Category: ES|QL
Articles tagged ES|QL
Know your facts: How Elasticsearch AI Indices let agents skip the reading and keep the answer
A technical walkthrough of precomputing facts into an Elasticsearch AI Index, so agents answer from a single ES|QL query instead of reading whole documents, with fewer tokens and lower latency.
Taming PUNKs: How ES|QL queries Elasticsearch fields it was never told about
In Elasticsearch 9.5, ES|QL can query unmapped fields. It reads them from _source or returns nulls, so a query keeps working when a field drops out of the mapping and you avoid a reindex that takes hours.
Three SLOs every search team needs: monitoring search latency, availability and quality with OpenTelemetry
Your OpenTelemetry search spans already carry the signals for SLOs, burn rate alerts, anomaly detection and incident response, and this post shows how to build all four in Elastic Observability.
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.
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.
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.
One query, three data sources: ES|QL subqueries get FROM, TS and ROW
Filter application logs by live metric behavior and combine indexed data with inline test values. Your filter lists pull from time-series data on the fly, so nothing is hard-coded.
One ES|QL query instead of two: WHERE IN subquery replaces the copy-paste loop in Elasticsearch
ES|QL's WHERE clause can filter by another Elasticsearch subquery's results instead of a static ID list you copied by hand, with nested subqueries, NOT IN and compound conditions built in.
