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README
Apache-2.0

DevLake

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什么是 DevLake?

DevLake 将你所有的 DevOps 数据以实用、个性化、可扩展的视图呈现。通过 DevLake,从不断增加的开发者工具列表中收集、分析和可视化数据。

DevLake 适用于希望更好地通过数据了解其开发过程的开发团队,以及希望以数据驱动提升自身实践的开发团队。有了 DevLake,你可以向你的开发过程提出任何问题,只要连接数据并查询。

一键体验 DevLake

在本地运行 Teamcode 查看手册

User Flow

用户使用流程


为什么选择 DevLake?

  1. 全面了解软件研发生命周期,挖掘工作流瓶颈
  2. 及时回顾团队迭代表现,快速反馈,敏捷调整
  3. 快速搭建场景化数据仪表盘,下钻分析洞察问题根因

DevLake 可以完成什么?

  1. 归集 DevOps 全流程效能数据
  2. 同类工具共用一套标准的数据抽象层,输出标准化效能数据
  3. 内置 20+ 效能指标与下钻分析能力
  4. 支持自定义 SQL 分析及拖拽搭建场景化数据视图
  5. 灵活的架构与插件设计,支持快速接入新数据源

查看 Demo

点击这里 查看 Demo, Demo里呈现的数据来自此仓库。
用户名/密码: test/test



用户安装

  • 如果你只打算运行 DevLake,你只需要阅读这一小节
  • 如果你想在云端安装 DevLake,你可以使用 Tin 来进行
  • 写成 这样 的命令需要在你的终端中运行

需要安装的软件包

注:安装完 Docker 后,你可能需要运行 Docker 应用程序并重新启动你的终端

在你的终端中运行以下命令

IMPORTANT(新用户可以忽略): DevLake暂不支持向前兼容。当 DB Schema 发生变化时,直接更新已有实例可能出错,建议已经安装 DevLake 的用户在升级时,重新部署实例并导入数据。

  1. 最新版本列表 下载 docker-compose.ymlenv.example

  2. env.example 重命名为 .env

  3. 启动 Docker,然后运行 docker-compose up -d 启动服务。

  4. 访问 localhost:4000 来设置 DevLake 的配置文件

    • 在 Integration 页面上找到到所需的插件页面
    • 你需要为你打算使用的插件输入必要的信息
    • 请参考以下内容,以了解如何配置每个插件的更多细节 -> Jira -> GitLab -> Jenkins -> GitHub
    • 提交表单,通过点击每个表单页面上的Save Connection按钮来更新数值。
    • devlake需要一段时间才能完全启动。如果config-ui提示 API 无法访问,请等待几秒钟并尝试刷新页面。
    • 如果想收集一个 Repo 进行快速预览,请在数据集成/Github页面提供一个 Github 的个人 Token。
  5. 访问 localhost:4000/create-pipeline,创建 1个Pipeline run,并触发数据收集

    Pipeline Runs 可以通过新的 "Create Run"界面启动。只需启用你希望运行的数据源,并指定数据收集的范围,比如Gitlab的项目ID和GitHub的仓库名称。

    一旦创建了有效的 Pipeline Run 配置,按Create Run来启动/运行该 Pipeline。 Pipeline Run 启动后,你会被自动转到Pipeline Activity界面,以监控采集活动。

    Pipelines可从 config-ui 的主菜单进入。

    • 管理所有Pipeline http://localhost:4000/pipelines
    • 创建Pipeline Run http://localhost:4000/create-pipeline
    • 查看Pipeline Activity http://localhost:4000/pipelines/activity/[RUN_ID]

    对于复杂度较高的用例,请使用Raw JSON API进行任务配置。使用cURL或图形API工具(如Postman)手动启动运行。POST以下请求到DevLake API端点。

    [
      [
        {
          "Plugin": "github",
          "Options": {
            "repo": "lake",
            "owner": "merico-dev"
          }
        }
      ]
    ]

    请参考这篇 wiki How to trigger data collection.

  6. 数据收集完成后,点击配置页面左上角的 View Dashboards 按钮或者访问 localhost:3002,访问 Grafana (用户名: admin, 密码: admin)

    我们使用 Grafana 作为可视化工具,为存储在我们数据库中的数据建立图表。可以使用SQL查询,添加面板来构建、保存和编辑自定义仪表盘。

    关于配置和定制仪表盘的所有细节可以在 Grafana 文档 中找到。

设置 Cron job

为了定期同步数据,我们提供了lake-cli以方便发送数据收集请求,我们同时提供了cron job以定期触发 cli 工具。


开发者安装

前期准备

  • Docker
  • Golang
  • Make
    • Mac (Already installed)
    • Windows: Download
    • Ubuntu: sudo apt-get install build-essential

如何设置开发环境

  1. 进入你想安装本项目的路径,并克隆资源库

    git clone https://github.com/merico-dev/lake.git
    cd lake
  2. 安装插件依赖

  3. 安装 go packages

    go get
  4. 将样本配置文件复制到新的本地文件

    cp .env.example .env
  5. .env文件中找到以DB_URL开头的那一行,把mysql:3306替换为127.0.0.1:3306

  6. 启动 MySQL 和 Grafana

    确保在此步骤之前 Docker 正在运行。

    docker-compose up -d mysql grafana
  7. 在 2 个终端种分别以开发者模式运行 lake 和 config UI:

    # run lake
    make dev
    # run config UI
    make configure-dev
  8. 访问 config-ui localhost:4000 来配置 DevLake 数据源

    • 在 "Integration"页面上找到到所需的插件页面
    • 你需要为你打算使用的插件输入必要的信息
    • 请参考以下内容,以了解如何配置每个插件的更多细节 -> Jira -> GitLab -> Jenkins -> GitHub
  9. 访问 localhost:4000/create-pipeline,创建 1个Pipeline run,并触发数据收集

    Pipeline Runs 可以通过新的 "Create Run"界面启动。只需启用你希望运行的数据源,并指定数据收集的范围,比如Gitlab的项目ID和GitHub的仓库名称。

    一旦创建了有效的 Pipeline Run 配置,按Create Run来启动/运行该 Pipeline。 Pipeline Run 启动后,你会被自动转到Pipeline Activity界面,以监控采集活动。

    Pipelines可从 config-ui 的主菜单进入。

    • 管理所有Pipeline http://localhost:4000/pipelines
    • 创建Pipeline Run http://localhost:4000/create-pipeline
    • 查看Pipeline Activity http://localhost:4000/pipelines/activity/[RUN_ID]

    对于复杂度较高的用例,请使用Raw JSON API进行任务配置。使用cURL或图形API工具(如Postman)手动启动运行。POST以下请求到DevLake API端点。

    [
      [
        {
          "Plugin": "github",
          "Options": {
            "repo": "lake",
            "owner": "merico-dev"
          }
        }
      ]
    ]

    请参考这篇 wiki How to trigger data collection.

  10. 数据收集完成后,点击配置页面左上角的 View Dashboards 按钮或者访问 localhost:3002(用户名: admin, 密码: admin)

    我们使用 Grafana 作为可视化工具,为存储在我们数据库中的数据建立图表。可以使用SQL查询,添加面板来构建、保存和编辑自定义仪表盘。

    关于配置和定制仪表盘的所有细节可以在 Grafana 文档 中找到。

  11. (可选)运行测试:

    make test

项目路线图


贡献

本节列出了所有与共建 DevLake 相关的文档


社区

  • Discord: 在 Discord 上给我们发消息
  • FAQ: 常见问题汇总
  • 加入 DevLake 微信讨论群

加入 DevLake 讨论群


License

此项目的许可证为 Apache License 2.0 - 查看 许可证 详情。

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DevLake: the open-source data lake & dashboard for your DevOps tools. 展开 收起
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