Elasticsearch and OpenSearch are two powerful platforms for data search and analysis, commonly used in the big data domain. Elasticsearch, developed by Elastic, has become the de facto standard for search and indexing. OpenSearch is its fork, created following changes in Elasticsearch's licensing terms. This article provides a detailed analysis and comparison of these two technologies to help users choose the best solution for their needs.

History and Context

Elasticsearch

Elasticsearch was first released in 2010 and quickly gained popularity due to its ability to process large volumes of data in real time and provide fast and efficient search capabilities. Elasticsearch is part of the Elastic Stack (formerly known as the ELK Stack), which also includes Logstash, Kibana, and Beats.

OpenSearch

OpenSearch was created in 2021 after Elastic changed Elasticsearch's license from Apache 2.0 to Server Side Public License (SSPL). Amazon Web Services (AWS) decided to create a fork of Elasticsearch named OpenSearch, which remains under the open-source Apache 2.0 license.

Key Differences

1. License

Elasticsearch: Uses Server Side Public License (SSPL), a controversial license requiring any services offering Elasticsearch as SaaS to open-source their code.

OpenSearch: Uses the open-source Apache 2.0 license, providing greater flexibility and broader acceptance within the open-source community.

2. Functionality

Elasticsearch: Offers a wide range of features, including advanced analytics tools, machine learning, and integration with other Elastic Stack components.

OpenSearch: Aims to maintain full compatibility with Elasticsearch while adding its own improvements and features, such as new plugins and better support for AWS services.

3. Compatibility and Migration

Elasticsearch: Migration to other platforms can be complex due to proprietary features and licensing changes.

OpenSearch: Designed to be compatible with existing Elasticsearch data and queries, making migration easier.

4. Ecosystem and Support

Elasticsearch: Strong ecosystem and support from Elastic, including commercial offerings and support services.

OpenSearch: Active support from AWS and the open-source community, which includes a wide range of contributions and extensions.

Use Cases

Elasticsearch

• Log Management: Frequently used for collecting, storing, and analyzing logs.
• Full-Text Search: Ideal for implementing full-text search functionalities in applications.
• Data Analysis: Combined with Kibana, provides visualization and analytical tools for large data volumes.

OpenSearch

• Monitoring and Diagnostics: Used in Cloud environments for monitoring and diagnosing applications and services.
• Security Information and Event Management (SIEM): Deployed in security operations for threat analysis and detection.
• IoT Data Management: Managing and analyzing data from IoT devices with high scalability and speed.

 

The choice between Elasticsearch and OpenSearch depends on the specific needs of your project and preferences regarding licensing and support. Elasticsearch offers a broad ecosystem and advanced features but with a more restrictive license. OpenSearch represents a more open and flexible alternative, with active support from AWS and the community. Careful consideration of these factors will help you select the right solution for effective data search and analysis in your environment.