Blogs
Developer insights and practical how-to articles from our experts to inspire and empower your search experience.
Bringing it together: How we rebuilt Elasticsearch as a columnar metrics engine; 6.6x less storage, 160x faster queries
Elasticsearch metrics in version 9.4 run on a fully columnar engine: 6.6x less storage, 160x faster queries, native PromQL and OTel support.
Talk to your Elasticsearch data: building a real-time voice agent with Google ADK and MCP in 3 components
Wire Google ADK's real-time voice streaming to your Elasticsearch data via Agent Builder's built-in MCP server; no custom integration code required.
Elasticsearch ES|QL: Now with Views, Subqueries, and Schema-on-Read
Query fields you never mapped, combine indices with different schemas in one pipeline, and reuse query logic as named views. ES|QL's most significant data access expansion yet.
The hash() Elasticsearch won't name and the 12 bytes that prove it's Murmur3
Elasticsearch's routing formula uses MurmurHash3, but the docs never say so. This post names the function, walks through the full shard calculation, and shows you how to reproduce it externally.
Elasticsearch DiskBBQ delivers 7x faster vector search than Qdrant on network-attached storage
Elasticsearch DiskBBQ achieves up to 7x higher vector search throughput than Qdrant at comparable recall on network-attached storage. Explore the benchmark methodology and full results.
jina-clip-v2 brings text-to-image search across 89 languages to Elasticsearch, no GPU needed
Run multimodal search across 89 languages inside Elasticsearch with jina-clip-v2: one embedding space for text and images, with no separate model infrastructure to manage.
Your data analyst doesn't need SQL: wiring Elastic Agent Builder to AWS AgentCore for natural-language Elasticsearch queries
Wire plain-English questions to your Elasticsearch data using Elastic Agent Builder MCP, AWS Bedrock AgentCore and the Strands SDK. Python code included.
Why your Elasticsearch cluster is hitting disk watermarks: 14 real-world causes explained
Learn how Elasticsearch disk watermarks work, why they trigger, and how to diagnose 14 of the most common scenarios Support encounters, from index bloat to ILM stalls.
Jingra: A Reproducible Framework for Vector Search Benchmarking
Jingra is an open source benchmarking framework that runs the same vector search workload across Elasticsearch, OpenSearch and Qdrant so you can compare engines under identical, reproducible conditions.
Extract chart data standard OCR misses: Elastic Agent Builder and LlamaParse in one pipeline
Build an end-to-end pipeline that extracts structured data (including values from charts) out of complex PDFs and into Elasticsearch, ready for agent queries with ES|QL.
How we built a persistent agent memory layer on Elasticsearch with 0.89 recall and zero tenant leaks
Discover the architecture behind a persistent, multi-tenant agent memory layer on Elasticsearch: three indices, hybrid retrieval with RRF and a reranker, supersession, decay, and per-user DLS isolation. R@10 0.89 across 168 questions. Full open-source implementation included.
