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.

Thank you for clarifying, sounds a lot better then my initial impression!