aws-cdk.aws-lambda-python 1.141.0

The CDK Construct Library for AWS Lambda in Python

Amazon Lambda Python Library

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The APIs of higher level constructs in this module are experimental and under active development. They are subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model and breaking changes will be announced in the release notes. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

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This library provides constructs for Python Lambda functions.

To use this module, you will need to have Docker installed.

Python Function

Define a PythonFunction:

lambda_.PythonFunction(self, "MyFunction",
    entry="/path/to/my/function",  # required
    runtime=Runtime.PYTHON_3_8,  # required
    index="my_index.py",  # optional, defaults to 'index.py'
    handler="my_exported_func"
)

All other properties of lambda.Function are supported, see also the AWS Lambda construct library.

Python Layer

You may create a python-based lambda layer with PythonLayerVersion. If PythonLayerVersion detects a requirements.txt or Pipfile or poetry.lock with the associated pyproject.toml at the entry path, then PythonLayerVersion will include the dependencies inline with your code in the layer.

Define a PythonLayerVersion:

lambda_.PythonLayerVersion(self, "MyLayer",
    entry="/path/to/my/layer"
)

A layer can also be used as a part of a PythonFunction:

lambda_.PythonFunction(self, "MyFunction",
    entry="/path/to/my/function",
    runtime=Runtime.PYTHON_3_8,
    layers=[
        lambda_.PythonLayerVersion(self, "MyLayer",
            entry="/path/to/my/layer"
        )
    ]
)

Packaging

If requirements.txt, Pipfile or poetry.lock exists at the entry path, the construct will handle installing all required modules in a Lambda compatible Docker container according to the runtime and with the Docker platform based on the target architecture of the Lambda function.

Python bundles are only recreated and published when a file in a source directory has changed. Therefore (and as a general best-practice), it is highly recommended to commit a lockfile with a list of all transitive dependencies and their exact versions. This will ensure that when any dependency version is updated, the bundle asset is recreated and uploaded.

To that end, we recommend using [pipenv] or [poetry] which have lockfile support.

• pipenv
• poetry

Packaging is executed using the Packaging class, which:

1. Infers the packaging type based on the files present.
2. If it sees a Pipfile or a poetry.lock file, it exports it to a compatible requirements.txt file with credentials (if they're available in the source files or in the bundling container).
3. Installs dependencies using pip.
4. Copies the dependencies into an asset that is bundled for the Lambda package.

Lambda with a requirements.txt

.
├── lambda_function.py # exports a function named 'handler'
├── requirements.txt # has to be present at the entry path

Lambda with a Pipfile

.
├── lambda_function.py # exports a function named 'handler'
├── Pipfile # has to be present at the entry path
├── Pipfile.lock # your lock file

Lambda with a poetry.lock

.
├── lambda_function.py # exports a function named 'handler'
├── pyproject.toml # your poetry project definition
├── poetry.lock # your poetry lock file has to be present at the entry path

Custom Bundling

Custom bundling can be performed by passing in additional build arguments that point to index URLs to private repos, or by using an entirely custom Docker images for bundling dependencies. The build args currently supported are:

• PIP_INDEX_URL
• PIP_EXTRA_INDEX_URL
• HTTPS_PROXY

Additional build args for bundling that refer to PyPI indexes can be specified as:

entry = "/path/to/function"
image = DockerImage.from_build(entry)

lambda_.PythonFunction(self, "function",
    entry=entry,
    runtime=Runtime.PYTHON_3_8,
    bundling=lambda.BundlingOptions(
        build_args={"PIP_INDEX_URL": "https://your.index.url/simple/", "PIP_EXTRA_INDEX_URL": "https://your.extra-index.url/simple/"}
    )
)

If using a custom Docker image for bundling, the dependencies are installed with pip, pipenv or poetry by using the Packaging class. A different bundling Docker image that is in the same directory as the function can be specified as:

entry = "/path/to/function"
image = DockerImage.from_build(entry)

lambda_.PythonFunction(self, "function",
    entry=entry,
    runtime=Runtime.PYTHON_3_8,
    bundling=lambda.BundlingOptions(image=image)
)

Custom Bundling with Code Artifact

To use a Code Artifact PyPI repo, the PIP_INDEX_URL for bundling the function can be customized (requires AWS CLI in the build environment):

from child_process import exec_sync


entry = "/path/to/function"
image = DockerImage.from_build(entry)

domain = "my-domain"
domain_owner = "111122223333"
repo_name = "my_repo"
region = "us-east-1"
code_artifact_auth_token = exec_sync(f"aws codeartifact get-authorization-token --domain {domain} --domain-owner {domainOwner} --query authorizationToken --output text").to_string().trim()

index_url = f"https://aws:{codeArtifactAuthToken}@{domain}-{domainOwner}.d.codeartifact.{region}.amazonaws.com/pypi/{repoName}/simple/"

lambda_.PythonFunction(self, "function",
    entry=entry,
    runtime=Runtime.PYTHON_3_8,
    bundling=lambda.BundlingOptions(
        build_args={"PIP_INDEX_URL": index_url}
    )
)

This type of an example should work for pip and poetry based dependencies, but will not work for pipenv.