OpenSearch Project Blog

OpenSearch Data Prepper 2.12 introduces new features to enhance your data ingestion and processing pipeline. Discover the unified OTLP source for streamlined telemetry data ingestion, the Amazon SQS sink for efficient message queuing, and integration with AWS X-Ray for observability.

The buzz at OpenSearchCon India was electric. And for good reason. In a packed keynote session, leaders from AWS and Freshworks unpacked how search is evolving in the age of...

OpenSearch Benchmark's new redline testing feature automatically determines cluster throughput limits through dynamic load scaling and real-time monitoring. Eliminate manual tuning and discover your true performance ceiling.

Learn how to simplify hybrid search filtering with common filter support in OpenSearch 3.0.

Learn how to use OpenSearch 3.0's new PPL commands to enhance your log analysis. Explore practical examples and understand the Calcite-powered optimization.

Discover how to implement agentic AI in OpenSearch using MCP server and Amazon Q Developer CLI for powerful, conversational search experiences and natural language analytics.

Discover the new Live Queries API in OpenSearch Query Insights 3.0, enabling real-time visibility into currently executing search queries to identify and debug performance issues as they happen.

Learn about derived source for vectors, introduced in OpenSearch 3.0: why it matters, how it works, and how to start using it to improve performance and reduce storage costs.

The Java Security Manager (JSM) has long been a foundational security mechanism in OpenSearch. With its deprecation underway, this blog post explores the alternative technologies adopted to replace JSM.

Learn how to optimize inference processors in OpenSearch to reduce redundant model calls, lower costs, and improve ingestion performance.

Optimize OpenSearch cluster performance by separating indexing and search operations. Discover write/search replicas and dedicated search nodes, with benchmark data showing 34% lower query latency through workload separation.

Learn how to build a hybrid search application using OpenSearch as a vector database. Bookmark this step-by-step guide to implementing semantic and keyword search with a recipe dataset.

Explore how the plan-execute-reflect agent in OpenSearch 3.0 transforms troubleshooting by automatically investigating system issues. See this AI agent in action as it analyzes data and provides solutions through a real-world debugging example.

OpenSearch now supports Model Context Protocol (MCP), a framework simplifying AI agent integration with search tools. Learn how MCP reduces development complexity and enables seamless AI-powered data access for enhanced search solutions.

OpenSearch 3.0 delivers major performance improvements, including 10x faster search queries, 2.5x vector search boost, and GPU acceleration. Learn about key enhancements and the future roadmap.

OpenSearch Benchmark is the de-facto performance benchmarking tool for OpenSearch.

Learn how star-tree index improves the performance of aggregations in OpenSearch.

Explore z-score normalization in OpenSearch 3.0-beta1 for hybrid search. Learn how this technique compares to min-max normalization through benchmarks on search relevance and performance.