AWS CodePipeline
Continuous integration and delivery service for automated releases
CodePipeline is like an assembly line for software releases. Instead of manually building, testing, and deploying code (error-prone and slow), CodePipeline automates the entire process. When a developer commits code to Git, CodePipeline automatically builds it, runs tests, and deploys to production, all without human intervention. It's like having a robot that takes your code from development to production, ensuring every step is executed correctly and consistently. Perfect for teams practicing continuous delivery: release software faster and more reliably.
CodePipeline orchestrates CI/CD workflows through stages (source, build, test, deploy) and actions (tasks within stages). Each stage can have multiple actions running sequentially or in parallel. CodePipeline integrates with source control (CodeCommit, GitHub, Bitbucket), build tools (CodeBuild, Jenkins), test tools, and deployment targets (CodeDeploy, ECS, Lambda, S3).
Key Capabilities
- Models release workflows as a sequence of stages (Source, Build, Test, Deploy) with one or more actions per stage
- Triggers automatically on source changes from CodeCommit, S3, ECR, GitHub, and Bitbucket without polling
- Supports parallel actions within a stage, allowing multiple build or test steps to run concurrently before progressing
- Manual approval actions pause pipeline execution and send SNS notifications until a designated reviewer approves or rejects
- Pipeline variables allow data (such as commit IDs or environment names) to be passed between stages and actions
- Cross-account and cross-region actions enable a single pipeline to deploy artifacts to multiple AWS accounts or regions in sequence
Gotchas & Constraints
Gotcha #1: CodePipeline charges per active pipeline per month; disable unused pipelines to save costs. Gotcha #2: Pipeline execution can fail at any stage; implement proper error handling and notifications. Constraints: Maximum 50 stages per pipeline, maximum 50 actions per stage, and artifacts stored in S3 (you pay S3 storage costs).
A development team releases software weekly. The manual process takes 4 hours and is error-prone. They create a CodePipeline: Source stage pulls code from GitHub when developers push to main branch. Build stage uses CodeBuild to compile code, run unit tests, and create Docker image. Test stage deploys to staging environment and runs integration tests. Manual approval stage requires QA lead to approve. Deploy stage uses CodeDeploy to deploy to production EC2 instances with blue/green deployment (zero downtime). If any stage fails, CodePipeline stops and sends SNS notification. They add a parallel action in the deploy stage to deploy to Lambda and ECS simultaneously. Release time drops from 4 hours to 20 minutes, and deployment failures drop by 90%.
The Result
faster releases, fewer errors, and consistent deployment process.