OpenSearch – enterprise-grade search and observability suite

Last Updated on June 18, 2026

OpenSearch is a community-driven search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.

Developers build with OpenSearch for use cases such as application search, log analytics, data observability, data ingestion, and more.

This is free and open source software.

Key Features

• Vector search provides a complete vector database solution for building efficient AI applications.
• Store and search vector embeddings alongside your existing data, making it easy to implement semantic search, retrieval-augmented generation (RAG), recommendation systems, and other AI-powered applications.
• Search features – customize your search use cases:
  • Search methods:
    • Exact matching and keywords
    • Keyword (BM25) search
    • Supports similarity (k-nearest neighbor) search using dense and sparse vector embeddings to power use cases such as semantic search, retrieval-augmented generation, and multimodal image search.
    • Supports AI-powered search capabilities beyond vector embeddings. OpenSearch’s AI search enables search and ingestion flows to be enriched by any AI service to power the full range of AI-enhanced search use cases.
  • Query languages:
    • Query domain-specific language (DSL): The primary OpenSearch query language that supports creating complex, fully customizable queries.
    • Query string query language: A scaled-down query language that you can use in a query parameter of a search request or in OpenSearch Dashboards.
    • SQL: A traditional query language that bridges the gap between traditional relational database concepts and the flexibility of OpenSearch’s document-oriented data storage.
    • Piped Processing Language (PPL): The primary language used with observability in OpenSearch. PPL uses a pipe syntax that chains commands into a query.
    • Dashboards Query Language (DQL): A simple text-based query language for filtering data in OpenSearch Dashboards.
  • Search performance:
    • Asynchronous search: Runs resource-intensive queries asynchronously.
    • Concurrent segment search: Searches segments concurrently.
  • Search relevance:
    • Search Relevance Workbench: A suite of tools that support search quality improvements through experimentation.
    • Querqy: Offers query rewriting capability.
    • User Behavior Insights: Links user behavior to user queries to improve search quality.

Website: github.com/opensearch-project/OpenSearch
Support:
Developer: OpenSearch Project
License: Apache License 2.0

OpenSearch is written in Java. Learn Java with our recommended free books and free tutorials.

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