OpenSearch Project Blog

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 marks a major milestone in the project’s ongoing performance journey and the first major release since 2.0 in April 2022. Building on the enhancements of the 2.x series, the...

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

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

In search engines and machine learning, data normalization is essential for making fair and accurate comparisons between different features or scores. Hybrid queries use multiple normalization techniques to prepare final results. The two...

Three years after the launch of 2.0, OpenSearch has taken the launch of Apache Lucene 10 as a chance to develop a new major version. Why the new development? Well,...

Data Prepper 2.11.0 offers better OpenTelemetry support and new integrations with external sources.

In this blog post, we'll explore why pagination depth is necessary for hybrid queries, show you how to use this feature, and share benchmarking results.

OpenSearch Query Insights gives you essential visibility into how search queries perform, helping you understand how queries run and how they use cluster resources. Since introducing Query Insights, we’ve aimed...

OpenSearch provides a wide range of capabilities to support modern search needs, including traditional keyword-based techniques like lexical search using BM25 and more advanced methods like semantic and hybrid search....

Vector search plays a crucial role in many machine learning (ML) and data science pipelines. In the context of large language models (LLMs), vector search powers retrieval-augmented generation (RAG), a technique...

Introduced in OpenSearch 2.18, Intel Advanced Vector Extensions 512 (Intel AVX-512) is a set of new instructions that can accelerate vector workload performance. Vector search benchmarks using OpenSearch Benchmark showed...

The rise of generative AI has transformed search technology, evolving it from simple keyword-based retrieval to intelligent, multimodal search experiences powered by deep learning. In this keynote address at the Linux Foundation Member...

OpenSearch is a scalable open-source search and analytics platform that can serve as the core of a Security Information and Event Management (SIEM) system. OpenSearch can centralize logs from diverse...

OpenSearch’s adoption as a vector database has grown significantly with the rise of generative AI applications. Vector search workloads are scaling from millions to billions of vectors, making traditional CPU-based indexing both...