AWS CI/CD Pipeline: Tutorial & Best Practices

Delivering high-quality software quickly and reliably is a constant challenge. Building an effective CI/CD pipeline requires integrating multiple components, such as version control, building, testing, deploying, and optional rollback plans. The aim is to support developers with automated deployments for smoother software delivery cycles.

The AWS CI/CD pipeline offers a comprehensive solution by automating the entire software delivery journey. AWS CodePipeline is a fully managed service that orchestrates well-defined stages for a consistent and reliable flow from development to production within the AWS ecosystem.

This article explores the best practices in streamlining delivery, from establishing clear milestones to integrating thorough testing processes. We also discuss how managed third-party tools integrate with AWS to enhance your workflow further.

Summary of AWS CI/CD pipeline best practices

The following table lists the best practices to implement a CI/CD pipeline for your application.

Best practices for implementing the AWS CI/CD pipeline

Now that we’ve identified the best practices, let’s review each and how they are implemented within AWS CodePipeline.

#1 Define stages

Each stage should be clearly articulated, detailing its purpose, inputs, outputs, and responsibilities. A typical CI/CD pipeline includes the following stages.

Source action

This stage handles and maintains the codebase using a version control system. AWS CodePipeline integrates with popular systems like GitLab, GitHub, BitBucket Cloud, CodeCommit, Elastic Container Registry (ECR), and S3 bucket. Developers can trigger pipelines based on code commits to their version control system.

Build stage

AWS CodeBuild, the fully managed continuous integration service within AWS, manages the build stage in the CodePipeline. It pulls the latest code from the version control system and runs build commands defined in the CodeBuild’s buildspec.yml file. It can resolve dependencies, compile code, and generate artifacts such as docker images or JAR files.

Test stage

AWS CodeBuild also runs tests in CodePipeline. It runs automated tests, including unit, integration, performance, and security tests to ensure code quality and functionalities before deployment.

Deployment stage

The deployment stage of CodePipeline can be managed via CodeDeploy, a fully managed deployment service. It ensures deployments are consistent and reliable by using strategies like blue/green and rolling deployments. It minimizes downtime and reduce the impact of deployment errors.

For organizations following a multi-account AWS architecture, pipelines are managed in the operations account while deployments are carried out to the workload accounts. CodeBuild triggers the CodeDeploy deployment in the workload account using a cross-account IAM role assumption.

CodeBuild can also deploy Infrastructure as Code (IaC) using CloudFormation, AWS CDK or Terraform. CloudFormation is a service that allows developers to provision and manage a collection of related AWS resources in a predictable fashion. Similarly, AWS CDK allows developers to define cloud resources using programming languages.

#2 Version control

For effective development, centrally store all your code and infrastructure configuration files. Version control tools like Git ensure that changes are tracked, reversible, and auditable. Using Git, teams can collaborate effectively, manage code changes, and maintain a history of modifications for troubleshooting and rollback procedures.

To ensure code integrity and traceability, implement practices such as:

• A clear branching strategy
• Consistent commit messages
• Using tags to mark release points

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#3 Automate tasks

Automation helps to increase efficiency and reduce manual errors that may arise during a deployment workflow. The following examples demonstrate how you can use CodeBuild to automate the application's build, test, and deployment.

Example buildspec.yml for a Node application build.

version: 0.2

phases:
 install:
   runtime-versions:
     nodejs: 18 
 pre_build:
   commands:
     - echo Installing NPM dependencies
     - npm install
 build:
   commands:
     - echo Build started on `date`
     - npm run build

Example buildspec.yml for performing a unit test.

version: 0.2

phases:
 install:
   runtime-versions:
     nodejs: 18
 pre_build:
   commands:
     - echo Installing NPM dependencies
     - npm install
 build:
   commands:
     - echo Running Tests
     - npm test

buildspec.yml for deploying a Node application to an S3 bucket.

version: 0.2

phases:
 install:
   runtime-versions:
     nodejs: 18 
 build:
   commands:
     - aws s3 sync build/ s3://$S3_BUCKET_NAME --delete

#4 Prioritize testing

Thorough testing throughout the development pipeline guarantees high code quality and performance efficiency. This encompasses unit, integration, and end-to-end tests. Automating these tests identifies bugs early in the development cycle, and reduces the resources required for later fixes. It also ensures that new code changes do not disrupt existing functionality for application stability.

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#5 Embrace Infrastructure as Code

Infrastructure as Code (IaC) streamlines infrastructure provisioning and management, for consistency and repeatability You can maintain uniformity across environments more efficiently. IaC tools such as Terraform, AWS CloudFormation, and CDK empower teams to develop their infrastructure in code, enabling version control and automation of the modifications required for it. By adopting IaC practices, organizations can systematically scale their environments and manage configurations.

The following code shows how users can use CodeBuild to perform build, test, or deploy stages using Terraform.

// Build, test, or deploy stage
resource "aws_codebuild_project" "buildstage" {
 name          = "codebuild-sampleApplication-build-01"
 service_role  = aws_iam_role.codebuild-test-service-role.arn
 artifacts {
   type = "CODEPIPELINE"
 }
 environment {
   compute_type                = "BUILD_GENERAL1_SMALL"
   image                       = "aws/codebuild/standard:7.0"
   type                        = "LINUX_CONTAINER"
   image_pull_credentials_type = "CODEBUILD"
 }
 source {
   type            = "CODEPIPELINE"
   buildspec       = "buildspec.yml"
   report_build_status = true
 }
}

Similarly, here’s a sample code showing how users can use AWS CDK to set up build, test, or deploy stages for their CodePipeline.

import * as cdk from 'aws-cdk-lib';
import { Construct } from 'constructs';

export class SampleCodepipelineStack extends cdk.Stack {
 constructor(scope: Construct, id: string, props?: cdk.StackProps) {
   super(scope, id, props);

   const sourceOutput = new cdk.aws_codepipeline.Artifact();

   // Build, test or deploy stage
   const build = new cdk.aws_codebuild.Project(this, 'Build', {
     projectName: 'codebuild-sampleApplication-build-01',
     environment: {
       buildImage: cdk.aws_codebuild.LinuxBuildImage.STANDARD_7_0,
     },
     buildSpec: cdk.aws_codebuild.BuildSpec.fromAsset('buildspec.yml'),
   });

   const buildAction = new cdk.aws_codepipeline_actions.CodeBuildAction({
     actionName: 'CodeBuild',
     project: build,
     input: sourceOutput,
   });
   }
}

#6 Focus on security

Implementing security at every layer of the CI/CD pipeline is crucial for the safety of code, infrastructure, and data. Here are some strategies.

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Secure code practices

Encourage developers to follow coding practices that involve

• Regular code reviews
• Static code analysis using tools like SonarQube or Fortify.
• Dependency scanning using tools like Snyk, Dependabot, or Amazon Inspector to scan for vulnerabilities in third-party libraries and dependencies.

Address them before deploying the code.

Access control

Implement role-based access control (RBAC) to control access to the pipeline so that only authorized personnel can run or make changes to it. Also, you can implement least privilege access for the pipeline components so that they only have the necessary permissions to perform their tasks. AWS Organizations can be used to create a separate operations account where users can create their pipelines, and admins can control the access to this account using the IAM Identity Center.

Audit logs

Enable audit logging for your AWS account using AWS CloudTrail to track changes, access attempts, and your pipeline's deployment history.

Secret management

Practice using secret management tools like AWS Secrets Manager or HashiCorp Vault to store and manage sensitive information such as API Keys, database credentials, passwords, and tokens.

Encryption

Ensure that the pipeline uses TLS/SSL to encrypt data in transit between the pipeline and external systems. Users can also use server-side encryption using KMS for the pipeline artifacts stored in S3 buckets.

#7 Monitor and analyze

Tools like Amazon CloudWatch (logs, alarms, reports), ELK Stack, Grafana, and Prometheus provide insights into pipeline performance and outcomes. Regularly review pipeline logs, CodeBuild CPU and memory utilization metrics. Set up alerts for pipeline failure to tackle issues and ensure a smooth and efficient CI/CD process.

#8 Maintain and adapt

The CI/CD pipeline must be regularly updated and adapted to evolving requirements for its long-term effectiveness. This involves keeping tools and dependencies up-to-date, addressing security vulnerabilities captured using static code and dependency analysis tools, and continuously improving the pipeline based on performance metrics and outcomes.

Updating the CodeBuild to the latest version ensures that it supports the latest dependencies and packages. This is crucial for the application's safety.

To update the CodeBuild for a pipeline from a lower version to a higher version using the AWS CLI, create a JSON file with the desired properties. The image property under the environment variable defines the version for your CodeBuild.

For example, to upgrade your CodeBuild from aws/codebuild/standard:6.0 to aws/codebuild/standard:7.0, create the following JSON file:

{
  "name": "<project-name>",
  "environment": {
    "type": "LINUX_CONTAINER",
    "computeType": "BUILD_GENERAL1_SMALL",
    "image": "aws/codebuild/standard:7.0"
  }
}

Next, run the CodeBuild update-project API call and pass the JSON file to it.

aws codebuild update-project --cli-input-json file://<update-project-file>

#9 Design for limitations

Here are some best practices and examples to design for common CI/CD limitations:

Complexity

Pipelines can become challenging to maintain and understand if they have a single component, such as a CodeBuild, performing all the steps. Use modular design principles and break down the pipeline into smaller, manageable stages, each with a specific focus and responsibility.

Security concerns

Since pipelines can modify a deployment and access your application network, implementing security best practices is essential.

• Secure the access—Follow the principle of least privilege access to control access to your pipeline environment by implementing strict IAM roles.
• Secure the data—Enable data encryption on the pipeline artifacts and build images and logs to ensure the safety of your application.
• Secure the network—Ensure that all the components within your CodePipeline are running within VPC to add an extra layer of network security.
• Perform audits—enable CloudTrail logging and perform regular audits to mitigate risks and keep track of access.

Integration challenges

Integrating third-party tools can be challenging if organizations use external code analysis tools, automated test providers, or secret managers. Use CodePipeline’s built-in integrations or develop strict IAM policies to connect with third-party tools, ensuring compatibility and seamless data flow.

Automation bottlenecks

There can be scenarios where the workflow might be too slow because CodeBuild underperforms. Identify and eliminate bottlenecks in the automation process by allocating enough resource configuration for individual stages. Users can also utilize CodeBuild’s batch build feature to run multiple builds in parallel and reduce the time for the build and test stages.

Monitoring and troubleshooting challenges

Tracing errors can be tricky without logging support for your CodePipeline. Implement comprehensive monitoring and troubleshooting mechanisms to identify and resolve issues using centralized logging, real-time monitoring, and CloudWatch Alarms to track pipeline performance and detect anomalies.

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Challenges associated with the AWS CI/CD pipeline

As evident from the previous section, the native CI/CD pipeline supported by AWS can introduce challenges, has a steep learning curve, and can be difficult to get along.

Complexity

Deployment scripts depend on the nature of the application or service. As the application grows, functional dependencies among services may increase. Your CodePipeline may also have multiple components such as CodeBuild, CodeDeploy, Lambda function, or S3 buckets.

Breaking down the workflow into different stages and maintaining the infrastructure becomes challenging. Creating and maintaining the IAM roles, managing secret credentials, ensuring data encryption and network safety also becomes troublesome and requires professional expertise.

Integration with third-party tools

Since CodePipeline’s built-in integrations are limited, integration with third-party tools for code scanning, analysis, automated tests, or secret management can be tricky and require extra overhead.

Automation bottlenecks

CodeBuild comes with pre-defined CPU and memory configurations. Frequent monitoring of resources is essential to eliminate bottlenecks without increasing manual resource configurations.

Monitoring and troubleshooting

Enabling logging and auditing is critical for tracing issues and ensuring operational visibility for the pipeline. However, logging and auditing come with extra charges, and users must set up CloudTrail on their accounts for auditing purposes.

Simplifying your workflow: Managed CI/CD by Coherence

Coherence is a Platform-as-a-Service (PaaS) offering that allows you to control and automate your workflow with little overhead. You can automate CI/CD in your cloud environments, deploy continuously from branches with GitHub integration or promote builds manually via UI or API. With Coherence, developers can also manage workflows and secrets and automate containerization.

Coherence provides the following advantages over the native AWS CI/CD solution:

Easy to set up

Coherence provides easy-to-use CI/CD that delivers secure, fast, production-ready builds and deployments. It also minimizes your vendor footprint and provides the most cost-effective and performant deployment automation.

Managed CI/CD

With a developer-friendly UI, Coherence offers managed CI/CD integrated with source providers like GitHub and Infrastructure as Code execution powered by CNC.

Preview environments

Coherence provides the ability to create, manage, and modify environments of different types, such as preview, static, or production, all from a single pane, improving the efficiency of code release and testing.

Access control

Coherence has RBAC built-in and allows developers to create new environments, add new services, and configure them easily.

Centralized monitoring

Coherence offers an internal developers platform that provides pipeline visibility and logging for efficient management.

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Conclusion

AWS CI/CD pipelines offer substantial benefits like automation, speed, scalability, and reliability. However, leveraging best practices is crucial for effective pipeline management. This includes implementing strong security measures, maintaining high code quality, and monitoring performance.

Coherence can significantly improve workflows by providing a managed solution. It simplifies pipeline management and automates the consistent implementation of best practices. Teams can focus on creating and improving applications as Coherence handles the complexities of CI/CD management, enhances efficiency, security, and overall effectiveness of the development cycle.