Install Elasticsearch with Dockeredit

Docker images for Elasticsearch are available from the Elastic Docker registry. A list of all published Docker images and tags is available at The source code is in GitHub.

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

If you just want to test Elasticsearch in local development, refer to Local dev setup (Docker). Please note that this setup is not suitable for production environments.

Run Elasticsearch in Dockeredit

Use Docker commands to start a single-node Elasticsearch cluster for development or testing. You can then run additional Docker commands to add nodes to the test cluster or run Kibana.

This setup doesn’t run multiple Elasticsearch nodes or Kibana by default. To create a multi-node cluster with Kibana, use Docker Compose instead. See Start a multi-node cluster with Docker Compose.

Start a single-node clusteredit

  1. Install Docker. Visit Get Docker to install Docker for your environment.

    If using Docker Desktop, make sure to allocate at least 4GB of memory. You can adjust memory usage in Docker Desktop by going to Settings > Resources.

  2. Create a new docker network.

    docker network create elastic
  3. Pull the Elasticsearch Docker image.

    docker pull
  4. Optional: Install Cosign for your environment. Then use Cosign to verify the Elasticsearch image’s signature.

    cosign verify --key

    The cosign command prints the check results and the signature payload in JSON format:

    Verification for --
    The following checks were performed on each of these signatures:
      - The cosign claims were validated
      - Existence of the claims in the transparency log was verified offline
      - The signatures were verified against the specified public key
  5. Start an Elasticsearch container.

    docker run --name es01 --net elastic -p 9200:9200 -it -m 1GB

    Use the -m flag to set a memory limit for the container. This removes the need to manually set the JVM size.

    The command prints the elastic user password and an enrollment token for Kibana.

  6. Copy the generated elastic password and enrollment token. These credentials are only shown when you start Elasticsearch for the first time. You can regenerate the credentials using the following commands.

    docker exec -it es01 /usr/share/elasticsearch/bin/elasticsearch-reset-password -u elastic
    docker exec -it es01 /usr/share/elasticsearch/bin/elasticsearch-create-enrollment-token -s kibana

    We recommend storing the elastic password as an environment variable in your shell. Example:

    export ELASTIC_PASSWORD="your_password"
  7. Copy the http_ca.crt SSL certificate from the container to your local machine.

    docker cp es01:/usr/share/elasticsearch/config/certs/http_ca.crt .
  8. Make a REST API call to Elasticsearch to ensure the Elasticsearch container is running.

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

Add more nodesedit

  1. Use an existing node to generate a enrollment token for the new node.

    docker exec -it es01 /usr/share/elasticsearch/bin/elasticsearch-create-enrollment-token -s node

    The enrollment token is valid for 30 minutes.

  2. Start a new Elasticsearch container. Include the enrollment token as an environment variable.

    docker run -e ENROLLMENT_TOKEN="<token>" --name es02 --net elastic -it -m 1GB
  3. Call the cat nodes API to verify the node was added to the cluster.

    curl --cacert http_ca.crt -u elastic:$ELASTIC_PASSWORD https://localhost:9200/_cat/nodes

Run Kibanaedit

  1. Pull the Kibana Docker image.

    docker pull
  2. Optional: Verify the Kibana image’s signature.

    cosign verify --key
  3. Start a Kibana container.

    docker run --name kib01 --net elastic -p 5601:5601
  4. When Kibana starts, it outputs a unique generated link to the terminal. To access Kibana, open this link in a web browser.
  5. In your browser, enter the enrollment token that was generated when you started Elasticsearch.

    To regenerate the token, run:

    docker exec -it es01 /usr/share/elasticsearch/bin/elasticsearch-create-enrollment-token -s kibana
  6. Log in to Kibana as the elastic user with the password that was generated when you started Elasticsearch.

    To regenerate the password, run:

    docker exec -it es01 /usr/share/elasticsearch/bin/elasticsearch-reset-password -u elastic

Remove containersedit

To remove the containers and their network, run:

# Remove the Elastic network
docker network rm elastic

# Remove Elasticsearch containers
docker rm es01
docker rm es02

# Remove the Kibana container
docker rm kib01

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.

Start a multi-node cluster with Docker Composeedit

Use Docker Compose to start a three-node Elasticsearch cluster with Kibana. Docker Compose lets you start multiple containers with a single command.

Configure and start the clusteredit

  1. Install Docker Compose. Visit the Docker Compose docs to install Docker Compose for your environment.

    If you’re using Docker Desktop, Docker Compose is installed automatically. Make sure to allocate at least 4GB of memory to Docker Desktop. You can adjust memory usage in Docker Desktop by going to Settings > Resources.

  2. Create or navigate to an empty directory for the project.
  3. Download and save the following files in the project directory:

  4. In the .env file, specify a password for the ELASTIC_PASSWORD and KIBANA_PASSWORD variables.

    The passwords must be alphanumeric and can’t contain special characters, such as ! or @. The bash script included in the docker-compose.yml file only works with alphanumeric characters. Example:

    # Password for the 'elastic' user (at least 6 characters)
    # Password for the 'kibana_system' user (at least 6 characters)
  5. In the .env file, set STACK_VERSION to the current Elastic Stack version.

    # Version of Elastic products
  6. By default, the Docker Compose configuration exposes port 9200 on all network interfaces.

    To avoid exposing port 9200 to external hosts, set ES_PORT to in the .env file. This ensures Elasticsearch is only accessible from the host machine.

    # Port to expose Elasticsearch HTTP API to the host
  7. To start the cluster, run the following command from the project directory.

    docker-compose up -d
  8. After the cluster has started, open http://localhost:5601 in a web browser to access Kibana.
  9. Log in to Kibana as the elastic user using the ELASTIC_PASSWORD you set earlier.

Stop and remove the clusteredit

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" WSL instance before the Elasticsearch container will properly start. There are several ways to do this, depending on your version of Windows and your version of WSL.

If you are on Windows 10 before version 22H2, or if you are on Windows 10 version 22H2 using the built-in version of WSL, you must either manually set it every time you restart Docker before starting your Elasticsearch container, or (if you do not wish to do so on every restart) you must globally set every WSL2 instance to have the vm.max_map_count changed. This is because these versions of WSL do not properly process the /etc/sysctl.conf file.

To manually set it every time you reboot, you must run the following commands in a command prompt or PowerShell window every time you restart Docker:

wsl -d docker-desktop -u root
sysctl -w vm.max_map_count=262144

If you are on these versions of WSL and you do not want to have to run those commands every time you restart Docker, you can globally change every WSL distribution with this setting by modifying your %USERPROFILE%\.wslconfig as follows:

kernelCommandLine = "sysctl.vm.max_map_count=262144"

This will cause all WSL2 VMs to have that setting assigned when they start.

If you are on Windows 11, or Windows 10 version 22H2 and have installed the Microsoft Store version of WSL, you can modify the /etc/sysctl.conf within the "docker-desktop" WSL distribution, perhaps with commands like this:

wsl -d docker-desktop -u root
vi /etc/sysctl.conf

and appending a line which reads:

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 /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 1GB, use the following command.

docker run -e ES_JAVA_OPTS="-Xms1g -Xmx1g" -e ENROLLMENT_TOKEN="<token>" --name es01 -p 9200:9200 --net elastic -it

The ES_JAVA_OPTS variable overrides all other JVM options. We do not recommend using ES_JAVA_OPTS in production.

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.

For example:

docker run -it --rm \
-v full_path_to/config:/usr/share/elasticsearch/config \ \
bin/elasticsearch-keystore create -p

You can also use a docker run command to add or update secure settings in the keystore. You’ll be prompted to enter the setting values. If the keystore is encrypted, you’ll also be prompted to enter the keystore password.

docker run -it --rm \
-v full_path_to/config:/usr/share/elasticsearch/config \ \
bin/elasticsearch-keystore \
add \

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.