OpenSearch 2.x-3.x: New Features and Ecosystem Evolution
Explore OpenSearch versions 2.x and 3.x: vector search, performance improvements, security enhancements, and the evolving ecosystem.
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OpenSearch
OpenSearch tutorials covering basics, operations, internal architecture, vector search, and production use cases.
OpenSearch Overview
OpenSearch is a distributed, RESTful search and analytics engine built on Apache Lucene. Originally forked from Elasticsearch, it provides powerful full-text search, real-time analytics, and visualization capabilities.
OpenSearch was created in 2021 when Elastic changed its licensing, and AWS forked the last Apache 2.0 version of Elasticsearch and Kibana. Since then, OpenSearch has developed independently, adding features like built-in vector database support with k-NN (k-nearest neighbors) search, advanced security plugins (fine-grained access control, audit logging), and anomaly detection — all under the Apache 2.0 license. OpenSearch retains the core Elasticsearch API while introducing its own innovations in search relevance, observability, and AI integration.
OpenSearch’s architecture uses Apache Lucene for indexing and searching, with data organized into indices that are sharded across nodes in a cluster. Each document is JSON and indexed into inverted lists for full-text search, with configurable analyzers for language-specific tokenization, stemming, and stop-word filtering. The OpenSearch Dashboards (forked from Kibana) provides visualization and dashboarding for log analytics, application monitoring, and security event correlation. Recent developments include the Neural Search plugin for integrating ML models into search pipelines, conversational search with RAG support, and improved indexing performance through segment replication and remote-backed storage.
Why It Matters
OpenSearch is the leading open-source search and observability platform under a permissive license, serving as the primary alternative to Elasticsearch for organizations that require Apache 2.0 licensing. It powers log analytics infrastructure at thousands of companies and is increasingly used for AI-powered search applications.
Featured Articles
- OpenSearch Basics - DSL, mappings, and queries
- OpenSearch Operations - Backup, scaling, and cluster management
- OpenSearch Internals - Lucene, sharding, and replication
- OpenSearch Features - Vector search and new capabilities
- OpenSearch for AI - Vector search and RAG pipelines
- OpenSearch Use Cases - Logging, analytics, and security
All OpenSearch Articles
See the full list below.
OpenSearch for AI: Vector Search, RAG Pipelines, and Semantic Search 2026
Comprehensive guide to using OpenSearch for AI applications including k-NN vector search, RAG pipelines, embedding storage, hybrid search, and production best practices.
OpenSearch in Production: Logging, Analytics, and Security
Discover OpenSearch production use cases: log analytics, application search, security analytics, business intelligence, and observability.
OpenSearch Internals: Lucene, Sharding, and Replication
Deep dive into OpenSearch architecture. Understand Apache Lucene, segment-based storage, sharding, replication, and near real-time search internals.
OpenSearch Operations: Backup, Scaling, and Cluster Management
Learn OpenSearch administration: index management, snapshots, cluster scaling, performance tuning, and security configuration.
OpenSearch: The Complete Guide to Distributed Search and Analytics
Master OpenSearch from installation to advanced queries. Learn OpenSearch DSL, index management, mappings, and search operations with practical examples.