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How To Use AWS Lambda Layers for Code Reuse and Organization?

Last Updated : 18 Jan, 2024
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AWS lambda is an AWS service that works with Serverless Architecture, The infrastructure management is automatically managed by the AWS. It facilitates the developers to focus on the Code development completely. AWS Lambda Layers extends the core functionalities of the AWS Lambda functions through an efficient approach for managing dependencies and reusability of code. Mainly lambda layers allow the users to separate and externalize the dependencies making the efficient code organization bringing develop enhancement in the workflow.

What are AWS Lambda Layers?

AWS Lambda Layers is a distribution mechanism for libraries, custom runtimes, and other function dependencies. Layers can be used to separate different components of an application,  By separating these components, you can reuse the same code (in the form of layers) across multiple AWS lambda functions or even other AWS accounts.

For instance, we can separate our business logic and the application dependencies, so that our application will look organized, the common codes (layers) can be reused across other lambda functions, and we can even share the layers of other lambda functions which require the same dependencies.

AWS-Lambda-Layer

Advantages of AWS Lambda Layers

1. Code Reusability

  • Code reusability is the prime benefit of using Lambda Layers, it can be used to share common code or libraries across multiple Lambda functions. This reduces the need for duplicating code across functions and also makes it easier to update the code when needed.

2. Custom Binaries/Libraries Support

  • By default, lambda provides us with very lightweighters is a distribution mechanism for libraries, custom runtimes, and other function dependencies. Layers can be used to separate different components of an application,  By separating these components, you can reuse the same code (in the form of layers) across multiple lambda functions or even other AWS accounts.
  • For instance, we can separate our business logic and the application dependencies, so that our application will look organized, the common codes (layers) can be reused across other lambda functions, and we can even share the layers of other lambda functions which require the same dependencies. These base images with only a few and most necessary libraries/binaries, and if we require any additional libraries/binaries which are not natively supported by AWS Lambda, we need to include them in our lambda application code.

3. Versioning

  • Lambda Layers can be used to maintain different versions of the same code or libraries. This can be useful for rolling back to an earlier version of the code or libraries if needed.

4. Speed of Deployment

  • Lambda Layers can help speed up the deployment of new Lambda functions. By sharing common code and libraries across functions, there is no need to download and install the same code and libraries repeatedly, so if there is any dependency update, we can release the patched dependencies layer quickly, and business logic need not be updated in this case which reduces the time to deployment.

Challenges of AWS Lambda Layers

1. Dependency Coordination

  • Management of updates across multiple layers and ensuring their compatibility with layers and functions are quite challenging.

2. Learning Curve

  • Learning and Implementing lambda layers are a bit difficult for those who are unfamiliar with the lambda and serverless concepts in adoption and maintenance initially.

3. Versioning Precision Struggle

  • Providing the right balance in layer version maintenance is very crucial. Overcomplicating the degree of detailing in version control may lead to management challenges and an Insufficient degree of detailing might affect the compromise of the control with dependencies.

4. Integration Challenges

  • Understanding and integrating existing workflow seamlessly raises the challenges in Integration with different functions and services, It requires proper planning, and careful requirement consideration is needed to resolve those.

Guidelines for Effective Usage of AWS Lambda Layer

The following are steps for effective Usage of AWS Lambda Layers to streamline the code management, simplify the deployment and enhance the resuability of shared resources across multiple lambda functions

Step 1: Create a Lambda Layer

  • Navigate to the Lambda service in the AWS Management Console, go to the layers section in the sidebar, and then click on the “Create Layer” button.
  • Specify the name, description, and runtime compatibility for your layer. After that upload your code or libraries as .zip files or provide a link to the package.

Step 2: Publish the Layer Version

  • After once creating the layer, Select “Publish new Version” in the drop-down section of “Actions” and assign the Description and Version number to it.

Step 3: Adding Layer to Lambda Function

  • Open the Lambda function that you want to add to the layer. Click on the “Add a layer” in the layers section of the function overview.

Step 4: Configuring the Lambda Function

  • Try to cross-check whether the lambda function references the libraries or dependencies provided by the layer. To increase the performance of the shared resources try to modify the function’s code.

Step 5: Deploy the Lambda Function

  • Check the changes made to your lambda function after cross-checking confirmation and deploy the updated version.

Step 6 – Test the Lambda Function

  • Triggering the Lambda function to verify whether the shared code or libraries working properly or not from the layer.

Step 7: Update Layer Version ( Optional )

  • For updating the layer with new code or libraries, repeat the same process by creating a new version of the layer.

Step 8: Managing the Layer Permissions

  • Try adjusting the permissions of the layer to ensure that necessary lambda functions have required access to the layer.

Layers And Layer Versions

Aspects

Layers

Layer Versions

Definition

AWS Layers are meant sharing Packages, Libraries and Codes

AWS Layer Versions are meant for specifying the versions of a layer with containing its code or dependencies

Attachment

These are attached to the lambda functions

These are also attached to the lambda functions but controlled with version specification.

Reusability

It helps in reusability of Code and Dependencies

It allow reusage of specific versions of a layer for providing consistent behaviour

Updation

Updation of content of the Layer is possible

Updation of specific versions of Layers can be done independently

Management

It follows centralized managementers is a distribution mechanism for libraries, custom runtimes, and other function dependencies. Layers can be used to separate different components of an application,  By separating these components, you can reuse the same code (in the form of layers) across multiple lambda functions or even other AWS accounts.

For instance, we can separate our business logic and the application dependencies, so that our application will look organized, the common codes (layers) can be reused across other lambda functions, and we can even share the layers of other lambda functions which require the same dependencies.

It follows granular control over different versions for better control

Cost Implications

These are commonly shared across multiple functions

It shares only needed versions for cost Optimization

Flexibility

It has the flexibility in management of shared resources

It offers specific version controls and rollback options

Usage of Lambda Layers for Code Reuse and Organization

As discussed above, using Lambda Layers allows for code reuse and better organization by separating reusable code from function-specific code. Here In this section you are going to learn how to separate the dependencies of a Python application into layers and reuse it in other functions, and share it with other accounts as well.

Step by Step Implementation to Create a Lambda Layer for Python Application

The following steps of implementation to create a lambda layer for the dependencies of the Python application.

Step 1: Create a virtual environment

python3 -m venv myvenv

Step 2: Activate the virtual environment

  • Activate the virtual environment, by running the below command, Now install the dependencies in this isolated environment.
source myenv/bin/activate

Create & activate the virtual environment

Step 3: Install dependencies

  • Install the dependencies using pip package manager, the dependencies of the application are listed in the requirements.txt file.
pip install -r requirements.txt

To know about automating the installation of dependencies in python, please refer the article – Automating Installation of python libraries

Step 4: Package the dependencies

After running the above command, The dependencies will be installed in myvenv/lib/python3.8/site-packages directory, here we need to package those dependencies inside the python directory so that they can be used inside the lambda function.

create a new directory with the name python, copy the dependencies to this directory, then compress this directory using the below command.

zip -r dependencies.zip python/ 

Step 5: Creating AWS Lambda Layer

  • Open the Lambda service in the AWS console.
  • Open the layers page (available at the left panel)
  • Click on Create layer, fill in the layer details, and upload the compressed dependencies file.

Create a Lambda Layer

Step 6: Usage of Lambda Layer in Multiple Lambda Functions

After creating the layer successfully, you can now use them in any layer, to add any layer follow the below steps.

  • Open Lambda Function.
  • Under the Code tab, you can find the layers section.
  • Click on Add Layer, and choose the layer you want to attach to this function.
  • Click on save.

Add Lambda LayerAdd Lambda Layer to the function

Now you can use any dependency in the layer, and you can reuse the same layer in multiple functions.

Sharing Layer with other Accounts

We can share the lambda layer with specific accounts/organizations or even you can make the layer public, using Serverless Application Repository. 

As of now we can share layers using CLI only, run the below command to share the layer with all users (public).

aws lambda add-layer-version-permission
--layer-name dependencies_layer
--version-number 1
--statement-id ShareLayertoPublic
--principal "*"
--action lambda:GetLayerVersion

Using Shared Layer

You can use the shared layer ARN directly to use in the lambda function.

Use Shared Layer by using Arn

AWS Lambda Layers Configurations and Permissions

Configuration Of Lambda Layers

Step 1: Creating Lambda Layer Archive

  • Organizing your shared code and dependencies into a folder.
  • Create an archived ZIP of the folder contents maintaining the structure.

Step 2: Publishing the Lambda Layer to AWS Lambda

  • Navigate to Lambda Layer in AWS Management Console and click on the “Create Layer”
  • Upload the ZIP archive by providing the name, description and select your compatible runtime, and finally click on “Create”.

Step 3: Attaching the Lambda Layer to the Lambda Function

  • Open the Lambda Functidon and scroll down to the “Layers” section.
  • Click on “Add a Layer” and select the created layer along with specifying the version. Finally save your function as a final step of Configuration.

Permissions of Lambda Layers

Step 4: Create an IAM Policy for the Lambda Layer

  • Navigate to the IAM from the AWS Management Console. Click on the “Create Policy” in the “Policies” section.
  • Define the Permissions by specifying the action;  attach the policies to the role.

Step 5: Attaching the IAM Role to the Lambda Function

  • Go to the “Execution Role” section in the lambda function configurations. Click on the Role link and attach the IAM policies that are created in the previous step for the role.

Step 6: Granting Public Access to the Lambda Layer ( Optional )

  • On using the command `add-layer-version-permission` command in AWS CLI with specifying the argumental information, you can grant the public access to the layer version.

Step 7: Verification of Configuration

  • Try on testing the Lambda function for safe guarding its access and usage of shared layers.

Conclusion

In summarizing the enhancement of AWS Lambda Layers usage provides better code reusability in organization as a highly effective strategy. Management of command code and its dependencies into Lambda layers helping the developers facilitate an efficient deployment process by reducing the size of individual functional packages. It supports simple maintenance of updates to improve operational efficiency and promote a modular approach in a structured way. It contributes more scalable and manageable serverless architecture on the AWS Lambda platforms.

AWS Lambda Layer Usage-FAQs

What’s The Main Purpose Of AWS Lambda Layers?

Lambda Layers provides efficient code reusability by sharing the common dependencies across multiple Lambda Functions to improve efficiency and organization.

How Do Lambda Layers Reduce Size Of The Deployment Functional Package?

The maintenance of command code and dependencies separately and sharing them as per needs with other lambda functions, It reduces the size of the deployment packages.

Can Lambda Layers Manage Different Versions Of Shared Code?

Yes. Through Version supporting features lambda layers provide precise control and rollback options for maintaining consistent behavior.

What Considerations Are Crucial For Maintaining Lambda Layers?

Keeping the Lambda layers small and Organizing regular updates are some of the crucial considerations for maintaining Lambda layers for improvements and security patches.

How Do Lambda Layers Enhance Collaboration Among Development Teams?

Lambda Layers enhances collaboration among development teams by providing a centralized repository and efficient shared resources across different functions. 



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