Abstract
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.
Published Version
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