Elasticsearch

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Elasticsearch is a powerful open source search and analytics engine that makes data easy to explore.

GitHub repo: https://github.com/docker-library/elasticsearch

Library reference

This content is imported from the official Docker Library docs, and is provided by the original uploader. You can view the Docker Store page for this repo at https://store.docker.com/images/elasticsearch.

DEPRECATED

This image is officially deprecated in favor of the elasticsearch image provided by elastic.co which is available to pull via docker.elastic.co/elasticsearch/elasticsearch:[version] like 5.2.1. This image will receive no further updates after 2017-06-20 (June 20, 2017). 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

For detailed information about the published artifacts of each of the above supported tags (image metadata, transfer size, etc), please see the repos/elasticsearch directory in the docker-library/repo-info GitHub repo.

For more information about this image and its history, please see the relevant manifest file (library/elasticsearch). This image is updated via pull requests to the docker-library/official-images GitHub repo.

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.

wikipedia.org/wiki/Elasticsearch

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How to use this image

Cluster

Note: since 5.0, Elasticsearch only listens on localhost by default on both http and transport, so this image sets http.host to 0.0.0.0 (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=0.0.0.0 -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.

Image Variants

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

elasticsearch:<version>

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.

elasticsearch:alpine

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).

License

View license information for the software contained in this image.

Supported Docker versions

This image is officially supported on Docker version 17.04.0-ce.

Support for older versions (down to 1.6) is provided on a best-effort basis.

Please see the Docker installation documentation for details on how to upgrade your Docker daemon.

User Feedback

Issues

If you have any problems with or questions about this image, please contact us through a GitHub issue. If the issue is related to a CVE, please check for a cve-tracker issue on the official-images repository first.

You can also reach many of the official image maintainers via the #docker-library IRC channel on Freenode.

Contributing

You are invited to contribute new features, fixes, or updates, large or small; we are always thrilled to receive pull requests, and do our best to process them as fast as we can.

Before you start to code, we recommend discussing your plans through a GitHub issue, especially for more ambitious contributions. This gives other contributors a chance to point you in the right direction, give you feedback on your design, and help you find out if someone else is working on the same thing.

Documentation

Documentation for this image is stored in the elasticsearch/ directory of the docker-library/docs GitHub repo. Be sure to familiarize yourself with the repository’s README.md file before attempting a pull request.

library, sample, Elasticsearch