Główne pojęcia
LLM can be effectively utilized to design a novel policy-based multi-modal query optimizer, eliminating the need for optimization rules and significantly improving execution speed.
Streszczenie
This content discusses the development of a novel LLM-enabled policy-based multi-modal query optimizer called LaPuda. It explores the use of LLM in query optimization, focusing on operator movement, merge, and removal policies. The paper introduces a two-level guidance strategy, including coarse-level error detection and finer-level cost estimation feedback. Experiments evaluate the performance against baselines using diverse datasets and metrics.
Abstract:
Investigates query optimization with LLM.
Introduces LaPuda as a novel multi-modal query optimizer.
Discusses operator movement, merge, and removal policies.
Presents a two-level guidance strategy for optimization.
Introduction:
Highlights the emergence of large language models in technology.
Explores LLM's potential as a planner for human-language tasks.
Discusses challenges in designing multi-modal query optimizers.
Methodology:
Describes the architecture and workflow of LaPuda.
Explains operator movement, merge, and removal policies.
Details the guided cost descent algorithm for optimization.
Results:
Evaluates performance against baselines using diverse datasets.
Compares execution time, improvement metrics, and valid plan ratio.
Statystyki
"Given the fact that modern optimizers include hundreds to thousands of rules..."
"the optimized plans generated by our methods result in 1∼3x higher execution speed than those by the baselines."
Cytaty
"No more optimization rules: LLM-enabled policy-based multi-modal query optimizer."
"Our experiments indicate that providing examples solely for non-SQL operators suffices for LLM..."