Install Elasticsearch with Docker

Elasticsearch is also available as Docker images. The images use centos:7 as the base image and are available with X-Pack.

A list of all published Docker images and tags can be found in The source code can be found on GitHub.

Image types

The images are available in three different configurations or "flavors". The basic flavor, which is the default, ships with X-Pack Basic features pre-installed and automatically activated with a free licence. The platinum flavor features all X-Pack functionally under a 30-day trial licence. The oss flavor does not include X-Pack, and contains only open-source Elasticsearch.

X-Pack Security is enabled in the platinum image. To access your cluster, it’s necessary to set an initial password for the elastic user. The initial password can be set at start up time via the ELASTIC_PASSWORD environment variable:

docker run -e ELASTIC_PASSWORD=MagicWord

The platinum image includes a trial license for 30 days. After that, you can obtain one of the available subscriptions or revert to a Basic licence. The Basic license is free and includes a selection of X-Pack features.

Obtaining Elasticsearch for Docker is as simple as issuing a docker pull command against the Elastic Docker registry.

Docker images can be retrieved with the following commands:

docker pull
docker pull
docker pull

Running Elasticsearch from the command line

Development mode

Elasticsearch can be quickly started for development or testing use with the following command:

docker run -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node"

Production mode

The vm.max_map_count kernel setting needs to be set to at least 262144 for production use. Depending 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

    To apply the setting on a live system type: 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:

    $ screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty

    Just press enter and configure the sysctl setting as you would for Linux:

    sysctl -w vm.max_map_count=262144
  • Windows and macOS with Docker Toolbox

    The vm.max_map_count setting must be set via docker-machine:

    docker-machine ssh
    sudo sysctl -w vm.max_map_count=262144

The following example brings up a cluster comprising two Elasticsearch nodes. To bring up the cluster, use the docker-compose.yml and just type:

docker-compose up

docker-compose is not pre-installed with Docker on Linux. Instructions for installing it can be found on the Docker Compose webpage.

The node elasticsearch listens on localhost:9200 while elasticsearch2 talks to elasticsearch over a Docker network.

This example also uses Docker named volumes, called esdata1 and esdata2 which will be created if not already present.


version: '2.2'
    container_name: elasticsearch
      - bootstrap.memory_lock=true
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
        soft: -1
        hard: -1
      - esdata1:/usr/share/elasticsearch/data
      - 9200:9200
      - esnet
    container_name: elasticsearch2
      - bootstrap.memory_lock=true
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
      - ""
        soft: -1
        hard: -1
      - esdata2:/usr/share/elasticsearch/data
      - esnet

    driver: local
    driver: local


To stop the cluster, type docker-compose down. Data volumes will persist, so it’s possible to start the cluster again with the same data using docker-compose up. To destroy the cluster and the data volumes, just type docker-compose down -v.

Inspect status of cluster:

1472225929 15:38:49 docker-cluster green 2 2 4 2 0 0 0 0 - 100.0%

Log messages go to the console and are handled by the configured Docker logging driver. By default you can access logs with docker logs.

Configuring Elasticsearch with Docker

Elasticsearch loads its configuration from files under /usr/share/elasticsearch/config/. These configuration files are documented in Configuring Elasticsearch and Setting JVM options.

The image offers several methods for configuring Elasticsearch settings with the conventional approach being to provide customized files, that is to say, elasticsearch.yml. It’s also possible to use environment variables to set options:

A. Present the parameters via Docker environment variables

For example, to define the cluster name with docker run you can pass -e "". Double quotes are required.

B. Bind-mounted configuration

Create your custom config file and mount this over the image’s corresponding file. For example, bind-mounting a custom_elasticsearch.yml with docker run can be accomplished with the parameter:

-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, such as custom_elasticsearch.yml above, need to be accessible by this user. For the data and log dirs, such as /usr/share/elasticsearch/data, write access is required as well. Also see note 1 below.

C. Customized image

In some environments, it may make more sense to prepare a custom image containing your configuration. A Dockerfile to achieve this may be as simple as:

COPY --chown=elasticsearch:elasticsearch elasticsearch.yml /usr/share/elasticsearch/config/

You could then build and try the image with something like:

docker build --tag=elasticsearch-custom .
docker run -ti -v /usr/share/elasticsearch/data elasticsearch-custom

D. Override the image’s default CMD

Options can be passed as command-line options to the Elasticsearch process by overriding the default command for the image. For example:

docker run <various parameters> bin/elasticsearch

Configuring SSL/TLS with the Elasticsearch Docker image

See Encrypting Communications in an Elasticsearch Docker Image.

Notes for production use and defaults

We have collected a number of best practices for production use. Any Docker parameters mentioned below assume the use of docker run.

  1. By 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 will present persistent volumes with the gid set to 0 which will work without any adjustments.

    If you are bind-mounting a local directory or file, ensure it is readable by this user, while the data and log dirs additionally require write access. A good strategy is to grant group access to gid 1000 or 0 for the local directory. As an 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 also 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. In this case, they will be owned by uid:gid 1000:0 providing read/write access to the Elasticsearch process as required.

  2. It is important to ensure increased ulimits for nofile and nproc are available for the Elasticsearch containers. Verify the init system for the Docker daemon is already setting those to acceptable values and, if needed, adjust them in the Daemon, or override them per container, for example using docker run:

    --ulimit nofile=65536:65536

    One way of checking the Docker daemon defaults for the aforementioned ulimits is by running:

    docker run --rm centos:7 /bin/bash -c 'ulimit -Hn && ulimit -Sn && ulimit -Hu && ulimit -Su'
  3. Swapping needs to be disabled for performance and node stability. This can be achieved through any of the methods mentioned in the Elasticsearch docs. If you opt for the bootstrap.memory_lock: true approach, apart from defining it through any of the configuration methods, you will additionally need the memlock: true ulimit, either defined in the Docker Daemon or specifically set for the container. This is demonstrated above in the docker-compose.yml. If using docker run:

    -e "bootstrap.memory_lock=true" --ulimit memlock=-1:-1
  4. The image exposes TCP ports 9200 and 9300. For clusters it is recommended to randomize the published ports with --publish-all, unless you are pinning one container per host.
  5. Use the ES_JAVA_OPTS environment variable to set heap size. For example, to use 16GB, use -e ES_JAVA_OPTS="-Xms16g -Xmx16g" with docker run.
  6. Pin your deployments to a specific version of the Elasticsearch Docker image. For example,
  7. Always use a volume bound on /usr/share/elasticsearch/data, as shown in the production example, for the following reasons:

    1. The data of your Elasticsearch node won’t be lost if the container is killed
    2. Elasticsearch is I/O sensitive and the Docker storage driver is not ideal for fast I/O
    3. It allows the use of advanced Docker volume plugins
  8. If you are using the devicemapper storage driver, make sure you are not using the default loop-lvm mode. Configure docker-engine to use direct-lvm instead.
  9. Consider 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.

Next steps

You 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: