OpenSearch 3.3 delivers an all-in-one observability experience for search
The OpenSearch project announced OpenSearch 3.3 is now available for download with an array of new features designed to help search, observability, and AI-powered applications.
With this release, OpenSearch delivers its most capable, comprehensive, and user-friendly observability offering yet, according to the vendor.
Dramatic enhancements to query functionality and visualization tools can help users build more robust and efficient observability pipelines and discover more from logs, metrics, and trace data.
This release brings a preview version of a completely redesigned OpenSearch Dashboards interface. Expected to become the default view with the 3.4 release, the new interface brings log analytics, distributed tracing, and intelligent visualizations into a single user experience.
Users gain the ability to analyze and correlate observability data in an application that features auto-visualizations, context-aware log and trace analysis, and AI-powered query construction. This release also expands the available chart types, offering more visualization options.
The new Discover experience also delivers columnar query results and recommends the most appropriate visualization.
Additional customization options include axis labels and ranges, thresholds for data alerts, configuring titles and tooltips, adjusting color schemes, and modifying visual styles. Twelve preset rules cover common data patterns, from simple metric displays to complex multi-dimensional scatter plots, the company said.
Built on the Discover interface and new in 3.3, Discover Traces provides a central interface for querying and exploring traces across large distributed systems.
This release adds the React Flow library to OpenSearch Dashboards core as an experimental feature, providing a standardized framework for interactive node-based visualizations. This integration eliminates version conflicts that occurred when individual plugins bundled copies of the library, providing a consistent user experience.
The library is currently being used with the Discover Traces feature to render service maps that visualize trace spans and service dependencies. Unlike traditional charting libraries, React Flow specializes in workflow and network diagrams, offering drag-and-drop interactions, custom node components, and efficient rendering of thousands of nodes while maintaining accessibility compliance, the vendor said.
Apache Calcite has become the default query engine for PPL in OpenSearch 3.3, introducing better portability for a wide range of data management systems. Calcite’s mature framework provides new optimization capabilities, improvements to query processing efficiency, and an extensive library of new PPL commands.
OpenSearch 3.3 introduces comprehensive benchmarking infrastructure to validate PPL’s performance capabilities. The ClickBench and Big5 datasets support standardized performance testing across a range of analytical scenarios.
Furthermore, this release brings experimental AI capabilities to OpenSearch Dashboards that can transform how users interact with their data through intelligent context awareness, conversational interfaces, and agent integration. Three new AI features are available to explore: the Context Provider plugin for automatic context capture, the OSD-Agents package with a reference AI agent, and an enhanced Chat UI that creates an intelligent assistant experience on the Discover page.
OpenSearch 3.3 brings to production an array of vector search enhancements to support more sophisticated and performant generative AI applications.
OpenSearch 3.3 brings the general availability of agentic search, allowing users to interact with data though natural language inputs. Agentic search leverages intelligent agents to automatically select the right tools and generate optimized queries based on user intent.
OpenSearch 3.3 introduces processor chains, a powerful new feature that enables flexible data transformation pipelines within artificial intelligence and machine learning (AI/ML) workflows.
OpenSearch 3.3 introduces experimental streaming support for both model prediction and agent execution, offering a significant advancement in how users interact with AI models and agents. This streaming feature introduces real-time data streaming capabilities through SSE, delivering data in chunks as it becomes available.
This release also delivers several new features to improve search results and advance performance and scalability.
OpenSearch 3.3 now supports multi-terms aggregations to be resolved via star-tree. Multi-terms aggregations are among the slowest-running aggregations, particularly when applied to large datasets. Now, star-tree can help speed up multi-terms aggregations for large datasets, and users can now also see query failures, with newly introduced failure statistics included as part of index/node/shard-level statistics.
OpenSearch 3.3 also delivers functionality that helps users maintain the stability, availability, and resiliency of OpenSearch deployments, according to the vendor.
For more information about this news, visit https://opensearch.org.