ottertune VS how-query-engines-work

The thing this most makes me think of is Ottertune, that the Carnegie Mellon DB group came up with several years ago for MySQL, https://github.com/cmu-db/ottertune, and eventually commercialised https://ottertune.com/ for MySQL and Postgres.

I keep thinking about this from a JVM perspective, there's so many settings you can tweak that can have such a drastic impact on performance, depending on your workload, that it seems likely there's some value in automated tuning. The main difficult there (from my probably naive perspective) is figuring out the right feedback loop.

Does it work?

Seems it's a now a commercial enterprise that has sprung from a CMU DB group project. An old version's still on Github: https://github.com/cmu-db/ottertune

They also had Peloton, a "self-driving SQL database management system", that's a dead project now.

I highly recommend "How Query Engines Work" by Andy Grove (author of the Apache Datafusion Query Engine + Apache Ballista Distributed scheduler)

It's written in Kotlin and is very intelligible + succinct (99 pages):

https://leanpub.com/how-query-engines-work

https://github.com/andygrove/how-query-engines-work

https://leanpub.com/how-query-engines-work. It covers a fair chunk of DB technology but not storage. Definitely check out their repository at https://github.com/andygrove/how-query-engines-work/tree/mai... .

A companion to DDIA would be https://www.amazon.com/Database-Internals-Deep-Distributed-S... (especially its treatment of LSM trees which is harder to come by).