This image has been deprecated in favor of the official elasticsearch image provided and maintained by elastic.co. The upstream images are available to pull via docker.elastic.co/elasticsearch/elasticsearch:[version] like 5.4.2. The images found here will receive no further updates once the 5.6.0 release is available upstream. Please adjust your usage accordingly.

Elastic provides open-source support for Elasticsearch via the elastic/elasticsearch GitHub repository and the Docker image via the elastic/elasticsearch-docker GitHub repository, as well as community support via its forums.

Supported tags and respective Dockerfile links

Quick reference

What is Elasticsearch?

Elasticsearch is a search server based on Lucene. It provides a distributed, multitenant-capable full-text search engine with a RESTful web interface and schema-free JSON documents.

Elasticsearch is a registered trademark of Elasticsearch BV.


How to use this image


Note: since 5.0, Elasticsearch only listens on localhost by default on both http and transport, so this image sets http.host to (given that localhost is not terribly useful in the Docker context).

As a result, this image does not support clustering out of the box and extra configuration must be set in order to support it.

Supporting clustering implies having Elasticsearch in a production mode which is more strict about the bootstrap checks that it performs, especially when checking the value of vm.max_map_count which is not namespaced and thus must be set to an acceptable value on the host (as opposed to simply using --sysctl on docker run).

One example of adding clustering support is to pass the configuration on the docker run:

$ docker run -d --name elas elasticsearch -Etransport.host= -Ediscovery.zen.minimum_master_nodes=1

See the following sections of the upstream documentation for more information:

This comment in elastic/elasticsearch#4978 shows why this change was added in upstream.

Elasticsearch will not start in production mode if vm.max_map_count is not high enough. […] If the value on your system is NOT high enough, then your cluster is going to crash and burn at some stage and you will lose data.

Running Containers

You can run the default elasticsearch command simply:

$ docker run -d elasticsearch

You can also pass in additional flags to elasticsearch:

$ docker run -d elasticsearch -Des.node.name="TestNode"

This image comes with a default set of configuration files for elasticsearch, but if you want to provide your own set of configuration files, you can do so via a volume mounted at /usr/share/elasticsearch/config:

$ docker run -d -v "$PWD/config":/usr/share/elasticsearch/config elasticsearch

This image is configured with a volume at /usr/share/elasticsearch/data to hold the persisted index data. Use that path if you would like to keep the data in a mounted volume:

$ docker run -d -v "$PWD/esdata":/usr/share/elasticsearch/data elasticsearch

This image includes EXPOSE 9200 9300 (default http.port), so standard container linking will make it automatically available to the linked containers.

… via docker stack deploy or docker-compose

Example stack.yml for elasticsearch:

version: '3.1'


        image: elasticsearch

        image: kibana
            - 5601:5601

Run docker stack deploy -c stack.yml elasticsearch (or docker-compose -f stack.yml up), wait for it to initialize completely, and visit http://swarm-ip:5601, http://localhost:5601, or http://host-ip:5601 (as appropriate).

Image Variants

The elasticsearch images come in many flavors, each designed for a specific use case.


This is the defacto image. If you are unsure about what your needs are, you probably want to use this one. It is designed to be used both as a throw away container (mount your source code and start the container to start your app), as well as the base to build other images off of.


This image is based on the popular Alpine Linux project, available in the alpine official image. Alpine Linux is much smaller than most distribution base images (~5MB), and thus leads to much slimmer images in general.

This variant is highly recommended when final image size being as small as possible is desired. The main caveat to note is that it does use musl libc instead of glibc and friends, so certain software might run into issues depending on the depth of their libc requirements. However, most software doesn’t have an issue with this, so this variant is usually a very safe choice. See this Hacker News comment thread for more discussion of the issues that might arise and some pro/con comparisons of using Alpine-based images.

To minimize image size, it’s uncommon for additional related tools (such as git or bash) to be included in Alpine-based images. Using this image as a base, add the things you need in your own Dockerfile (see the alpine image description for examples of how to install packages if you are unfamiliar).


View license information for the software contained in this image.

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