Fn Development#

Fn is an open-source framework for creating a Functions-as-a-Service (FaaS) compute platform. In real terms this means we make a HTTP POST call to our Fn server, ask it to call a function which then returns a result.

Getting setup#

To run through this tutorial you must have both Docker and Fn.

For Docker installation instructions please visit the Docker installation instruction.

To install Fn follow the instructions for your operating system given on their GitHub repo.

Starting Fn#

As we’ll be developing functions locally we’ll want to start up an instance of Fn to allow us to run (invoke) the functions we build. During development setting the log level of Fn to DEBUG is highly recommended. This pipes errors that may occur within the containers to the terminal. Without setting the log level the only error reporting shown is a less helpful Error invoking function. status: 502 message: function failed message.

So, open a terminal and run (and leave running)

fn start --log-level DEBUG

If you don’t want extra debug information from each invocation just run

fn start

First function#

To call functions within OpenGHG we use a routing function with Fn. This function (link to route.py) routes calls made to our Fn triggers (more on triggers later). To setup a function we first need to create an app. To do this we use

fn init --runtime python openghg_fn

This will create a folder called openghg_fn with some boilerplate code inside. These can form a template for creation of your own functions. If we look inside this directory we’ll see three files

$ cd openghg_fn/
$ ls
func.py  func.yaml  requirements.txt

Here func.py is the function we’re going to call, func.yaml tells Fn how to setup and run the function and requirements.txt contains the Python requirements and is used when a Docker image is built for the function.

Function code#

First we’ll modify func.py to contain a simple response to being called. Paste the following into func.py:

import io
import json
import logging
from typing import Dict, Optional, Union

from fdk.response import Response

def route(ctx, data: Optional[Union[io.BytesIO, Dict]] = None) -> Response:
    message = {"message": "Hello from OpenGHG"}
    return_str = json.dumps(message)
    headers = {"Content-Type": "application/json"}

    return Response(ctx, response_data=return_str, headers=headers)

Function parameters#

Next we’ll modify func.yaml to contain

schema_version: 20180708
name: openghg_fn
version: 0.0.1
runtime: python
entrypoint: /python/bin/fdk /function/func.py route
# Memory limit for function in MB
memory: 256

Here note the change of handler to route for the entrypoint.

Seeing as we’re just using a bare Fn function here we can leave requirements.txt as it is with our only requirement being fdk. As each function needs to be part of an app we create an app called openghg.

fn create app openghg

We’re now ready to deploy our function and call it.


To deploy the app we can run

fn --verbose deploy --local

This tells Fn give us verbose output and --local tells it not to push our Docker image to DockerHub. After you run this command you’ll see a lot of output as Fn builds a Docker. Hopefully at the end of the output you’ll have something similar to

Successfully built 22ed08c1f99e
Successfully tagged openghg_fn:0.0.2

Updating function openghg_fn using image openghg_fn:0.0.2...
Successfully created function: openghg_fn with openghg_fn:0.0.2

Don’t worry if some values are slightly different here.


You should then be able to invoke / call the function using

[user@computer openghg_fn]$ fn invoke openghg openghg_fn
{"message": "Hello from OpenGHG"}

We can also use curl to trigger the function. If we do

[user@computer openghg_fn]$ fn inspect function openghg openghg_fn
    "annotations": {
        "fnproject.io/fn/invokeEndpoint": "http://localhost:8080/invoke/01ES9D6TA5NG8G00GZJ0000009"
    "app_id": "01ES9D6T23NG8G00GZJ0000008",
    "created_at": "2020-12-11T17:22:35.461Z",
    "id": "01ES9D6TA5NG8G00GZJ0000009",
    "idle_timeout": 30,
    "image": "openghg:0.0.82",
    "memory": 2048,
    "name": "openghg",
    "timeout": 30,
    "updated_at": "2021-06-08T13:26:48.066Z"

We can see that there is an invocation endpoint at http://localhost:8080/invoke/01ES9D6TA5NG8G00GZJ0000009, using curl we can call the function like so

[user@computer openghg_fn]$ curl -X POST http://localhost:8080/invoke/01ES9D6TA5NG8G00GZJ0000009
{"message": "Hello from OpenGHG"}

Note that your invocation endpoint may differ slightly from the one shown above.


As our functions will be more complex than the example given above we need to create our own custom Docker image. To create our own Docker image for the function we’ve created above create a Dockerfile in the openghg_fn folder that contains the following:

FROM fnproject/python:3.8.5

ADD requirements.txt func.py function/
WORKDIR /function

RUN pip3 install pip==20.2.4 wheel setuptools
RUN pip3 install --target /python/ -r requirements.txt
RUN rm -rf requirements.txt


ENTRYPOINT ["/python/bin/fdk", "/function/func.py", "route"]

Here we’ve installed a specific pip version 20.2.4 as this was the last version before the new resolver was introduced.

After creating our Dockerfile we must also update func.yaml to create tell Fn that we’re now using our own customer Docker container

schema_version: 20180708
name: openghg_fn
version: 0.0.4
runtime: docker
- name: route
type: http
source: /openghg_fn

We can then tell Fn to deploy the image again. This will build the container using our custom Dockerfile.

fn --verbose deploy --local

Hopefully at the end of the build you’ll see something like:

Updating function openghg_fn using image openghg_fn:0.0.4...
Successfully created trigger: route
Trigger Endpoint: http://localhost:8080/t/openghg/openghg_fn

We now have a much cleaner endpoint we can use to trigger the function. Using curl again to trigger the function. Note that trigger/invoke/call are all used interchangeably here.

[user@computer openghg_fn]$ curl -X POST http://localhost:8080/t/openghg/openghg_fn
{"message": "Hello from OpenGHG"}

Now we have an understanding of how Fn works and how to create functions and call them we will cover the functions available in OpenGHG.

OpenGHG functions#

With OpenGHG we use a single routing function to route calls to a number of separate functions. This routing function can be found in services/route.py. To use this routing function we first need to setup a Docker container within which we can perform the computation and return the data to the caller. As OpenGHG requires a number of packages we use a two step build process. First we create a base image, called openghg-base which contains all the requirements for OpenGHG. We then use this base image to create a second image, called openghg-complete, into which we copy our OpenGHG library code and services/function code.

Base image#

First we’ll look at the base image which can be found in docker/base_image. This folder contains a Python script that makes building the image easier and a Dockerfile.

FROM fnproject/python:3.8.5-dev as build-stage

ADD requirements-server.txt ./

RUN apt-get update && apt-get install git -y
# pip 20.2.4 is the version before the new resolver was introduced
RUN pip3 install pip==20.2.4 wheel setuptools && \
    pip3 install --target /python/ -r requirements-server.txt

FROM fnproject/python:3.8.5

COPY --from=build-stage /python/ /python/


This Dockerfile is very similar to the one shown above. Some differences are that we copy an extra requirements file into the image and install git to allow pip to install Acquire from GitHub. Another difference is that we use two build stages. The first using the fnproject/python:3.8.5-dev as build-stage. After cloning Acquire and installing all the packages into /python we start with a fresh image and copy only the contents of /python into this image. This helps limit the size of the image.

To build this image run

python build.py

This will build a Docker image with the tag openghg/openghg-base:latest. To see the available options when building the image run the command above with -h.

Complete image#

Now we’ve built the base image we can build the complete image containing the OpenGHG library and services code. The files to build this image can be found in docker/. It contains func.yaml which tells Fn how to run our function. The Dockerfile (shown below) uses the openghg-base image we build in the previous step, adding route.py to the /function folder and then copying the OpenGHG code into /python. We also copy the services code which form the functions that calls are routed to by route.py.

FROM openghg/openghg-base:latest

ADD openghg_services/route.py /function/
WORKDIR /function

# Copy in openghg
ADD openghg /python/openghg
RUN python3 -m compileall /python/openghg/*

# Copy in the service files
RUN mkdir /python/openghg_services
ADD openghg_services /python/openghg_services
RUN python3 -m compileall /python/openghg_services/*.py

# Become the $FN_USER so that nothing runs as root

ENTRYPOINT ["/python/bin/fdk", "/function/route.py", "handle_invocation"]

We have also modified func.yaml to increase the amount of memory available to this function to 2048 MB / 2 GB. If you notice functions failing unexpectedly it may be worth trying changing this value.

schema_version: 20180708
name: openghg
version: 0.0.100
runtime: docker
memory: 2048
- name: route
  type: http
  source: /

To build this image we use the build_deploy.py Python script.

[user@computer docker]$ python build_deploy.py -h
usage: build_deploy.py [-h] [--tag TAG] [--push] [--build] [--deploy] [--build-base]

Build the base Docker image and optionally push to DockerHub

optional arguments:
-h, --help    show this help message and exit
--tag TAG     tag name/number, examples: 1.0 or latest. Not full tag name such as openghg/openghg-complete:latest. Default: latest
--push        push the image to DockerHub
--build       build the docker image. Disables Fn deploy.
--deploy      buid image and deploy the Fn functions
--build-base  build the base docker image before building the complete image

This script takes care of building the base image as well if you want it to. To build both the base and complete image and deploy the functions to Fn run

python build.py --build-base

If you have the base image built and have only made changes to the OpenGHG code you can just run

python build.py

Note - if you’ve made changes to either requirements.txt or requirements-server.txt you’ll need to do a rebuild of the base image. This ensures all dependencies are installed in the base image.

Test a function#

Say we want to test a function such as the testconnection function that is a part of OpenGHG. This simple function returns a simple string of the timestamp at which the function was called to the user.

As above we inspect the funtion and find its endpoint.

[user@computer openghg_fn]$ fn inspect function openghg openghg
    "annotations": {
        "fnproject.io/fn/invokeEndpoint": "http://localhost:8080/invoke/01ES9D6TA5NG8G00GZJ0000009"
    "app_id": "01ES9D6T23NG8G00GZJ0000008",
    "created_at": "2020-12-11T17:22:35.461Z",
    "id": "01ES9D6TA5NG8G00GZJ0000009",
    "idle_timeout": 30,
    "image": "openghg:0.0.82",
    "memory": 2048,
    "name": "openghg",
    "timeout": 30,
    "updated_at": "2021-06-08T13:26:48.066Z"

We can then call the function using

curl -X POST -d '{"function" : "testconnection", "args": {}}' http://localhost:8080/invoke/01ES9D6TA5NG8G00GZJ0000009


Your endpoint URL will differ from the one above.

And we should recieve a response such as

{'results': 'Function run at 2021-06-08 13:34:17.095579+00:00'}

Now we know our Dockerised function can be called and works correctly.

Calling from OpenGHG#

For information on how we’ve setup calling functions from OpenGHG please see the Fn usage section of the documentation.