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Continuous Delivery Pattern

CD Pipeline

Continuous Delivery (CD) is a software development practice where code changes are automatically prepared for production release. According to DORA research, it's one of the key capabilities that drives high performance in technology organizations.

Core Principles

Principle Description Implementation
Trunk-Based Development Work in small batches with short-lived branches Git workflow with feature flags
Automation Automate build, test, and deployment processes Jenkins, GitHub Actions
Comprehensive Testing Implement automated testing at all levels Selenium, JUnit, Cypress
GitOps Use Git as single source of truth ArgoCD, Flux

Implementation Guide

1. Version Control Practices

# Trunk-based development workflow
git checkout -b feature/small-change
# Make small, incremental changes
git commit -am "feat: add new feature behind flag"
git push origin feature/small-change
# Merge to trunk within 24 hours

2. Deployment Pipeline

# Example Jenkins Pipeline
pipeline {
    agent any
    stages {
        stage('Build') {
            steps {
                sh 'make build'
            }
        }
        stage('Test') {
            parallel {
                stage('Unit') {
                    steps { sh 'make test-unit' }
                }
                stage('Integration') {
                    steps { sh 'make test-integration' }
                }
            }
        }
        stage('Deploy') {
            steps {
                sh 'make deploy'
            }
        }
    }
}

3. Feature Flags

public class FeatureFlags {
    private static final String FLAG_NEW_FEATURE = "new-feature";

    public boolean isEnabled(String flag) {
        return LaunchDarkly.client().boolVariation(flag, user, false);
    }
}

Key Metrics

Based on DORA research, track these metrics:

Metric Elite Performance Implementation
Deployment Frequency Multiple deploys per day deployment_frequency = deploys / time_period
Lead Time for Changes Less than one hour lead_time = time_to_production - commit_time
Change Failure Rate 0-15% failure_rate = failed_deploys / total_deploys
Time to Restore Less than one hour mttr = restore_time - failure_time

Best Practices

1. Build Process

  • Use deterministic builds
  • Cache dependencies
  • Implement parallel processing

2. Testing Strategy

  • Maintain test pyramid
  • Automate all tests
  • Include security testing

3. Deployment Process

# Example ArgoCD Application
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: fawkes-app
spec:
  source:
    repoURL: https://github.com/paruff/fawkes.git
    path: kubernetes
    targetRevision: HEAD
  destination:
    server: https://kubernetes.default.svc
    namespace: production

Common Anti-Patterns

Avoid These Practices: - Long-lived feature branches - Manual deployment steps - Infrequent integration - Environment-specific builds

Instead Do This: - Merge to trunk daily - Automate everything - Practice continuous integration - Build once, deploy many times

Tools Integration

Category Tools Purpose
CI/CD Jenkins, GitHub Actions Pipeline automation
Version Control Git Source code management
Testing Selenium, JUnit Automated testing
Deployment Spinnaker, ArgoCD Deployment automation
Monitoring Prometheus, Grafana Performance tracking

References

View Examples Implementation Guide