https://aws.amazon.com/blogs/compute/choosing-between-aws-lambda-data-storage-options-in-web-apps/
https://derekurizar.medium.com/aws-lambda-python-pil-cannot-import-name-imaging-11b2377d31c4
Your Lambda functions may use additional libraries as part of the deployment package. You can bundle these in the deployment archive or optionally move to a layer instead. A Lambda function can have up to five layers, and is subject to the maximum deployment size of 50 MB (zipped, for direct upload). Packages in layers are available in the /opt directory during invocations. While layers are private to you by default, you can also share layers with other AWS accounts, or make layers public.
<img src="https://pingfan.s3.amazonaws.com/pic4/xr990.png" alt="Lambda layers in the console">
There are many benefits to using layers throughout the functions in your serverless application. It’s best practice to include the AWS SDK instead of depending on the version bundled with the Lambda service. This enables you to pin the version of the SDK. By using a layer, you don’t need to bundle the package with each function, which can increase your deployment package size and slow down deployments. You can create an AWS SDK layer and then include a reference to the layer in each function.
Layers can be an effective way to bundle large dependencies, or share compiled libraries with binaries that vary by operating system. For example, the Happy Path application uses the Sharp npm graphics library to process images. Similarly, the Innovator Island workshop uses the OpenCV library to perform image manipulation, and this is imported using a shared layer.
Layers are static once they are deployed. You can only change the contents of a layer by deploying a new version. Any Lambda function using the layer binds to a specific version and must be updated to change layer versions. To learn more, see using Lambda layers to simplify your development process .
If you deploy your function code using a .zip file archive, you can use Lambda layers as a distribution mechanism for libraries, custom runtimes, and other function dependencies. Layers enable you to manage your in-development function code independently from the unchanging code and resources that it uses. You can configure your function to use layers that you create, layers that AWS provides, or layers from other AWS customers.
You do not use layers with container images. Instead, you package your preferred runtime, libraries, and other dependencies into the container image when you build the image.
For more information about layers, see Creating and sharing Lambda layers .