Run Fn Functions on K8s on Google Cloud Platform

Recently, I have been playing a lot with Functions and Project Fn. Eventually, I got to the point where I had to go beyond a playground on my laptop and go to the real wild world. An idea of running Fn on a K8s cluster seemed very attractive to me and I decided to do that somewhere on prem or in the cloud.  After doing some research on how to install and configure K8s cluster on your own on a bare metal I came to a conclusion that I was too lazy for that. So, I went (flew) to the cloud.

In this post I am going to show how to run Fn on Kubernetes cluster hosted on the Google Cloud Platform. Why Google? There are plenty of other cloud providers with the K8s services.
The thing is that Google really has Kubernetes cluster in the cloud which is available for everyone. They give you the service right away without asking to apply for a preview mode access (aka we'll reach out to you once we find you good enough for that), explaining why you need it, checking your background, credit history, etc. So, Google.

Once you got through all formalities and finally have access to the Google Kubernetes Engine, go to the Quickstarts page and follow the instructions to install Google Cloud SDK.

If you don't have kubectl installed on your machine you can install it with gcloud:

gcloud components install kubectl

Follow the instructions on Kubernetes Engine Quickstart to configure gcloud and create a K8s cluster by invoking the following commands:

gcloud container clusters create fncluster
gcloud container clusters get-credentials fncluster

Check the result with kubectl:

kubectl cluster-info

This will give you a list of K8s services in your cluster and their URLs.

Ok, so this is our starting point. We have a new K8s cluster in the cloud on one hand and Fn project on another hand. Let's get them married.

We need to install a tool managing Kubernetes packages (charts). Something similar to apt/yum/dnf/pkg on Linux. The tool is Helm. Since I am a happy Mac user I just did that:

brew install kubernetes-helm

The rest of Helm installation options are available here.

The next step is to install Tiller in the K8s cluster. This is a server part of Helm:

kubectl create serviceaccount --namespace kube-system tiller
kubectl create clusterrolebinding tiller-cluster-rule --clusterrole=cluster-admin --serviceaccount=kube-system:tiller
helm init --service-account tiller --upgrade

If you don't have Fn installed locally, you will want to install it so you have Fn CLI on your machine (on Mac or Linux):  

curl -LSs https://raw.githubusercontent.com/fnproject/cli/master/install > setup.sh
chmod u+x setup.sh
sudo ./setup.sh

Install Fn on K8s cluster with Helm (assuming you do have git client):

git clone git@github.com:fnproject/fn-helm.git && cd fn-helm
helm dep build fn

helm install --name fn-release fn

Wait (a couple of minutes) until Google Kubernetes Engine assigns an external IP to the Fn API in the cluster. Check it with:

kubectl get svc --namespace default -w fn-release-fn-api

Configure your local Fn client with access to Fn running on K8s cluster

export FN_API_URL=http://$(kubectl get svc --namespace default fn-release-fn-api -o jsonpath='{.status.loadBalancer.ingress[0].ip}'):80

Basically, it's done. Let's check it:

  fn apps create adfbuilderapp 
  fn apps list

Now we can build ADF applications with an Fn function as it is described in my previous post. Only this time the function will run and therefore building job will be performed somewhere high in the cloud.


That's it!

Fn Function to build an Oracle ADF application

In one of my previous posts I described how to create a Docker container serving as a builder machine for ADF applications. Here I am going to show how to use this container as a function on Fn platform.

First of all let's update the container so that it meets requirements of a function, meaning that it can be invoked as a runnable binary accepting some arguments. In an empty folder I have created a Dockerfile (just a simple text file with this name) with the following content:

FROM efedorenko/adfbuilder

ENTRYPOINT ["xargs","mvn","package","-DoracleHome=/opt/Oracle_Home","-f"]

This file contains instructions for Docker on how to create a new Docker image out of existing one (efedorenko/adfbuilder from the previous post) and specifies an entry point, so that a container knows what to do once it has been initiated by the Docker run command. In this case whenever we run a container it executes Maven package goal for the pom file with the name fetched from stdin. This is important as Fn platform uses stdin/stdout for functions input/output as a standard approach.

In the same folder let's execute a command to build a new Docker image (fn_adfbuilder) out of our Docker file:

docker build -t efedorenko/fn_adfbuilder .

Now, if we run the container passing pom file name through stdin like this:

echo -n "/opt/MySampleApp/pom.xml" | docker run -i --rm efedorenko/fn_adfbuilder

The container will execute inside itself what we actually need:

mvn package -DoracleHome=/opt/Oracle_Home -f /opt/MySampleApp/pom.xml

Basically, having done that, we got a container acting as a function. It builds an application for the given pom file.

Let's use this function in Fn platform. The installation of Fn on your local machine is as easy as invoking a single command and described on GitHub Fn project page.  Once Fn is installed we can specify Docker registry where we store images of our functions-containers and start Fn server:

export FN_REGISTRY=efedorenko 
fn start

The next step is to create an Fn application which is going to use our awesome function:

fn apps create adfbuilderapp

For this newly created app we have to specify a route to our function-confiner, so that the application knows when and how to invoke it:

fn routes create --memory 1024 --timeout 3600 --type async adfbuilderapp /build efedorenko/fn_adfbuilder:latest

We have created a route saying that whenever /build resource is requested for adfbuilderapp, Fn platform should create a new Docker container basing on the latest version of fn_adfbuilder image from  efedorenko repository and run it granting with 1GB of memory and passing arguments to stdin (the default mode). Furthermore, since the building is a time/resource consuming job, we're going to invoke the function in async mode with an hour timeout.  Having the route created we are able to invoke the function with Fn Cli:

echo -n "/opt/MySampleApp/pom.xml" | fn call adfbuilderapp /build

or over http:

curl -d "/opt/MySampleApp/pom.xml" http://localhost:8080/r/adfbuilderapp/build

In both cases the platform will put the call in a queue (since it is async) and return the call id:

{"call_id":"01C5EJSJC847WK400000000000"}

The function is working now and we can check how it is going in a number of different ways. Since function invocation is just creating and running a Docker container, we can see it by getting a list of all running containers:

docker ps 

CONTAINER ID        IMAGE                               CREATED             STATUS                NAMES

6e69a067b714        efedorenko/fn_adfbuilder:latest     3 seconds ago       Up 2 seconds          01C5EJSJC847WK400000000000

e957cc54b638        fnproject/ui                        21 hours ago        Up 21 hours           clever_turing
68940f3f0136        fnproject/fnserver                  27 hours ago        Up 27 hours           fnserver

Fn has created a new container and used function call id as its name. We can attach our stdin/stdout to the container and see what is happening inside:

docker attach 01C5EJSJC847WK400000000000

Once the function has executed we can use Fn Rest API (or Fn Cli) to request information about the call:

http://localhost:8080/v1/apps/adfbuilderapp/calls/01C5EJSJC847WK400000000000

{"message":"Successfully loaded call","call":{"id":"01C5EJSJC847WK400000000000","status":"success","app_name":"adfbuilderapp","path":"/build","completed_at":"2018-02-03T19:52:33.204Z","created_at":"2018-02-03T19:46:56.071Z","started_at":"2018-02-03T19:46:57.050Z","stats":[{"timestamp":"2018-02-03T19:46:58.189Z","metrics":

....

http://localhost:8080/v1/apps/adfbuilderapp/calls/01C5EJSJC847WK400000000000/log

{"message":"Successfully loaded log","log":{"call_id":"01C5EKA5Y747WK600000000000","log":"[INFO] Scanning for projects...\n[INFO] ------------------------------------------------------------------------\n[INFO] Reactor Build Order:\n[INFO] \n[INFO] Model\n[INFO] ViewController\n[INFO]

....

We can also monitor function calls in a fancy way by using Fn UI dashboard:

The result of our work is a function that builds ADF applications. The beauty of it is that the consumer of the function, the caller, just uses Rest API over http to get the application built and the caller does not care how and where this job will be done. But the caller knows for sure that computing resources will be utilized no longer than it is needed to get the job done.

Next time we'll try to orchestrate the function in Fn Flow.

That's it!