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.
April 1, 2026
LINQ to Elasticsearch ES|QL: Write C#, query Elasticsearch
Exploring the new LINQ to Elasticsearch ES|QL provider in the Elasticsearch .NET client, which allows you to write C# code that’s automatically translated to ES|QL queries.
March 20, 2026
Fast vs. accurate: Measuring the recall of quantized vector search
Explaining how to measure recall for vector search in Elasticsearch with minimal setup.
March 2, 2026
Adaptive early termination for HNSW in Elasticsearch
Introducing a new adaptive early termination strategy for HNSW in Elasticsearch.