So I was tuning one of our services in order to speed up some MongoDB queries. Incidentally, my attention was caught by the size of one of the collections that contains archived objects and therefore is rarely used. Unfortunately I wasn’t able to reduce the size of the documents stored there, but I started to wonder: is it possible to store the same data in a more compact way? Mongo stores JSON that allows many different ways of expressing similar data, so there seems to be room for improvements.
Programmers who have ever developed software for Apple platforms in the early days of Swift language might remember ridiculous times it took to compile the whole project. For large and complicated codebase times used to range from 10 up to 40 minutes. Over the years our toolset has improved alongside with compilation times, but slow build times of source code can still be a nightmare.
When we developed our Allegro iOS app adding new features and with more people contributing to the codebase, we noticed that build times began to grow. In order to have precise metrics, we started to track clean build time as well as the amount of code we had. Do these two metrics grow at the same pace?
Marketing is a very important department in every company. In case of Allegro, marketing is especially difficult because you have so many products to promote. In this post we will tell the story of a platform we built for marketing purposes.
We are excited to announce that we have just released BigFlow 1.0 as open source. It’s a Python framework for big data processing on the Google Cloud Platform.