How to add layers in the Lambda function

Key takeaways:

• AWS Lambda runs code without managing servers, simplifying deployment.

• Lambda layers add external libraries, tools, or custom runtimes to functions.

• Layers improve reusability, reduce package size, and simplify updates.

• They can be created using AWS CLI or AWS Management Console with runtime and architecture compatibility.

• Layers decouple dependencies from code, making maintenance easier.

• They optimize execution environments for specific use cases, enhancing efficiency.

AWS Lambda is a powerful tool that allows us to run our code without the hassle of managing the computers it operates on. When we use AWS Lambda, all we need to do is write our code, and Lambda takes care of the rest, such as deciding the best place and method to run it. However, sometimes, our code requires additional components, like specific tools or libraries, that aren’t included in the standard AWS Lambda setup. In these situations, Lambda layers prove to be extremely useful.

Understanding Lambda layers

Lambda layers are essentially add-on features that enhance the functionality of our AWS Lambda functions. They are similar to zip files and are packed with various elements that our Lambda function might need. Here’s what they can include:

1. Libraries: Our Lambda function might require additional code libraries to function correctly. Using Lambda layers, we can easily add these libraries to our function, ensuring it has all the necessary components to execute successfully.

2. Custom runtimes: Sometimes, our Lambda function might need to run in a specific programming environment that isn’t available by default in AWS Lambda. Lambda layers allow us to incorporate these custom runtimes, giving us the flexibility to use the programming environment that our project demands.

3. Configuration files: These files are crucial as they contain settings and instructions that guide our Lambda function. They dictate how the function should operate and can be used to fine-tune its behavior to suit our specific needs.

Create a Lambda layer package

Let’s say we need an image processing library in Lambda to process the images, but the default environment doesn’t provide it. We can create a Lambda layer with an image-processing pillow library. We can use the following commands to create the Lambda layer package:

• Create a PIL_Layer named folder and go to this folder on our system. Here, create the python named folder and install the pillow library.

• Execute the following command to compress the contents into a .zip file:

We’ve successfully created the pillow library’s package for the Lambda layer.

Deploy a Lambda layer

There are different ways to upload the Lambda layer packages to our created Lambda function.

Upload Lambda layer package using CLI

The Lambda layer package can be uploaded using the CLI. We can execute the following command to create the “pillow_layer” with the package we’ve created with the pillow library.

Note: AWS credentials should be configured before executing this command.

This command is used to publish a new version of a Lambda layer using the AWS Command Line Interface (CLI). It creates a layer named pillow_layer with the contents of the pillow_layer.zip file. The layer is specified as compatible with the Python 3.13 runtime. The --no-cli-pager option ensures the output is displayed directly in the command line window without invoking a pager, like “less” or “more,” which is useful for scripting or automation.

Upload Lambda layer package using AWS Management Console

The Lambda layer package can be uploaded using the AWS Management Console. Follow these steps to add the layer to the Lambda function:

• Go to the “Lambda” dashboard.

• Click the “Layers” option in the “Additional resources” section in the left navigation bar.

• Click the “Create layer” button, and it’ll open a new page.

• Enter “pillow_layer” in the “Name” field. In the “Description-optional” field, add the description of our choice.

• Ensure that the “Upload a .zip file” option is selected, and then click the “Upload” button. Now, select the “pillow_layer.zip” file we recently created.

• Select the “x86_64” option in the “Compatible architectures-optional” field.

• Select the “Python 3.13” option in the “Compatible runtimes” field.

• Keep the rest of the fields as it is, and click the “Create” button to create the layer for the Lambda function.

We’ve successfully created the Lambda layer. Now, we can attach it to a Lambda function and start using the pillow library in the Lambda function code.

Benefits of AWS Lambda layers

Here are some benefits of using Lambda layers:

Try it yourself

Click the terminal given below to connect it and execute the following command:

You need to provide your AWS credentials to configure AWS. After executing the above command, provide the following:

• Enter your AWS Access Key ID.

• Enter your AWS Secret Access Key.

• Enter your preferred region, such as us-east-1 or us-west-2.

• In the output format, you can leave this as the default json or choose table/text.

Conclusion

AWS Lambda layers make developing easier and faster by turning common tools and dependencies into reusable parts. This approach helps in managing and deploying code more efficiently and allows it to fit various programming needs without overcomplicating the main function code. Developers get a strong tool in Lambda layers, ensuring necessary components like libraries and settings are included cleanly and effectively.