AWS Lambda

Create a Lambda with Python code.

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Pants can create a Lambda-compatible zip file from your Python code, allowing you to develop your Lambdas in your repository instead of using the online Cloud9 editor.

FYI: how Pants does this

Under-the-hood, Pants uses the Lambdex project. First, Pants will convert your code into a Pex file. Then, Pants will use Lambdex to convert the Pex into a zip file understood by AWS.

Step 1: Activate the Python AWS Lambda backend

Add this to your pants.toml:

pants.toml

[GLOBAL]
backend_packages.add = [
  "pants.backend.awslambda.python",
  "pants.backend.python",
]

This adds the new python_awslambda target, which you can confirm by running pants help python_awslambda

Step 2: Define a python_awslambda target

First, add your lambda function in a Python file like you would normally do with AWS Lambda. Specifically, create a function def my_handler_name(event, context) with the name you want.

Then, in your BUILD file, make sure that you have a python_source or python_sources target with the handler file included in the sources field. You can use pants tailor :: to automate this.

Add a python_awslambda target and define the runtime and handler fields. The runtime should be one of the values from https://docs.aws.amazon.com/lambda/latest/dg/lambda-python.html. The handler has the form handler_file.py:handler_func, which Pants will convert into a well-formed entry point. Alternatively, you can set handler to the format path.to.module:handler_func.

For example:

project/BUILD

# The default `sources` field will include our handler file.
python_sources(name="lib")

python_awslambda(
    name="lambda",
    runtime="python3.8",
    # Pants will convert this to `project.lambda_example:example_handler`.
    handler="lambda_example.py:example_handler",
)

project/lambda_example.py

def example_handler(event, context):
    print("Hello AWS!")

Pants will use dependency inference based on the handler field, which you can confirm by running pants dependencies path/to:lambda. You can also manually add to the dependencies field.

You can optionally set the output_path field to change the generated zip file's path.

Use resource instead of file

file / files targets will not be included in the built AWS Lambda because filesystem APIs like open() would not load them as expected. Instead, use the resource and resources target. See Assets and archives for further explanation.

Step 3: Run package

Now run pants package on your python_awslambda target to create a zipped file.

For example:

$ pants package project/awslambda_example.py
Wrote code bundle to dist/project.zip
  Runtime: python3.8
  Handler: lambdex_handler.handler

Running from macOS and failing to build?

AWS Lambdas must run on Linux, so Pants tells PEX and Pip to build for Linux when resolving your third party dependencies. This means that you can only use pre-built wheels (bdists). If your project requires any source distributions (sdists) that must be built locally, PEX and pip will fail to run.

If this happens, you must either change your dependencies to only use dependencies with pre-built wheels or find a Linux environment to run pants package.

Step 4: Upload to AWS

You can use any of the various AWS methods to upload your zip file, such as the AWS console or the AWS CLI via aws lambda create-function and aws lambda update-function-code.

You must specify the AWS lambda handler as lambdex_handler.handler.

Docker Integration

To deploy a Python lambda function with container images, you can use Pants's Docker support.

For example:

project/Dockerfile

FROM public.ecr.aws/lambda/python:3.8

RUN yum install unzip -y
COPY project/lambda.zip .
RUN unzip lambda.zip -d "${LAMBDA_TASK_ROOT}"
CMD ["lambdex_handler.handler"]

project/BUILD

python_sources()

python_awslambda(
    name="lambda",
    runtime="python3.8",
    handler="main.py:lambda_handler"
)

docker_image(
    name="my_image",
    dependencies = [":lambda"],
)

Then, use pants package project:my_image, for example. Pants will first build your AWS Lambda, and then will build the Docker image and copy it into the AWS Lambda.