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().
In this article I look at several different ways to use Elasticsearch to implement autosuggest.
Most of us make use of some sort of autosuggest functionality several times per day to the point of barely even noticing it. Autosuggest functionality can help a user fill form input fields by prompting them with likely completions and even alternatives to the text they are typing as they type it. In what follows, I will use the the term “autocomplete” in the strict sense of completing what the user has typed so far, and “autosuggest” in the wider sense of suggesting not just completions, but also alternatives that the user may have intended.
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
Rabbits, they are small, the are cute, they are fluffy…. and they are terrible fighters*.
Which is why evolution has favoured the skittish amongst them.
The rabbit which was overly cautious and ran away and the first hint of danger survived and went on to produce more rabbits. But the rabbit which was more laid back and waited until it was sure it was in peril before trying to flee did not. Of course running away does have a cost, and if you spend all your time running at the slightest sound you’ll expend a lot of energy and have no time to nibble at the grass and gain more**.
So what do rabbits have to do with Auto Scaling Groups (ASGs)?
Let’s face it, writing software is hard. And frankly we humans suck at it. We need all the help we can get. Our industry has developed many tools and techniques over the years to provide “safety rails”, from the invention of the macro assembler through to sophisticated integration and automated testing frameworks. But somewhere along the way the idea of static analysis went out of favour.
I’m here to convince you that static analysis tools still have a place in modern software engineering.
As previously discussed we’re pretty keen on micro services at REA. Our delivery teams are organised around small, autonomous “squads” that get to choose pretty much any language and technology stack they wish to implement their solutions.
This inevitably leads to a fairly broad church of language use. 🙂