Streamlining Serverless Deployments: Building CI/CD Pipelines for AWS Lambda Functions and Layers

In today’s cloud-native development landscape, AWS Lambda enables serverless architecture. AWS Lambda layers come into play to improve code reuse, reduce deployment times, and promote better management of shared libraries. This post will explore how to build a CI/CD pipeline that integrates AWS Lambda Layers and functions to streamline deployment.

Introduction to AWS Lambda Layers and Their Benefits

AWS Lambda Layers allow developers to package shared libraries, dependencies, or custom runtimes into reusable components, reducing duplication and promoting modularity across multiple functions. Some key benefits include:

• Reducing Deployment Package Size and Promoting Modularity: By isolating dependencies into layers, you can slim down the deployment packages, allowing faster uploads and simplifying function code.
• Sharing Dependencies Across Multiple Functions: Layers enable you to use the same dependencies across different Lambda functions without bundling them with each function, saving storage and deployment time.
• Enabling Console Code Editor: Since layers are external to the function code, it becomes easier to edit Lambda functions directly within the AWS Management Console.

Understanding Layer Versions and Immutable Snapshots

AWS Lambda Layers are immutable, meaning they cannot be modified once a layer is created. However, new versions can be published, allowing you to update dependencies while maintaining the stability of older functions using earlier versions.

Guidelines for Creating and Utilizing Lambda Layers

To create Lambda Layers, follow these best practices:

• Package your dependencies: Ensure your layer contains only the necessary libraries or code required for your functions.
• Runtime-specific considerations: Each layer must be compatible with the runtime environment of the Lambda functions. Python functions, for example, require a layer that includes libraries in the /python directory.
• Version control: Keep track of layer versions to prevent disruptions when updates occur.

Packaging Layer Content into a .zip Archive and Runtime-Specific Considerations

Lambda layers must be packaged into a .zip archive. For Python, for instance, you would install dependencies locally and ensure they are structured in a /python folder before zipping. This ensures compatibility when added to Lambda functions.

Creating Layers in AWS Lambda via Upload or Amazon S3

You can create Lambda layers by directly uploading the .zip archive or by referencing an archive stored in Amazon S3. The latter is useful when dealing with more extensive layers.

Adding Layers to Lambda Functions and Managing Dependencies

After creating the layer, you can attach it to any Lambda function. When adding multiple layers, Lambda merges them at runtime, and the function code can access dependencies from the attached layers.

Step-by-Step CI/CD Pipeline Implementation for Lambda Functions and Layers

Here’s how you can implement a CI/CD pipeline for automating the deployment of AWS Lambda functions and layers:

Folder Structure, Requirements File, and Lambda Function Code

Start by structuring your project as follows:

/my-lambda-project

  /layer

    └── requirements.txt

  /lambda_function

    └── app.py

  └── buildspec.yml

  └── template.yml

  └── pipeline.yml

• requirements.txt: List of dependencies for the layer.
• app.py: Lambda function code.

Build Specification (buildspec.yml) with SAM Build and Deploy Commands

Here’s a sample buildspec.yml for building the Lambda layer and deploying the stack:

version: 0.2

phases:

  install:

    commands:

      – pip install aws-sam-cli –upgrade

  build:

    commands:

      – sam build

  post_build:

    commands:

      – sam deploy –no-confirm-changeset –stack-name my-lambda-stack –capabilities CAPABILITY_IAM

SAM Template (template.yml) for Defining Resources

In the template.yml, define the Lambda function, layer, and necessary permissions:

Resources:

  MyLambdaLayer:

    Type: AWS::Lambda::LayerVersion

    Properties:

      Content:

        S3Bucket: my-bucket

        S3Key: my-layer.zip

      CompatibleRuntimes:

        – python3.8

  MyLambdaFunction:

    Type: AWS::Serverless::Function

    Properties:

      Handler: app.lambda_handler

      Runtime: python3.8

      Layers:

        – !Ref MyLambdaLayer

CloudFormation Script (pipeline.yml) for Creating the CI/CD Pipeline

Use the following CloudFormation script (pipeline.yml) to define the CI/CD pipeline:

Resources:

  MyPipeline:

    Type: AWS::CodePipeline::Pipeline

    Properties:

      Stages:

        – Name: Source

          Actions:

            – Name: SourceAction

              ActionTypeId:

                Category: Source

                Owner: AWS

                Version: 1

                Provider: CodeCommit

              OutputArtifacts:

                – Name: SourceOutput

        – Name: Build

          Actions:

            – Name: BuildAction

              ActionTypeId:

                Category: Build

                Owner: AWS

                Version: 1

                Provider: CodeBuild

              InputArtifacts:

                – Name: SourceOutput

              OutputArtifacts:

                – Name: BuildOutput

Observing the CI/CD Pipeline in Action and Validating Deployment

Once the pipeline is deployed, you can observe the following stages:

• Monitoring Code Build Project: Ensure the build and deployment phases are completed successfully.
• Lambda Stack Creation: Validate the creation of the Lambda function and layer.
• Resource Deployment: Verify that all required resources have been deployed correctly.

Verifying Library Inclusion in Layer and Absence from Function Code

You can verify that your Lambda function uses the correct layer by checking that dependencies are in the layer and not included in the function code itself. This can be done by observing the function’s code in the AWS console and checking for references to layer versions.

Testing Lambda Function and Confirming Layer Version in Console

After deployment, test the Lambda function and confirm it uses the correct layer version by checking the Lambda console under the “Layers” section.

Updating Library Versions and Observing Layer Version Changes

To update dependencies, modify the requirements.txt file and commit the changes. The CI/CD pipeline will trigger automatically and rebuild the layer with the updated dependencies.

Modifying Requirements File, Triggering Pipeline, and Reviewing Change Set

When you modify the requirements.txt file, the pipeline rebuilds the layer and generates a new version. Review the CloudFormation change set to ensure the Lambda function uses the latest version.

Confirming New Layer Version in Lambda Function and Testing Updated Code

Once the new layer version is deployed, test the Lambda function again to confirm that it uses the updated layer and that the function behaves as expected.

Conclusion and Key Takeaways

In this guide, we’ve explored the power of AWS Lambda Layers and how to build a CI/CD pipeline to streamline function and layer deployments. Key benefits of using Lambda layers include reducing deployment package size, promoting code modularity, and enabling easier management of shared dependencies.

Encouragement for Further Exploration and Customization

Our pipeline can be customized further to suit more complex workflows, such as integrating testing frameworks, additional stages, or security checks. By automating Lambda function and layer management, you reduce the overhead of manual deployments and focus on delivering scalable serverless applications.

References

Deploying an AWS Lambda function with the serverless framework and Tekton

Deploying AWS Lambda layers automatically across multiple Regions