mikelorant/elasticsearch repository overview

⁠Elasticsearch

An opinionated Docker implementation for Elasticsearch focused on providing a reliable yet flexible solution based on the upstream official image.

• Features⁠
• Getting Started⁠
  • Prerequisites⁠
  • Installing⁠
  • Environmental Variables⁠
  • Extra Settings⁠
  • Memory Allocation⁠
• Deployment⁠
• Components⁠
• Contributing⁠
• Versioning⁠
• Authors⁠
• License⁠
• Acknowledgments⁠

⁠Features

There are a number of features that differentiate this implementation from the official or other solutions.

• Configuration determined at runtime by a powerful templating engine.
• AWS availability zone awareness auto detection.
• Extra configuration snippets easy to add.
• Heap sized based on container memory and percentages.
• Enables AWS S3 plugin for snapshots.
• Enables Prometheus plugin for metrics.

⁠Getting Started

This implementation is designed to be used with a Helm chart. However, there is a docker compose file included for testing this implementation locally. Bringing up a test cluster and discovering the client endpoint is easily done with just 2 commands.

docker-compose up

It will likely take a few minutes for the containers to start and you can verify when it is available using curl.

curl $(docker-compose port client 9200)

⁠Prerequisites

The only requirements for bringing up this implementation locally is Docker.

Docker 17.12.0+

However, be aware that Elasticsearch has significant memory requirements especially since it is Java based and it is necessary to allocate the heap on start to prevent memory fragmentation. With 4 Elasticsearch containers (Discovery, Master, Data and Client), Kibana and Cerebro at least 4388Mi of RAM is required in heap allocation, preferably 6Gi if there is capacity.

⁠Installing

It is expected that the Helm chart will be used to install this container. For local development, there are a number of ways to interact with Elasticsearch.

The client endpoint is the main way applications interact with Elasticsearch.

docker-compose port client 9200

Management of Elasticsearch is available using Kibana.

docker-compose port kibana 5601

An alternative management interface is available using Cerebro.

docker-compose port cerebro 9000

⁠Environmental Variables

⁠Extra Settings

The existence of /usr/share/elasticsearch/config/elasticsearch_custom.yml will automatically be appended to the elasticsearch.yml settings at runtime.

⁠Memory Allocation

Running a JVM in a container requires an understand of the relationship of the cgroups memory limit and the heap size. There is an overhead required in running a JVM as well which is approximate 348Mi.

A simple formula to use to determine the RAM percentage is as follows:

(Memory Limit - 384Mi / Memory Limit) * 100

⁠Deployment

For use on live systems, see the documentation for the Helm chart.

⁠Components

• Elasticsearch⁠ - Open Source, Distributed, RESTful Search Engine
• Elasticsearch-Docker⁠ - Official Elasticsearch Docker image from Elastic.
• Gomplate⁠ - A flexible commandline tool for template rendering. Supports lots of local and remote datasources.
• Kibana⁠ - Kibana analytics and search dashboard for Elasticsearch
• Cerebro⁠ - Cerebro is an open source Elasticsearch web admin tool built using Scala, Play Framework, AngularJS and Bootstrap
• Prometheus Exporter Plugin for Elasticsearch⁠ - Prometheus exporter plugin for Elasticsearch

⁠Contributing

Please read CONTRIBUTING.md⁠ for details on our code of conduct, and the process for submitting pull requests to us.

⁠Versioning

We use SemVer⁠ for versioning. For the versions available, see the tags on this repository⁠.

⁠Authors

• Michael Lorant - Initial work - Nine⁠

See also the list of contributors⁠ who participated in this project.

⁠License

This project is licensed under the MIT License - see the LICENSE.md⁠ file for details

⁠Acknowledgments

• Elastic
• Nine