Blogs

Developer insights and practical how-to articles from our experts to inspire and empower your search experience.

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Bringing Fire to Elasticsearch: Adding Native Prometheus API Support
Elasticsearch Labs

Bringing Fire to Elasticsearch: Adding Native Prometheus API Support

Query Elasticsearch directly from Prometheus-compatible clients via native PromQL, discovery, and metadata endpoints. Send data to Elasticsearch with Prometheus Remote Write.

Felix Barnsteiner
From averages to any percentile: Elasticsearch ships native exponential histogram support in ES|QL
Elasticsearch Labs

From averages to any percentile: Elasticsearch ships native exponential histogram support in ES|QL

Query any percentile at any time. Elasticsearch natively stores OTel exponential histograms and lets you analyze distributions in ES|QL without fixed buckets or lossy conversions.

Jonas Kunz
What’s new in Kibana Dashboards: flexible controls, improved defaults and a visual refresh
Elasticsearch Labs

What’s new in Kibana Dashboards: flexible controls, improved defaults and a visual refresh

Kibana 9.4 ships a batch of dashboard improvements — from better defaults to layout controls to a visual refresh. Here's what changed and why.

30x faster than Prometheus: How we rebuilt Elasticsearch as a leading columnar metrics datastore
Elasticsearch Labs

30x faster than Prometheus: How we rebuilt Elasticsearch as a leading columnar metrics datastore

Elasticsearch now stores OTel metrics at 3.75 bytes per data point and queries them up to 30x faster than Prometheus. Here's how we rebuilt TSDS and ES|QL.

Kostas Krikellas
Three indices walk into a FROM clause: ES|QL subqueries in Elasticsearch
Elasticsearch Labs

Three indices walk into a FROM clause: ES|QL subqueries in Elasticsearch

ES|QL subqueries give each data source its own pipeline and filters, eliminating CASE chains, restoring predicate pushdown, and making multi-index queries extensible by design.

Tyler Perkins
Elasticsearch's BBQ vs. TurboQuant: 10–40× faster on CPU and lower ranking noise
Elasticsearch Labs

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.

Thomas Veasey
Multi-tier search with Elastic for ecommerce search governance: Fixing poor recall
Elasticsearch Labs

Multi-tier search with Elastic for ecommerce search governance: Fixing poor recall

Learn how to build a multi-tier retrieval strategy used to execute ecommerce governed search plans and improve recall management. We'll cover how to orchestrate semantic matching while maintaining stable results, facets, and pagination.

Alexander Marquardt
Elastic Agent Builder: How we taught AI agents to manage their own context
Elasticsearch Labs

Elastic Agent Builder: How we taught AI agents to manage their own context

Agent Builder in Elasticsearch 9.4 ships dynamically loaded skills, a conversation context store, selective compaction, and external connectors to cut token costs by 40% and let agents handle their own context management.

Anish Mathur
Elasticsearch ES|QL views: One query to rule twelve dashboards
Elasticsearch Labs

Elasticsearch ES|QL views: One query to rule twelve dashboards

With ES|QL views, you only need one query for multiple dashboards. Define it once and let Elasticsearch keep everything in sync.

Tyler Perkins
Elasticsearch percolator for ecommerce search governance: translating ambiguous queries into controlled retrieval strategies
Elasticsearch Labs

Elasticsearch percolator for ecommerce search governance: translating ambiguous queries into controlled retrieval strategies

Learn how to use the Elasticsearch percolator to implement search governance. In this blog, we outline the patterns needed to create a governed policy engine in production and create a controlled retrieval strategy.

Alexander Marquardt
How to measure and improve Elasticsearch search recall: from 0.43 to 0.75 with hybrid search
Elasticsearch Labs

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.

Jeffrey Rengifo
Building a control plane to govern ecommerce search
Elasticsearch Labs

Building a control plane to govern ecommerce search

How to build a governed control plane for ecommerce that composes conflicting search policies into a single execution plan (without code changes).

Alexander Marquardt