- Elasticsearch Guide: other versions:
- Elasticsearch introduction
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- G1GC check
- All permission check
- Discovery configuration check
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Full-cluster restart and rolling restart
- Set up X-Pack
- Configuring X-Pack Java Clients
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- Stats Aggregation
- Sum Aggregation
- Top Hits Aggregation
- Value Count Aggregation
- Median Absolute Deviation Aggregation
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram Aggregation
- Children Aggregation
- Composite Aggregation
- Date histogram aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- GeoTile Grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Parent Aggregation
- Range Aggregation
- Rare Terms Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Subtleties of bucketing range fields
- Pipeline Aggregations
- Avg Bucket Aggregation
- Derivative Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Moving Function Aggregation
- Cumulative Sum Aggregation
- Cumulative Cardinality Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Bucket Sort Aggregation
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Metrics Aggregations
- Query DSL
- Search across clusters
- Scripting
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Char Group Tokenizer
- Classic Tokenizer
- Edge n-gram tokenizer
- Keyword Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- N-gram tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Pattern Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Standard Tokenizer
- Thai Tokenizer
- UAX URL Email Tokenizer
- Whitespace Tokenizer
- Token Filters
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten Graph Token Filter
- Hunspell Token Filter
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Length Token Filter
- Limit Token Count Token Filter
- Lowercase Token Filter
- MinHash Token Filter
- Multiplexer Token Filter
- N-gram
- Normalization Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Phonetic Token Filter
- Porter Stem Token Filter
- Predicate Token Filter Script
- Remove Duplicates Token Filter
- Reverse Token Filter
- Shingle Token Filter
- Snowball Token Filter
- Stemmer Token Filter
- Stemmer Override Token Filter
- Stop Token Filter
- Synonym Token Filter
- Synonym Graph Token Filter
- Trim Token Filter
- Truncate Token Filter
- Unique Token Filter
- Uppercase Token Filter
- Word Delimiter Token Filter
- Word Delimiter Graph Token Filter
- Character Filters
- Modules
- Index modules
- Ingest node
- Pipeline Definition
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Bytes Processor
- Circle Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Dissect Processor
- Dot Expander Processor
- Drop Processor
- Fail Processor
- Foreach Processor
- GeoIP Processor
- Grok Processor
- Gsub Processor
- HTML Strip Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Pipeline Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Set Security User Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- URL Decode Processor
- User Agent processor
- Managing the index lifecycle
- Getting started with index lifecycle management
- Policy phases and actions
- Set up index lifecycle management policy
- Using policies to manage index rollover
- Update policy
- Index lifecycle error handling
- Restoring snapshots of managed indices
- Start and stop index lifecycle management
- Using ILM with existing indices
- Getting started with snapshot lifecycle management
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Monitor a cluster
- Frozen indices
- Roll up or transform your data
- Set up a cluster for high availability
- Secure a cluster
- Overview
- Configuring security
- User authentication
- Built-in users
- Internal users
- Token-based authentication services
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Security privileges
- Document level security
- Field level security
- Granting privileges for indices and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enabling audit logging
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Alerting on cluster and index events
- Command line tools
- How To
- Testing
- Glossary of terms
- REST APIs
- API conventions
- cat APIs
- Cluster APIs
- Cross-cluster replication APIs
- Document APIs
- Explore API
- Index APIs
- Add index alias
- Analyze
- Clear cache
- Clone index
- Close index
- Create index
- Delete index
- Delete index alias
- Delete index template
- Flush
- Force merge
- Freeze index
- Get field mapping
- Get index
- Get index alias
- Get index settings
- Get index template
- Get mapping
- Index alias exists
- Index exists
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists
- Open index
- Put index template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Index lifecycle management API
- Ingest APIs
- Info API
- Licensing APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
- Create datafeeds
- Create filter
- Delete calendar
- Delete datafeeds
- Delete events from calendar
- Delete filter
- Delete forecast
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Find file structure
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Revert model snapshots
- Set upgrade mode
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filter
- Update jobs
- Update model snapshots
- Machine learning data frame analytics APIs
- Migration APIs
- Reload search analyzers
- Rollup APIs
- Search APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect authenticate API
- OpenID Connect logout API
- SSL certificate
- Snapshot lifecycle management API
- Transform APIs
- Watcher APIs
- Definitions
- Release highlights
- Breaking changes
- Release notes
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
Install Elasticsearch with Docker
editInstall Elasticsearch with Docker
editElasticsearch is also available as Docker images. The images use centos:7 as the base image.
A list of all published Docker images and tags is available at www.docker.elastic.co. The source files are in Github.
These images are free to use under the Elastic license. They contain open source and free commercial features and access to paid commercial features. Start a 30-day trial to try out all of the paid commercial features. See the Subscriptions page for information about Elastic license levels.
Pulling the image
editObtaining Elasticsearch for Docker is as simple as issuing a docker pull
command
against the Elastic Docker registry.
docker pull docker.elastic.co/elasticsearch/elasticsearch:7.4.2
Alternatively, you can download other Docker images that contain only features available under the Apache 2.0 license. To download the images, go to www.docker.elastic.co.
Starting a single node cluster with Docker
editTo start a single-node Elasticsearch cluster for development or testing, specify single-node discovery to bypass the bootstrap checks:
docker run -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.4.2
Starting a multi-node cluster with Docker Compose
editTo get a three-node Elasticsearch cluster up and running in Docker, you can use Docker Compose:
-
Create a
docker-compose.yml
file:
version: '2.2' services: es01: image: docker.elastic.co/elasticsearch/elasticsearch:7.4.2 container_name: es01 environment: - node.name=es01 - cluster.name=es-docker-cluster - discovery.seed_hosts=es02,es03 - cluster.initial_master_nodes=es01,es02,es03 - bootstrap.memory_lock=true - "ES_JAVA_OPTS=-Xms512m -Xmx512m" ulimits: memlock: soft: -1 hard: -1 volumes: - data01:/usr/share/elasticsearch/data ports: - 9200:9200 networks: - elastic es02: image: docker.elastic.co/elasticsearch/elasticsearch:7.4.2 container_name: es02 environment: - node.name=es02 - cluster.name=es-docker-cluster - discovery.seed_hosts=es01,es03 - cluster.initial_master_nodes=es01,es02,es03 - bootstrap.memory_lock=true - "ES_JAVA_OPTS=-Xms512m -Xmx512m" ulimits: memlock: soft: -1 hard: -1 volumes: - data02:/usr/share/elasticsearch/data networks: - elastic es03: image: docker.elastic.co/elasticsearch/elasticsearch:7.4.2 container_name: es03 environment: - node.name=es03 - cluster.name=es-docker-cluster - discovery.seed_hosts=es01,es02 - cluster.initial_master_nodes=es01,es02,es03 - bootstrap.memory_lock=true - "ES_JAVA_OPTS=-Xms512m -Xmx512m" ulimits: memlock: soft: -1 hard: -1 volumes: - data03:/usr/share/elasticsearch/data networks: - elastic volumes: data01: driver: local data02: driver: local data03: driver: local networks: elastic: driver: bridge
This sample Docker Compose file brings up a three-node Elasticsearch cluster.
Node es01
listens on localhost:9200
and es02
and es03
talk to es01
over a Docker network.
The Docker named volumes
data01
, data02
, and data03
store the node data directories so the data persists across restarts.
If they don’t already exist, docker-compose
creates them when you bring up the cluster.
-
Make sure Docker Engine is allotted at least 4GiB of memory. In Docker Desktop, you configure resource usage on the Advanced tab in Preference (macOS) or Settings (Windows).
Docker Compose is not pre-installed with Docker on Linux. See docs.docker.com for installation instructions: Install Compose on Linux
-
Run
docker-compose
to bring up the cluster:docker-compose up
-
Submit a
_cat/nodes
request to see that the nodes are up and running:curl -X GET "localhost:9200/_cat/nodes?v&pretty"
Log messages go to the console and are handled by the configured Docker logging driver.
By default you can access logs with docker logs
.
To stop the cluster, run docker-compose down
.
The data in the Docker volumes is preserved and loaded
when you restart the cluster with docker-compose up
.
To delete the data volumes when you bring down the cluster,
specify the -v
option: docker-compose down -v
.
Start a multi-node cluster with TLS enabled
editSee Encrypting communications in an Elasticsearch Docker Container and Run the Elastic Stack in Docker with TLS enabled.
Using the Docker images in production
editThe following requirements and recommendations apply when running Elasticsearch in Docker in production.
Set vm.max_map_count
to at least 262144
editThe vm.max_map_count
kernel setting must be set to at least 262144
for production use.
How you set vm.max_map_count
depends on your platform:
-
Linux
The
vm.max_map_count
setting should be set permanently in/etc/sysctl.conf
:grep vm.max_map_count /etc/sysctl.conf vm.max_map_count=262144
To apply the setting on a live system, run:
sysctl -w vm.max_map_count=262144
-
macOS with Docker for Mac
The
vm.max_map_count
setting must be set within the xhyve virtual machine:-
From the command line, run:
screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
-
Press enter and use`sysctl` to configure
vm.max_map_count
:sysctl -w vm.max_map_count=262144
-
To exit the
screen
session, typeCtrl a d
.
-
-
Windows and macOS with Docker Desktop
The
vm.max_map_count
setting must be set via docker-machine:docker-machine ssh sudo sysctl -w vm.max_map_count=262144
Configuration files must be readable by the elasticsearch
user
editBy default, Elasticsearch runs inside the container as user elasticsearch
using
uid:gid 1000:1000
.
One exception is Openshift,
which runs containers using an arbitrarily assigned user ID.
Openshift presents persistent volumes with the gid set to 0
, which works without any adjustments.
If you are bind-mounting a local directory or file, it must be readable by the elasticsearch
user.
In addition, this user must have write access to the data and log dirs.
A good strategy is to grant group access to gid 1000
or 0
for the local directory.
For example, to prepare a local directory for storing data through a bind-mount:
mkdir esdatadir chmod g+rwx esdatadir chgrp 1000 esdatadir
As a last resort, you can force the container to mutate the ownership of
any bind-mounts used for the data and log dirs through the
environment variable TAKE_FILE_OWNERSHIP
. When you do this, they will be owned by
uid:gid 1000:0
, which provides the required read/write access to the Elasticsearch process.
Increase ulimits for nofile and nproc
editIncreased ulimits for nofile and nproc must be available for the Elasticsearch containers. Verify the init system for the Docker daemon sets them to acceptable values.
To check the Docker daemon defaults for ulimits, run:
docker run --rm centos:7 /bin/bash -c 'ulimit -Hn && ulimit -Sn && ulimit -Hu && ulimit -Su'
If needed, adjust them in the Daemon or override them per container.
For example, when using docker run
, set:
--ulimit nofile=65535:65535
Disable swapping
editSwapping needs to be disabled for performance and node stability. For information about ways to do this, see Disable swapping.
If you opt for the bootstrap.memory_lock: true
approach,
you also need to define the memlock: true
ulimit in the
Docker Daemon,
or explicitly set for the container as shown in the sample compose file.
When using docker run
, you can specify:
-e "bootstrap.memory_lock=true" --ulimit memlock=-1:-1
Randomize published ports
editThe image exposes
TCP ports 9200 and 9300. For production clusters, randomizing the
published ports with --publish-all
is recommended,
unless you are pinning one container per host.
Set the heap size
editUse the ES_JAVA_OPTS
environment variable to set the heap size.
For example, to use 16GB, specify -e ES_JAVA_OPTS="-Xms16g -Xmx16g"
with docker run
.
You must configure the heap size even if you are limiting memory access to the container.
Pin deployments to a specific image version
editPin your deployments to a specific version of the Elasticsearch Docker image. For
example docker.elastic.co/elasticsearch/elasticsearch:7.4.2
.
Always bind data volumes
editYou should use a volume bound on /usr/share/elasticsearch/data
for the following reasons:
- The data of your Elasticsearch node won’t be lost if the container is killed
- Elasticsearch is I/O sensitive and the Docker storage driver is not ideal for fast I/O
- It allows the use of advanced Docker volume plugins
Avoid using loop-lvm
mode
editIf you are using the devicemapper storage driver, do not use the default loop-lvm
mode.
Configure docker-engine to use
direct-lvm.
Centralize your logs
editConsider centralizing your logs by using a different logging driver. Also note that the default json-file logging driver is not ideally suited for production use.
Configuring Elasticsearch with Docker
editWhen you run in Docker, the Elasticsearch configuration files are loaded from
/usr/share/elasticsearch/config/
.
To use custom configuration files, you bind-mount the files over the configuration files in the image.
You can set individual Elasticsearch configuration parameters using Docker environment variables. The sample compose file and the single-node example use this method.
You can also override the default command for the image to pass Elasticsearch configuration parameters as command line options. For example:
docker run <various parameters> bin/elasticsearch -Ecluster.name=mynewclustername
While bind-mounting your configuration files is usually the preferred method in production, you can also create a custom Docker image that contains your configuration.
Mounting Elasticsearch configuration files
editCreate custom config files and bind-mount them over the corresponding files in the Docker image.
For example, to bind-mount custom_elasticsearch.yml
with docker run
, specify:
-v full_path_to/custom_elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml
The container runs Elasticsearch as user elasticsearch
using
uid:gid 1000:1000
**. Bind mounted host directories and files must be accessible by this user,
and the data and log directories must be writable by this user.
Using custom Docker images
editIn some environments, it might make more sense to prepare a custom image that contains
your configuration. A Dockerfile
to achieve this might be as simple as:
FROM docker.elastic.co/elasticsearch/elasticsearch:7.4.2 COPY --chown=elasticsearch:elasticsearch elasticsearch.yml /usr/share/elasticsearch/config/
You could then build and run the image with:
docker build --tag=elasticsearch-custom . docker run -ti -v /usr/share/elasticsearch/data elasticsearch-custom
Some plugins require additional security permissions. You must explicitly accept them either by:
-
Attaching a
tty
when you run the Docker image and allowing the permissions when prompted. -
Inspecting the security permissions and accepting them (if appropriate) by adding the
--batch
flag to the plugin install command.
See Plugin management for more information.
Next steps
editYou now have a test Elasticsearch environment set up. Before you start serious development or go into production with Elasticsearch, you must do some additional setup:
- Learn how to configure Elasticsearch.
- Configure important Elasticsearch settings.
- Configure important system settings.
On this page
- Pulling the image
- Starting a single node cluster with Docker
- Starting a multi-node cluster with Docker Compose
- Start a multi-node cluster with TLS enabled
- Using the Docker images in production
- Set
vm.max_map_count
to at least262144
- Configuration files must be readable by the
elasticsearch
user - Increase ulimits for nofile and nproc
- Disable swapping
- Randomize published ports
- Set the heap size
- Pin deployments to a specific image version
- Always bind data volumes
- Avoid using
loop-lvm
mode - Centralize your logs
- Configuring Elasticsearch with Docker
- Mounting Elasticsearch configuration files
- Using custom Docker images
- Next steps