CI/CD Pipeline Overview

Continuous Integration and Continuous Deployment (CI/CD) transforms your development workflow from manual, error-prone deployments into automated, repeatable, and reliable processes. A well-designed CI/CD pipeline catches bugs early, ensures consistent builds, and deploys changes safely to production.

This guide covers the complete CI/CD philosophy and workflow patterns for Python/Django applications deployed to AWS infrastructure.

Philosophy

CI/CD is not just automation—it's about building confidence. Every commit should flow through a series of increasingly strict gates: automated tests prove correctness, container builds prove reproducibility, and deployment processes prove stability. The goal is to deploy multiple times per day with zero fear.

The CI/CD Mindset

Continuous Integration Philosophy

Continuous Integration means every code change is automatically validated before it reaches production. This validation includes:

Code Quality Gates: - Linting ensures consistent style and catches common errors - Type checking prevents runtime type errors - Security scanning identifies vulnerable dependencies - Test execution proves functional correctness

Integration Validation: - Database migrations run successfully - External service connections work as expected - Static assets compile and bundle correctly - Container images build without errors

The core principle: Problems detected early are cheap to fix; problems discovered in production are expensive.

Continuous Deployment Philosophy

Continuous Deployment extends CI by automatically pushing validated changes to production. This requires:

Deployment Confidence: - Every commit must pass all quality gates - Deployment processes must be fully automated - Rollback mechanisms must be instant and reliable - Monitoring must immediately detect issues

Progressive Delivery: - Changes flow through environments: dev → staging → production - Each environment validates increasingly production-like conditions - Blue-green deployments enable zero-downtime updates - Health checks prevent bad deployments from completing

The core principle: If it hurts, do it more often. Automation turns pain into routine.

Pipeline Architecture

Three-Stage Pipeline

A production CI/CD pipeline consists of three interconnected stages:

graph LR
    A[Code Push] --> B[Build Stage]
    B --> C[Test Stage]
    C --> D[Deploy Stage]
    D --> E[Health Check]
    E --> F{Healthy?}
    F -->|Yes| G[Complete]
    F -->|No| H[Rollback]
    H --> I[Alert Team]

Build Stage

The build stage creates reproducible artifacts from source code:

Objectives: - Generate requirements from dependency specifications - Build Docker containers with multi-stage optimization - Tag images with commit SHA for traceability - Push to container registry (ECR) - Cache layers for faster subsequent builds

Success Criteria: - Container builds without errors - All dependencies resolve correctly - Static assets compile successfully - Image size stays within acceptable limits

Failure Handling: - Stop pipeline immediately - Notify developer of build errors - Preserve build logs for debugging - No deployment proceeds with broken builds

Test Stage

The test stage validates application behavior:

Test Types: - Unit Tests: Verify individual functions and methods - Integration Tests: Validate database interactions and external services - UI Tests: Ensure frontend functionality works correctly - Security Tests: Check for vulnerable dependencies

Test Environment: - Database containers mirror production schema - Mock external services for isolated testing - Environment variables match production structure - Test fixtures provide consistent data

Success Criteria: - All tests pass - Code coverage meets minimum threshold - No security vulnerabilities in dependencies - Database migrations apply cleanly

Failure Handling: - Stop pipeline before deployment - Report test failures with detailed logs - Preserve test artifacts (screenshots, logs) - Block merging until tests pass

Deploy Stage

The deploy stage pushes validated code to target environments:

Deployment Flow: 1. Update ECS task definition with new image 2. Create CodeDeploy deployment 3. Perform blue-green traffic shift 4. Monitor health checks 5. Complete or rollback based on health

Deployment Strategies: - Blue-Green: Run old and new versions simultaneously, switch traffic atomically - Rolling: Gradually replace old tasks with new tasks - Canary: Route small percentage of traffic to new version first

Success Criteria: - All new tasks reach healthy state - Health checks pass for specified duration - Application metrics remain normal - No error rate spikes

Failure Handling: - Automatic rollback to previous version - Preserve logs from failed deployment - Alert on-call engineer - Prevent subsequent deployments until resolved

Environment Strategy

Environment Hierarchy

Applications flow through a series of increasingly production-like environments:

graph TD
    A[Local Development] --> B[Development/Staging]
    B --> C[Production]
    B --> D[Production Validation]
    D --> C

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#ffe1e1
    style D fill:#f0ffe1

Local Development

Purpose: Individual developer experimentation and rapid iteration

Characteristics: - Runs entirely on developer machine (devcontainer) - Uses local database containers or LocalStack for AWS services - Hot reloading for immediate feedback - Debug mode enabled with detailed error pages - Test data fixtures for consistent starting state

When to Use: - Feature development - Bug investigation - Exploratory testing - Database migration development

Not For: - Performance testing (limited resources) - Integration testing with real services - Security validation - Load testing

Development/Staging

Purpose: Team integration and pre-production validation

Characteristics: - Deployed to AWS ECS in isolated environment - Uses separate RDS instances with production-like data - Connected to sandbox versions of external services - Debug logging enabled for troubleshooting - Allows destructive testing without risk

When to Use: - Integration testing across services - Manual QA validation - Stakeholder demos - Database migration testing - Performance profiling

Deployment Frequency: On every merge to main branch (automatic)

Production

Purpose: Live application serving real users

Characteristics: - Deployed to AWS ECS with high availability - Uses production RDS with backups and replicas - Connected to production external services - Optimized logging (info and above) - Zero-downtime deployment required - Automatic rollback on health check failure

When to Use: - Always (serves real traffic) - Only after successful staging validation - During business hours for major changes

Deployment Frequency: Multiple times per day after validation

Environment Configuration

Each environment requires specific configuration without code changes:

Environment Variables: - DJANGO_SETTINGS_MODULE: Points to environment-specific settings - DATABASE_URL: Connection string for RDS instance - AWS_REGION: AWS region for service discovery - ENVIRONMENT: Identifier for logging and monitoring

AWS SSM Parameters: - Database credentials stored in Parameter Store - API keys for external services - Feature flags for gradual rollout - Third-party integration credentials

Secrets Management: - Never commit secrets to source control - Use AWS Secrets Manager for sensitive data - Rotate credentials regularly - Audit access to secrets

Deployment Strategies

Blue-Green Deployment

Blue-green deployment maintains two identical production environments. Traffic switches atomically from blue (old) to green (new), enabling instant rollback.

Process:

1. Green Environment Preparation
2. Deploy new version to green environment
3. Run health checks

4. Warm up application (cache, connections)

5. Traffic Switch

6. Update load balancer to route traffic to green
7. Blue remains running as fallback

8. Switch happens in seconds

9. Validation

10. Monitor error rates, response times, resource usage
11. Validate critical user flows

12. Check background job processing

13. Blue Environment Teardown

14. After successful validation period (30-60 minutes)
15. Terminate blue environment
16. Green becomes new blue for next deployment

Advantages: - Instant rollback (just switch load balancer back) - Zero downtime during deployment - Full environment available for testing before traffic switch - Previous version available for comparison

Disadvantages: - Requires double the infrastructure during deployment - Database migrations must be backward compatible - More complex to implement than rolling updates

Rolling Deployment

Rolling deployment gradually replaces old tasks with new tasks across the ECS cluster.

Process:

1. Task Replacement
2. Stop one old task
3. Start one new task
4. Wait for health check to pass

5. Repeat until all tasks updated

6. Traffic Distribution

7. Load balancer sends traffic to both old and new tasks
8. Percentage of new version increases gradually
9. Complete when all old tasks replaced

Advantages: - No extra infrastructure required - Simpler to implement than blue-green - Natural rollout pace reduces risk

Disadvantages: - Slower rollback (must deploy previous version) - Mixed versions running simultaneously - Cannot test full new environment before traffic

Canary Deployment

Canary deployment routes a small percentage of traffic to the new version while the old version handles the majority.

Process:

1. Canary Release
2. Deploy new version with minimal instances (e.g., 1 task)
3. Configure load balancer for 5% traffic to canary

4. Monitor canary metrics closely

5. Validation Window

6. Run for 15-30 minutes
7. Compare error rates between canary and baseline

8. Check latency, success rates, resource usage

9. Progressive Rollout

10. If metrics are healthy, increase to 25% traffic
11. Continue increasing: 50%, 75%, 100%

12. Each stage includes validation window

13. Completion or Rollback

14. Complete if all stages pass validation
15. Rollback immediately if any metric degrades

Advantages: - Limits blast radius of bad deployments - Real production traffic tests new version - Data-driven deployment decisions - Early detection of production-only issues

Disadvantages: - More complex implementation - Requires sophisticated metrics and alerting - Longer deployment duration - Potential for inconsistent user experience

Rollback Procedures

Automatic Rollback

The deployment system automatically rolls back when:

Health Check Failures: - New tasks fail to reach healthy state within timeout - Health check endpoint returns errors - Tasks crash repeatedly

CloudWatch Alarms: - Error rate exceeds threshold (e.g., >1% 5xx responses) - Response time degrades significantly - Resource exhaustion (CPU, memory, connections)

Rollback Process: 1. Stop deploying new tasks 2. Revert to previous task definition 3. Terminate unhealthy tasks 4. Restore traffic to healthy old version 5. Alert engineering team 6. Preserve logs for investigation

Timeframe: Automatic rollback completes within 2-5 minutes

Manual Rollback

Engineers initiate manual rollback when automatic rollback doesn't trigger but issues are detected:

Manual Rollback Scenarios: - Business logic errors discovered in production - Data corruption or consistency issues - Performance degradation not caught by alarms - External dependency failures

Manual Rollback Steps:

# 1. Identify the previous stable task definition
aws ecs list-task-definitions --family-prefix poseidon-webapp --status ACTIVE

# 2. Create deployment with previous task definition
aws deploy create-deployment \
  --application-name AppECS-cluster-app \
  --deployment-group-name DgpECS-cluster-app \
  --task-definition arn:aws:ecs:region:account:task-definition/app:123

# 3. Monitor deployment progress
aws deploy get-deployment --deployment-id d-XXXXXXXXX

# 4. Verify application health
curl https://api.example.com/health

Rollback Validation: - Check application logs for errors - Verify key user flows function correctly - Confirm database state is consistent - Monitor metrics for return to normal

Database Rollback Considerations

Database migrations complicate rollbacks because data changes persist:

Forward-Compatible Migrations: - Add columns as nullable initially - Create new tables without foreign keys - Deploy in two phases: schema first, code second

Backward-Compatible Code: - New code must work with old schema - Handle missing columns gracefully - Avoid removing fields until data migrated

Data Rollback Strategy: - Maintain backups before each deployment - Test restoration procedures regularly - Document manual data fix procedures - Consider feature flags instead of rollback

Pipeline Best Practices

Fast Feedback Loops

Optimize for Speed: - Cache dependencies between runs - Run tests in parallel when possible - Use incremental builds for Docker layers - Skip redundant validations

Fail Fast: - Run fastest tests first (linting, type checking) - Stop pipeline immediately on failure - Don't waste resources on doomed builds

Feedback Visibility: - Notify developers immediately on failure - Include failure context in notifications - Link directly to logs and artifacts - Show exactly which step failed

Security Integration

Automated Security Scanning: - Scan dependencies for known vulnerabilities (pip-audit) - Check for secrets accidentally committed - Validate IAM permissions follow least privilege - Scan container images for CVEs

Deployment Security: - Use OIDC for AWS authentication (no long-lived credentials) - Rotate credentials regularly - Audit access to production systems - Require MFA for manual deployments

Monitoring and Observability

Deployment Metrics: - Track deployment frequency (good teams: multiple per day) - Measure lead time from commit to production - Monitor deployment failure rate - Track mean time to recovery (MTTR)

Application Health Metrics: - Request rate, error rate, duration (RED metrics) - Resource utilization (CPU, memory, connections) - Business metrics (signups, purchases, etc.) - Dependency health (database, external APIs)

Documentation and Runbooks

Pipeline Documentation: - Diagram pipeline flow with decision points - Document environment-specific configurations - Explain deployment strategies and when to use each - Maintain troubleshooting guide for common failures

Incident Runbooks: - Step-by-step rollback procedures - Emergency contacts and escalation paths - Common failure scenarios and resolutions - Post-incident review template

Common Failure Scenarios

Build Failures

Dependency Resolution Issues:

ERROR: Could not find a version that satisfies the requirement package==1.2.3

Causes: - Package removed from PyPI - Version constraint conflict - Private package registry unreachable

Resolution: - Pin all dependencies with exact versions - Use private package mirror for critical dependencies - Lock files ensure reproducible builds

Test Failures in CI

Tests pass locally, fail in CI:

AssertionError: Expected 200, got 404

Causes: - Different environment configuration - Race condition in parallel tests - Database state not properly isolated - Timezone or locale differences

Resolution: - Use containers for consistent environment - Run tests in random order locally - Ensure test isolation with transactions - Set explicit timezone in CI environment

Deployment Failures

Task Startup Failures:

Task failed to start: CannotPullContainerError

Causes: - ECR authentication expired - Image tag doesn't exist - Insufficient memory to start container - Health check endpoint not responding

Resolution: - Verify image was pushed successfully - Check ECR permissions for ECS task role - Review CloudWatch logs for startup errors - Adjust health check grace period

Health Check Failures:

Deployment failed: Health check returned 503

Causes: - Application dependencies not ready (database, cache) - Insufficient warm-up time - Configuration missing in new environment - Database migrations not applied

Resolution: - Run migrations before task startup - Increase health check initial delay - Verify all configuration parameters set - Check application logs for startup errors

Pipeline Metrics and SLOs

Key Performance Indicators

Deployment Frequency: - Target: Multiple deployments per day - Measure: Count of successful production deployments - Indicates: Team velocity and confidence in pipeline

Lead Time for Changes: - Target: Less than 1 hour from commit to production - Measure: Time from commit to deployed in production - Indicates: Pipeline efficiency and automation

Change Failure Rate: - Target: Less than 15% of deployments cause issues - Measure: Percentage of deployments requiring rollback - Indicates: Test effectiveness and code quality

Mean Time to Recovery: - Target: Less than 15 minutes - Measure: Time from problem detection to resolution - Indicates: Rollback effectiveness and incident response

Service Level Objectives

Pipeline Availability: - 99.5% of pipeline runs complete successfully (excluding code failures) - Pipeline infrastructure downtime less than 2 hours per month

Build Performance: - 90% of builds complete in under 10 minutes - 99% of builds complete in under 20 minutes

Deployment Performance: - 95% of deployments complete in under 15 minutes - 99% of deployments complete in under 30 minutes

Rollback Performance: - 100% of automatic rollbacks complete in under 5 minutes - 100% of manual rollbacks complete in under 10 minutes

Pipeline Evolution

Continuous Improvement

Regular Pipeline Audits: - Review pipeline duration monthly - Identify bottlenecks and optimization opportunities - Update dependencies and tooling - Validate security scanning effectiveness

Feedback from Incidents: - Document what went wrong - Add automated checks to prevent recurrence - Improve rollback procedures - Update runbooks with lessons learned

Experimentation: - Try new testing strategies - Experiment with deployment techniques - Evaluate new tools and services - A/B test pipeline changes

Advanced Patterns

Progressive Deployment Strategies: - Feature flags for gradual rollout - Dark launching to test with real traffic - Shadow deployments for performance validation - Ring-based deployment (internal → beta → all)

Testing in Production: - Synthetic monitoring simulates user flows - Chaos engineering tests resilience - Load testing validates scalability - A/B testing measures impact

Multi-Region Deployment: - Deploy to multiple regions for resilience - Coordinate deployments across regions - Handle region-specific configuration - Implement global traffic routing

Next Steps

After understanding CI/CD pipeline philosophy and architecture:

1. GitHub Actions: Learn how to implement the CI/CD pipeline using GitHub Actions workflows
2. Container Building: Deep dive into Docker multi-stage builds and optimization strategies
3. ECS Deployment: Explore AWS ECS, Fargate, and CodeDeploy integration patterns
4. Monitoring: Implement comprehensive monitoring and alerting for your pipeline

Start Simple

Don't try to implement everything at once. Start with basic CI (linting, tests), then add automated deployments to staging, then implement blue-green deployments to production. Each step builds confidence for the next.

Related Resources

Internal Documentation: - Environment Variables: Managing configuration across environments - AWS SSM Parameters: Storing secrets and configuration in AWS - Secrets Management: Secure handling of sensitive data - Docker Development: Container development patterns - 12-Factor App Principles: Foundational design principles

External Resources: - Continuous Delivery Book by Jez Humble and David Farley - Release It! by Michael T. Nygard - The DevOps Handbook by Gene Kim et al. - AWS ECS Best Practices Guide - GitHub Actions Documentation