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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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elastic.co
Joined October 2009
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  • user avatar
    Elastic
    @elastic
    5h
    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
    973
    user avatar
    Elastic
    @elastic
    5h
    Full results, methodology, and dataset: go.es.io/4aqI4sA Benchmark tool (Jingra): go.es.io/442FHZn
    Elasticsearch DiskBBQ delivers 7x faster vector search than Qdrant at comparable recall on network-attached storage. Explore full results and methodology.
    Elasticsearch vs. Qdrant: 7x faster vector search - Elasticsearch Labs
    From elastic.co
    354
  • user avatar
    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
    123K
    user avatar
    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
    489
    user avatar
    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 in version 9.4 run on a fully columnar engine: 6.6x less storage, 160x faster queries, native PromQL and OTel support.
    Elasticsearch metrics: Columnar engine, 160x faster queries - Elasticsearch Labs
    From elastic.co
    357
  • user avatar
    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
    1.1M
    user avatar
    Elastic
    @elastic
    Jun 29
    Full walkthrough of the architecture, benchmarks, and migration paths:
    Elasticsearch is now best-in-class for metrics: 30× faster than Prometheus, up to 2.5× more storage-efficient, 50% less than Datadog. Learn about all the capabilities we’ve added.
    Elasticsearch: best-in-class for logs, now best-in-class for metrics — Elastic Observability Labs
    From elastic.co
    709
  • user avatar
    Elastic
    @elastic
    Jun 26
    Hybrid search = BM25 + vector search, merged and reranked in 1 request. Most search implementations pick one. Lexical search misses semantic intent. Vector search misses exact keyword matches. Hybrid covers both. Here's how it fits together in a single Elasticsearch query: -
    4K
    user avatar
    Elastic
    @elastic
    Jun 26
    Resource:
    What is hybrid search? How it works and when to use it
    From elastic.co
    991
  • user avatar
    Elastic
    @elastic
    Jun 25
    Your Claude Code agent forgets everything between sessions. So you bolt on a memory service. Another API, another thing to run. If you already run Elasticsearch, you already have the parts. semantic_text handles embeddings at index time. ES|QL gives you hybrid recall in one
    3.5K
    user avatar
    Elastic
    @elastic
    Jun 25
    Full walkthrough, schema, and the bridge CLI:
    Give your AI agent in Claude Code persistent memory using Elasticsearch: hybrid recall, a knowledge graph, and cross-device handoffs.
    Persistent memory for agents: Claude Code on Elasticsearch - Elasticsearch Labs
    From elastic.co
    905
  • Elastic reposted
    user avatar
    JP Hwang
    @_jphwang
    Jun 25
    Cooking up a new video + a live session to demo video search with @JinaAI_ omni v5 model and @elastic. Here's a preview of the demo app running entirely locally & live on my Mac. More soon 😉
    00:00
    982
  • user avatar
    Elastic
    @elastic
    Jun 24
    Most data analysis still feels like translation work. Someone asks which products drive revenue. Then you write queries, join tables, check dashboards, validate assumptions. An hour later, maybe you have an answer. This tutorial wires Elastic Agent Builder MCP to @awscloud
    GIF
    1.9K
    user avatar
    Elastic
    @elastic
    Jun 24
    Tutorial and full code: go.es.io/4uVdj6j GitHub repo: go.es.io/4wdcEyl
    Build an Elasticsearch AI agent with Elastic Agent Builder MCP and AWS AgentCore using the Strands Agents SDK. Python tutorial included.
    Elastic Agent Builder MCP and AWS AgentCore: Python tutorial - Elasticsearch Labs
    From elastic.co
    757
  • user avatar
    Elastic
    @elastic
    Jun 23
    Most search benchmarks only tell half the story. You test relevance. You ship it. Then p99 latency tanks under real concurrency and users start filing tickets. Or you optimize for speed, and your top-k results are fast garbage. The fix: measure both sides every time. 10
    3.5M
  • user avatar
    Elastic
    @elastic
    Jun 22
    3 patterns for multimodal RAG. Here's how they differ and when each one breaks down. Most RAG systems add multimodal support by converting everything to text first. Is your system natively multimodal, or just a conversion pipeline? The architecture choice shapes what you can
    4.7M
    user avatar
    Elastic
    @elastic
    Jun 22
    Learn more:
    Learn how to build a Multimodal RAG system with Elasticsearch that integrates text, audio, video, and image data to provide richer, contextualized information retrieval.
    Multimodal RAG: Building a multimodal RAG system with Elastic - Elasticsearch Labs
    From elastic.co
    1.2K
  • user avatar
    Elastic
    @elastic
    Jun 19
    Run the same vector search benchmark across 3 engines. Jingra is a new open-source framework that uses YAML config to run identical workloads on Elasticsearch, OpenSearch, and Qdrant. The part that matters: with await_index_ready enabled, it holds evaluation until the index
    2.2K
    user avatar
    Elastic
    @elastic
    Jun 19
    Blog: go.es.io/4vXMUpl Repo: go.es.io/4oNsPjd
    Jingra benchmarks vector search across Elasticsearch, OpenSearch and Qdrant under identical conditions. Open source, config-driven, Apache 2.0.
    Vector search benchmarking with Jingra: A reproducible framework - Elasticsearch Labs
    From elastic.co
    1.1K

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