GitHub 操作

2025-05-10

重要

此功能目前以公共预览版提供。

GitHub Actions 允许自动执行生成、测试和部署 CI/CD 管道。 Databricks 为 GitHub 上的 CI/CD 工作流开发了以下 GitHub Actions。将 GitHub Actions YAML 文件添加到存储库的 .github/workflows 目录中。

注释

本文介绍由第三方开发的 GitHub Actions。若要联系提供商，请参阅 GitHub Actions 支持。

GitHub 操作	DESCRIPTION
databricks/setup-cli	在 GitHub Actions 工作流中设置 Databricks CLI 的复合操作。

使用 GitHub Actions 部署捆绑包

GitHub Actions 可用于部署 Databricks 资产捆绑包，并从 GitHub 存储库触发 CI/CD 工作流的运行。有关 Databricks 的其他 CI/CD 功能和最佳做法的信息，请参阅 Azure Databricks 上的 CI/CD 和最佳做法以及 Databricks 上的推荐 CI/CD 工作流。

使用运行管道更新的捆绑包运行 CI/CD 工作流

以下示例 GitHub Actions YAML 文件会触发一项测试部署，该部署验证、部署并运行捆绑包配置文件内定义的名为“dev”的预生产目标中的捆绑包中的指定作业。

此示例要求有：

在存储库根目录中的一个捆绑配置文件，它需要通过 GitHub Actions YAML 文件中的设置 working-directory: . 显式声明。该捆绑配置文件应定义一个名为 my-job 的 Azure Databricks 工作流，以及一个名为 dev 的目标。请参阅 Databricks 资产捆绑包配置。
名为 SP_TOKEN 的 GitHub 机密，表示与部署和运行此捆绑包的 Azure Databricks 工作区相关联的 Azure Databricks 服务主体的 Azure Databricks 访问令牌。请参阅加密的机密。

# This workflow validates, deploys, and runs the specified bundle
# within a pre-production target named "dev".
name: 'Dev deployment'

# Ensure that only a single job or workflow using the same concurrency group
# runs at a time.
concurrency: 1

# Trigger this workflow whenever a pull request is opened against the repo's
# main branch or an existing pull request's head branch is updated.
on:
  pull_request:
    types:
      - opened
      - synchronize
    branches:
      - main

jobs:
  # Used by the "pipeline_update" job to deploy the bundle.
  # Bundle validation is automatically performed as part of this deployment.
  # If validation fails, this workflow fails.
  deploy:
    name: 'Deploy bundle'
    runs-on: ubuntu-latest

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Download the Databricks CLI.
      # See https://github.com/databricks/setup-cli
      - uses: databricks/setup-cli@main

      # Deploy the bundle to the "dev" target as defined
      # in the bundle's settings file.
      - run: databricks bundle deploy
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: dev

  # Validate, deploy, and then run the bundle.
  pipeline_update:
    name: 'Run pipeline update'
    runs-on: ubuntu-latest

    # Run the "deploy" job first.
    needs:
      - deploy

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Use the downloaded Databricks CLI.
      - uses: databricks/setup-cli@main

      # Run the Databricks workflow named "my-job" as defined in the
      # bundle that was just deployed.
      - run: databricks bundle run my-job --refresh-all
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: dev

可能还需要触发生产部署。以下 GitHub Actions YAML 文件可以在与前面文件相同的存储库中存在。此文件在捆绑配置文件中定义的名为“prod”的生产目标内验证、部署和运行指定的捆绑包。

# This workflow validates, deploys, and runs the specified bundle
# within a production target named "prod".
name: 'Production deployment'

# Ensure that only a single job or workflow using the same concurrency group
# runs at a time.
concurrency: 1

# Trigger this workflow whenever a pull request is pushed to the repo's
# main branch.
on:
  push:
    branches:
      - main

jobs:
  deploy:
    name: 'Deploy bundle'
    runs-on: ubuntu-latest

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Download the Databricks CLI.
      # See https://github.com/databricks/setup-cli
      - uses: databricks/setup-cli@main

      # Deploy the bundle to the "prod" target as defined
      # in the bundle's settings file.
      - run: databricks bundle deploy
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: prod

  # Validate, deploy, and then run the bundle.
  pipeline_update:
    name: 'Run pipeline update'
    runs-on: ubuntu-latest

    # Run the "deploy" job first.
    needs:
      - deploy

    steps:
      # Check out this repo, so that this workflow can access it.
      - uses: actions/checkout@v3

      # Use the downloaded Databricks CLI.
      - uses: databricks/setup-cli@main

      # Run the Databricks workflow named "my-job" as defined in the
      # bundle that was just deployed.
      - run: databricks bundle run my-job --refresh-all
        working-directory: .
        env:
          DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
          DATABRICKS_BUNDLE_ENV: prod

运行生成 JAR 并部署捆绑包的 CI/CD 工作流

如果你有基于 Java 的生态系统，那么你的 GitHub Action 需要在部署包之前构建并上传 JAR。以下示例 GitHub Actions YAML 文件会触发一项部署，该部署生成 JAR 并将其上传到卷，然后验证捆绑包并将其部署到捆绑包配置文件中定义的名为“prod”的生产目标。它编译基于 Java 的 JAR，但基于 Scala 的项目的编译步骤类似。