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When complex SQL queries suffer slow execution despite query optimization, DBAs typically invoke automated query rewriting tools to recommend "lean" equivalents that are conducive to faster execution. The rewritings are typically achieved through transformation rules; however, these rules are limited in scope and difficult to update in production systems. We investigate here how the remarkable cognitive capabilities of LLMs can be leveraged for performant query rewriting while incorporating safeguards and optimizations to ensure correctness and efficiency. Our study shows that these goals can be progressively achieved by incorporating (a) an ensemble suite of basic prompts, (b) database-sensitive prompts via redundancy removal and selectivity-based rewriting rules, and (c) LLM token probability-guided rewrite paths. Further, a suite of logic-based and statistical tools can be used to check for semantic violations in the rewrites prior to DBA consideration.

We have implemented the above LLM-infused techniques in the LITHE system, and evaluated complex analytic queries from standard benchmarks on contemporary database platforms. The results show significant performance improvements for slow queries, over both SOTA rewriters and the native optimizer. Overall, LITHE is a promising step toward viable LLM-based advisory tools for enhancing enterprise query performance.