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databend

databendlabsApache-2.0活跃维护

基于 Rust 重写的云原生数据仓库，统一支持分析、搜索、AI 与 Python 沙箱，直接架在 S3 上，省去多套系统拼装。

Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.

v1.2.961-nightly发布于 2026-11-08 23:04 UTC9,492star901fork同步于 2026-11-10 11:30 UTC
数据仓库云原生湖仓一体OLAPRust
对比 GitHub 官网aibigdatacloud-nativedatabaseelasticsearch

怎么上手

由 README 提炼，执行前请确认
原文 
docker run -d -p 127.0.0.1:8000:8000 datafuselabs/databend
bendsql -u root
硬件要求按 README 说明

README 未说明具体 CPU、内存或显存要求；平台标注支持 Linux、macOS、ARM，存储后端支持 S3、Azure、GCS。

有 Docker 镜像
下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼
没人工点评？写下你的经验

Rust 写的开源数据仓库，把 SQL 分析、向量检索和全文检索放在同一张表上，省去多系统同步

基于 Rust 重写的云原生数据仓库，统一支持分析、搜索、AI 与 Python 沙箱，直接架在 S3 上，省去多套系统拼装。

核心能力

  • 同一张表同时支持 SQL 分析、向量检索与全文检索
  • 用 Rust 实现向量化引擎，计算弹性伸缩在对象存储上
  • 提供沙箱化 Python UDF，用 SQL 编排 Agent 逻辑
  • 支持类 Git 的数据分支，可在生产快照上安全试验
  • 支持自动 schema 演进与事务

容易踩的坑

自托管需另装 BendSQL 客户端连接；默认只把 8000 端口绑定到 127.0.0.1，远程访问需自行调整；生产部署的凭据与存储配置需参考 self-hosted 文档，README 未给出默认值。

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

最近发版

5 个版本v1.2.961-nightly
全部 release
  1. v1.2.961-nightlypre-release最新正式版2026-11-08 · 2026-11-08 23:04 UTC

    What's Changed

    Exciting New Features ✨

    Thoughtful Bug Fix 🔧

  2. v1.2.960-nightlypre-release2026-11-02 · 2026-11-02 07:47 UTC
    查看更新说明

    What's Changed

    Thoughtful Bug Fix 🔧

    Code Refactor 🎉

    Full Changelog: https://github.com/databendlabs/databend/compare/v1.2.959-nightly...v1.2.960-nightly

  3. v1.2.959-nightlypre-release2026-11-01 · 2026-11-01 23:12 UTC
    查看更新说明

    What's Changed

    Thoughtful Bug Fix 🔧

    Build/Testing/CI Infra Changes 🔌

    Others 📒

    Full Changelog: https://github.com/databendlabs/databend/compare/v1.2.958-nightly...v1.2.959-nightly

  4. v1.2.958-nightlypre-release2026-10-30 · 2026-10-30 05:25 UTC
    查看更新说明

    What's Changed

    Exciting New Features ✨

    Thoughtful Bug Fix 🔧

    Code Refactor 🎉

    Full Changelog: https://github.com/databendlabs/databend/compare/v1.2.957-nightly...v1.2.958-nightly

  5. v1.2.957-nightlypre-release2026-10-29 · 2026-10-29 11:51 UTC
    查看更新说明

    What's Changed

    Exciting New Features ✨

    Thoughtful Bug Fix 🔧

    Build/Testing/CI Infra Changes 🔌

    Documentation 📔

    Others 📒

    Full Changelog: https://github.com/databendlabs/databend/compare/v1.2.956-nightly...v1.2.957-nightly

用户点评

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    README 原文

    未翻译
    在 GitHub 打开

    ☁️ Try Cloud • 🚀 Quick Start • 📈 Performance • 📖 Documentation

    💡 Why Databend?

    Databend is an open-source data warehouse built in Rust. Analytics, search, and agent workloads run on the same tables, so there is no second system to sync.

    📊 One WarehouseSQL analytics, vector search, full-text search, auto schema evolution, transactions.🤖 Agent-ReadySandboxed Python UDFs for agent logic, SQL for orchestration.
    🏢 Enterprise ScaleFast vectorized engine, elastic compute on object storage: S3, Azure, GCS.🌿 BranchingGit-like data versioning. Agents experiment safely on production snapshots.

    Databend Architecture

    📈 Performance

    Published results on Databend Cloud with default settings. Each page includes the SQL to reproduce it.

    BenchmarkResult
    TPC-H SF100 vs. Snowflake1.25× faster, ~60% lower cost
    Data ingestion vs. Snowflake5.15× faster, 91% lower cost
    Vector & full-text search10M docs: vector p50 41 ms, full-text p50 35 ms
    TPC-H SF1000 (1 TB) scaling4.1× faster on Large vs. Small warehouse

    See all benchmarks.

    ⚡ Quick Start

    Cloud (Recommended) — Sign up, create a warehouse, and run SQL in the worksheet. Cloud guide →

    Self-hosted — Run the all-in-one image and connect with BendSQL:

    docker run -d -p 127.0.0.1:8000:8000 datafuselabs/databend
bendsql -u root

    See Docker image options and self-hosted deployment for credentials, storage, and production setup.

    Your First Query

    Analytics, full-text search, and vector search in one table:

    CREATE TABLE docs (
    id INT,
    content STRING,
    embedding VECTOR(3),
    INVERTED INDEX content_idx(content),
    VECTOR INDEX embedding_idx(embedding) distance = 'cosine'
);

INSERT INTO docs VALUES
    (1, 'SQL analytics for agents', [1.0, 0.0, 0.0]),
    (2, 'Vector search for documents', [0.0, 1.0, 0.0]);

SELECT id, content FROM docs WHERE MATCH(content, 'analytics');

SELECT id, content FROM docs
ORDER BY COSINE_DISTANCE(embedding, [1.0, 0.0, 0.0]::VECTOR(3))
LIMIT 1;

    Both queries return document 1. Learn more: Vector search · JSON & search.

    🤖 Agent-Ready Architecture

    Databend's Sandbox UDF enables flexible agent orchestration with a three-layer architecture:

    • Control Plane: Resource scheduling, permission validation, sandbox lifecycle management
    • Execution Plane (Databend): SQL orchestration, issues requests via Arrow Flight
    • Compute Plane (Sandbox Workers): Isolated sandboxes running your agent logic
    -- Define your agent logic
CREATE FUNCTION my_agent(input STRING) RETURNS STRING
LANGUAGE python HANDLER = 'run'
AS $$
def run(input):
    # Your agent logic: LLM calls, tool use, reasoning...
    answer = f"processed: {input}"
    return answer
$$;

-- Orchestrate agents with SQL
SELECT my_agent(question) FROM tasks;

    🚀 Use Cases

    • AI Agents: Sandbox UDF + SQL orchestration + branching for safe operations
    • Analytics & BI: Large-scale SQL analytics — Learn more
    • Search & RAG: Vector + full-text search — Learn more

    🤝 Community & Support

    Contributors are immortalized in the system.contributors table 🏆

    📄 License

    Databend is licensed under Apache 2.0. A small set of enterprise features is under Elastic 2.0, see LICENSING.md | Licensing FAQ

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