Lambda layers for shared code in AWS - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Lambda layer in AWS?

A Lambda layer is a way to package and share code or libraries that multiple Lambda functions can use. It helps avoid repeating the same code in each function.

beginner

How do Lambda layers help with code management?

Lambda layers let you keep shared code in one place. When you update the layer, all functions using it get the update without changing each function separately.

intermediate

Can a Lambda function use multiple layers?

Yes, a Lambda function can use up to five layers at the same time. This allows combining different shared code packages.

intermediate

What is the maximum size of a Lambda layer?

Each Lambda layer can be up to 50 MB compressed (zipped) and 250 MB uncompressed when deployed.

intermediate

How do you update shared code in Lambda layers?

You create a new version of the layer with the updated code and then update your Lambda functions to use the new layer version.

What is the main benefit of using Lambda layers?

A To monitor Lambda function performance

B To increase the memory of a Lambda function

C To share common code across multiple Lambda functions

D To schedule Lambda functions automatically

Lambda layers allow sharing common code or libraries across multiple Lambda functions, reducing duplication.

How many layers can a single Lambda function use at once?

A 3

B 5

C 1

D 10

A Lambda function can use up to 5 layers simultaneously.

What must you do to apply updated shared code in a Lambda layer to your functions?

A Update the Lambda function code only

B Restart the Lambda service

C Delete the old layer

D Create a new layer version and update the function to use it

You create a new version of the layer with updated code and then update your Lambda functions to use this new version.

What is the maximum compressed size of a Lambda layer?

A 50 MB

B 10 MB

C 100 MB

D 250 MB

Each Lambda layer can be up to 50 MB compressed (zipped).

Which of the following is NOT a use case for Lambda layers?

A Storing environment variables

B Sharing utility libraries

C Including custom runtimes

D Sharing configuration files

Environment variables are set directly on Lambda functions, not stored in layers.

Explain how Lambda layers help reduce code duplication in AWS Lambda functions.

Think about how one package can be used by many functions instead of copying code.

Describe the steps to update shared code using Lambda layers and apply it to your Lambda functions.

Focus on versioning and function configuration.

Practice

1. What is the main purpose of using Lambda layers in AWS Lambda functions?

easy

A. To share common code and libraries across multiple Lambda functions

B. To increase the memory size of a Lambda function

C. To schedule Lambda functions to run at specific times

D. To monitor Lambda function execution logs

Solution

1. Step 1: Understand Lambda layers purpose

   Lambda layers are designed to hold shared code or libraries that multiple Lambda functions can use.

2. Step 2: Compare options with purpose

   Options A, C, and D describe unrelated Lambda features like memory, scheduling, and logging.

3. Final Answer:

   To share common code and libraries across multiple Lambda functions -> Option A

4. Quick Check:

   Lambda layers = shared code [OK]

Hint: Layers share code across functions, not manage memory or logs [OK]

Common Mistakes:

• Confusing layers with memory settings
• Thinking layers schedule functions
• Mixing layers with logging features

2. Which of the following is the correct way to add a Lambda layer to a function using AWS CLI?

easy

A. aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1

B. aws lambda create-layer-version --function-name MyFunction --layer-name MyLayer

C. aws lambda add-layer-version --function-name MyFunction --layer-name MyLayer

D. aws lambda attach-layer --function-name MyFunction --layer-name MyLayer

Solution

1. Step 1: Identify correct CLI command to add layer

   The correct command to add a layer to a Lambda function is update-function-configuration with the --layers parameter specifying the layer ARN.

2. Step 2: Review options for syntax correctness

   aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1 uses the correct command and syntax. Options A, B, and D use invalid or non-existent commands.

3. Final Answer:

   aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1 -> Option A

4. Quick Check:

   Update function config with layers ARN = aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1 [OK]

Hint: Use update-function-configuration with --layers ARN [OK]

Common Mistakes:

• Using create-layer-version to attach layers
• Trying non-existent commands like add-layer-version
• Not specifying full ARN for the layer

3. Given this Python Lambda function code snippet using a layer for shared utilities:

import shared_utils

def lambda_handler(event, context):
    return shared_utils.process(event)

What will happen if the Lambda function is invoked but the layer is not attached?

medium

A. The function will timeout waiting for the layer

B. The function will run successfully without errors

C. The function will return None silently

D. The function will raise a ModuleNotFoundError for 'shared_utils'

Solution

1. Step 1: Understand import behavior without layer

   If the layer providing shared_utils is missing, Python cannot find the module and raises an error.

2. Step 2: Identify the error type

   Python raises ModuleNotFoundError when an import fails.

3. Final Answer:

   The function will raise a ModuleNotFoundError for 'shared_utils' -> Option D

4. Quick Check:

   Missing layer = ModuleNotFoundError [OK]

Hint: Missing layer means missing module, causing import error [OK]

Common Mistakes:

• Assuming function runs without the layer
• Expecting silent failure or None return
• Thinking function times out due to missing layer

4. You updated your Lambda layer code and published a new version. After updating your function configuration to use the new layer version, the function still uses old code. What is the most likely cause?

medium

A. The function needs a restart to pick up the new layer version

B. The function configuration was not updated to the new layer version ARN

C. The new layer version was not published correctly

D. Lambda layers automatically update all functions, so this is impossible

Solution

1. Step 1: Understand layer versioning

   Each layer version has a unique ARN. Functions must explicitly reference the new version ARN to use updated code.

2. Step 2: Identify common mistake

   If the function still uses old code, it likely still references the old layer version ARN.

3. Final Answer:

   The function configuration was not updated to the new layer version ARN -> Option B

4. Quick Check:

   Update function config with new layer ARN to use new code [OK]

Hint: Update function config with new layer ARN after publishing [OK]

Common Mistakes:

• Assuming layers auto-update all functions
• Not publishing new layer version properly
• Thinking function restart is needed

5. You have two Lambda functions, both using a shared layer with version 3. You want to update the shared code without affecting the functions immediately. What is the best practice to update the shared code safely?

hard

A. Delete version 3 and create a new layer with the same version number

B. Overwrite version 3 of the layer with new code so both functions get updates automatically

C. Publish a new layer version 4 with updated code, then update each function to use version 4 when ready

D. Update the functions' code directly instead of using layers

Solution

1. Step 1: Understand layer version immutability

   Lambda layer versions are immutable; you cannot overwrite an existing version.

2. Step 2: Apply best practice for safe updates

   Publish a new version with updated code and update functions individually to control rollout.

3. Final Answer:

   Publish a new layer version 4 with updated code, then update each function to use version 4 when ready -> Option C

4. Quick Check:

   New layer version + update functions individually = safe update [OK]

Hint: Publish new layer version; update functions selectively [OK]

Common Mistakes:

• Trying to overwrite existing layer version
• Deleting and recreating layers with same version
• Updating function code instead of using layers