DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger

In this paper we explore the ability of Large Language Models (LLMs) in analyzing and comparing query plans, and resolving query performance regressions. We present DBG-PT, a query regression debugging framework powered by LLMs. DBG-PT keeps track of query execution instances, and detects slowdowns according to a user-defined regression factor. Once a regression is detected, DBG-PT leverages the capabilities of the underlying LLM in order to compare the regressed plan with a previously effective one, and comes up with tuning knob configurations in order to alleviate the regression. By exploiting textual information of the executed query plans, DBG-PT is able to integrate with close-to-zero implementation effort with any database system that supports the EXPLAIN clause. During the demonstration, we will showcase DBG-PT's ability to resolve query regressions using several real-world inspired scenarios, including plan changes because of index creations/deletions, or configuration changes. Furthermore, users will be able to experiment using ad-hoc, or predefined queries from the Join Order Benchmark (JOB) and TPC-H, and over MySQL and Postgres.