OpenSearch Project Blog

Discover how random rotation in OpenSearch 3.2 improves binary quantization accuracy by removing axis correlations without changing true distances in your data.

Discover OpenSearch's experimental Apache Arrow integration. Learn how Arrow Flight enables zero-copy data transfer between nodes, improving throughput for large-scale analytics and observability workloads.

OpenSearch is a modern, open source search and analytics suite designed for performance, scalability, and flexibility. It’s widely used for use cases such as log analysis, application monitoring, real-time data...

OpenSearch 3.3 introduces resource sharing and access control, enabling teams to securely share ML models, anomaly detectors, and other plugin resources with fine-grained permissions.

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OpenSearch 3.3 delivers breakthrough performance with 11× faster queries, optimized vector search, and enhanced AI capabilities. Discover the latest innovations in search and analytics performance.

Discover how late interaction models enhance vector search accuracy by preserving token-level information, balancing efficiency with precision for superior semantic search results.

Data Prepper 2.13 adds Prometheus sink support, native OpenSearch data streams, cross-region S3 ingestion, and 20% performance improvements. Download it now.

Learn how OpenSearch 3.3's agentic memory system enables AI agents to maintain context and learn from past interactions using persistent memory and intelligent processing capabilities.

Learn how asymmetric distance computation (ADC) in OpenSearch achieves 32-fold memory compression for vector search while maintaining better recall than traditional binary quantization methods.

OpenSearch 3.3, PPL, Piped Processing Language, log analytics, observability, Apache Calcite, data analysis, time-series analysis, log processing, data visualization, OpenSearch 3.3, database querying, data monitoring, security analytics

With OpenSearch 3.3, we are excited to introduce _agentic search_, a major evolution in the way you interact with your data. Agentic search lets you explore your data using a natural language interface, without needing to construct query domain-specific language (DSL) queries manually.

Segment replication is a powerful index replication strategy for high QPS workloads that allows physical isolation and decoupling of indexing and search workloads, rapid replica failovers, and seamless point-in-time restores. However, this efficiency comes with its own unique challenges. During high indexing bursts, or if replication is delayed due to network issues, the system can accumulate large replication checkpoints. Searchers (replica shards) struggle to absorb these checkpoints, experiencing high page faults, thrashing for in-flight queries, and degraded search performance. This blog post describes how to address these issues and build a resilient segment-replicated system using Lucene's powerful new hooks for "Adaptive Refresh".

Learn how to integrate Amazon Q CLI with OpenSearch's log pattern analysis and data distribution tools for streamlined command-line diagnostics and enhanced system troubleshooting capabilities.

In today’s fast-paced digital world, waiting isn’t an option, especially when it comes to AI-powered applications. Streaming technology has emerged as a critical solution to this challenge, fundamentally changing how...

Learn how to improve search result diversity using OpenSearch 3.3's native Maximal Marginal Relevance (MMR) for k-NN and neural queries. Includes implementation guide and performance benchmarks.

Discover how to build reliable RAG pipelines by combining OpenSearch and Docling, an open-source tool that transforms complex documents into structured, metadata-rich data suitable for AI workflows.

Learn how OpenSearch automatically generates and executes search processors during query evaluation, how plugin developers can extend this mechanism, and how to monitor system-generated processors using the Search Pipeline Stats API.

Explore the diverse range of AI agents in OpenSearch, from simple flow agents to advanced ReAct multi-agent systems. Learn how to choose the right agent for tasks like RAG, chatbots, research, and automated root cause analysis.