Hark! What is this Jest you speak of?
Think of it as several layers of improvement stuck on top of Jasmine. Some of the neat features Jest provides are:
- Automatically finds tests to run in your project
- Has in built support for fake DOM APIs, such as jsdom, that you can run from the command line
- You can test asynchronous code more easily using inbuilt mocked timer functions
- Tests are run in parallel so they go faster! Vroom vroom.
But the big drawcard is Jest’s automatic mocking of CommonJS dependencies using the require() function. Instead of specifying all the dependencies you want mocked, you do the opposite. For the subject under test, you just use jest.dontMock().
On a Friday a few weeks ago, we deployed a set of minor changes to one of our Rails apps. That evening, our servers started alerting on memory usage (> 95%). Our initial attempts to remedy this situation by reducing the Puma worker count on each EC2 instance didn’t help, and the memory usage remained uncomfortably high through the weekend. On Monday, we popped open NewRelic and had a look in the Ruby VM section. Indeed, both the Ruby heap and memory usage of each web worker process had begun a fairly sharp climb when we deployed on Friday, after being totally flat previously:
However, over the same period of time, the number of objects allocated in each request remained fairly static:
If our requests aren’t creating more objects, but there are more and more objects in memory over time, some of them must be escaping garbage collection somehow. Continue reading
Previously at REA we’d had very special tools for Load and Performance testing that were quite expensive, very richly featured but completely disconnected from our every day development tools. The main outcome of this was that we ended up with a couple of engineers who were quite good at L & P testing with our enterprise tools while the majority of engineers found the barriers too great. We have moved to an approach which is far more inclusive and utilises many of the tools our engineers are working with on a daily basis. I’ll talk about how we did this for the most recent project I worked on.
So, you’re writing microservices! You’re feeling pretty smug, because microservices are all the rage. All the cool kids are doing it. You’re breaking up your sprawling monoliths into small services that Do One Thing Well. You’re even using consumer driven contract testing to ensure that all your services are compatible.
Then… you discover that your consumer’s requirements have changed, and you need to make a change to your provider. You coordinate the consumer and provider codebases so that the contract tests still pass, and then, because you’re Deploying Early and Often, you release the provider. Immediately, your monitoring system goes red – the production consumer still expects the provider’s old interface. You realise that when you make a change, you need to deploy your consumer and your provider together. You think to yourself, screw this microservices thing, if I have to deploy them together anyway, they may as well go in the one codebase.
How I learned to stop worrying and love bash scripting
It’s 3am and PagerDuty is waking you up. You just want to re-deploy an app because that’s the quickest way to get things going again and you like sleep. But wait – it turns out this deploy relies on a version of ruby you don’t have, the bundle won’t install because nokogiri is having problems and you wonder if gardening might have been a more rewarding career.
Bash scripting provides a simple way to get things done and avoids many of the above dramas. However, we hear that bash scripts are ok for really short things that can be tested manually and anything else is better left to a “Real Programming Language™” (whether ruby qualifies is left as an exercise for the reader).
bash-spec-2 to the rescue. With this nifty little testing framework you can TDD your way to a set of comprehensible, documented functions. Continue reading
Please don’t use mocks or stubs in tests. While they are seemingly ubiquitous in enterprise development, they have serious drawbacks, and typically mask easily fixable deficiencies in the underlying code.
Even their most ardent defenders concede that mocks and stubs have flaws. These include:
Dependence on fragile implementation details
Mocks and stubs require intimate knowledge of how code interacts with other modules. Even if the implementation is correctly refactored without altering public contracts, these tests will tend to break, and draw your attention away from more productive tasks.
Testing incidental properties with no bearing on correctness
What is the point of this code? This is an essential question to ask, in order to understand it. Tests have a story to tell here, and mocks invariably tell the wrong one. Is the point of makeCoffee() that we made a coffee, or that we opened the fridge to get the milk? When we payShopkeeper(), do we care that we completed a transaction, or that we rummaged though our wallet for change? When mocking tests fail, the poor maintainer is left to reconstruct the real intent from a trail of indirect clues and anecdotes.
Web of lies
It is good practice to write data structures that are correct-by-construction; any constructor or sequence of method calls is guaranteed to leave the data in a meaningful state. Stubs introduce test-only fictions that are stripped of any of the safety latches and guarantees that may have been built in; they introduce fresh sources of error that are not present in the codebase. There is no value in detecting any failure that arises in such a way.
As time goes on, lies beget more lies. It is not unusual for a stubbed input in one place to result in another here, and another there; the fiction leaks and spreads into some kind of evil facsimile of the original code, but with more bulk, complexity and defects.