# deployment-pipeline-design — detailed patterns and worked examples

## Pipeline Stages

### Standard Pipeline Flow

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

### Detailed Stage Breakdown

1. **Source** - Code checkout, dependency graph resolution
2. **Build** - Compile, package, containerize, sign artifacts
3. **Test** - Unit, integration, SAST/SCA security scans
4. **Staging Deploy** - Deploy to staging environment with smoke tests
5. **Integration Tests** - E2E, contract tests, performance baselines
6. **Approval Gate** - Manual or automated metric-based gate
7. **Production Deploy** - Canary, blue-green, or rolling strategy
8. **Verification** - Deep health checks, synthetic monitoring
9. **Rollback** - Automated rollback on failure signals

## Approval Gate Patterns

### Pattern 1: Manual Approval (GitHub Actions)

```yaml
production-deploy:
  needs: staging-deploy
  environment:
    name: production
    url: https://app.example.com
  runs-on: ubuntu-latest
  steps:
    - name: Deploy to production
      run: kubectl apply -f k8s/production/
```

Environment protection rules in GitHub enforce required reviewers before this job starts. Configure reviewers at **Settings → Environments → production → Required reviewers**.

### Pattern 2: Time-Based Approval (GitLab CI)

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

### Pattern 3: Multi-Approver (Azure Pipelines)

```yaml
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"
```

### Pattern 4: Automated Metric Gate

Use an AnalysisTemplate (Argo Rollouts) or a custom gate script to block promotion when error rates exceed a threshold:

```yaml
# Argo Rollouts AnalysisTemplate — blocks canary promotion automatically
apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: success-rate
spec:
  metrics:
  - name: success-rate
    interval: 60s
    successCondition: "result[0] >= 0.95"
    failureCondition: "result[0] < 0.90"
    inconclusiveLimit: 3
    provider:
      prometheus:
        address: http://prometheus:9090
        query: |
          sum(rate(http_requests_total{status!~"5..",job="my-app"}[2m]))
          / sum(rate(http_requests_total{job="my-app"}[2m]))
```

## Deployment Strategies

### Decision Table

| Strategy     | Downtime | Rollback Speed | Cost Impact     | Best For                        |
|-------------|----------|----------------|-----------------|----------------------------------|
| Rolling      | None     | ~minutes       | None            | Most stateless services          |
| Blue-Green   | None     | Instant        | 2x infra (temp) | High-risk or database migrations |
| Canary       | None     | Instant        | Minimal         | High-traffic, metric-driven      |
| Recreate     | Yes      | Fast           | None            | Dev/test, batch jobs             |
| Feature Flag | None     | Instant        | None            | Gradual feature exposure         |

### 1. Rolling Deployment

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 2         # at most 12 pods during rollout
      maxUnavailable: 1   # at least 9 pods always serving
```

Characteristics: gradual rollout, zero downtime, easy rollback, best for most applications.

### 2. Blue-Green Deployment

```bash
# Switch traffic from blue to green
kubectl apply -f k8s/green-deployment.yaml
kubectl rollout status deployment/my-app-green

# Flip the service selector
kubectl patch service my-app -p '{"spec":{"selector":{"version":"green"}}}'

# Rollback instantly if needed
kubectl patch service my-app -p '{"spec":{"selector":{"version":"blue"}}}'
```

Characteristics: instant switchover, easy rollback, doubles infrastructure cost temporarily, good for high-risk deployments with long warm-up times.

### 3. Canary Deployment (Argo Rollouts)

```yaml
apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: my-app
spec:
  replicas: 10
  strategy:
    canary:
      analysis:
        templates:
          - templateName: success-rate
        startingStep: 2
      steps:
        - setWeight: 10
        - pause: { duration: 5m }
        - setWeight: 25
        - pause: { duration: 5m }
        - setWeight: 50
        - pause: { duration: 10m }
        - setWeight: 100
```

Characteristics: gradual traffic shift, real-user metric validation, automated promotion or rollback, requires Argo Rollouts or a service mesh.

### 4. Feature Flags

```python
from flagsmith import Flagsmith

flagsmith = Flagsmith(environment_key="API_KEY")

if flagsmith.has_feature("new_checkout_flow"):
    process_checkout_v2()
else:
    process_checkout_v1()
```

Characteristics: deploy without releasing, A/B testing, instant rollback per user segment, granular control independent of deployment.

## Pipeline Orchestration

### Multi-Stage Pipeline Example (GitHub Actions)

```yaml
name: Production Pipeline

on:
  push:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    outputs:
      image: ${{ steps.build.outputs.image }}
    steps:
      - uses: actions/checkout@v4
      - name: Build and push Docker image
        id: build
        run: |
          IMAGE=myapp:${{ github.sha }}
          docker build -t $IMAGE .
          docker push $IMAGE
          echo "image=$IMAGE" >> $GITHUB_OUTPUT

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

  deploy-staging:
    needs: test
    environment:
      name: staging
    runs-on: ubuntu-latest
    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
    environment:
      name: production        # blocks here until required reviewers approve
    runs-on: ubuntu-latest
    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: Deep health check
        run: |
          for i in {1..12}; do
            STATUS=$(curl -sf https://app.example.com/health/ready | jq -r '.status')
            [ "$STATUS" = "ok" ] && exit 0
            sleep 10
          done
          exit 1
      - name: Notify on success
        run: |
          curl -X POST ${{ secrets.SLACK_WEBHOOK }} \
            -d '{"text":"Production deployment successful: ${{ github.sha }}"}'
```

## Health Checks

### Shallow vs Deep Health Endpoints

A shallow `/ping` returns 200 even when downstream dependencies are broken. Use a deep readiness endpoint that verifies actual dependencies before promoting traffic.

```python
# /health/ready — checks real dependencies, used by pipeline gate
@app.get("/health/ready")
async def readiness():
    checks = {
        "database": await check_db_connection(),
        "cache":    await check_redis_connection(),
        "queue":    await check_queue_connection(),
    }
    status = "ok" if all(checks.values()) else "degraded"
    code = 200 if status == "ok" else 503
    return JSONResponse({"status": status, "checks": checks}, status_code=code)
```

### Post-Deployment Verification Script

```bash
#!/usr/bin/env bash
# verify-deployment.sh — run after every production deploy
set -euo pipefail

ENDPOINT="${1:?usage: verify-deployment.sh <base-url>}"
MAX_ATTEMPTS=12
SLEEP_SECONDS=10

for i in $(seq 1 $MAX_ATTEMPTS); do
  STATUS=$(curl -sf "$ENDPOINT/health/ready" | jq -r '.status' 2>/dev/null || echo "unreachable")
  if [ "$STATUS" = "ok" ]; then
    echo "Health check passed after $((i * SLEEP_SECONDS))s"
    exit 0
  fi
  echo "Attempt $i/$MAX_ATTEMPTS: status=$STATUS — retrying in ${SLEEP_SECONDS}s"
  sleep "$SLEEP_SECONDS"
done

echo "Health check failed after $((MAX_ATTEMPTS * SLEEP_SECONDS))s"
exit 1
```

## Rollback Strategies

### Automated Rollback in Pipeline

```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 --timeout=5m

    - name: Post-deployment health check
      id: health
      run: ./scripts/verify-deployment.sh https://app.example.com

    - name: Rollback on failure
      if: failure()
      run: |
        kubectl rollout undo deployment/my-app
        echo "Rolled back to previous revision"
```

### Manual Rollback Commands

```bash
# List revision history with change-cause annotations
kubectl rollout history deployment/my-app

# Rollback to previous version
kubectl rollout undo deployment/my-app

# Rollback to a specific revision
kubectl rollout undo deployment/my-app --to-revision=3

# Verify rollback completed
kubectl rollout status deployment/my-app
```

For advanced rollback strategies including database migration rollbacks and Argo Rollouts abort flows, see [`references/advanced-strategies.md`](references/advanced-strategies.md).

## Monitoring and Metrics

### Key DORA Metrics to Track

| Metric                    | Target (Elite) | How to Measure                           |
|--------------------------|----------------|------------------------------------------|
| Deployment Frequency      | Multiple/day   | Pipeline run count per day               |
| Lead Time for Changes     | < 1 hour       | Commit timestamp → production deploy     |
| Change Failure Rate       | < 5%           | Failed deploys / total deploys           |
| Mean Time to Recovery     | < 1 hour       | Incident open → service restored         |

### Post-Deployment Metric Verification

```yaml
- name: Verify error rate post-deployment
  run: |
    sleep 60  # allow metrics to accumulate

    ERROR_RATE=$(curl -sf "$PROMETHEUS_URL/api/v1/query" \
      --data-urlencode 'query=sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))' \
      | jq '.data.result[0].value[1]')

    echo "Current error rate: $ERROR_RATE"
    if (( $(echo "$ERROR_RATE > 0.01" | bc -l) )); then
      echo "Error rate $ERROR_RATE exceeds 1% threshold — triggering rollback"
      exit 1
    fi
```

## Pipeline Best Practices

1. **Fail fast** — Run quick checks (lint, unit tests) before slow ones (E2E, security scans)
2. **Parallel execution** — Run independent jobs concurrently to minimize total pipeline time
3. **Caching** — Cache dependency layers and build artifacts between runs
4. **Artifact promotion** — Build once, promote the same artifact through all environments
5. **Environment parity** — Keep staging infrastructure as close to production as possible
6. **Secrets management** — Use secret stores (Vault, AWS Secrets Manager, GitHub encrypted secrets) — never hardcode
7. **Deployment windows** — Prefer low-traffic windows; enforce change freeze periods via gate policies
8. **Idempotent deploys** — Ensure re-running a deploy produces the same result
9. **Rollback automation** — Trigger rollback automatically on health check or metric threshold failure
10. **Annotate deployments** — Send deployment markers to monitoring tools (Datadog, Grafana) for correlation
