Google Cloud Functions
Create a Cloud Function with Python.
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
[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
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_sources target with the handler file included in the
sources field. You can use
./pants tailor to automate this.
python_google_cloud_function target and define the
type fields. The
type should be either
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
# 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", )
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
You can optionally set the
output_path field to change the generated zip file's path.
filestargets will not be included in the built Cloud Function because filesystem APIs like
open()would not load them as expected. Instead, use the
resourcestarget. See Assets and archives for further explanation.
Step 3: Run
./pants package on your
python_google_cloud_function target to create a zipped file.
$ ./pants package project/google_cloud_function_example.py Wrote code bundle to dist/project.zip Runtime: python3.8 Handler: main.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
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
Updated about 1 year ago