Amazon Lambda Python Library¶---
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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.
This library provides constructs for Python Lambda functions.
To use this module, you will need to have Docker installed.
lambda_.PythonFunction(self, "MyFunction", entry="/path/to/my/function", # required index="my_index.py", # optional, defaults to 'index.py' handler="my_exported_func", # optional, defaults to 'handler' runtime=Runtime.PYTHON_3_6 )
All other properties of
lambda.Function are supported, see also the AWS Lambda construct library.
Pipfile 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.
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 # has to be present at the entry path ├── poetry.lock # your poetry lock file
Lambda Layer Support
You may create a python-based lambda layer with
PythonLayerVersion detects a
poetry.lock with the associated
pyproject.toml at the entry path, then
PythonLayerVersion will include the dependencies inline with your code in the
lambda_.PythonFunction(self, "MyFunction", entry="/path/to/my/function", layers=[ lambda_.PythonLayerVersion(self, "MyLayer", entry="/path/to/my/layer" ) ] )