The AOSC (Automatic Online Schema Change) plugin for OpenSearch enables live index migrations with different mappings, settings, or shard counts within a single cluster. It automat…
In response to community feedback regarding the lack of a dedicated Apache Lucene conference, OpenSearchCon has expanded its Search track to include "Search & Apache Lucene," focus…
OpenSearch 3.8 introduces an experimental memory retention feature for agentic memory containers, allowing users to set age-based or count-based limits to automatically delete stal…
The OpenSearch Observability Technical Advisory Group (TAG) is celebrating its first anniversary of providing strategic technical guidance and driving industry standards within the…
The massive volume of telemetry produced by AI agents makes traditional per-GB observability pricing unsustainable and leads to costly, fragmented tool sprawl. The OpenSearch Obser…
OpenSearch 3.8 introduces significant performance and functionality upgrades across search, AI, and observability workflows. Key enhancements include up to 4x faster vector ingesti…
Introduced at OpenSearchCon Europe 2026, Agentic Relevance Tuning (ART) is a new framework that uses specialized LLM agents to automate the entire search relevance lifecycle in Ope…
OpenSearch ML Commons enables secure, low-latency connections to machine learning endpoints hosted within private VPCs or behind firewalls. While self-managed OpenSearch can be con…
At OpenSearchCon Europe 2026, Unnati Mishra and Akshat Khanna presented a method for handling search privacy during the data ingest phase rather than through retroactive access con…
The OpenSearch Software Foundation has announced the schedule for OpenSearchCon North America 2026, taking place from September 22-24 in San Jose, California. The event will featur…
The new OpenSearch KEDA scaler, introduced in KEDA 2.20, allows Kubernetes workloads to scale directly based on any arbitrary query result from OpenSearch, such as error rates or l…
At OpenSearchCon Europe 2026, Amazon’s Shubham Chaudhary demonstrated how bypassing JVM limitations to implement native SIMD architecture achieved a 60% reduction in Lucene vector …
