Blogs
Developer insights and practical how-to articles from our experts to inspire and empower your search experience.
Skip the mapping explosion: ES|QL queries schemaless JSON keys without dynamic mapping
Flattened fields turn Elasticsearch into a schema-on-read store where you index schemaless data under one mapping, then use ES|QL's FIELD_EXTRACT to pull out any JSON key you need to filter, group or join on, with predicates pushed into the columnar store.
Ask the source: Scaling code search to a billion lines with Elasticsearch and Elastic Agent Builder
Sourcerer matches Claude Code and Codex on code retrieval quality and searches up to thousands of times faster than grep. Every answer links back to the exact files and lines across repos and versions.
Skip the stateful OTel Collector: Elasticsearch 9.5 natively stores both metric temporalities
Ingest cumulative and delta OpenTelemetry metrics under the same metric name while ES|QL and PromQL queries auto-detect temporality per series, with no new syntax or conversion pipelines required.
Search relevance from click streams: Using Learn To Rank and behavioral signals with OpenTelemetry
Learn how to turn click streams and behavioral signals from OpenTelemetry search analytics into judgment lists, rank features and Learn To Rank models that make search relevance improve over time.
Your AI agent doesn't need your API key: OAuth 2.1 for Elasticsearch MCP server authentication
OAuth 2.1 lets you connect AI agents to the Elasticsearch MCP server with a browser sign-in instead of an API key. Your agent gets a short-lived token tied to your permissions that you can revoke any time.
Two lines of JSON to replace your ILM policy: data stream lifecycle adds frozen tier support
In Elasticsearch 9.5, frozen_after in data stream lifecycle moves indices to searchable snapshots on object storage on their own, keeping them queryable alongside downsampling and retention.
0.35% trained, 100% competitive: the frozen-tower architecture behind jina-embeddings-v5-omni
The latest jina embeddings model generates multimodal embeddings for text, images, video and audio, competing with models nearly 6x its size on vector search while training just 0.35% of the weights.
Faster, cheaper support investigations with precomputed context
Precomputed context cut input tokens by 58% and latency by 40% in Elastic’s support agent, making support investigations more efficient by reducing repeated retrieval.
Building context in Elasticsearch: how AI Indices power smarter agents using fewer tokens
Store AI agent context in an AI Index and power smarter agents using fewer tokens. Step-by-step walkthrough with ES|QL and Kibana Workflows included.
How Elasticsearch's batched query phase improves search performance at scale
The batched query phase can cut search execution time in half by reducing transport overhead and better distributing reduction work across the cluster.
Elasticsearch as one platform: What a second data system really costs
Running search, analytics, metrics, logs, and vector retrieval in five systems costs more than five licenses. Here's what one platform looks like in practice.
