Install Elasticsearch with Dockeredit

Elasticsearch is also available as Docker images. A list of all published Docker images and tags is available at The source files are in Github.

This package contains both free and subscription features. Start a 30-day trial to try out all of the features.

Starting in Elasticsearch 8.0, security is enabled by default. With security enabled, Elastic Stack security features require TLS encryption for the transport networking layer, or your cluster will fail to start.

Install Docker Desktop or Docker Engineedit

Install the appropriate Docker application for your operating system.

Make sure that Docker is allotted at least 4GiB of memory. In Docker Desktop, you configure resource usage on the Advanced tab in Preference (macOS) or Settings (Windows).

Pull the Elasticsearch Docker imageedit

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

Version 8.2.0 of Elasticsearch has not yet been released, so no Docker image is currently available for this version.

Now that you have the Elasticsearch Docker image, you can start a single-node or multi-node cluster.

Start a single-node cluster with Dockeredit

Version 8.2.0 of the Elasticsearch Docker image has not yet been released.

If you’re starting a single-node Elasticsearch cluster in a Docker container, security will be automatically enabled and configured for you. When you start Elasticsearch for the first time, the following security configuration occurs automatically:

  • Certificates and keys are generated for the transport and HTTP layers.
  • The Transport Layer Security (TLS) configuration settings are written to elasticsearch.yml.
  • A password is generated for the elastic user.
  • An enrollment token is generated for Kibana.

You can then start Kibana and enter the enrollment token, which is valid for 30 minutes. This token automatically applies the security settings from your Elasticsearch cluster, authenticates to Elasticsearch with the kibana_system user, and writes the security configuration to kibana.yml.

The following commands start a single-node Elasticsearch cluster for development or testing.

  1. Create a new docker network for Elasticsearch and Kibana

    docker network create elastic
  2. Start Elasticsearch in Docker. A password is generated for the elastic user and output to the terminal, plus an enrollment token for enrolling Kibana.

    Version 8.2.0 of Elasticsearch has not yet been released, so no Docker image is currently available for this version.

    You might need to scroll back a bit in the terminal to view the password and enrollment token.

  3. Copy the generated password and enrollment token and save them in a secure location. These values are shown only when you start Elasticsearch for the first time.

    If you need to reset the password for the elastic user or other built-in users, run the elasticsearch-reset-password tool. This tool is available in the Elasticsearch /bin directory of the Docker container. For example:

    docker exec -it es01 /usr/share/elasticsearch/bin/elasticsearch-reset-password
  4. Copy the http_ca.crt security certificate from your Docker container to your local machine.

    docker cp es01:/usr/share/elasticsearch/config/certs/http_ca.crt .
  5. Open a new terminal and verify that you can connect to your Elasticsearch cluster by making an authenticated call, using the http_ca.crt file that you copied from your Docker container. Enter the password for the elastic user when prompted.

    curl --cacert http_ca.crt -u elastic https://localhost:9200

Enroll additional nodesedit

When you start Elasticsearch for the first time, the installation process configures a single-node cluster by default. This process also generates an enrollment token and prints it to your terminal. If you want a node to join an existing cluster, start the new node with the generated enrollment token.

  1. In the terminal where you started your first node, copy the generated enrollment token for adding new Elasticsearch nodes.
  2. On your new node, start Elasticsearch and include the generated enrollment token.

    Version 8.2.0 of Elasticsearch has not yet been released, so no Docker image is currently available for this version.

    Elasticsearch is now configured to join the existing cluster.

Setting JVM heap sizeedit

If you experience issues where the container where your first node is running exits when your second node starts, explicitly set values for the JVM heap size. To manually configure the heap size, include the ES_JAVA_OPTS variable and set values for -Xms and -Xmx when starting each node. For example, the following command starts node es02 and sets the minimum and maximum JVM heap size to 1 GB:

docker run -e ES_JAVA_OPTS="-Xms1g -Xmx1g" -e ENROLLMENT_TOKEN="<token>" --name es02 -p 9201:9200 --net elastic -it

Next stepsedit

You now have a test Elasticsearch environment set up. Before you start serious development or go into production with Elasticsearch, review the requirements and recommendations to apply when running Elasticsearch in Docker in production.

Security certificates and keysedit

When you install Elasticsearch, the following certificates and keys are generated in the Elasticsearch configuration directory, which are used to connect a Kibana instance to your secured Elasticsearch cluster and to encrypt internode communication. The files are listed here for reference.

The CA certificate that is used to sign the certificates for the HTTP layer of this Elasticsearch cluster.
Keystore that contains the key and certificate for the HTTP layer for this node.
Keystore that contains the key and certificate for the transport layer for all the nodes in your cluster.

http.p12 and transport.p12 are password-protected PKCS#12 keystores. Elasticsearch stores the passwords for these keystores as secure settings. To retrieve the passwords so that you can inspect or change the keystore contents, use the bin/elasticsearch-keystore tool.

Use the following command to retrieve the password for http.p12:

bin/elasticsearch-keystore show

Use the following command to retrieve the password for transport.p12:

bin/elasticsearch-keystore show

Start a multi-node cluster with Docker Composeedit

To get a multi-node Elasticsearch cluster and Kibana up and running in Docker with security enabled, you can use Docker Compose.

This configuration provides a simple method of starting a secured cluster that you can use for development before building a distributed deployment with multiple hosts.


Install the appropriate Docker application for your operating system.

If you’re running on Linux, install Docker Compose.

Make sure that Docker is allotted at least 4GB of memory. In Docker Desktop, you configure resource usage on the Advanced tab in Preferences (macOS) or Settings (Windows).

Prepare the environmentedit

Create the following configuration files in a new, empty directory. These files are also available from the elasticsearch repository on GitHub.

Version 8.2.0 of Elasticsearch has not been released, so the sample Docker Compose and configuration files are not yet available for this version. See the current version for the latest sample files.

Start your cluster with security enabled and configurededit

  1. Modify the .env file and enter strong password values for both the ELASTIC_PASSWORD and KIBANA_PASSWORD variables.

    You must use the ELASTIC_PASSWORD value for further interactions with the cluster. The KIBANA_PASSWORD value is only used internally when configuring Kibana.

  2. Create and start the three-node Elasticsearch cluster and Kibana instance:

    docker-compose up -d
  3. When the deployment has started, open a browser and navigate to http://localhost:5601 to access Kibana, where you can load sample data and interact with your cluster.

Stop and remove the deploymentedit

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.

docker-compose down

To delete the network, containers, and volumes when you stop the cluster, specify the -v option:

docker-compose down -v

Next stepsedit

You now have a test Elasticsearch environment set up. Before you start serious development or go into production with Elasticsearch, review the requirements and recommendations to apply when running Elasticsearch in Docker in production.

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.


To view the current value for the vm.max_map_count setting, run:

grep vm.max_map_count /etc/sysctl.conf

To apply the setting on a live system, run:

sysctl -w vm.max_map_count=262144

To permanently change the value for the vm.max_map_count setting, update the value in /etc/sysctl.conf.

macOS with Docker for Macedit

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 Desktopedit

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

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

The vm.max_map_count setting must be set in the docker-desktop container:

wsl -d docker-desktop
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 1000:0.

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 config, data and log dirs (Elasticsearch needs write access to the config directory so that it can generate a keystore). A good strategy is to grant group access to gid 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 0 esdatadir

You can also run an Elasticsearch container using both a custom UID and GID. You must ensure that file permissions will not prevent Elasticsearch from executing. You can use one of two options:

  • Bind-mount the config, data and logs directories. If you intend to install plugins and prefer not to create a custom Docker image, you must also bind-mount the plugins directory.
  • Pass the --group-add 0 command line option to docker run. This ensures that the user under which Elasticsearch is running is also a member of the root (GID 0) group inside the container.

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{version} /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.

Manually set the heap sizeedit

By default, Elasticsearch automatically sizes JVM heap based on a nodes’s roles and the total memory available to the node’s container. We recommend this default sizing for most production environments. If needed, you can override default sizing by manually setting JVM heap size.

To manually set the heap size in production, bind mount a JVM options file under /usr/share/elasticsearch/config/jvm.options.d that includes your desired heap size settings.

For testing, you can also manually set the heap size using the ES_JAVA_OPTS environment variable. For example, to use 16GB, specify -e ES_JAVA_OPTS="-Xms16g -Xmx16g" with docker run. The ES_JAVA_OPTS variable overrides all other JVM options. We do not recommend using ES_JAVA_OPTS in production. The docker-compose.yml file above sets the heap size to 512MB.

Pin deployments to a specific image versionedit

Pin your deployments to a specific version of the Elasticsearch Docker image. For example

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. You can use the setting name directly as the environment variable name. If you cannot do this, for example because your orchestration platform forbids periods in environment variable names, then you can use an alternative style by converting the setting name as follows.

  1. Change the setting name to uppercase
  2. Prefix it with ES_SETTING_
  3. Escape any underscores (_) by duplicating them
  4. Convert all periods (.) to underscores (_)

For example, -e bootstrap.memory_lock=true becomes -e ES_SETTING_BOOTSTRAP_MEMORY__LOCK=true.

You can use the contents of a file to set the value of the ELASTIC_PASSWORD or KEYSTORE_PASSWORD environment variables, by suffixing 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/bootstrapPassword.txt, specify:

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

You can override the default command for the image to pass Elasticsearch configuration parameters as command line options. For example:

docker run <various parameters> bin/elasticsearch

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

If you bind-mount a custom elasticsearch.yml file, ensure it includes the setting. This setting ensures the node is reachable for HTTP and transport traffic, provided its ports are exposed. The Docker image’s built-in elasticsearch.yml file includes this setting by default.

The container runs Elasticsearch as user elasticsearch using uid:gid 1000:0. Bind mounted host directories and files must be accessible by this user, and the data and log directories must be writable by this user.

Create an encrypted Elasticsearch keystoreedit

By default, Elasticsearch will auto-generate a keystore file for secure settings. This file is obfuscated but not encrypted.

To encrypt your secure settings with a password and have them persist outside the container, use a docker run command to manually create the keystore instead. The command must:

  • Bind-mount the config directory. The command will create an elasticsearch.keystore file in this directory. To avoid errors, do not directly bind-mount the elasticsearch.keystore file.
  • Use the elasticsearch-keystore tool with the create -p option. You’ll be prompted to enter a password for the keystore.

If you’ve already created the keystore and don’t need to update it, you can bind-mount the elasticsearch.keystore file directly. You can use the KEYSTORE_PASSWORD environment variable to provide the keystore password to the container at startup. For example, a docker run command might have the following options:

-v full_path_to/config/elasticsearch.keystore:/usr/share/elasticsearch/config/elasticsearch.keystore

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:

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.

Troubleshoot Docker errors for Elasticsearchedit

Here’s how to resolve common errors when running Elasticsearch with Docker.

elasticsearch.keystore is a directoryedit

Exception in thread "main" org.elasticsearch.bootstrap.BootstrapException: Is a directory: SimpleFSIndexInput(path="/usr/share/elasticsearch/config/elasticsearch.keystore") Likely root cause: Is a directory

A keystore-related docker run command attempted to directly bind-mount an elasticsearch.keystore file that doesn’t exist. If you use the -v or --volume flag to mount a file that doesn’t exist, Docker instead creates a directory with the same name.

To resolve this error:

  1. Delete the elasticsearch.keystore directory in the config directory.
  2. Update the -v or --volume flag to point to the config directory path rather than the keystore file’s path. For an example, see Create an encrypted Elasticsearch keystore.
  3. Retry the command.

elasticsearch.keystore: Device or resource busyedit

Exception in thread "main" java.nio.file.FileSystemException: /usr/share/elasticsearch/config/elasticsearch.keystore.tmp -> /usr/share/elasticsearch/config/elasticsearch.keystore: Device or resource busy

A docker run command attempted to update the keystore while directly bind-mounting the elasticsearch.keystore file. To update the keystore, the container requires access to other files in the config directory, such as keystore.tmp.

To resolve this error:

  1. Update the -v or --volume flag to point to the config directory path rather than the keystore file’s path. For an example, see Create an encrypted Elasticsearch keystore.
  2. Retry the command.