How to start with OpenFaaS
Introduction
Before we get into what is serverless or openfaas, I would like to talk about the concept behind this. So, before going into the details, I want to give you the big picture of this new way of developing applications and how the users are supposed to use them.
Containers are now more efficient than ever
So let’s go back in time, when computers were enormous things and filled complete
rooms. They were very long lived and hard to change. Today, we have got smaller
computers, and especially with the IoT we have lot’s of tiny hardware which
they still fill rooms. Even these tiny things were still “too large” and
“big enough” to manage, so we created the virtual machines, so to slice up these
computers without taking space. We still use these virtual machines and we still are
managing them like normal computers (unless you are Netflix). But these virtual servers
were still slow enough until we realise they exist: I mean they still need about a minute
or so to boot up and shutdown again. So, we still manage them as if they were normal
computers. But then we have got some really really small VMs, and this are containers.
These containers are much much much faster and much much much more efficient. In a matter
of fact, they are so fast and efficient that you can wrap every process inside a container.
See for example RacherOS, where everything runs inside
a docker container. System processes like udev and ntp are running inside docker. So,
really fast and efficient small things. Lately, we’ve got cluster-kind-of-schedulers
(so called orchestrators) for our containers.
That is essential simplified and changed the infrastructure into one big process-list.
It’s a list of processes that are running inside containers. And the infrastructure then
started to look like something like a very big computer. In the same way you can run ps
in your laptop, you can now run ps on your infrastructure and just see everything that
is running. When you see a process running in your laptop, you don’t care in which stick
of RAM it runs. In the same way, you don’t care where your process is running in Kubernetes.
If you start to care less about things, then you start not to observe the existence of
those things.The interesting thing about this that from the developers point of view, is
that the whole infrastructure became a whole lot more simpler.
So this is the first the first thing that changed. The VMs (containers) became so efficient that people are wrapping all the dependencies needed inside them.
A cloud for containers
What is new is how computing is supplied for people who are writing applications and that’s the cloud. As I’ve told you earlier, we indeed are slicing up our computers, but they are still hosted inside a physical computer. In the past that was constrained by the number of computers you had in room and from the space of this room, but that’s not the case anymore. We have this sort of infinite supply of computing which comes sort of out of nowhere. For example, from the POV of the user, Amazon EC2 looks like a limitless supply of machines. Nobody worries about the servers (e.g. where they were), because it didn’t really matter. What about running containers in the cloud? In other words, what about running processes in the cloud? So, in some sense now we combine containers and cloud that computer looks like an enormous computer that we run code on. We don’t really have constraints any more. This is because we have containerization that allows us to wrap up our application inside with all its dependencies and then have it boot up really quickly anywhere, regardless of where it is. So that kind of generally is sort of a trend that is happening, so why am I talking about this …
Actually, Google does this kind of stuff, and they do those stuff for years. So, what changes today is that this technology has started to become available for everyone and lot’s of companies are jumping in.
What is serverless
Serveless doesn’t mean there are no servers. There are servers, they do exist. They are just someone’s else servers who is responsible for managing and scaling them up for you. You just send the code, the code runs and you forget about it.
What do you mean forget about it? Well, there’s an element of a system managing them for you. You see, when I told you earlier there are somebody’s else servers, I never told you that this somebody is a real person. When you deploy a function in openFaaS, Kubernetes takes care of managing these functions: if they are have problems, k8s will fix them for you. If the usage increases, k8s will auto-scale them for you. If there’s a new version, k8s will update them for you. So, we have system doing the hard work for us. In this way, there’s no need to have different people maintaining different services. We just need to have one guy, who is the k8s administrator.
Kubernetes acts like Man-In-The-Middle between the user and the actual service. The service is not online and it’s not listening on any port. It will be activated on demand, and die after it completes its purpose. Because it can boot and shutdown within milliseconds, the actual user doesn’t understand or feel this delay, so he feels like he is accessing an online service. While this service is dead by default. It’s not running, it’s not listening on any socket. That’s why we call it serverless, because although we are talking abouts services and serves, in reality none of them is actually running - they run, only when there’s a request for them. And this can’t be more efficient that that. Another funny name for Serveless would be: Not-Yet-a-Service-as-a-Service.
Benefits of Serveless
Serveless is kind of a way that allows you run code on this internet-size-computer. It’s about treating the internet as one big computer where you can run code on and not having to worry about where it’s been run. The reason that is happening right now is because we had these two things that have been around for a long time: the internet and distributed systems. But the timing now is perfect that these two things are becoming good enough to cooperate. So, serveress is happening because of the intersection of container and cloud and it’s allowing to do all sorts of stuff.
- You don’t have to manage or provision any servers
- The application is automatically scaling
- You pay for what you use (event-based)
Function as a Service
openFaaS is an easy way to put your functions in Kubernetes by packaging functions as docker or OCI containers. The idea is that your write your application as series of functions which are triggered based on various things.
We are actually talking about a new architectural pattern of building systems. The dinosaurs in the room, they should remember a time where people were building monoliths. Heavyweight applications, doing far too many things, slow to deploy, with trouble to test them, release every 6 months etc. Then we broke those down into microservices, delegating the responsibilities, so each component be responsible for less stuff to do. The focus is to be composable and we deploy them usually with docker containers. Today, we are looking at Functions, as the next step of that architectural evolution. Functions do one thing and do it well – kind of similar with the Unix philosophy of things. You can think of a function as small discreet and reusable piece of code that you can deploy once and then forget about.
Functions in Serverless are not a long-running daemon.. I’d get bored that way. I work with webhooks - so stick me in a serverless framework like OpenFaaS and forget about me. Just apply oil from time to time.
Functions or microservices, are not going to replace completely your monoliths, but they can work alongside with them. How? Building integrations. Helping the event flow between ecosystem.
An alternative for AWS Lamda
One popular example of FaaS is Amazon Lambda. The idea with Amazon Lambda is that you can upload your function to Amazon and then you can trigger that piece of code based on a bunch of things that happen in Amazon. So you don’t have to worry about deploying a service, about scaling this thing – it just sorts of runs on cloud. The developer writes his function in a programming language that Amazon supports, he installs all the dependencies on the local file system, bundle this in a zip compressed file and upload it to the AWS cloud. At that point, Amazon will manage all the infrastructure for you, the billing and the lifecycle of the application, and you don’t have to think about your service anymore.
So, what if I don’t want to use Amazon?
The developer of openFaas was learning about AWS Lambda and he wanted to create his own function. But, he had to spend some money, bill his credit card for 12 months and also deal with zip files and stuff. All of these just didn’t felt right, so as docker captain he knew that it has to be a better way of doing this. This project exists since May 2017 and since then it became the top trending project overall on github. They were getting about 700 stars per month and lot of production users getting in touch. Now they have Kubernetes support, Docker Swarm and also asynchronous processing if needed (useful for machine learning).
This project, is really a community project. It’s a very healthy project, over a thousand of commits and over 45 contributors and a ton of forks.
If you:
- I want to be able to use whatever language I want.
- I want to run it on whatever platform I need.
- I want to be limited in billing or five-minute windows.
Then use openFaaS.
How it works
This is a cloud-native stack. Completely written in GoLang, completely open source with MIT license.
The API Gateway: is where you define all of your functions. Each of them has a public route and the users can access them.
The Watchdog is embedded in every container and this is the magic thing that allows the container to become serveless. It does all the work for you.
The Prometheus underpins the whole stack and collects statistics. With these statistics we can build customizable dashboard and and when a certain functions gets a lot of traffic, then it automatically autoscales using the Docker Swarm or Kubernetes API.
How to setup openFaaS in SUSE Containers as a Service Platform
Contact your k8s cluster
I have setup this cluster using CaaSP v2.0
which is important to use a version equal or higher than 1.7. Kubernetes upstream is already at version 1.8, while the CaaSP ships 1.7,
we are still fine going with that. Just make sure you have the kubeconfig at: cp ~/Downloads/kubeconfig ~/.kube/config
or export KUBECONFIG=~/Downloads/kubeconfig
Install kubectl
In this article, I am using openSUSE Tumbleweed as my client machine. So, in order to
install kubectl I have to do the following:
sudo zypper in kubernetes-node
Make sure you can contact your cluster:
$ kubectl cluster-info
Kubernetes master is running at https://d100.qam.suse.de:6443
Dex is running at https://d100.qam.suse.de:6443/api/v1/namespaces/kube-system/services/dex/proxy
KubeDNS is running at https://d100.qam.suse.de:6443/api/v1/namespaces/kube-system/services/kube-dns/proxy
Tiller is running at https://d100.qam.suse.de:6443/api/v1/namespaces/kube-system/services/tiller/proxy
To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.
The cluster doesn’t have any deployments at the moment:
$ kubectl get deployments
No resources found.
Install Helm
If you have bootstrapped your CaaSP v2.0 cluster with tiller then it makes
sense to use Helm. Helm is a tool that streamlines installing and managing
Kubernetes applications. Think of it like apt/yum/homebrew/zypper for Kubernetes.
Helm has two parts: a client (helm) and a server (tiller) Tiller runs inside of your Kubernetes cluster, and manages releases (installations) of your charts. Helm runs on your laptop, CI/CD, or wherever you want it to run. Charts are Helm packages that contain at least two things:
- A description of the package (Chart.yaml)
- One or more templates, which contain Kubernetes manifest files
Charts can be stored on disk, or fetched from remote chart repositories (like Debian or RedHat packages)
Install Helm client: (currently it’s officially supported only in TW)
sudo zypper in helm
More info: Helm Documentation
Clone the repo:
git clone https://github.com/openfaas/faas-netes
cd faas-netes
How to Install it:
$ helm upgrade --install --debug --reset-values --set async=false openfaas openfaas/
or without RBAC:
helm upgrade --install --debug --reset-values --set async=false --set rbac=false openfaas openfaas/
How to delete it (in case you don’t want it anymore):
helm delete --purge openfaas
release "openfaas" deleted
Make sure it works:
See the deployments, there must be 4:
$ kubectl get deploy
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
alertmanager 1 1 1 1 1m
faas-netesd 1 1 1 1 1m
gateway 1 1 1 1 1m
prometheus 1 1 1 1 1m
See the services:
$ kubectl get svc
NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE
alertmanager 172.24.251.90 <none> 9093/TCP 1h
alertmanager-external 172.24.200.36 <nodes> 9093:31113/TCP 1h
faas-netesd 172.24.225.192 <none> 8080/TCP 1h
faas-netesd-external 172.24.132.11 <nodes> 8080:31111/TCP 1h
gateway 172.24.186.132 <none> 8080/TCP 1h
gateway-external 172.24.193.30 <nodes> 8080:31112/TCP 1h
kubernetes 172.24.0.1 <none> 443/TCP 2h
mememachine 172.24.159.94 <none> 8080/TCP 1h
prometheus 172.24.69.16 <none> 9090/TCP 1h
prometheus-external 172.24.212.7 <nodes> 9090:31119/TCP 1h
Create a function from the UI
Go to the OpenFaaS Portal by accessing on of your pods:
# kubectl describe pod gateway-640487255-0l6kv | grep Node:
Node: 452cc28514da4ab3a8c7089a2291be9e.infra.caasp.local/10.161.229.39
# nslookup 10.161.229.39
39.229.161.10.in-addr.arpa name = d295.qam.suse.de.
Perfect, so I know that I can access d295.qam.suse.de. Well, pretty much
I can access any node from my cluster, and in order to prove that to you
I am going to access d100.qam.suse.de instead:
Go to: https://d100.qam.suse.de:31112/ui/
Click at: Create New Function
Image: functions/alpine <--- the docker image: https://hub.docker.com/r/functions/alpine/
Service name: stronghash <--- the name of the function to call
fProcess: sha512sum <--- the binary
Network: func_functions <--- always the same
Test it from the command line:
curl -X POST https://d100.qam.suse.de:31112/function/stronghash -d 'opensuse'
It should return the hash:
410656168586fbe6717f934180e79184b441932ff2ac449af5b89237bb28b754e0491ab9bcd5651f354190fc592b8566caf37edfe6b4ea39ebe3f1210d8535c4 -
Install the command line tool
Run this:
curl -sSL https://cli.openfaas.com | sudo sh
This will download the binary and then it will also move it to /usr/local/bin
Make sure it works:
drpaneas@localhost:~/github/faas-netes> faas-cli list --gateway https://d100.qam.suse.de:31112
Function Invocations Replicas
mememachine
Let me show you how you can call the function from the command-line:
faas-cli invoke stronghash --gateway https://d100.qam.suse.de:31112/
echo "hello world" | faas-cli invoke stronghash --gateway https://d100.qam.suse.de:31112/
curl -X POST https://d100.qam.suse.de:31112/function/stronghash -d 'panos'
Try other people’s functions:
GitHub Repo with functions: FaaS and Furious
Import a function:
faas-cli deploy -f https://raw.githubusercontent.com/faas-and-furious/openfaas-mememachine/master/mememachine.yml -e read_timeout=60 -e write_timeout=60 --gateway https://d100.qam.suse.de:31112
Expected outout would be:
Parsed: https://raw.githubusercontent.com/faas-and-furious/openfaas-mememachine/master/mememachine.yml
Deploying: mememachine.
No existing service to remove
Deployed.
URL: https://192.168.178.122:31112/function/mememachine
202 Accepted
Test it:
echo '{"image": "https://vignette4.wikia.nocookie.net/factpile/images/6/66/Lotr-boromir-1280jpg-
b6a4d5_1280w.jpg","top": "ONE DOES NOT SIMPLY JUST","bottom": "DEPLOY TO PRODUCTION"}' | faas-cli invoke mememachine --gateway https://192.168.178.122:31112/ > meme.jpg
However, if you don’t want to force the users to use faas-cli invoke they can use curl also:
curl --request POST --data-binary '{"image": "https://vignette4.wikia.nocookie.net/factpile/images/6/66/Lotr-boromir-1280jpg-b6a4d5_1280w.jpg","top": "ONE DOES NOT SIMPLY JUST","bottom": "DEPLOY TO PRODUCTION"}' https://d100.qam.suse.de:31112/function/mememachine > meme.jpg
Create your own functions
faas-cli new --lang python3 hello-python
Edit the yml file and use your DockerHub name:
e.g. image: drpaneas/hello-python
You can read the code in the handler.py and include any Python3 module in requirements file.
After that, let’s build: faas-cli build -f hello-python.yml.
Then we push the image to DockerHub: docker push drpaneas/hello-python
and last thing is to deploy it: faas-cli deploy -f hello-python.yml --gateway https://d100.qam.suse.de:31112/
To test if it works:
curl -X POST https://d100.qam.suse.de:31112/function/hello-python -d 'Lunch and Learn'
Dig deeper
One way to start playing with OpenFaaS is to study other’s people functions and code. So in this example, I am going through the youtubedl funtion:
Pull the Docker image: docker pull crosbymichael/youtubedl
The dockerfile of this image can be found at: https://hub.docker.com/r/crosbymichael/youtubedl/~/dockerfile/
FROM crosbymichael/python RUN pip install --upgrade youtube_dl && mkdir /download
WORKDIR /download
ENTRYPOINT ["youtube-dl"]
CMD ["--help"]
So it just uses another layer: crosbymichael/python and it installs the youtube-dl via pip.
It also creates a /download directory and cd into it. Last but not least, when somebody
is going to docker run this container it will automatically trigger the binary youtube-dl.
If no arguments are given, then the --help will be called. In order to pass the downloaded
video into the host machine, we need to mount a volume (the parent directory) to the /download.
This is an example of usage:
localhost:~ # mkdir test
localhost:~ # cd test/
localhost:~/test # docker run -v $(pwd):/download crosbymichael/youtubedl "https://www.youtube.com/watch?v=Nw42q1ofrV0"
[youtube] Confirming age
[youtube] Nw42q1ofrV0: Downloading webpage
[youtube] Nw42q1ofrV0: Downloading video info webpage
[youtube] Nw42q1ofrV0: Extracting video information
WARNING: [youtube] Nw42q1ofrV0: Skipping DASH manifest: u'dashmpd'
[download] Destination: Service Discovery for Docker via DNS-Nw42q1ofrV0.mp4
[download] 100% of 83.34MiB in 00:1528MiB/s ETA 00:005n ETA
localhost:~/test # ls
Service Discovery for Docker via DNS-Nw42q1ofrV0.mp4
So it worked !
Before we move on, I would like to pause for a minute and check the base image: crosbymichael/python
The dockerfile can be found at: https://hub.docker.com/r/crosbymichael/python/~/dockerfile/
FROM debian:jessie
RUN apt-get update && apt-get install -y --no-install-recommends \
git \
libxml2-dev \
python \
build-essential \
make \
gcc \
python-dev \
locales \
python-pip
RUN dpkg-reconfigure locales && \
locale-gen C.UTF-8 && \
/usr/sbin/update-locale LANG=C.UTF-8
ENV LC_ALL C.UTF-8
So, as you can see this just a debian jessie container with some pre-installed packages and some basic
locale configuration. Nothing really special, but good to know.
In order to convert this youtube-dl container into a function, we create the following Dockerfile:
FROM crosbymichael/youtubedl
ENTRYPOINT []
ADD https://github.com/openfaas/faas/releases/download/0.6.7a/fwatchdog /usr/bin
RUN chmod +x /usr/bin/fwatchdog
COPY entry.sh .
RUN chmod +x entry.sh
ENV fprocess="./entry.sh"
CMD ["fwatchdog"]
We are using the crosbymichael/youtubedl simply because it already contains the youtube-dl binary.
Then we pass whatever parameters the user is going to pass: e.g. /dev/stdin
What is standard is the fwatchdog thingy:
ADD https://github.com/alexellis/faas/releases/download/0.5.8-alpha/fwatchdog /usr/bin
RUN chmod +x /usr/bin/fwatchdog
Then we need to run the actual binary youtube-dl. This time we are going to differ a little bit and run it through a script: The entry.sh is the following:
#!/bin/sh
while read line
do
echo "$line"
done < "${1:-/dev/stdin}"
youtube-dl $line --no-warnings --quiet -o -
The script uses the read command which is used to read from the standard input. Usually it is used for user input.
e.g.
echo "What is your name?"
read name
It is reading line by line and the return code of the read command is zero, unless an end-of-file
character is encountered, Used in a while loop it actually reads a file line by line assigning the
value to a called line.
The ${1:-/dev/stdin} is an application of bash parameter expansion that says:
return the value of $1, unless $1 is undefined (no argument was passed) or its value is the empty string (““or ’’ was passed).
Notice: The variation ${1-/dev/stdin} would only return /dev/stdin if $1 is undefined (if it contains any value, even the empty string, it would be returned).
Also To output to stdout use -o -. Which means that the output of youtube-dl will be on … terminal.
So, an example would be:
youtube-dl https://www.youtube.com/watch?v=Nw42q1ofrV0 --no-warnings --quiet -o - > video.mp4
Last but not least, we build the image:
sudo docker build -t drpaneas/faas-youtubedl .
[sudo] password for root:
Sending build context to Docker daemon 79.36 kB
Step 1 : FROM crosbymichael/youtubedl
---> fe8cd02e824c
Step 2 : ENTRYPOINT
---> Using cache
---> 60dd05daf068
Step 3 : ADD https://github.com/openfaas/faas/releases/download/0.6.7a/fwatchdog /usr/bin
Downloading [==================================================>] 4.111 MB/4.111 MB
---> 501c6b53a744
Removing intermediate container 5f3791264b52
Step 4 : RUN chmod +x /usr/bin/fwatchdog
---> Running in debc6877965e
---> d612ca111d3e
Removing intermediate container debc6877965e
Step 5 : COPY entry.sh .
---> c43786aa59ce
Removing intermediate container c23f07b009cc
Step 6 : RUN chmod +x entry.sh
---> Running in 2d7dcfa1e849
---> 49f17f1f383f
Removing intermediate container 2d7dcfa1e849
Step 7 : ENV fprocess "./entry.sh"
---> Running in dc8636221f1c
---> 5bdcd93093e8
Removing intermediate container dc8636221f1c
Step 8 : CMD fwatchdog
---> Running in f844ace7ad7e
---> 7ced4553f05c
Removing intermediate container f844ace7ad7e
Successfully built 7ced4553f05c
Next, it’s time to push that image to dockerhub. Let’s try it:
First authenticate yourself:
sudo docker login -u <drpaneas> -p $PASSWORD
Password:
Login Succeeded
Then push:
sudo docker push drpaneas/faas-youtubedl
The push refers to a repository [docker.io/drpaneas/faas-youtubedl]
58e5092cc396: Pushed
af14a52c65fd: Pushed
e2c247661ac2: Pushed
a2ce316698cd: Pushed
5f70bf18a086: Layer already exists
280da4fe2a80: Layer already exists
151ecc7d9364: Layer already exists
8df1ad35a1bf: Pushed
1646024fc401: Layer already exists
latest: digest: sha256:efa600d5123d4a91d15eec53ea7cc00e7e102ed8c45cb7b9f00095590210c1b4 size: 3234
Now use ‘faas-cli’ to deploy it:
faas-cli deploy \
--gateway https://192.168.178.122:31112 \
--image drpaneas/faas-youtubedl \
--name youtubedl \
--fprocess="sh ./entry.sh"
No existing service to remove
Deployed.
URL: https://192.168.178.122:31112/function/youtubedl
202 Accepted
Test it:curl https://192.168.178.122:31112/function/youtubedl -d "https://www.youtube.com/watch?v=nG2rNBFzkGE" > cat_jump.mov
From an idea to a function
Idea: We want to pass an image and resize it by 50%
ImageMagick permits image data to be read and written from the standard streams
STDIN (standard in) and STDOUT (standard out), respectively, using a pseudo-filename of -
example: cat input.jpg | convert - -resize "50%" output.jpg
Other pipes can be accessed via their file descriptors (as of version 6.4.9-3).
The file descriptors 0, 1, and 2 are reserved for the standard streams STDIN, STDOUT,
and STDERR, respectively, but a pipe associated with a file descriptor number N>2 can be accessed
using the pseudonym fd:N. (The pseudonyms fd:0 and fd:1 can be used for STDIN and STDOUT).
example: cat input.jpg | convert - -resize "50%" fd:1 > output.jpg
As a result, the fprocess will be:
fprocess="convert - -resize 50% fd:1"
We just need an image with ‘imagemagick’ pkg installed. Then I will add the ‘watchdog’.
mkdir imagemagick && cd imagemagick
vi Dockerfile
FROM opensuse:latest
ADD https://github.com/openfaas/faas/releases/download/0.6.7a/fwatchdog /usr/bin
RUN chmod +x /usr/bin/fwatchdog \
&& zypper -n in -y -l ImageMagick
ENV fprocess="convert - -resize 50% fd:1"
HEALTHCHECK --interval=5s CMD [ -e /tmp/.lock ] || exit 1
CMD ["fwatchdog"]
Build it: sudo docker build -t drpaneas/resize .
Push: sudo docker push drpaneas/resize
Convert: faas-cli deploy --gateway https://192.168.178.122:31112 --image drpaneas/resize --name resize --fprocess="convert - -resize 50% fd:1"
Call it: curl https://192.168.178.122:31112/function/resize --data-binary @meme.jpg > smaller.png
Call it alternative: cat whale.jpg | faas-cli invoke shrink-image > whale-small.jpg
Build a generic image
Every single function I’ve encountered upon in the repositories, it’s consisted of its own Docker image, usually hosted in DockerHub. So, is this a problem? Well, no … and yes … I guess it doesn’t feel right to create so much overlap. Especially when you are a lazy guy - like me - then you start to look for base patterns which you could possibly re-iterate. Then, not exactly out of the sudden, I end up with the fellowship of the tabs.
But before changing things, you must first understand them.
You see, I am not a developer. Truth is I speak a little bit of C++ and Python, but I am not close to anything like a native speaker. And this is a good thing. Maybe. So, how a non-developer guy write his own functions? Easy: He doesn’t, because he doesn’t have to. You see, in our age, there’s always Someone Out There (TM), who has possibly think of what you’re thinking. Fortunately, this Someone or else, has already implemented it.
OpenFaaS works also with binaries. So, I thought … let’s create a functions based on these utilities or combinations of those. Well, someone could ask: why you should do such a thing when you already have this functionality in your PC?
So, pick up a distro:
FROM alpine:latest
Next, include the watchdog:
ADD https://github.com/openfaas/faas/releases/download/0.6.5/fwatchdog /usr/bin
RUN chmod +x /usr/bin/fwatchdog
Optionally, include a healthcheck:
HEALTHCHECK --interval=5s CMD [ -e /tmp/.lock ] || exit 1
And run watchdog:
CMD ["fwatchdog"]
That’s it. That’s my basic image.
Now, for every utility I need, can just call this one and add my function, or to be precise: my fprocess:
FROM functions/alpine:latest
ENV fprocess "/bin/cat"
Build them all: faas-cli build -f samples.yml --parallel 4
[https://github.com/openfaas/faas/tree/master/sample-functions](Sample Functions)