The Six QA Hats

Quality assurance does not merely belong to testers but rather belongs to the whole team, so we need to empower everyone on the team with basic testing mind sets in a way that is memorable and enjoyable. Introducing the “Six QA Hats” – a mind-map with six different branches, each of which represents a dimension of focus on testing. The the “Six QA Hats” could help us effectively brainstorm and organise our next major testing activities. Continue reading

Implementing Autosuggest in Elasticsearch

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.

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Hunting Down Memory Issues in Rails

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:

Heap Size Growing

However, over the same period of time, the number of objects allocated in each request remained fairly static:

Object Allocations Over the Same Period

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

What your AutoScaling Groups can learn from rabbits

Rabbits, they are small, the are cute, they are fluffy…. and they are terrible fighters*.

rabbit-705759_640

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)?

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Static analysis—what’s it good for?

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.

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