Do We Still Need People to Write Database Systems?
Database management systems (DBMSs) are complex software. They are typically worked on by an elite few seasoned in writing performant code that requires strict correctness guarantees. But there is a new trend toward replacing traditional, hand-optimized DBMS components with “learned” components that rely on machine learning (ML). Such learned components include index data structures, query optimizers, and configuration managers. Proponents of ML methods argue that they reduce the engineering overhead of DBMSs.
This talk discusses recent advancements in both human-devised DBMS optimizations and learned DBMS components, and covers both academic research and real-world implementations.
Andy Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. His (unnatural) infatuation with database systems has inadvertently caused him to incur several distinctions, such as VLDB Early Career Award (2021), NSF CAREER (2019), Sloan Fellowship (2018), and the ACM SIGMOD Jim Gray Best Dissertation Award (2014). He is also the CEO and co-founder of the OtterTune database tuning startup (2020).
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