Question

What are Lambda layers?

Answer 1

Lambda layers are a feature of AWS Lambda that allows you to reuse common code and dependencies across multiple Lambda functions. Instead of including all dependencies in each function's deployment package, you can use layers to manage libraries, custom runtimes, and other dependencies centrally.

Key Points about Lambda Layers:

1. Code and Dependencies Sharing:

   • Lambda layers let you package and deploy libraries, runtime dependencies, and other files that are common to multiple functions.
   • This helps in reducing the size of individual Lambda deployment packages and simplifies updating dependencies across functions.

2. Separation of Concerns:

   • Layers allow you to separate your function code from its dependencies, making the function code cleaner and easier to maintain.
   • You can manage layers independently of your Lambda functions, which facilitates versioning and updates without modifying the function code.

3. Usage Scenarios:

   • Library Dependencies: Share common libraries (e.g., database drivers, SDKs) across multiple Lambda functions without bundling them in each function's deployment package.
   • Custom Runtimes: Define and deploy custom runtime environments (e.g., Python with specific libraries) as layers, allowing multiple functions to use the same runtime.
   • Additional Files: Include additional files like configuration files or resource files needed by your functions.

4. Layer Versions and Permissions:

   • Each layer can have multiple versions, allowing you to manage updates and rollback changes as needed.
   • You can control access to layers using AWS Identity and Access Management (IAM) policies, ensuring that only authorized functions can use specific layers.

5. Size and Limits:

   • Each Lambda function can use up to 5 layers.
   • The total unzipped size of a function and all its layers cannot exceed the limit of 250 MB.
   • Each layer can be up to 50 MB when zipped, and 250 MB when uncompressed.

How to Use Lambda Layers:

1. Creating a Layer:

   • Package your code or dependencies into a ZIP file.
   • Create a Lambda layer using the AWS Management Console, AWS CLI, or AWS SDKs.
   • Upload the ZIP file as a new version of the layer.

2. Attaching Layers to Lambda Functions:

   • When creating or updating a Lambda function, specify the ARN (Amazon Resource Name) of the layer versions to attach.
   • Layers are mounted in the function's filesystem at /opt.

Example Use Case:

Let's say you have multiple Lambda functions written in Python that need to use the same set of utility functions and libraries (e.g., helper functions, logging setup, database connections):

• Create a Layer: Package your utility functions and necessary libraries into a Lambda layer.
• Attach to Functions: Attach this layer to each of your Python Lambda functions.
• Benefits: This reduces duplication of code across functions, simplifies maintenance (updates are done in one place), and helps manage dependencies efficiently.

Lambda layers thus provide a powerful mechanism to improve code reuse, simplify dependency management, and enhance the overall maintainability of serverless applications built on AWS Lambda.