Get some python in google cloud functions
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[NOTE]: This is a highly experimental (and proof of concept) library so do not expect all python packages to work flawlessly. Also, cloud functions are now (Summer 2018) rolling out native support for python3 in EAP so that also might be an option, check out the #functions channel on where the product managers hang around and open to help you out!


py-cloud-fn is a CLI tool that allows you to write and deploy Google cloud functions in pure python, supporting python 2.7 and 3.5 (thanks to @MitalAshok for helping on the code compatibility). No javascript allowed! The goal of this library is to be able to let developers write light weight functions in idiomatic python without needing to worry about node.js. It works OOTB with pip, just include a file named requirements.txt that is structured like this:


as you normally would when building any python application. When building (for production), the library will pick up this file and make sure to install the dependencies. It will do so while caching all dependencies in a virtual environment, to speed up subsequent builds.

TLDR, look at the examples

Run pip install pycloudfn to get it. You need to have Google cloud SDK installed, as well as the Cloud functions emulator and npm if you want to test your function locally.

You also need Docker installed and running as well as the gcloud CLI. Docker is needed to build for the production environment, regardless of you local development environment.

Currently, http, pubsub and bucket events are supported (no firebase).



usage: py-cloud-fn [-h] [-p] [-f FILE_NAME] [--python_version {2.7,3.5,3.6}]
                   function_name {http,pubsub,bucket}

Build a GCP Cloud Function in python.

positional arguments:
  function_name         the name of your cloud function
  {http,pubsub,bucket}  the trigger type of your cloud function

optional arguments:
  -h, --help            show this help message and exit
  -p, --production      Build function for production environment
  -i, --production_image
                        Docker image to use for building production environment
  -f FILE_NAME, --file_name FILE_NAME
                        The file name of the file you wish to build
  --python_version {2.7,3.5}
                        The python version you are targeting, only applies
                        when building for production

Usage is meant to be pretty idiomatic:

Run py-cloud-fn <function_name> <trigger_type> to build your finished function. Run with -h to get some guidance on options. The library will assume that you have a file named if not specified.

The library will create a cloudfn folder wherever it is used, which can safely be put in .gitignore. It contains build files and cache for python packages.

$DJANGO_SETTINGS_MODULE=mysite.settings py-cloud-fn my-function http -f --python_version 3.5

  _____                  _                 _         __
 |  __ \                | |               | |       / _|
 | |__) |   _ ______ ___| | ___  _   _  __| |______| |_ _ __
 |  ___/ | | |______/ __| |/ _ \| | | |/ _` |______|  _| '_ \
 | |   | |_| |     | (__| | (_) | |_| | (_| |      | | | | | |
 |_|    \__, |      \___|_|\___/ \__,_|\__,_|      |_| |_| |_|
         __/ |

Function: my-function
Trigger: http
Python version: 3.5
Production: False

⠴    Building, go grab a coffee...
⠋    Generating javascript...
⠼    Cleaning up...

Elapsed time: 37.6s
Output: ./cloudfn/target/index.js


This library works with pip OOTB. Just add your requirements.txt file in the root of the repo and you are golden. It obviously needs pycloudfn to be present.


Since this is not really supported by google, there is one thing that needs to be done to make this work smoothly: You can't use the default clients directly. It's solvable though, just do

from cloudfn.google_account import get_credentials

biquery_client = bigquery.Client(credentials=get_credentials())

And everything is taken care off for you!! no more actions need be done.

Handling a http request

Look at the Request object for the structure

from cloudfn.http import handle_http_event, Response

def handle_http(req):
      return Response(
        body={'key': 2},
        headers={'content-type': 'application/json'},


If you don't return anything, or return something different than a cloudfn.http.Response object, the function will return a 200 OK with an empty body. The body can be either a string, list or dictionary, other values will be forced to a string.

Handling http with Flask

Flask is a great framework for building microservices. The library supports flask OOTB. If you need to have some routing / parsing and verification logic in place, flask might be a good fit! Have a look at the example to see how easy it is!

from cloudfn.flask_handler import handle_http_event
from cloudfn.google_account import get_credentials
from flask import Flask, request
from flask.json import jsonify
from import bigquery

app = Flask('the-function')
biquery_client = bigquery.Client(credentials=get_credentials())

@app.route('/',  methods=['POST', 'GET'])
def hello():
    print request.headers
    return jsonify(message='Hello world!', json=request.get_json()), 201

def helloLol():
    return 'Hello lol!'

@app.route('/bigquery-datasets',  methods=['POST', 'GET'])
def bigquery():
    datasets = []
    for dataset in biquery_client.list_datasets():
    return jsonify(message='Hello world!', datasets={
        'datasets': datasets
    }), 201


Handling http with Django

Django is a great framework for building microservices. The library supports django OOTB. Assuming you have setup your django application in a normal fashion, this should be what you need. You need to setup a pretty minimal django application (no database etc) to get it working. It might be a little overkill to squeeze django into a cloud function, but there are some pretty nice features for doing request verification and routing in django using for intance django rest framework.

See the example for how you can handle a http request using django.

from cloudfn.django_handler import handle_http_event
from mysite.wsgi import application


Handling a bucket event

look at the Object for the structure, it follows the convention in the Storage API

from import handle_bucket_event
import jsonpickle

def bucket_handler(obj):
    print jsonpickle.encode(obj)


Handling a pubsub message

Look at the Message for the structure, it follows the convention in the Pubsub API

from cloudfn.pubsub import handle_pubsub_event
import jsonpickle

def pubsub_handler(message):
    print jsonpickle.encode(message)


Deploying a function

I have previously built go-cloud-fn, in which there is a complete CLI available for you to deploy a function. I did not want to go there now, but rather be concerned about building the function and be super light weight. Deploying a function can be done like this:

(If you have the emulator installed, just swap gcloud beta functions with npm install && functions and you are golden!).


py-cloud-fn my-function http --production && \
cd cloudfn/target && gcloud beta functions deploy my-function \
--trigger-http --stage-bucket <bucket> && cd ../..


py-cloud-fn  my-bucket-function bucket -p && cd cloudfn/target && \
gcloud beta functions deploy my-bucket-function --trigger-bucket \
<trigger-bucket> --stage-bucket <stage-bucket> && cd ../..


py-cloud-fn my-topic-function bucket -p && cd cloudfn/target && \
gcloud beta functions deploy my-topic-function --trigger-topic <topic> \
--stage-bucket <bucket> && cd ../..

Adding support for packages that do not work

  • Look at the build output for what might be wrong.
  • Look for what modules might be missing.
  • Add a line-delimited file for hidden imports and a folder called cloudfn-hooks in the root of your repo, see more at Pyinstaller for how it works. Check out this for how to add hooks.


When things blow up, the first thing to try is to delete the cloudfn cache folder. Things might go a bit haywire when builds are interrupted or other circumstances. It just might save the day! Please get in touch at twitter if you bump into anything: @MartinSahlen


Copyright © 2017 Martin Sahlen

Distributed under the MIT License