Lambda layers for shared code in AWS - Step-by-Step Execution

Process Flow - Lambda layers for shared code

Create shared code package

↓

Package as Lambda Layer

↓

Publish Layer to AWS

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Attach Layer to Lambda function

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Invoke Lambda function

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Lambda uses shared code from Layer

This flow shows how shared code is packaged as a Lambda Layer, published, attached to a Lambda function, and then used during function execution.

Execution Sample

AWS

1. zip -r layer.zip .
2. aws lambda publish-layer-version --layer-name shared-code --zip-file fileb://layer.zip
3. aws lambda update-function-configuration --function-name MyFunction --layers <layer-arn>
4. Invoke MyFunction which uses shared code

This sequence packages shared code, publishes it as a Lambda Layer, attaches it to a Lambda function, and then runs the function using the shared code.

Process Table

Step	Action	AWS Service	Result	Notes
1	Package shared code into zip	Local	layer.zip created	Shared code ready for upload
2	Publish Lambda Layer	AWS Lambda	Layer version published	Layer ARN returned
3	Attach Layer to Lambda function	AWS Lambda	Function updated with layer	Function now uses shared code
4	Invoke Lambda function	AWS Lambda	Function runs successfully	Shared code from layer is accessible
5	Function execution ends	AWS Lambda	Execution complete	Output depends on function logic

💡 Function execution completes using shared code from attached Lambda Layer

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
layer.zip	none	created	uploaded	attached	used	used
Lambda Layer ARN	none	none	generated	attached	attached	attached
Lambda Function config	no layer	no layer	no layer	layer attached	layer attached	layer attached
Function execution	not started	not started	not started	not started	running	completed

Key Moments - 3 Insights

Why do we need to package shared code into a zip file before publishing as a Lambda Layer?

AWS Lambda requires the shared code to be zipped so it can be uploaded and stored as a single artifact. This is shown in execution_table step 1 where the zip is created before publishing.

What happens if the Lambda function is not updated to include the new layer ARN?

The function will not have access to the shared code in the layer. Execution_table step 3 shows attaching the layer is necessary for the function to use the shared code.

Can multiple Lambda functions use the same layer?

Yes, once a layer is published, its ARN can be attached to multiple functions. This is implied in the flow where the layer is published once and can be reused.

Visual Quiz - 3 Questions

Look at the execution_table, at which step is the Lambda Layer ARN generated?

A Step 2

B Step 1

C Step 3

D Step 4

💡 Hint

Check the 'Result' column in execution_table for when the layer version is published.

According to variable_tracker, what is the state of the Lambda Function config after Step 3?

A No layer attached

B Layer attached

C Layer created but not attached

D Function deleted

💡 Hint

Look at the 'Lambda Function config' row under 'After Step 3' in variable_tracker.

If the shared code zip file was not created, which step in execution_table would fail?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Publishing the layer requires the zip file created in Step 1, see execution_table Step 2.

Concept Snapshot

Lambda Layers let you package shared code separately.
Package shared code as a zip file.
Publish it as a Lambda Layer in AWS.
Attach the layer to your Lambda function.
Function can then use the shared code at runtime.

Full Transcript

This visual execution shows how to use Lambda Layers for shared code. First, you package your shared code into a zip file locally. Then you publish this zip as a Lambda Layer in AWS, which creates a layer version and returns an ARN. Next, you update your Lambda function configuration to attach this layer ARN. When you invoke the Lambda function, it runs and can access the shared code from the attached layer. The execution table traces each step from packaging to function execution. The variable tracker shows how key variables like the zip file, layer ARN, and function configuration change at each step. Key moments clarify why packaging is needed, the importance of attaching the layer, and that layers can be reused by multiple functions. The quiz tests understanding of when the layer ARN is generated, the function config state after attaching the layer, and what happens if the zip file is missing. The concept snapshot summarizes the process in simple steps for quick recall.

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