Category: Inside Elastic
Articles tagged Inside Elastic
No more allocation delays: Decoupling snapshots from shard relocation in stateless Elasticsearch
Clusters scale out under load without waiting for a snapshot to finish, because snapshots now read straight from the object store and no longer pin shards in place.
Avoiding and Correcting Hotspots: How Elasticsearch Serverless Balances Shards
Elasticsearch Serverless replaces the Elasticsearch node-weight based shard rebalancing algorithm with resource usage aware rebalancing that avoids index shard colocation, OOM events and write load hotspotting
Migrating 1,100 files to Redux Toolkit v2 without freezing the Kibana monorepo
Kibana gave Redux Toolkit v2 the default package name and pushed v1 onto an explicit alias, which inverts the usual migration order. Webpack externals, yarn resolutions and an ESLint rule keep React Redux v7 and v9 out of each other's way.
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.
ES95: Adaptive Compression for Elasticsearch Time-Series Metrics
ES95 is Elasticsearch 9.5's new adaptive time series codec that cuts @timestamp storage by 92% and floating point fields by up to 74%, with zero configuration.
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.
Why Elasticsearch is becoming a columnar database
Elasticsearch is becoming a first-class columnar database. Columnar Mode ships in 9.5, storing data once alongside the existing modes and cutting storage footprints while speeding up analytical queries.
Your compliance posture just got an upgrade: Elasticsearch now supports FIPS 140-3
Elastic 9.4 brings FIPS 140-3 support for Elasticsearch and Kibana to GA. Here's what changes for federal, defense and regulated deployments, and how to migrate from 140-2.
A simdvec deep-dive: How Elasticsearch uses neural-net and video-codec CPU instructions for vector search
Four ways Elasticsearch's vector search engine reuses neural-network, video-codec and cryptography CPU instructions for up to 6x speedups; with the math, the failed attempts and the benchmarks.
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.
How Elasticsearch cut metrics storage by 41% by dropping sequence numbers after replication
Find out how Elasticsearch trims sequence numbers at merge time to cut TSDS storage by 41%, what you give up, and why it's safe for metrics workloads.
Elasticsearch simdvec deep-dive: Walking the memory tightrope to 2x better vector throughput
A deep dive into four optimizations (cascade unrolling, batch prefetching, dim-axis unrolling, a structural refactor) that pushed Elasticsearch simdvec to 2x vector throughput by working with the CPU, not against it.