deployment-pipeline-design

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Deployment Pipeline Design

部署流水线设计

Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.

带有审批门和部署策略的多阶段CI/CD流水线架构模式。

Purpose

目标

Design robust, secure deployment pipelines that balance speed with safety through proper stage organization and approval workflows.

通过合理的阶段组织和审批工作流，设计兼具速度与安全性的健壮、安全的部署流水线。

When to Use

适用场景

Design CI/CD architecture
Implement deployment gates
Configure multi-environment pipelines
Establish deployment best practices
Implement progressive delivery

CI/CD架构设计
部署门配置
多环境流水线搭建
部署最佳实践落地
渐进式交付实施

Pipeline Stages

流水线阶段

Standard Pipeline Flow

标准流水线流程

┌─────────┐   ┌──────┐   ┌─────────┐   ┌────────┐   ┌──────────┐
│  Build  │ → │ Test │ → │ Staging │ → │ Approve│ → │Production│
└─────────┘   └──────┘   └─────────┘   └────────┘   └──────────┘

┌─────────┐   ┌──────┐   ┌─────────┐   ┌────────┐   ┌──────────┐
│  Build  │ → │ Test │ → │ Staging │ → │ Approve│ → │Production│
└─────────┘   └──────┘   └─────────┘   └────────┘   └──────────┘

Detailed Stage Breakdown

详细阶段分解

Source - Code checkout
Build - Compile, package, containerize
Test - Unit, integration, security scans
Staging Deploy - Deploy to staging environment
Integration Tests - E2E, smoke tests
Approval Gate - Manual approval required
Production Deploy - Canary, blue-green, rolling
Verification - Health checks, monitoring
Rollback - Automated rollback on failure

Source - 代码检出
Build - 编译、打包、容器化
Test - 单元测试、集成测试、安全扫描
Staging Deploy - 部署到预发布环境
Integration Tests - 端到端测试、冒烟测试
Approval Gate - 需要人工审批
Production Deploy - 金丝雀、蓝绿、滚动部署
Verification - 健康检查、监控
Rollback - 失败时自动回滚

Approval Gate Patterns

审批门模式

Pattern 1: Manual Approval

模式1：人工审批

yaml

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yaml

undefined

GitHub Actions

production-deploy: needs: staging-deploy environment: name: production url: https://app.example.com runs-on: ubuntu-latest steps: - name: Deploy to production run: | # Deployment commands

undefined

production-deploy: needs: staging-deploy environment: name: production url: https://app.example.com runs-on: ubuntu-latest steps: - name: Deploy to production run: | # Deployment commands

undefined

Pattern 2: Time-Based Approval

模式2：基于时间的审批

yaml

undefined

yaml

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GitLab CI

deploy:production: stage: deploy script: - deploy.sh production environment: name: production when: delayed start_in: 30 minutes only: - main

undefined

deploy:production: stage: deploy script: - deploy.sh production environment: name: production when: delayed start_in: 30 minutes only: - main

undefined

Pattern 3: Multi-Approver

模式3：多审批人

yaml

undefined

yaml

undefined

Azure Pipelines

stages:

stage: Production dependsOn: Staging jobs:
- deployment: Deploy environment: name: production resourceType: Kubernetes strategy: runOnce: preDeploy: steps: - task: ManualValidation@0 inputs: notifyUsers: "team-leads@example.com" instructions: "Review staging metrics before approving"


**Reference:** See `assets/approval-gate-template.yml`

stages:

stage: Production dependsOn: Staging jobs:
- deployment: Deploy environment: name: production resourceType: Kubernetes strategy: runOnce: preDeploy: steps: - task: ManualValidation@0 inputs: notifyUsers: "team-leads@example.com" instructions: "Review staging metrics before approving"


**参考：** 查看 `assets/approval-gate-template.yml`

Deployment Strategies

部署策略

1. Rolling Deployment

1. 滚动部署

yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 2
      maxUnavailable: 1

Characteristics:

Gradual rollout
Zero downtime
Easy rollback
Best for most applications

yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 2
      maxUnavailable: 1

特点：

渐进式发布
零停机
回滚简单
适用于大多数应用

2. Blue-Green Deployment

2. 蓝绿部署

yaml

undefined

yaml

undefined

Blue (current)

Blue (当前版本)

kubectl apply -f blue-deployment.yaml kubectl label service my-app version=blue

Green (new)

Green (新版本)

kubectl apply -f green-deployment.yaml

Test green environment

测试绿色环境

kubectl label service my-app version=green

Rollback if needed

如需回滚

kubectl label service my-app version=blue


**Characteristics:**

- Instant switchover
- Easy rollback
- Doubles infrastructure cost temporarily
- Good for high-risk deployments

kubectl label service my-app version=blue


**特点：**

- 即时切换
- 回滚简单
- 临时双倍基础设施成本
- 适合高风险部署

3. Canary Deployment

3. 金丝雀部署

yaml

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    canary:
      steps:
        - setWeight: 10
        - pause: { duration: 5m }
        - setWeight: 25
        - pause: { duration: 5m }
        - setWeight: 50
        - pause: { duration: 5m }
        - setWeight: 100

Characteristics:

Gradual traffic shift
Risk mitigation
Real user testing
Requires service mesh or similar

yaml

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    canary:
      steps:
        - setWeight: 10
        - pause: { duration: 5m }
        - setWeight: 25
        - pause: { duration: 5m }
        - setWeight: 50
        - pause: { duration: 5m }
        - setWeight: 100

特点：

渐进式流量切换
风险缓解
真实用户测试
需要服务网格或类似工具支持

4. Feature Flags

4. 功能开关

python

from flagsmith import Flagsmith

flagsmith = Flagsmith(environment_key="API_KEY")

if flagsmith.has_feature("new_checkout_flow"):
    # New code path
    process_checkout_v2()
else:
    # Existing code path
    process_checkout_v1()

Characteristics:

Deploy without releasing
A/B testing
Instant rollback
Granular control

python

from flagsmith import Flagsmith

flagsmith = Flagsmith(environment_key="API_KEY")

if flagsmith.has_feature("new_checkout_flow"):
    # 新代码路径
    process_checkout_v2()
else:
    # 现有代码路径
    process_checkout_v1()

特点：

部署但不发布
A/B测试
即时回滚
精细化控制

Pipeline Orchestration

流水线编排

Multi-Stage Pipeline Example

多阶段流水线示例

yaml

name: Production Pipeline

on:
  push:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build application
        run: make build
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      - name: Push to registry
        run: docker push myapp:${{ github.sha }}

  test:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Unit tests
        run: make test
      - name: Security scan
        run: trivy image myapp:${{ github.sha }}

  deploy-staging:
    needs: test
    runs-on: ubuntu-latest
    environment:
      name: staging
    steps:
      - name: Deploy to staging
        run: kubectl apply -f k8s/staging/

  integration-test:
    needs: deploy-staging
    runs-on: ubuntu-latest
    steps:
      - name: Run E2E tests
        run: npm run test:e2e

  deploy-production:
    needs: integration-test
    runs-on: ubuntu-latest
    environment:
      name: production
    steps:
      - name: Canary deployment
        run: |
          kubectl apply -f k8s/production/
          kubectl argo rollouts promote my-app

  verify:
    needs: deploy-production
    runs-on: ubuntu-latest
    steps:
      - name: Health check
        run: curl -f https://app.example.com/health
      - name: Notify team
        run: |
          curl -X POST ${{ secrets.SLACK_WEBHOOK }} \
            -d '{"text":"Production deployment successful!"}'

yaml

name: Production Pipeline

on:
  push:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build application
        run: make build
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      - name: Push to registry
        run: docker push myapp:${{ github.sha }}

  test:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Unit tests
        run: make test
      - name: Security scan
        run: trivy image myapp:${{ github.sha }}

  deploy-staging:
    needs: test
    runs-on: ubuntu-latest
    environment:
      name: staging
    steps:
      - name: Deploy to staging
        run: kubectl apply -f k8s/staging/

  integration-test:
    needs: deploy-staging
    runs-on: ubuntu-latest
    steps:
      - name: Run E2E tests
        run: npm run test:e2e

  deploy-production:
    needs: integration-test
    runs-on: ubuntu-latest
    environment:
      name: production
    steps:
      - name: Canary deployment
        run: |
          kubectl apply -f k8s/production/
          kubectl argo rollouts promote my-app

  verify:
    needs: deploy-production
    runs-on: ubuntu-latest
    steps:
      - name: Health check
        run: curl -f https://app.example.com/health
      - name: Notify team
        run: |
          curl -X POST ${{ secrets.SLACK_WEBHOOK }} \
            -d '{"text":"Production deployment successful!"}'

Pipeline Best Practices

流水线最佳实践

Fail fast - Run quick tests first
Parallel execution - Run independent jobs concurrently
Caching - Cache dependencies between runs
Artifact management - Store build artifacts
Environment parity - Keep environments consistent
Secrets management - Use secret stores (Vault, etc.)
Deployment windows - Schedule deployments appropriately
Monitoring integration - Track deployment metrics
Rollback automation - Auto-rollback on failures
Documentation - Document pipeline stages

快速失败 - 优先运行快速测试
并行执行 - 并发运行独立任务
缓存机制 - 在多次运行间缓存依赖
制品管理 - 存储构建制品
环境一致性 - 保持各环境一致
密钥管理 - 使用密钥存储工具（如Vault等）
部署窗口 - 合理安排部署时间
监控集成 - 跟踪部署指标
自动回滚 - 失败时自动回滚
文档化 - 记录流水线阶段

Rollback Strategies

回滚策略

Automated Rollback

自动回滚

yaml

deploy-and-verify:
  steps:
    - name: Deploy new version
      run: kubectl apply -f k8s/

    - name: Wait for rollout
      run: kubectl rollout status deployment/my-app

    - name: Health check
      id: health
      run: |
        for i in {1..10}; do
          if curl -sf https://app.example.com/health; then
            exit 0
          fi
          sleep 10
        done
        exit 1

    - name: Rollback on failure
      if: failure()
      run: kubectl rollout undo deployment/my-app

yaml

deploy-and-verify:
  steps:
    - name: Deploy new version
      run: kubectl apply -f k8s/

    - name: Wait for rollout
      run: kubectl rollout status deployment/my-app

    - name: Health check
      id: health
      run: |
        for i in {1..10}; do
          if curl -sf https://app.example.com/health; then
            exit 0
          fi
          sleep 10
        done
        exit 1

    - name: Rollback on failure
      if: failure()
      run: kubectl rollout undo deployment/my-app

Manual Rollback

手动回滚

bash

undefined

bash

undefined

List revision history

查看版本历史

kubectl rollout history deployment/my-app

Rollback to previous version

回滚到上一个版本

kubectl rollout undo deployment/my-app

Rollback to specific revision

回滚到指定版本

kubectl rollout undo deployment/my-app --to-revision=3

undefined

kubectl rollout undo deployment/my-app --to-revision=3

undefined

Monitoring and Metrics

监控与指标

Key Pipeline Metrics

关键流水线指标

Deployment Frequency - How often deployments occur
Lead Time - Time from commit to production
Change Failure Rate - Percentage of failed deployments
Mean Time to Recovery (MTTR) - Time to recover from failure
Pipeline Success Rate - Percentage of successful runs
Average Pipeline Duration - Time to complete pipeline

部署频率 - 部署发生的频次
前置时间 - 从代码提交到生产环境的时间
变更失败率 - 失败部署的百分比
平均恢复时间(MTTR) - 从故障中恢复的时间
流水线成功率 - 成功运行的百分比
平均流水线时长 - 完成流水线的时间

Integration with Monitoring

与监控系统集成

yaml

- name: Post-deployment verification
  run: |
    # Wait for metrics stabilization
    sleep 60

    # Check error rate
    ERROR_RATE=$(curl -s "$PROMETHEUS_URL/api/v1/query?query=rate(http_errors_total[5m])" | jq '.data.result[0].value[1]')

    if (( $(echo "$ERROR_RATE > 0.01" | bc -l) )); then
      echo "Error rate too high: $ERROR_RATE"
      exit 1
    fi

yaml

- name: Post-deployment verification
  run: |
    # 等待指标稳定
    sleep 60

    # 检查错误率
    ERROR_RATE=$(curl -s "$PROMETHEUS_URL/api/v1/query?query=rate(http_errors_total[5m])" | jq '.data.result[0].value[1]')

    if (( $(echo "$ERROR_RATE > 0.01" | bc -l) )); then
      echo "Error rate too high: $ERROR_RATE"
      exit 1
    fi

deployment-pipeline-design

Original

Translation

Deployment Pipeline Design

部署流水线设计

Purpose

目标

When to Use

适用场景

Pipeline Stages

流水线阶段

Standard Pipeline Flow

标准流水线流程

Detailed Stage Breakdown

详细阶段分解

Approval Gate Patterns

审批门模式

Pattern 1: Manual Approval

模式1：人工审批

GitHub Actions

GitHub Actions

Pattern 2: Time-Based Approval

模式2：基于时间的审批

GitLab CI

GitLab CI

Pattern 3: Multi-Approver

模式3：多审批人

Azure Pipelines

Azure Pipelines

Deployment Strategies

部署策略

1. Rolling Deployment

1. 滚动部署

2. Blue-Green Deployment

2. 蓝绿部署

Blue (current)

Blue (当前版本)

Green (new)

Green (新版本)

Test green environment

测试绿色环境

Rollback if needed

如需回滚

3. Canary Deployment

3. 金丝雀部署

4. Feature Flags

4. 功能开关

Pipeline Orchestration

流水线编排

Multi-Stage Pipeline Example

多阶段流水线示例

Pipeline Best Practices

流水线最佳实践

Rollback Strategies

回滚策略

Automated Rollback

自动回滚

Manual Rollback

手动回滚

List revision history

查看版本历史

Rollback to previous version

回滚到上一个版本

Rollback to specific revision

回滚到指定版本

Monitoring and Metrics

监控与指标

Key Pipeline Metrics

关键流水线指标

Integration with Monitoring

与监控系统集成

Reference Files

参考文件

Related Skills

相关技能