17 points matt_d 3 hours ago 8 comments
schultzer 1 hour ago | parent
It’s not clear from the paper or their website how it works, the paper seams to talk about an optimizer where the websites states its AI maybe this is just slop. Hard to determine when skimming it, although seams like a neat idea if it’s a proper engine and not just AI that anyone could copy and paste into a chat with the statistics.
pkhuong 1 hour ago | parent
> Approach. QueryBrew builds a refined SQL statement by passing
an input query through Umbra’s [11] state-of-the-art optimizer
and distilling the resulting optimized plan back into SQL
hbirler 46 minutes ago | parent
Hello, paper co-author here.
QueryBrew is based on our research relational database Umbra (https://umbra-db.com/) which has been in development since around 2018.
Our optimizer needs to produce correct plans within milliseconds while considering thousands to millions of alternatives, so using machine learning based approaches is often not a great fit. We instead rely on purpose-built algorithms like query decorrelation (https://15799.courses.cs.cmu.edu/spring2025/papers/11-unnest...) and DP based join ordering (https://dl.acm.org/doi/pdf/10.1145/3183713.3183733).
We have used AI for fuzzing input queries to test the optimizer.
schultzer 14 minutes ago | parent
Thank you for clarifying, sounds a lot better then my initial impression!
remywang 1 hour ago | parent
Very practical approach to “query optimizer as a service”, but I find it cursed that we have decided SQL is the IR for databases