Lambda Layers

5 minutes 5 Questions

Lambda Layers is a concept in AWS Lambda that allows you to manage and distribute shared code, libraries, and other function dependencies that are required across multiple Lambda functions. A layer is an archive containing libraries, a custom runtime, or other kind of reusable code. You can include…

Guide on AWS Lambda Layers

AWS Lambda Layers is an important concept to grasp for any AWS Solution Architect.

What are Lambda Layers?
They are a distribution mechanism for libraries, custom runtimes, and other function dependencies. Layers promote code sharing and separation of responsibilities so that you can iterate faster on writing business logic.

Why are Lambda Layers important?
Lambda Layers allow you to manage your in-development AWS Lambda function’s code and resources more effectively, which can even lead to substantial cost reduction. They serve to separate your function’s code from its resources, sparing you much of the effort and resources previously taken up by bundling them together.

How do Lambda Layers work?
In a Lambda deployment package, dependencies are included alongside your code. When using layers, these dependencies are moved out of the deployment package and into a layer which is a .zip file that contains libraries, a custom runtime, or other dependencies. The layer is uploaded to Lambda, and the ARNs of those layers are then specified when you create a function.

Exam Tips: Answering Questions on Lambda Layers
- Remember that layers decouple your function code from its dependencies.
- Lambda function and its associated layers must be in the same region.
- A Lambda Layer can be used in any number of AWS Lambda functions.
- The total unzipped size of the function and all layers cannot exceed the unzipped deployment package size limit of 250 MB.

Lambda Layers is a powerful tool in a solution architect’s arsenal, making function updates easier, promoting code sharing and separation of responsibilities, and can potentially result in reduced costs.

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Question 1

You are using a shared library to handle logging in multiple Lambda functions. How can you efficiently manage the shared library using Lambda Layers?

Use Step Functions to manage the shared library. Copy the logging library into each Lambda function's source code. Use a Lambda Layer for the shared library and add the layer to all relevant Lambda Functions. Upload the library code to an S3 bucket and use an AWS Lambda Extension to pull it.

Correct Answer: Use a Lambda Layer for the shared library and add the layer to all relevant Lambda Functions.

By creating a Lambda Layer for the shared library, you can manage a single version of the library and easily add, modify or remove it in multiple Lambda functions. Copying the library to each function results in duplicate code and increased maintenance efforts. Other mentioned services are not suitable for managing Lambda Layers.

Question 2

You are developing a cross-account Lambda function that relies on a Lambda Layer created in another AWS account. To implement the Layer, what should be the next step?

Duplicate the Lambda Layer to your account. Grant cross-account access to the Lambda Layer and reference it in your Lambda function. Use AWS AppConfig to pull the Layer. Create an IAM Role for Lambda to pull the Layer.

Correct Answer: Grant cross-account access to the Lambda Layer and reference it in your Lambda function.

By granting cross-account access to the Lambda Layer, you can manage a single version and share it between accounts without duplicating the layer. Other methods like duplicating layer or creating IAM roles are not suitable or efficient for sharing Lambda Layers.

Question 3

You operate an AWS Lambda function that processes images from Amazon S3 and depends on many native image-processing libraries. You want to manage these dependencies using Lambda Layers. Given the per-function limit of five layers and typical layer size constraints, what is the best approach?

Use Amazon ECR to store the image libraries. Combine all the libraries into one layer. Create a layer for each library and add it to the Lambda function. Include all libraries in the function's deployment package.

Correct Answer: Combine all the libraries into one layer.

AWS Lambda lets you attach up to five layers per function, and the total unzipped size of the deployment package plus layers is limited. Creating a separate layer per library quickly hits the layer-count limit and adds management overhead. Packaging all required image-processing libraries together into a single layer (or as few layers as possible) simplifies versioning, respects the layer limit, and enables reuse across functions. Using ECR would imply a container image deployment model, which is an alternative to layers but not the best answer when the requirement is to use Lambda Layers.

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