Elastic (@elastic) / X

Elastic

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Elastic

@elastic

Where developers learn, build, and share. Your source for hands-on demos, cheat sheets, explainers and more.

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Joined October 2009

Elastic
@elastic
Jul 10
Search feels simple until you start getting back irrelevant results. Know which of these 3 retrieval strategies to reach for a furniture store site: - BM25 matches exact terms. Finds an ottoman from "Product ID 43926". - Vector matches meaning. Figures out what "padded stool for
Elastic
@elastic
Jul 9
We're introducing Elasticsearch Columnar Mode: A new index mode that stores data once, in columnar form, with no redundant copies and no indexes the workload doesn't need. Not replacing the document model. Adding a second way to organise data alongside it, for the workloads
Elastic
@elastic
Jul 9
The full deep dive on why Elasticsearch is going columnar, what Columnar Mode changes, and what it doesn't:
Elasticsearch columnar database: one platform for search and analytics - Elasticsearch Labs
From elastic.co
Elastic
@elastic
Jul 9
A red cluster is a decision tree, not one magic command. Bookmark these common commands to help you diagnose cluster, node and shard health issues
Elastic
@elastic
Jul 8
Your research agent returns confident answers from a single source. That's not research. That's a summary with extra steps. Cross-checking needs structure: multiple angles, independent findings, a way to compare them. @LangChain Deep Agents with Elasticsearch give you that
Elastic reposted
Elastic Security Labs
@elasticseclabs
Jul 7
We tracked a new activity cluster targeting Mexican banking customers. Elastic Security Labs discovered REF6045, an operator-assisted banking fraud campaign targeting customers of Mexican banks, fintechs, and cryptocurrency platforms through ClickFix fake-CAPTCHA lures. The
Elastic
@elastic
Jul 7
Your Claude API bill shouldn't be a monthly surprise. The Elastic Anthropic Metrics integration polls Anthropic's Admin API and routes org-wide usage, cost, and rate limit data into Elasticsearch. One Admin API key. Zero application code changes. 6 alert templates out of the
00:00
Elastic
@elastic
Jul 6
CPUs don't have vector search instructions. You borrow from neural nets and video codecs instead. Elasticsearch's simdvec engine reformulates vector math to fit whatever the CPU already runs fast. Four recent examples: - int7 quantization: fit unsigned-only multiply-accumulate
Elastic
@elastic
Jul 6
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.
SIMD vector search: Elasticsearch simdvec's 6x speedup - Elasticsearch Labs
From elastic.co
Elastic
@elastic
Jul 3
I always enjoy developer story time 📖
JP Hwang
@_jphwang
Jul 2
Here's a story about 3 engineers - Cora, Samantha and Ben, configuring vector search. For the same task, they end up with completely different setups. Who's right, and who's wrong?
Elastic
@elastic
Jul 3
You've got 10k slides, scans, and screenshots to search. Your first instinct is to throw a VLM at it. But a VLM reads one image at a time. Running it across your whole corpus on every query doesn’t scale. Split the pipeline instead. jina-clip embeds every image into a vector
Elastic
@elastic
Jul 2
Article
Query your Elasticsearch data from the terminal in plain English
The official Elastic GitHub Copilot CLI plugin generates and runs ES|QL queries against your cluster. No Kibana, no manual syntax. Type a natural language question in your terminal. The official...
Elastic
@elastic
Jul 1
7x higher vector search throughput at comparable recall. Elasticsearch 9.4.1 DiskBBQ vs Qdrant 1.18.1, tested on network-attached persistent storage. The storage topology most K8s and managed-cloud deployments actually run on. Not local NVMe. The gap is disk access. DiskBBQ
Elastic
@elastic
Jul 1
Full results, methodology, and dataset: go.es.io/4aqI4sA Benchmark tool (Jingra): go.es.io/442FHZn
Elasticsearch vs. Qdrant: 7x faster vector search - Elasticsearch Labs
From elastic.co
Elastic
@elastic
Jun 30
🧵 Elasticsearch now queries time series metrics up to 160x faster than previous versions. TSDS and ES|QL were rebuilt over the past year. Three areas changed: storage, queries, and Prometheus compatibility. The result: - A fully columnar metrics engine. - OTel indexing
Elastic
@elastic
Jun 30
Replying to @elastic and @timestamp
3/ Storage - OTel metrics: 25 bytes down to 3.75 per data point. Four TSDS changes cut storage by 6.6x: - Doc value skippers: replace inverted indices and BKD trees - Synthetic _id: derived from _tsid and @ timestamp, bloom filter dedup - Sequence numbers: trimmed at merge time
Elastic
@elastic
Jun 30
TL;DR: - Queries: up to 160x faster - PromQL runs inside ES|QL, same engine - Storage: 6.6x more efficient for OTel metrics - One platform: metrics, logs, traces, documents Full architecture deep dive with benchmarks in the blog:
Elasticsearch metrics: Columnar engine, 160x faster queries - Elasticsearch Labs
From elastic.co
Elastic
@elastic
Jun 29
Up to 30× faster than Prometheus on gauge averages and counter rates. Up to 2.5× more storage efficient than Prometheus. That's ES|QL running on a new columnar storage engine purpose-built for time series data. Cost approximately 50% less than Datadog. No custom metric
Elastic
@elastic
Jun 29
Full walkthrough of the architecture, benchmarks, and migration paths:
Elasticsearch: best-in-class for logs, now best-in-class for metrics — Elastic Observability Labs
From elastic.co