Practicing zone of proximal development

I was at a LIVE tech conference last week! The keynote was about learning through tinkering. The talk had a chapter that resonated with me quite profoundly: the zone of proximal development. Essentially, it means a student can only learn something "around" a given subject if the topic is familiar to them.

As I've written here a few times, one of the pieces of technology close to my heart is Postgres (and RDMSs in general). I know the basics + some more and can tune some common knobs. Having said that, database internals present a fascinating mystery. I don't understand how they work under the hood - not even close.

I believe that delving into the details and getting hands-on experience can take my understanding to a whole new level. This is the essence of the zone of proximal development - learning by doing and pushing the boundaries of what we already know.

I stumbled upon the Carnegie Mellon university database lecture series in some of the investigatory rabbit holes. It starts from the fundamental algorithms of how most databases work. Again, very cool, but can I get even the gist of them by watching YouTube videos? The answer is a harsh no. Glancing over some videos barely scratches the surface.

That's why I started to supply the graveyard of side projects with a fresh corpse. This time it is a key-value store. Can you think of a more wonderful project? Honestly, there are some pretty interesting problems to solve there, at least if you are really nerdy! 🤓

Why a key-value store if I am interested in Postgres?

The answer is ingrained in the practice of zone of proximal development. Learning about RDS internals is only possible if I know the basics. If you squint your eyes a bit, a relational database is a key-value store. Sure, there is the whole query language thingy, but I need help grasping it. I must know the foundation on which those algorithms are built.

In short, I've implemented a linear hashing KVS with a simplistic implementation of how data is persisted to disk. That means 4K pages, page directory, page buffer, LRU cache, free pages, etc. I also intend to be mindful of excess allocations, handle mostly raw byte arrays, and occasionally benchmark the thingy. All of these are present in Postgres on some level but, of course, in a more sophisticated form.

It is in it's early days but here is the repository

https://github.com/jjylik/aapari

Comments

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