Blogs
Developer insights and practical how-to articles from our experts to inspire and empower your search experience.
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.
Your FAQ bot doesn't need a PhD: LLM query routing with Elastic Workflows
Route LLM queries by complexity using Elasticsearch search metadata: Mistral Small for FAQ questions, Claude Sonnet for multi-source synthesis.
Your search index is already an agent memory system: Persistent agent memory for Claude Code with Elasticsearch
Give your AI agent persistent cross-session memory using Elasticsearch: Hybrid recall, a knowledge graph, and cross-device handoffs. Three commands to install.
How DocValuesSkippers in Lucene 10 make range queries faster without doubling your storage
DocValuesSkippers add block-level skipping to Lucene DocValues fields, speeding up range queries on sorted or insert-ordered indexes with less than 0.1% storage overhead.
Systematic research with LangChain's Deep Agents framework and Elasticsearch
Building a systematic research pipeline using LangChain's Deep Agents framework and Elasticsearch: