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LinkedIn recently passed the milestone of having conducted one million code reviews. The head of the social networking service’s tooling shared a few learned lessons along the way.
You start with the best of intentions. You hire a developer to build out your startup idea. But almost every week, it feels like the project needs tweaking. Features start creeping in, and the scope slowly expands. It’s as if the project has a life of its own, and is trying to destroy your life.
Until a few months ago, for me streams were no more than an interesting and relatively straightforward concept in the context of messaging. After Kafka popularized the concept, I mostly investigated their usefulness in the case of Disque, a message queue that is now headed to be translated into a Redis 4.2 module. Later I decided that Disque was all about AP messaging, which is, fault tolerance and guarantees of delivery without much efforts from the client, so I decided that the concept of streams was not a good match in that case.
I researched several data types, and I tried to be language agnostic. I wanted my decision to not be influenced by any particular language, and first see if there was a “best way” out there, solely based on operations. Of course, a “best way” rarely exists. However, in the case of text manipulation and storage, there are some clear “worst ways” and “better ways.”
In the early 90s, to write a 3D game engine for PCs largely meant to repurpose the machine. PCs of this era were built to run word processors and spreadsheets, not perform 3D calculations at 70 frames per second. A significant obstacle was the CPU which despite being powerful did not have an hardware floating point unit. Programmers only had an ALU available to crunch integers.