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Go 项目发布自动化工具，处理构建、打包和发布的复杂流程。",{"description":388,"readmeHtml":389},"Apache Airflow 是以代码方式编写、调度和监控工作流的开源编排平台，用 Python DAG 定义任务依赖，为 ETL、机器学习等数据管道提供调度、执行与可视化监控能力。","\u003Ch2 id=\"项目概述\">项目概述\u003C\u002Fh2>\u003Cp>Apache Airflow 是一个以编程方式编写、调度和监控工作流的开源平台。工作流（DAG）用 Python 代码定义，因此更易维护、可版本化、可测试、可协作；调度器按依赖关系在多个 worker 上执行任务，丰富的命令行工具与 Web 界面让查看生产管道运行状态、监控进度与排查问题变得直观。\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>项目\u003C\u002Fth>\n\u003Cth>内容\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>平台\u003C\u002Ftd>\n\u003Ctd>Python 环境、Docker 等\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>开发语言\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>许可证\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>核心概念\u003C\u002Ftd>\n\u003Ctd>DAG、任务依赖、调度与监控\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Cblockquote>\n\u003Cp>工作流即代码：定义一次，调度、监控、协作全托管。\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ch2 id=\"主要功能\">主要功能\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>工作流即代码\u003C\u002Fstrong>：用 Python 定义 DAG，天然支持版本管理与代码评审\u003C\u002Fli>\n\u003Cli>\u003Cstrong>任务调度\u003C\u002Fstrong>：调度器按依赖与时序在多个 worker 上执行任务\u003C\u002Fli>\n\u003Cli>\u003Cstrong>可视化监控\u003C\u002Fstrong>：Web 界面展示管道运行状态、进度与失败详情\u003C\u002Fli>\n\u003Cli>\u003Cstrong>命令行工具\u003C\u002Fstrong>：丰富的 CLI 可对 DAG 执行复杂操作\u003C\u002Fli>\n\u003Cli>\u003Cstrong>场景广泛\u003C\u002Fstrong>：编排 ETL、ELT、机器学习与 MLOps 等各类流程\u003C\u002Fli>\n\u003Cli>\u003Cstrong>容器化部署\u003C\u002Fstrong>：提供官方 Docker 镜像，便于在多种基础设施上运行\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"适用人群\">适用人群\u003C\u002Fh2>\u003Cul>\n\u003Cli>数据工程师\u003C\u002Fli>\n\u003Cli>数据平台团队\u003C\u002Fli>\n\u003Cli>MLOps 工程师\u003C\u002Fli>\n\u003Cli>批处理任务调度需求者\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"快速上手\">快速上手\u003C\u002Fh2>\u003Col>\n\u003Cli>通过 PyPI 安装：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install apache-airflow\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>一键启动本地开发环境：\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">airflow standalone\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>打开 Web 界面查看内置示例 DAG\u003C\u002Fli>\n\u003Cli>参照官方教程编写自己的第一个 DAG\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"核心优势\">核心优势\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>代码可协作\u003C\u002Fstrong>：工作流以代码形式存在，可评审、可回滚、可测试\u003C\u002Fli>\n\u003Cli>\u003Cstrong>界面直观\u003C\u002Fstrong>：管道运行状况一目了然，问题定位成本低\u003C\u002Fli>\n\u003Cli>\u003Cstrong>生态成熟\u003C\u002Fstrong>：Apache 软件基金会项目，社区庞大、集成丰富\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"系统要求\">系统要求\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>项目\u003C\u002Fth>\n\u003Cth>要求\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>运行环境\u003C\u002Ftd>\n\u003Ctd>Python 或 Docker\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>部署方式\u003C\u002Ftd>\n\u003Ctd>PyPI 安装或官方 Docker 镜像\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"注意事项\">注意事项\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>学习成本\u003C\u002Fstrong>：DAG 模型与调度语义有概念门槛，建议先完成官方教程再上线\u003C\u002Fli>\n\u003Cli>\u003Cstrong>生产部署\u003C\u002Fstrong>：\u003Ccode>airflow standalone\u003C\u002Fcode> 仅适合本地体验，生产环境需自行规划元数据库与 worker 等组件\u003C\u002Fli>\n\u003C\u002Ful>\n",1793228992054]