May 21, 2026
Up to 3x faster stored-vector queries in Elasticsearch
Elasticsearch 9.4 provides a simpler way to search with vectors stored in an Elasticsearch index, with up to 3x lower latency.
May 19, 2026
12x faster Elasticsearch vector indexing: deploying NVIDIA cuVS with GPU and CPU tiers
Two patterns for deploying NVIDIA cuVS GPU-accelerated HNSW indexing in Elasticsearch: combined build-and-serve nodes for small clusters and a dedicated GPU ingest tier with ILM handoff to CPU for production at scale.
May 13, 2026
Elasticsearch Vector DiskBBQ filter search is now 3–5x faster
Learn how Elasticsearch 9.4 makes restrictive filtered DiskBBQ vector search 3–5x faster and more stable by avoiding wasted centroid and postings-list work when selectivity is high.
May 6, 2026
Elasticsearch's BBQ vs. TurboQuant: 10–40× faster on CPU and lower ranking noise
A head-to-head look at Elasticsearch BBQ and TurboQuant, including throughput, ranking accuracy, and why uniform quantization wins for CPU vector search with up to 40× faster comparisons and smaller ranking noise.
May 4, 2026
How to measure and improve Elasticsearch search recall: from 0.43 to 0.75 with hybrid search
Learn how to measure and improve search recall in Elasticsearch by combining BM25 lexical search with Jina AI vector embeddings, using the rank_eval API to validate the improvement with real numbers.
April 27, 2026
Preconditioning Vectors: Making Elasticsearch VectorDB Better Binary Quantization work for every vector
Modern quantization techniques can hurt recall when using older models or embeddings that aren’t normally distributed. Learn how preconditioning fixes these vectors through random orthogonal projection, making BBQ more effective and recovering recall.
April 23, 2026
How we built Elasticsearch simdvec to make vector search one of the fastest in the world
How we built Elasticsearch simdvec, the hand-tuned SIMD kernel library behind every vector search query in Elasticsearch.
April 10, 2026
Unsupervised document clustering with Elasticsearch + Jina embeddings
A practical, reproducible approach to unsupervised document clustering with Elasticsearch and Jina embeddings.
April 2, 2026
When TSDS meets ILM: Designing time series data streams that don't reject late data
How TSDS time bounds interact with ILM phases; and how to design policies that tolerate late-arriving metrics.