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Elasticsearch is a distributed search and analytics engine for performing real-time search for a wide variety of use cases such as storing and analyzing logs, adding a search box on an app or website, managing and integrating spatial information, and many more. Fast search on all types of data such as structured or unstructured text, numerical data, or geospatial data is possible through optimized smart indexing of stored data. As the volume of your data grows, Elasticsearch is able to grow with it because of its distributed nature. If you have a small problem or even an enormous petabyte-sized problem, Elasticsearch gives you the opportunity to design and implement an efficient working solution for search and analytics. Some examples of use cases:

  • Add a search box to an app or websiteStore and analyze logs, metrics, and security event data

  • Use machine learning to automatically model the behavior of your data in real time

  • Automate business workflows using Elasticsearch as a storage engine

  • Manage, integrate, and analyze spatial information using Elasticsearch as a geographic information system (GIS)

  • Store and process genetic data using Elasticsearch as a bioinformatics research tool

Please refer to Elasticsearch's website for more information.


The Elasticsearch services can be accessed by the following means:

  • Access via Kibana which provides a graphical user interface
  • Access via a Programmatic Access using REST API

Hardware configuration#

An Elasticsearch deployment requires selecting a hardware configuration.

Virtual Machine Size vCPU Memory (GiB) VM Storage (GiB) Network Bandwidth (Gbps) Block Storage Bandwidth (Mbps)
m5.xlarge.elasticsearch 4 16 Block Storage-Only Up to 10 Up to 3,500
m5.2xlarge.elasticsearch 8 32 Block Storage-Only Up to 10 Up to 3,500
m5.4xlarge.elasticsearch 16 64 Block Storage-Only Up to 10 3,500
m5.12xlarge.elasticsearch 48 192 Block Storage-Only 10 7,000