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Module 4: Deploying 2

Module Overview

Advanced deployment strategies, continuous integration, and deployment automation. Build on your deployment knowledge with more sophisticated techniques and tools.

Learning Objectives

Implement continuous integration and continuous deployment (CI/CD) pipelines
Configure auto-scaling for deployed applications
Set up monitoring and alerting for deployed services
Implement deployment strategies such as blue-green and canary deployments
Optimize application performance in production environments
Classify the purpose of each part of a given Continuous Deployment pipeline into source, build, test, and deploy
Explain how having different deployment stages separates test data from production data
Examine where in a provided pipeline a given test should be executed
Compare and contrast integration tests and unit tests
Explain why tests should be run and catch bugs in the earliest stage possible
Explain why separating test data from production data is valuable when testing
Recall that an approval workflow only runs after a successful promotion to that stage

Advanced CI/CD Pipeline Strategies

Continuous Integration and Continuous Deployment (CI/CD) pipelines automate the software delivery process. Advanced CI/CD pipelines include sophisticated testing, deployment, and monitoring strategies.

CI/CD Pipeline Components

Source Control Integration: Automatic triggers based on code commits
Automated Testing Suite: Unit, integration, and end-to-end tests
Quality Gates: Code coverage, security checks, and linting
Deployment Automation: Scripted deployment processes for all environments
Monitoring Integration: Automatic monitoring of newly deployed services

Modern CI/CD pipelines balance speed with quality by implementing comprehensive testing while keeping the pipeline efficient enough to enable multiple deployments per day.

Advanced Deployment Strategies

Modern deployment practices go beyond simple updates to minimize risk and downtime when deploying to production environments.

Blue-Green Deployment

Blue-green deployment involves maintaining two identical production environments, with only one active at a time:

Two identical environments: "Blue" (current) and "Green" (new version)
Deploy new version to the inactive environment
Test thoroughly in the inactive environment
Switch traffic from active to inactive environment when ready
Previous environment becomes standby for quick rollback if needed

Canary Deployment

Canary deployment gradually shifts traffic to the new version:

Deploy new version alongside the current version
Route a small percentage of traffic (e.g., 5%) to the new version
Monitor for issues and performance
Gradually increase traffic to the new version if stable
Revert to previous version if problems occur

Feature Flags

Feature flags allow toggling features on/off without redeploying:

Deploy code with new features disabled by default
Enable features via configuration changes rather than code deployments
Roll out features to specific user segments
Quickly disable problematic features without rollback

Auto-Scaling and Infrastructure as Code

Modern applications need to scale dynamically based on demand and be deployed consistently across environments.

Auto-Scaling Configuration

Scaling Policies: Define conditions for scaling (CPU usage, request count, memory)
Scale-Out Rules: Add instances when load increases
Scale-In Rules: Remove instances when load decreases
Cooldown Periods: Prevent rapid scaling oscillations
Predictive Scaling: Scale based on anticipated load patterns

Infrastructure as Code (IaC)

IaC tools like AWS CloudFormation, Terraform, or AWS CDK allow defining infrastructure in code:

Version-controlled infrastructure definitions
Consistent environments across development, testing, and production
Automated infrastructure provisioning
Self-documenting infrastructure
Reproducible deployments

// Example AWS CloudFormation template snippet for auto-scaling
{
  "Resources": {
    "WebServerGroup": {
      "Type": "AWS::AutoScaling::AutoScalingGroup",
      "Properties": {
        "MinSize": "1",
        "MaxSize": "5",
        "DesiredCapacity": "2",
        "LaunchConfigurationName": { "Ref": "LaunchConfig" },
        "VPCZoneIdentifier": { "Ref": "Subnets" }
      }
    },
    "CPUHighAlarm": {
      "Type": "AWS::CloudWatch::Alarm",
      "Properties": {
        "AlarmDescription": "Scale up if CPU > 90% for 5 minutes",
        "MetricName": "CPUUtilization",
        "Namespace": "AWS/EC2",
        "Statistic": "Average",
        "Period": "300",
        "Threshold": "90",
        "ComparisonOperator": "GreaterThanThreshold",
        "Dimensions": [
          {"Name": "AutoScalingGroupName", "Value": {"Ref": "WebServerGroup"}}
        ],
        "EvaluationPeriods": "2",
        "AlarmActions": [{"Ref": "WebServerScaleUpPolicy"}]
      }
    }
  }
}

Monitoring and Performance Optimization

Comprehensive monitoring is crucial for maintaining application health and optimizing performance in production.

Monitoring Strategy Components

Application Metrics: Response times, throughput, error rates
System Metrics: CPU, memory, disk usage, network I/O
Business Metrics: User logins, transactions, conversions
Log Aggregation: Centralized logging for troubleshooting
Distributed Tracing: Track requests across services
Alerting: Notifications when metrics exceed thresholds

Performance Optimization Techniques

Caching: Implement application and database caching
Database Optimization: Index tuning, query optimization
Content Delivery Networks (CDNs): Distribute static content globally
Asynchronous Processing: Move long-running tasks to background jobs
Load Testing: Regularly test application under various load conditions
Resource Right-Sizing: Adjust instance types based on actual usage patterns

Effective monitoring enables proactive identification of issues before they impact users and provides data for continuous optimization of application performance.