Easy lambda dependency management is here!
This is an AWS CDK resource that acts as a LayerVersion resource but with more convenient approach to specify and bundle dependencies.

AWS CDK already makes it incredibly simple to package code for lambda functions and layers by exposing Code resource and methods like Code.from_asset(). However, it is very difficult to add additional dependencies to your layer code e.g. jose, requests, newer boto3 library, etc. This custom layer resource makes packaging code with dependencies extremely simple like 2 2! You simply specify a dictionary of dependencies to include and this resource will do the rest.

You should note that this resource uses docker to bundle code and dependencies.

Biomapas aims to modernise life-science industry by sharing its IT knowledge with other companies and the community. This is an open source library intended to be used by anyone. Improvements and pull requests are welcome.

• Python3
• Docker
• AWS CDK
• AWS CloudFormation
• AWS Lambda
• AWS Lambda Layer
• AWS Lambda Layer bundling with Docker

This project assumes you know what Lambda functions are and how code is being shared between them (Lambda layers).

• Excellent knowledge in IaaC (Infrastructure as a Code) principles.
• Excellent knowledge in Lambda functions and Lambda layers.
• Good experience in AWS CDK and AWS CloudFormation.
• Good Python skills and basis of OOP.

• AWS CDK:
  https://docs.aws.amazon.com/cdk/api/latest/docs/aws-construct-library.html
• AWS CloudFormation:
  https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/Welcome.html
• Lambda layers:
  https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html
• Lambda layers in AWS CDK:
  https://docs.aws.amazon.com/cdk/api/latest/python/aws_cdk.aws_lambda/README.html

Before installing this library, ensure you have these tools setup:

• Python / Pip
• AWS CDK
• Docker

To install this project from source run:

pip install .

Or you can install it from a PyPi repository:

pip install b-cfn-lambda-layer

The most convenient feature of this resource is easy dependency management. When creating a new layer you simply supply a dictionary of dependencies, and they will be installed and packaged to you layer at a deployment level:

from b_cfn_lambda_layer.package_version import PackageVersion

dependencies = {
    'python-jose': PackageVersion.from_string_version('3.3.0'),
    'boto3': PackageVersion.from_string_version('1.16.35'),
    'botocore': PackageVersion.from_string_version('1.19.35')
}

This is a full example where we create a lambda layer and use it in lambda function.

from aws_cdk.aws_lambda import Function, Code, Runtime
from aws_cdk import Stack

from b_cfn_lambda_layer.lambda_layer import LambdaLayer
from b_cfn_lambda_layer.package_version import PackageVersion

# Create layer with custom dependencies.
layer = LambdaLayer(
    scope=Stack(...),
    name='TestLayer',
    # You can conveniently specify path to source code to include.
    # Or not specify it at all if you care only about dependencies.
    # source_path=None,
    source_path='/path/to/your/layer/source/code',
    code_runtimes=[Runtime.PYTHON_3_8, Runtime.PYTHON_3_9, Runtime.PYTHON_3_10],
    # You can conveniently specify dependencies to include.
    dependencies={
        'python-jose': PackageVersion.from_string_version('3.3.0'),
        'boto3': PackageVersion.from_string_version('1.16.35'),
        'botocore': PackageVersion.from_string_version('1.19.35')
    }
)

# Create a function with a layer.
Function(
    scope=Stack(...),
    id='MyFunction',
    code=Code.from_asset('/path/to/lambda/function/code'),
    handler='index.handler',
    runtime=Runtime.PYTHON_3_10,
    # Specify layers.
    layers=[layer]
)

We all know that we can not really share a lambda layer instance in multiple stacks. If we do, the next update to the source code in the lambda layer would result in a failed deployment. This is because of cross-stack exports-imports. An example error is given below:

2022-04-28 17:29:34 [ INFO] Stack | 2/4 | 5:29:31 PM | UPDATE_ROLLBACK_IN_P | AWS::CloudFormation::Stack | Stack Export Stack:Export cannot be updated as it is in use by StackB

Thankfully, this resource has solved the problem very conveniently.

Firstly, create a layer in any stack you want:

# Create the layer in any stack.
layer = LambdaLayer(
    scope=Stack(...),
    ...
)

Then add it to a lambda function:

# Create a lambda function in any stack.
function = Function(
    scope=Stack(...),
    # DO NOT supply the layer directly as it will create a direct dependency
    # between the function and the layer. This is what causes the deployments
    # to fail if we update layer's source code.
    # layers=[layer]
    ...
)

# Instead, call "add_to_function" layer's method to enable that layer for the function.
# This method does not create a direct dependency effectively solving cross-stack problems.
layer.add_to_function(function)

This package has integration tests based on pytest. To run tests simply run:

pytest b_cfn_lambda_layer_test/integration/tests

Found a bug? Want to add or suggest a new feature? Contributions of any kind are gladly welcome. You may contact us directly, create a pull-request or an issue in github platform. Lets modernize the world together.