Vespa Product Updates, October/November 2019: Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance, and Datadog Monitoring Support

Vespa's October/November 2019 product updates introduce several enhancements aimed at improving performance and integration, particularly in AI and big data applications. Key updates include the introduction of nearest neighbor and tensor ranking, which demonstrated significant speed advantages over Elastic in tests using dense tensor dot products. An optimized JSON tensor feed format has been released, improving feed rates by over ten times, while the new matched-elements-only setting in complex multi-value fields enhances performance by returning only query-relevant matches. Furthermore, performance improvements in updating large weighted sets have been achieved, with an 86.5% increase in speed for sets with 10,000 elements. Additionally, Vespa now supports integration with Datadog for enhanced monitoring capabilities in large-scale, mission-critical applications. Developed largely by Yahoo engineers, Vespa is an open-source big data processing and serving engine used by platforms like Yahoo News and the Verizon Media Ad Platform, and continues to evolve with community feedback and contributions.