methods. This helps ensure that exceptions and any resource management that maybe required are performed consistency. While this was of much greater need in the case of JDBC and JMS than with MongoDB, it still offers a single spot for exception translation and logging to occur. As such, using these execute callback is the preferred way to access the MongoDB driver’s DB and DBCollection objects to perform uncommon operations that were not exposed as methods on MongoTemplate.

Here is a list of execute callback methods.

<T> T execute (Class<?> entityClass, CollectionCallback<T> action) Executes the given CollectionCallback for the entity collection of the specified class.
<T> T execute (String collectionName, CollectionCallback<T> action) Executes the given CollectionCallback on the collection of the given name.
<T> T execute (DbCallback<T> action) Spring Data MongoDB provides support for the Aggregation Framework introduced to MongoDB in version 2.2. Executes a DbCallback translating any exceptions as necessary.
<T> T execute (String collectionName, DbCallback<T> action) Executes a DbCallback on the collection of the given name translating any exceptions as necessary.
<T> T executeInSession (DbCallback<T> action) Executes the given DbCallback within the same connection to the database so as to ensure consistency in a write heavy environment where you may read the data that you wrote.

Here is an example that uses the CollectionCallback to return information about an index

boolean hasIndex = template.execute("geolocation", new CollectionCallbackBoolean>() {
  public Boolean doInCollection(Venue.class, DBCollection collection) throws MongoException, DataAccessException {
    List<Document> indexes = collection.getIndexInfo();
    for (Document document : indexes) {
      if ("location_2d".equals(document.get("name"))) {
        return true;
      }
    }
    return false;
  }
});

GridFS support

MongoDB supports storing binary files inside it’s filesystem GridFS. Spring Data MongoDB provides a GridFsOperations interface as well as the according implementation GridFsTemplate to easily interact with the filesystem. You can setup a GridFsTemplate instance by handing it a MongoDbFactory as well as a MongoConverter:

Example 23. JavaConfig setup for a GridFsTemplate

class GridFsConfiguration extends AbstractMongoConfiguration {

  // … further configuration omitted

  @Bean
  public GridFsTemplate gridFsTemplate() {
    return new GridFsTemplate(mongoDbFactory(), mappingMongoConverter());
  }
}

An according XML configuration looks like this:

Example 24. XML configuration for a GridFsTemplate

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:mongo="http://www.springframework.org/schema/data/mongo"
  xsi:schemaLocation="http://www.springframework.org/schema/data/mongo
                      http://www.springframework.org/schema/data/mongo/spring-mongo.xsd
                      http://www.springframework.org/schema/beans
                      http://www.springframework.org/schema/beans/spring-beans.xsd">

  <mongo:db-factory id="mongoDbFactory" dbname="database" />
  <mongo:mapping-converter id="converter" />

  <bean class="org.springframework.data.mongodb.gridfs.GridFsTemplate">
    <constructor-arg ref="mongoDbFactory" />
    <constructor-arg ref="converter" />
  </bean>

</beans>

The template can now be injected and used to perform storage and retrieval operations.

Example 25. Using GridFsTemplate to store files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void storeFileToGridFs() {

    FileMetadata metadata = new FileMetadata();
    // populate metadata
    Resource file = … // lookup File or Resource

    operations.store(file.getInputStream(), "filename.txt", metadata);
  }
}

The store(…) operations take an InputStream, a filename and optionally metadata information about the file to store. The metadata can be an arbitrary object which will be marshaled by the MongoConverter configured with the GridFsTemplate. Alternatively you can also provide a Document as well.

Reading files from the filesystem can either be achieved through the find(…) or getResources(…) methods. Let’s have a look at the find(…) methods first. You can either find a single file matching a Query or multiple ones. To easily define file queries we provide the GridFsCriteria helper class. It provides static factory methods to encapsulate default metadata fields (e.g. whereFilename(), whereContentType()) or the custom one through whereMetaData().

Example 26. Using GridFsTemplate to query for files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void findFilesInGridFs() {
    GridFSFindIterable result = operations.find(query(whereFilename().is("filename.txt")))
  }
}

Note	Currently MongoDB does not support defining sort criteria when retrieving files from GridFS. Thus any sort criteria defined on the `Query` instance handed into the `find(…)` method will be disregarded.

The other option to read files from the GridFs is using the methods introduced by the ResourcePatternResolver interface. They allow handing an Ant path into the method ar thus retrieve files matching the given pattern.

Example 27. Using GridFsTemplate to read files

class GridFsClient {

  @Autowired
  GridFsOperations operations;

  @Test
  public void readFilesFromGridFs() {
    GridFsResources[] txtFiles = operations.getResources("*.txt");
  }
}

GridFsOperations extending ResourcePatternResolver allows the GridFsTemplate e.g. to be plugged into an ApplicationContext to read Spring Config files from a MongoDB.

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MongoDB support

Getting Started

Examples Repository

Connecting to MongoDB with Spring

Registering a Mongo instance using Java based metadata

Registering a Mongo instance using XML based metadata

The MongoDbFactory interface

Registering a MongoDbFactory instance using Java based metadata

Registering a MongoDbFactory instance using XML based metadata

Introduction to MongoTemplate

Instantiating MongoTemplate

WriteResultChecking Policy

WriteConcern

WriteConcernResolver

Saving, Updating, and Removing Documents

How the _id field is handled in the mapping layer

Type mapping

Customizing type mapping

Configuring custom type mapping

Methods for saving and inserting documents

Which collection will my documents be saved into?

Inserting or saving individual objects

Inserting several objects in a batch

Updating documents in a collection

Methods for executing updates for documents

Methods for the Update class

Upserting documents in a collection

Finding and Upserting documents in a collection

Methods for removing documents

Optimistic locking

Querying Documents

Querying documents in a collection

Methods for the Criteria class

Methods for the Query class

Methods for querying for documents

GeoSpatial Queries

Geo near queries

GeoJSON Support

GeoJSON types in domain classes

GeoJSON types in repository query methods

Full Text Queries

Full Text Search

Collations

Fluent Template API

Map-Reduce Operations

Example Usage

Script Operations

Example Usage

Group Operations

Example Usage

Aggregation Framework Support

Basic Concepts

Supported Aggregation Operations

Projection Expressions

Faceted classification

Buckets

Multi-faceted aggregation

Spring Expression Support in Projection Expressions

Complex calculations with SpEL expressions

Aggregation Framework Examples

Overriding default mapping with custom converters

Saving using a registered Spring Converter

Reading using a Spring Converter

Registering Spring Converters with the MongoConverter

Converter disambiguation

Index and Collection management

Methods for creating an Index

Creating an index using the MongoTemplate

Accessing index information

Methods for working with a Collection

Executing Commands

Methods for executing commands

Lifecycle Events

Exception Translation

How the `_id` field is handled in the mapping layer