Query Performance Explanation through Large Language Model for HTAP Systems
Haibo Xiu, Li Zhang, Tieying Zhang, Jun Yang, Jianjun Chen
TL;DR
This work addresses the difficulty of understanding HTAP query performance differences by combining Retrieval-Augmented Generation with a lightweight plan-embedding retriever. A knowledge base of historical queries, plan embeddings, and expert explanations grounds the LLM's explanations, producing context-aware, cross-engine performance rationales. Experiments show high explanation accuracy and improved user comprehension, validated by a human study, while comparing favorably to methods like DBG-PT. The approach offers a scalable, cost-efficient path to automated, understandable database performance analysis in HTAP environments.
Abstract
In hybrid transactional and analytical processing (HTAP) systems, users often struggle to understand why query plans from one engine (OLAP or OLTP) perform significantly slower than those from another. Although optimizers provide plan details via the EXPLAIN function, these explanations are frequently too technical for non-experts and offer limited insights into performance differences across engines. To address this, we propose a novel framework that leverages large language models (LLMs) to explain query performance in HTAP systems. Built on Retrieval-Augmented Generation (RAG), our framework constructs a knowledge base that stores historical query executions and expert-curated explanations. To enable efficient retrieval of relevant knowledge, query plans are embedded using a lightweight tree-CNN classifier. This augmentation allows the LLM to generate clear, context-aware explanations of performance differences between engines. Our approach demonstrates the potential of LLMs in hybrid engine systems, paving the way for further advancements in database optimization and user support.