OpenSearch Project Blog

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...

As organizations increasingly rely on cloud services, monitoring and detecting anomalies in system behavior has become crucial for maintaining reliable operations. In this post, we’ll explore recent improvements to the...

Trail of Bits, an independent research and consulting company, recently completed a detailed analysis of OpenSearch performance. Read the blog post and detailed analysis to learn about key performance metrics for the Big5 workload! OpenSearch is...

OpenSearch is a powerful open-source search and analytics engine that enables you to efficiently search and filter large datasets. A common search pattern involves filtering documents based on whether a...

Vector search has gained significant attention in the area of information retrieval thanks to advances in natural language embedding models. These models map domain-specific data into a vector space, where...

In February 2024, we decided to revamp the OpenSearch API Specification repository by adopting the OpenAPI Specification. While this change offered significant benefits, we encountered some challenges during implementation. Most...

OpenSearch 2.19 introduces reciprocal rank fusion (RRF), a new feature in the Neural Search plugin that enhances hybrid search. RRF merges ranked results from multiple query sources, such as neural...

The latest version of OpenSearch expands capabilities for search and observability use cases, boosts performance and ease of use, and introduces an array of advancements for machine learning (ML) and...