Chaos testing with F# and Azure

Written by avsm (Anil Madhavapeddy)
Published: 2016-09-17 (last updated: 2016-09-24)

Why Chaos engineering? There are lots of ways to test. If youre building systems that scale, have you tested various things like timeouts that are hard to comprehensively cover. The Volkswagen emissions scandal was detected because the results were too uniform, and not enough chaos! Its super important to test the interactions in your environment. is a NYC based startup that launched in July 2015 and is an ecommerce company that was bought for 3.3 billion purchase by Walmart. Has around 700 microservices and is a heavy user of F# and is not a heavy user of AWS since they are in competition with Amazon. Also a large variety of technologies like Python, Node, R, Python etc and not afraid to find the right tool for the job.

"I have now delivered three business critical applications written in F#. I am still waiting for the first bug to come in"

  • Simon Cousins , UK Power Company

Most loved languages in a recent survey on SO were mostly functional programming languages.

What is Chaos Engineering

Chaos engineering is about breaking things, but in a controlled way to see what will go wrong. "lets try a negative one of something and see what happens". Especially with a distributed system its important to test all the interactions so that it will actually work at scale.

Principles of chaos engineering:

  • Define "normal" behaviour (e.g. a shopping cart)
  • Assume normal will continue in both a control group and an experimental group
  • Introduce chaos -- servers that crash, hard drives that malfunction, network connections that are severed, etc.
  • Look for differences in behaviour between control and experimental groups.


  • self service as you write your own tests
  • design for failure
  • learn from the experiment

Is this just Chaos Monkey? They have a bunch of related products (Janitor Monkey, looks for unused resources). There is an AzMonkey that runs on Azure. They were all too large scale (take down a cluster) rather than a more manageable scale to start with (take down an instance). Dont want to destroy the engineering team with bugs!

Its obviously very very important to not test in production to start with!

The code looks like straightfoward:

|> getHostedService
|> Seq.filter ignoreList
|> knuthShuffle
|> Seq.distinctBy (fun a -> a.ServiceName)
|> (fun hostedService -> async {
   restartRandomInstance compute hostedService
|> Async.ParallelIgnore

ignoreList is to get ability to stop random testing for short period of time. Not encouraged to maintain long term ignore lists but the ability is useful. The map then restarts one service among the list and runs rest in parallel.

Did this help? One good example was ElasticSearch restarting. Search noticed that data wasnt coming back and the pricing team noticed errors. It turns out that ES was down and had been cascading issues out. This was found because Chaos restarted it and it took days for devops to get QA back up and running. If you want to be prepared then test for failure!


Q: How long before this gets to be standard part of engineering culture. A: Senior leadership has bought into it, but it will take some time to really embed.

Q: Are Azure services too stable and so chaos engineering less useful? A: Not quite -- all of the things have bugs!