GitHub Actions

這很重要

這項功能目前處於公開預覽版。

GitHub Actions 會觸發你從 GitHub 倉庫執行 CI/CD 流程，並讓你自動化建置、測試和部署的 CI/CD 流程。

本文提供 Databricks 所開發的 GitHub Actions 相關信息，以及常見使用案例的範例。如需 Databricks 上其他 CI/CD 功能和最佳做法的相關信息，請參閱 Azure Databricks 上的 CI/CD 和最佳做法，以及 Databricks 上建議的 CI/CD 工作流程。

Databricks GitHub 動作

Databricks 已針對 GitHub 上的 CI/CD 工作流程開發下列 GitHub Actions 。將 GitHub Actions YAML 檔案新增至存放庫的 .github/workflows 目錄。

備註

本文涵蓋由第三方開發的 GitHub Actions。若要連絡提供者，請參閱 GitHub Actions 支援。

GitHub 工作流程	說明
databricks/setup-cli	在 GitHub Actions 工作流程中設定 Databricks CLI 的複合動作。

執行一個 CI/CD 工作流程，更新 Git 資料夾

下列範例 GitHub Actions YAML 檔案會在遠端分支更新時更新工作區 Git 資料夾。關於 CI/CD 的 Git 資料夾方法，請參見「其他版本控制工具」。

需求

這個範例使用了 GitHub Actions 的工作負載身份聯合來增強安全性，並需要你已經建立聯合政策。請參閱啟用 GitHub Actions 的工作負載身分識別同盟。

創造行動

現在，將檔案 .github/workflows/sync_git_folder.yml 放入你的儲存庫，內容如下 YAML：

name: Sync Git Folder

concurrency: prod_environment

on:
  push:
    branches:
      # Set your base branch name here
      - git-folder-cicd-example

permissions:
  id-token: write
  contents: read

jobs:
  deploy:
    runs-on: ubuntu-latest
    name: 'Update git folder'
    environment: Prod
    env:
      DATABRICKS_AUTH_TYPE: github-oidc
      DATABRICKS_HOST: ${{ vars.DATABRICKS_HOST }}
      DATABRICKS_CLIENT_ID: ${{ secrets.DATABRICKS_CLIENT_ID }}

    steps:
      - uses: actions/checkout@v3
      - uses: databricks/setup-cli@main
      - name: Update git folder
        # Set your workspace path and branch name here
        run: databricks repos update /Workspace/<git-folder-path> --branch git-folder-cicd-example

執行包含管線更新的套件組合以運行 CI/CD 工作流程

以下範例 GitHub Actions YAML 檔案觸發測試部署，驗證、部署並執行套件中指定的工作，並於套件組設定檔中定義 dev 的預生產目標中執行。

需求

此範例需要有：

存放庫根目錄的套件組合組態檔，此套件組合組態檔是透過 GitHub Actions YAML 檔案的設定 working-directory: . 明確宣告的。此配套組態檔應該定義名為 sample_job 的 Azure Databricks 工作流程，以及名為 dev的目標。例如：

# This is a Databricks asset bundle definition for pipeline_update.
bundle:
  name: pipeline_update

include:
  - resources/*.yml

variables:
  catalog:
    description: The catalog to use
  schema:
    description: The schema to use

resources:
  jobs:
    sample_job:
      name: sample_job

      parameters:
        - name: catalog
          default: ${var.catalog}
        - name: schema
          default: ${var.schema}

      tasks:
        - task_key: refresh_pipeline
          pipeline_task:
            pipeline_id: ${resources.pipelines.sample_pipeline.id}

      environments:
        - environment_key: default
          spec:
            environment_version: '4'

  pipelines:
    sample_pipeline:
      name: sample_pipeline
      catalog: ${var.catalog}
      schema: ${var.schema}
      serverless: true
      root_path: '../src/sample_pipeline'

      libraries:
        - glob:
            include: ../src/sample_pipeline/transformations/**

      environment:
        dependencies:
          - --editable ${workspace.file_path}

targets:
  dev:
    mode: development
    default: true
    workspace:
      host: <dev-workspace-url>
    variables:
      catalog: my_catalog
      schema: ${workspace.current_user.short_name}
  prod:
    mode: production
    workspace:
      host: <production-workspace-url>
      root_path: /Workspace/Users/someone@example.com/.bundle/${bundle.name}/${bundle.target}
    variables:
      catalog: my_catalog
      schema: prod
    permissions:
      - user_name: someone@example.com
        level: CAN_MANAGE

欲了解更多關於套件組組的資訊，請參閱 Databricks 資產組組設定。

名為 SP_TOKEN的 GitHub 秘密，代表 Azure Databricks 服務主體的 Azure Databricks 存取令牌，其與部署和執行此套件組合的 Azure Databricks 工作區相關聯。要建立代幣：
1. 建立一個 Databricks 服務主體。請參閱將服務主體新增至您的帳戶。
2. 為服務主體產生一個秘密。請參閱步驟 1：建立 OAuth 密碼。複製秘密和客戶端 ID 的數值。
3. 手動使用複製的密碼和客戶端 ID 值來生成 Databricks 存取權杖（帳戶或工作區）。請參閱產生帳戶層級存取權杖。
4. 複製 access_token JSON 回應的值。在你的倉庫裡新增一個名為 SP_TOKEN 的 GitHub 機密至 Actions，並使用 Databricks 的存取權杖作為機密值。請參閱加密的秘密。

創造行動

現在，將檔案 .github/workflows/pipeline_update.yml 放入你的儲存庫，內容如下 YAML：

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

您也可以觸發生產部署。下列 GitHub Actions YAML 檔案可以存在於與上述檔案相同的存放庫中。此檔案會在名為「prod」的生產目標內驗證、部署及執行指定的套件組合，如套件組合組態檔中所定義。

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

運行 CI/CD 工作流程以建置 JAR 並部署套件組合

如果你的生態系統是基於 Java 的，你的 GitHub Action 需要先建置並上傳 JAR，才能部署套件。下列範例 GitHub Actions YAML 檔案會觸發部署，以建置 JAR 並將其上傳至磁碟區，然後將套件組合驗證並部署至名為 “prod” 的生產目標，如套件組合組態檔中所定義。它會編譯 Java 型 JAR，但 Scala 型專案的編譯步驟很類似。

需求

此範例需要有：

在儲存庫的根目錄中，有一個套件組合的組態檔，透過 GitHub Actions YAML 檔案的設定明確地指定 working-directory: .
DATABRICKS_TOKEN環境變數，代表與部署和執行此套件組合之 Azure Databricks 工作區相關聯的 Azure Databricks 存取令牌。
環境變數 DATABRICKS_HOST ，代表 Azure Databricks 主機工作區。

創造行動

現在，將檔案 .github/workflows/build_jar.yml 放入你的儲存庫，內容如下 YAML：

name: Build JAR and deploy with bundles

on:
  pull_request:
    branches:
      - main
  push:
    branches:
      - main

jobs:
  build-test-upload:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Set up Java
        uses: actions/setup-java@v4
        with:
          java-version: '17' # Specify the Java version used by your project
          distribution: 'temurin' # Use a reliable JDK distribution

      - name: Cache Maven dependencies
        uses: actions/cache@v4
        with:
          path: ~/.m2/repository
          key: ${{ runner.os }}-maven-${{ hashFiles('**/pom.xml') }}
          restore-keys: |
            ${{ runner.os }}-maven-

      - name: Build and test JAR with Maven
        run: mvn clean verify # Use verify to ensure tests are run

      - name: Databricks CLI Setup
        uses: databricks/setup-cli@v0.9.0 # Pin to a specific version

      - name: Upload JAR to a volume
        env:
          DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }} # Add host for clarity
        run: |
          databricks fs cp target/my-app-1.0.jar dbfs:/Volumes/artifacts/my-app-${{ github.sha }}.jar --overwrite

  validate:
    needs: build-test-upload
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Databricks CLI Setup
        uses: databricks/setup-cli@v0.9.0

      - name: Validate bundle
        env:
          DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
        run: databricks bundle validate

  deploy:
    needs: validate
    if: github.event_name == 'push' && github.ref == 'refs/heads/main' # Only deploy on push to main
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Databricks CLI Setup
        uses: databricks/setup-cli@v0.9.0

      - name: Deploy bundle
        env:
          DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
        run: databricks bundle deploy --target prod

其他資源

意見反應

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Last updated on 2026-01-17

共用方式為

GitHub Actions

Databricks GitHub 動作

執行一個 CI/CD 工作流程，更新 Git 資料夾

需求

創造行動

執行包含管線更新的套件組合以運行 CI/CD 工作流程

需求

創造行動

運行 CI/CD 工作流程以建置 JAR 並部署套件組合

需求

創造行動

其他資源

意見反應

其他資源