OpenSearch Project Blog

OpenSearch doc value skip indexes deliver up to 28x faster aggregations on uncorrelated filter and aggregation fields, overcoming key filter rewrite limitations.

Discover why AI agents fail in silence and how OpenSearch Agent Health solves it with open-source trace observability, automated benchmarking, and LLM judge evaluation.

Discover how OpenSearch FP16 vector search performance improved up to 310% throughput using bulk SIMD in OpenSearch 3.5, with progressive optimizations across 3.1, 3.4, and 3.5 reducing p99 latency to 91 ms.

Explore OpenSearch 3.4's agentic search UI in Dashboards. Configure AI agents, test natural language queries, and export configurations with hands-on examples.

Discover how OpenSearch's Anomaly Detection handles missing data with configurable imputation, turning data silence into critical alert signals. Learn how configurable imputation strategies and Klarna's real-world monitoring use case.

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 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.

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.

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.

Storage is a key factor driving the infrastructure cost of your OpenSearch cluster. As your data grows, storage requirements can increase multifold, depending on whether OpenSearch stores documents in multiple...