This is the 2nd post in the Stream Processing focus series. Last week, we had a look at what is stream processing. In this post, I’d like to address one possible use-case for stream processing: the computation of mathematical approximations.

Recently, I stumbled upon a Reddit thread pointing to a repository comparing the performances of implementations of the Sieve of Eratosthenes in different languages. The results were, let’s say, intriguing. The worst surprise came from Kotlin. I couldn’t believe my eyes! Then, I looked at the code.

Last week, a colleague pointed out to my team an online developer recruitment challenge. As I find it fun and there was no need to disclose one’s email, I decided to try, just to check if I could to it. The problem is quite simple but not easy: consider a rectangular maze of finite size. One has to find a specific cell on the board - the exit, starting from the origin. One has 2 move options: one cell at a time in one of the 4 cardinal points or jumping to any previously visited cell.

I recently stumbled upon a post telling about the Josephus problem and trying to solve it in different scripting languages. For the sake of brevity, here’s the problem (taken from the referenced post): Flavius Josephus was a roman historian of Jewish origin. During the Jewish-Roman wars of the first century AD, he was in a cave with fellow soldiers, 40 men in all, surrounded by enemy Roman troops. They decided to commit suicide by standing in a ring and counting off each third man.