RUG::B

Memoization is an optimisation technique that works by caching the results of slow functions. It’s easy to implement, but a production-ready implementation is considerably trickier. And how do you test that your implementation is correct, and useful?

I’ll show you the tricky bits in implementing memoization, where I’ll touch on Ruby metaprogramming and memory management. Lastly, I’ll show how to measure and verify that memoization is meaningful.

References: ddmemoize — ddmetrics

We have some tasks that are trigger every couple of minutes. In case the cluster was slow, we had the problem that new tasks was scheduled before the previous one being done. Making the cluster exponentially slower. So we sit back and think. What can we do to actually prevent this from happening?

Feedback form: https://goo.gl/forms/LbsYT3caeln7UgQ83

Resources, notes, slides: https://github.com/dcarral/ruby-tools

“Tools amplify your talent. The better your tools, and the better you know how to use them, the more productive you can be.” — The Pragmatic Programmer

There are a myriad of open-source (Ruby) tools which can help us in our day-a-day coding activities. Most of them, however, are unknown or rarely used by most developers.

During this session we're gonna walk through some of them, covering interesting configuration options and discussing ideas on how to incorporate them into our development workflow.