Hands-on labs: Serverless and OpenFaaS with Python
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Swapping nodeinfo with figlet function from store for the scaling
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Signed-off-by: Martin Dekov (VMware) <mdekov@vmware.com>
Latest commit 495f72e Oct 15, 2018

README.md

openfaas-workshop

This is a self-paced workshop for learning how to build, deploy and run OpenFaaS functions.

Translations

This workshop starts by deploying OpenFaaS to your laptop with Docker for Mac or Windows and then shows how to build, deploy and invoke Serverless functions in Python. Topics will include: managing dependencies with pip, dealing with API tokens through secure secrets, monitoring functions with Prometheus, invoking functions asynchronously and chaining functions together to create applications. We finish by creating a GitHub bot which automatically respond to issues. The same method could be applied by connecting to online event-streams through IFTTT.com - this will enable you to build bots, auto-responders and integrations with social media and IoT devices.

Requirements:

We walk through how to install these requirements in Lab 1, but please do this before you attend an instructor-led workshop.

  • Functions will be written in Python, so prior programming or scripting experience is preferred
  • Install the recommended code-editor / IDE VSCode
  • For Windows install Git Bash
  • Preferred OS: MacOS, Windows 10 Pro/Enterprise, Ubuntu Linux

Docker:

  • Docker CE for Mac/Windows Edge edition
  • Docker CE for Linux

Note: As a last resort if you have an incompatible PC you can run the workshop on https://labs.play-with-docker.com/.

Instructor-led workshops

If you're taking an instructor-led workshops then a link will be shared to join the OpenFaaS Slack community. Use the designated channel for the workshop to discuss comments, questions and suggestions.

Lab 1 - Prepare for OpenFaaS

  • Install pre-requisites
  • Set up a single-node cluster with Docker Swarm
  • Docker Hub account
  • OpenFaaS CLI
  • Deploy OpenFaaS

Lab 2 - Test things out

  • Use the UI Portal
  • Deploy via the Function Store
  • Learn about the CLI
  • Find metrics with Prometheus

Lab 3 - Introduction to Functions

  • Scaffold or generate a new function
  • Build the astronaut-finder function
  • Add dependencies with pip
  • Troubleshooting: find the container's logs
  • Troubleshooting: verbose output with write_debug
  • Use third-party language templates

Lab 4 - Go deeper with functions

Lab 5 - Create a Gitbot

Build issue-bot - an auto-responder for GitHub Issues

  • Get a GitHub account
  • Set up a tunnel with ngrok
  • Create an webhook receiver issue-bot
  • Receive webhooks from GitHub
  • Deploy SentimentAnalysis function
  • Apply labels via the GitHub API
  • Complete the function

Lab 6 - HTML for your functions

  • Generate and return basic HTML from a function
  • Read and return a static HTML file from disk
  • Collaborate with other functions

Lab 7 - Asynchronous Functions

  • Call a function synchronously vs asynchronously
  • View the queue-worker's logs
  • Use an X-Callback-Url with requestbin and ngrok

Lab 8 - Advanced Feature - Timeouts

  • Adjust timeouts with read_timeout
  • Accommodate longer running functions

Lab 9 - Advanced Feature - Auto-scaling

  • See auto-scaling in action
    • Some insights on min and max replicas
    • Discover and visit local Prometheus
    • Execute and Prometheus query
    • Invoke a function using curl
    • Observe auto-scaling kicking in

Lab 10 - Advanced Feature - Secrets

  • Adapt issue-bot to use a secret
    • Create a Swarm secret
    • Access the secret within the function

You can start with the first lab Lab 1.

Tear down / Clear up

You can find how to stop and remove OpenFaaS here

Wrapping up

If you're taking an instructor-led workshop, we'll now take Q&A and cover some advanced topics:

  • Auto-scaling
  • Security
    • TLS / Basic Authentication
  • Object storage
  • Customisations for templates

The appendix contains some additional content.

Acknowledgements

Thanks to @iyovcheva, @BurtonR, @johnmccabe, @laurentgrangeau, @stefanprodan, @kenfdev, @templum & rgee0 for testing and contributing to the labs.