OpenSearch Project Blog

Explore OpenSearch 3.0's semantic highlighting, an AI-powered feature that intelligently identifies relevant passages based on meaning rather than exact keyword matches. Learn how to enhance your search applications with this powerful capability.

Discover OpenSearch 3.1's system ingest pipeline feature, enabling plugin developers to automate document processing during ingestion. Learn how it simplifies configuration, improves user experience, and enhances data transformation capabilities.

Learn how to use ML inference processors to implement semantic search and summarization in OpenSearch.

OpenSearch 3.1 delivers up to 3.8x faster hybrid query throughput by optimizing score collection, batching, and segment-level execution—resulting in lower latency and more predictable performance under load.

Discover how to implement neural sparse models in Sentence Transformers with OpenSearch. Learn to encode text into sparse vectors with just a few lines of code for efficient search.

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 3.1 brings further innovation into the 3.x line with a host of updates to help you boost indexing performance, improve search results, dig deeper into your observability data, build more powerful agentic AI solutions, and maintain security for your OpenSearch deployments.

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.