elasticsearchEstimated reading time: 7 minutes
Elasticsearch is a powerful open source search and analytics engine that makes data easy to explore.
GitHub repo: https://github.com/docker-library/elasticsearch
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 image at https://store.docker.com/images/elasticsearch
This image has been deprecated in favor of the official
elasticsearch image provided and maintained by elastic.co. The list of images available from Elastic can be found at www.docker.elastic.co. The images found here will receive no further updates once the
6.0.0 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
Where to file issues:
the Docker Community
Supported Docker versions:
the latest release (down to 1.6 on a best-effort basis)
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
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
One example of adding clustering support is to pass the configuration on the
$ 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:
- Setup Elasticsearch » Important System Configuration » Virtual memory
- Setup Elasticsearch » Bootstrap Checks » Maximum map count check
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_countis 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.
You can run the default
elasticsearch command simply:
$ docker run -d elasticsearch
You can also pass in additional flags to
$ 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
$ 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.
docker stack deploy or
version: '3.1' services: elasticsearch: image: elasticsearch kibana: image: kibana ports: - 5601:5601
docker stack deploy -c stack.yml elasticsearch (or
docker-compose -f stack.yml up), wait for it to initialize completely, and visit
http://host-ip:5601 (as appropriate).
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
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.
As with all Docker images, these likely also contain other software which may be under other licenses (such as Bash, etc from the base distribution, along with any direct or indirect dependencies of the primary software being contained).
Some additional license information which was able to be auto-detected might be found in the
As for any pre-built image usage, it is the image user’s responsibility to ensure that any use of this image complies with any relevant licenses for all software contained within.library, sample, elasticsearch