Oct 15, 2016

Databases as a log

I tweeted this a few days ago and I’ve been thinking more about it.

A database is a set of information. You can add, modify, or remove information from that set. All of these types of changes can be expressed as instructions. What happens when you put these instructions together? You get a log.

Starting with an append-only, immutable log as a database makes you think about all kinds of stuff differently. When I started working on lm2, I had a fancy (but still relatively simple) design with block allocation, MVCC, free lists and garbage collection, and the possibility of adding compression.

It was complicated. It was hard to think about. It looked like a mountain of work and I didn’t want to keep hacking away on something so complicated. At one point I just threw out everything, got rid of my notes, and started with something super simple. Now it’s just a log with a small linked list read optimization and some caching.

The fact that it’s so minimal means it doesn’t restrict much you in terms of what you can do. Records are never deleted. Ever. Of course, that means you’ll use more disk space, but that can be solved at a higher level. The upside is that you can have snapshot reads forever. That’s a pretty cool feature, in my opinion.

I’ve been thinking lately that it’s probably best to push read optimizations up the stack as much as possible. That’s the approach RDS Aurora took with their storage engine, and they can do some amazing things. I’m pushing for this kind of approach at work too. Our database is a log. MySQL is a read optimization.

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