Google Cloud Functions

Create a Cloud Function with Python.

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Pants can create a Google Cloud Function-compatible zip file from your Python code, allowing you to develop your functions in your repository.

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 Google Cloud Functions.

Step 1: Activate the Python Google Cloud Function backend

Add this to your pants.toml:

pants.toml

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

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

Step 2: Define a python_google_cloud_function target

First, add your Cloud function in a Python file like you would normally do with Google Cloud Functions, such as creating a function def my_handler_name(event, context) for event-based functions.

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_google_cloud_function target and define the runtime, handler, and type fields. The type should be either "event" or "http". The runtime should be one of the values from https://cloud.google.com/functions/docs/concepts/python-runtime. 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_google_cloud_function(
    name="cloud_function",
    runtime="python38",
    # Pants will convert this to `project.lambda_example:example_handler`.
    handler="google_cloud_function_example.py:example_handler",
    type="event",
)

project/google_cloud_function_example.py

def example_handler(event, context):
    print("Hello Google Cloud Function!")

Pants will use dependency inference based on the handler field, which you can confirm by running pants dependencies path/to:cloud_function. 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 Cloud Function because filesystem APIs like open() would not load them as expected. Instead, use the resource / resources target. See Assets and archives for further explanation.

Step 3: Run package

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

For example:

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

Running from macOS and failing to build?

Cloud Functions 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 Google Cloud

You can use any of the various Google Cloud methods to upload your zip file, such as the Google Cloud console or the Google Cloud CLI.

You must specify the handler as handler.