Collectively, we have a lot of experience with users suffering unexpected issues because they have not configured important settings. In previous versions of Elasticsearch, misconfiguration of some of these settings were logged as warnings. Understandably, users sometimes miss these log messages. To ensure that these settings receive the attention that they deserve, Elasticsearch has bootstrap checks upon startup.
These bootstrap checks inspect a variety of Elasticsearch and system settings and compare them to values that are safe for the operation of Elasticsearch. If Elasticsearch is in development mode, any bootstrap checks that fail appear as warnings in the Elasticsearch log. If Elasticsearch is in production mode, any bootstrap checks that fail will cause Elasticsearch to refuse to start.
There are some bootstrap checks that are always enforced to prevent Elasticsearch from running with incompatible settings. These checks are documented individually.
By default, {es} binds to loopback addresses for HTTP and transport (internal) communication. This is fine for downloading and playing with {es} as well as everyday development, but it’s useless for production systems. To join a cluster, an {es} node must be reachable via transport communication. To join a cluster via a non-loopback address, a node must bind transport to a non-loopback address and not be using single-node discovery. Thus, we consider an Elasticsearch node to be in development mode if it can not form a cluster with another machine via a non-loopback address, and is otherwise in production mode if it can join a cluster via non-loopback addresses.
Note that HTTP and transport can be configured independently via
http.host and transport.host; this
can be useful for configuring a single node to be reachable via HTTP for testing
purposes without triggering production mode.
We recognize that some users need to bind the transport to an external
interface for testing a remote-cluster configuration. For this situation, we
provide the discovery type single-node (configure it by setting
discovery.type to single-node); in this situation, a node will elect itself
master and will not join a cluster with any other node.
If you are running a single node in production, it is possible to evade the
bootstrap checks (either by not binding transport to an external interface, or
by binding transport to an external interface and setting the discovery type to
single-node). For this situation, you can force execution of the bootstrap
checks by setting the system property es.enforce.bootstrap.checks to true
in the JVM options. We strongly encourage you to do
this if you are in this specific situation. This system property can be used to
force execution of the bootstrap checks independent of the node configuration.
By default, {es} automatically sizes JVM heap based on a node’s
roles and total memory. If you manually override the default
sizing and start the JVM with different initial and max heap sizes, the JVM may
pause as it resizes the heap during system usage. If you enable
bootstrap.memory_lock, the JVM locks the initial heap
size on startup. If the initial heap size is not equal to the maximum heap size,
some JVM heap may not be locked after a resize. To avoid these issues, start the
JVM with an initial heap size equal to the maximum heap size.
File descriptors are a Unix construct for tracking open "files". In Unix
though, {wikipedia}/Everything_is_a_file[everything is
a file]. For example, "files" could be a physical file, a virtual file
(e.g., /proc/loadavg), or network sockets. Elasticsearch requires
lots of file descriptors (e.g., every shard is composed of multiple
segments and other files, plus connections to other nodes, etc.). This
bootstrap check is enforced on OS X and Linux. To pass the file
descriptor check, you might have to configure file
descriptors.
When the JVM does a major garbage collection it touches every page of
the heap. If any of those pages are swapped out to disk they will have
to be swapped back in to memory. That causes lots of disk thrashing that
Elasticsearch would much rather use to service requests. There are
several ways to configure a system to disallow swapping. One way is by
requesting the JVM to lock the heap in memory through mlockall (Unix)
or virtual lock (Windows). This is done via the Elasticsearch setting
bootstrap.memory_lock. However, there are
cases where this setting can be passed to Elasticsearch but
Elasticsearch is not able to lock the heap (e.g., if the elasticsearch
user does not have memlock unlimited). The memory lock check verifies
that if the bootstrap.memory_lock setting is enabled, that the JVM
was successfully able to lock the heap. To pass the memory lock check,
you might have to configure bootstrap.memory_lock.
Elasticsearch executes requests by breaking the request down into stages
and handing those stages off to different thread pool executors. There
are different thread pool executors for a variety
of tasks within Elasticsearch. Thus, Elasticsearch needs the ability to
create a lot of threads. The maximum number of threads check ensures
that the Elasticsearch process has the rights to create enough threads
under normal use. This check is enforced only on Linux. If you are on
Linux, to pass the maximum number of threads check, you must configure
your system to allow the Elasticsearch process the ability to create at
least 4096 threads. This can be done via /etc/security/limits.conf
using the nproc setting (note that you might have to increase the
limits for the root user too).
The segment files that are the components of individual shards and the translog
generations that are components of the translog can get large (exceeding
multiple gigabytes). On systems where the max size of files that can be created
by the Elasticsearch process is limited, this can lead to failed
writes. Therefore, the safest option here is that the max file size is unlimited
and that is what the max file size bootstrap check enforces. To pass the max
file check, you must configure your system to allow the Elasticsearch process
the ability to write files of unlimited size. This can be done via
/etc/security/limits.conf using the fsize setting to unlimited (note that
you might have to increase the limits for the root user too).
Elasticsearch and Lucene use mmap to great effect to map portions of
an index into the Elasticsearch address space. This keeps certain index
data off the JVM heap but in memory for blazing fast access. For this to
be effective, the Elasticsearch should have unlimited address space. The
maximum size virtual memory check enforces that the Elasticsearch
process has unlimited address space and is enforced only on Linux. To
pass the maximum size virtual memory check, you must configure your
system to allow the Elasticsearch process the ability to have unlimited
address space. This can be done via adding <user> - as unlimited
to /etc/security/limits.conf. This may require you to increase the limits
for the root user too.
Continuing from the previous point, to
use mmap effectively, Elasticsearch also requires the ability to
create many memory-mapped areas. The maximum map count check checks that
the kernel allows a process to have at least 262,144 memory-mapped areas
and is enforced on Linux only. To pass the maximum map count check, you
must configure vm.max_map_count via sysctl to be at least 262144.
Alternatively, the maximum map count check is only needed if you are using
mmapfs or hybridfs as the store type for your
indices. If you do not allow the use of mmap then this
bootstrap check will not be enforced.
There are two different JVMs provided by OpenJDK-derived JVMs: the client JVM and the server JVM. These JVMs use different compilers for producing executable machine code from Java bytecode. The client JVM is tuned for startup time and memory footprint while the server JVM is tuned for maximizing performance. The difference in performance between the two VMs can be substantial. The client JVM check ensures that Elasticsearch is not running inside the client JVM. To pass the client JVM check, you must start Elasticsearch with the server VM. On modern systems and operating systems, the server VM is the default.
There are various garbage collectors for the OpenJDK-derived JVMs
targeting different workloads. The serial collector in particular is
best suited for single logical CPU machines or extremely small heaps,
neither of which are suitable for running Elasticsearch. Using the
serial collector with Elasticsearch can be devastating for performance.
The serial collector check ensures that Elasticsearch is not configured
to run with the serial collector. To pass the serial collector check,
you must not start Elasticsearch with the serial collector (whether it’s
from the defaults for the JVM that you’re using, or you’ve explicitly
specified it with -XX:+UseSerialGC). Note that the default JVM
configuration that ships with Elasticsearch configures Elasticsearch to
use the G1GC garbage collector with JDK14 and later versions. For earlier
JDK versions, the configuration defaults to the CMS collector.
Elasticsearch installs system call filters of various flavors depending on the operating system (e.g., seccomp on Linux). These system call filters are installed to prevent the ability to execute system calls related to forking as a defense mechanism against arbitrary code execution attacks on Elasticsearch. The system call filter check ensures that if system call filters are enabled, then they were successfully installed. To pass the system call filter check you must fix any configuration errors on your system that prevented system call filters from installing (check your logs).
The JVM options OnError and OnOutOfMemoryError enable executing
arbitrary commands if the JVM encounters a fatal error (OnError) or an
OutOfMemoryError (OnOutOfMemoryError). However, by default,
Elasticsearch system call filters (seccomp) are enabled and these
filters prevent forking. Thus, using OnError or OnOutOfMemoryError
and system call filters are incompatible. The OnError and
OnOutOfMemoryError checks prevent Elasticsearch from starting if
either of these JVM options are used and system call filters are
enabled. This check is always enforced. To pass this check do not enable
OnError nor OnOutOfMemoryError; instead, upgrade to Java 8u92 and
use the JVM flag ExitOnOutOfMemoryError. While this does not have the
full capabilities of OnError nor OnOutOfMemoryError, arbitrary
forking will not be supported with seccomp enabled.
The OpenJDK project provides early-access snapshots of upcoming releases. These releases are not suitable for production. The early-access check detects these early-access snapshots. To pass this check, you must start Elasticsearch on a release build of the JVM.
The all permission check ensures that the security policy used during bootstrap
does not grant the java.security.AllPermission to Elasticsearch. Running with
the all permission granted is equivalent to disabling the security manager.
By default, when Elasticsearch first starts up it will try and discover other nodes running on the same host. If no elected master can be discovered within a few seconds then Elasticsearch will form a cluster that includes any other nodes that were discovered. It is useful to be able to form this cluster without any extra configuration in development mode, but this is unsuitable for production because it’s possible to form multiple clusters and lose data as a result.
This bootstrap check ensures that discovery is not running with the default configuration. It can be satisfied by setting at least one of the following properties:
-
discovery.seed_hosts -
discovery.seed_providers -
cluster.initial_master_nodes
Note that you must remove cluster.initial_master_nodes
from the configuration of every node after the cluster has started for the
first time. Instead, configure discovery.seed_hosts or
discovery.seed_providers. If you do not need any discovery configuration, for
instance if running a single-node cluster, set discovery.seed_hosts: [] to
disable discovery and satisfy this bootstrap check.