QueryBrew: System-Agnostic SQL-to-SQL Query Optimization [pdf] (vldb.org)
21 pointsby matt_d5 hours ago8 comments
schultzer 3 hours ago
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 3 hours ago
> 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
remywang 3 hours ago
What website are you talking about? This has nothing to do with AI.
schmitob 2 hours ago
schultzer 2 hours ago
I was looking at the wrong thing when I searched, this came up: querybrew dot com.
hbirler 2 hours ago
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 2 hours ago
Thank you for clarifying, sounds a lot better then my initial impression!
remywang 3 hours ago
Very practical approach to “query optimizer as a service”, but I find it cursed that we have decided SQL is the IR for databases