Install Elasticsearch with Dockeredit

Elasticsearch 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 imageedit

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

Version 8.0.0 of Elasticsearch has not yet been released, so no Docker image is currently available for this version.

Starting a single node cluster with Dockeredit

Version 8.0.0 of the Elasticsearch Docker image has not yet been released.

Starting a multi-node cluster with Docker Composeedit

To get a three-node Elasticsearch cluster up and running in Docker, you can use Docker Compose:

  1. Create a docker-compose.yml file:

    Version 8.0.0 of Elasticsearch has not yet been released, so a docker-compose.yml is not available for this version.

    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.

  2. 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

  3. Run docker-compose to bring up the cluster:

    docker-compose up
  4. 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.

Using the Docker images in productionedit

The following requirements and recommendations apply when running Elasticsearch in Docker in production.

Set vm.max_map_count to at least 262144edit

The 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:

    1. From the command line, run:

      screen ~/Library/Containers/com.docker.docker/Data/vms/0/tty
    2. Press enter and use`sysctl` to configure vm.max_map_count:

      sysctl -w vm.max_map_count=262144
    3. To exit the screen session, type Ctrl 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 useredit

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 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 nprocedit

Increased 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 swappingedit

Swapping 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 portsedit

The 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 sizeedit

Use 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 versionedit

Pin your deployments to a specific version of the Elasticsearch Docker image. For example docker.elastic.co/elasticsearch/elasticsearch:8.0.0.

Always bind data volumesedit

You should use a volume bound on /usr/share/elasticsearch/data 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

Avoid using loop-lvm modeedit

If you are using the devicemapper storage driver, do not use the default loop-lvm mode. Configure docker-engine to use direct-lvm.

Centralize your logsedit

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.

Configuring Elasticsearch with Dockeredit

When 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.

To use the contents of a file to set an environment variable, suffix the environment variable name with _FILE. This is useful for passing secrets such as passwords to Elasticsearch without specifying them directly.

For example, to set the Elasticsearch bootstrap password from a file, you can bind mount the file and set the ELASTIC_PASSWORD_FILE environment variable to the mount location. If you mount the password file to /run/secrets/password.txt, specify:

-e ELASTIC_PASSWORD_FILE=/run/secrets/bootstrapPassword.txt

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 filesedit

Create 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 imagesedit

In 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:8.0.0
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 stepsedit

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: