Query Performance Explanation through Large Language Model for HTAP Systems
This addresses a domain-specific problem for users of HTAP systems by providing more accessible query performance explanations, though it is incremental as it builds on existing LLM and RAG techniques.
The paper tackles the problem of users struggling to understand performance differences between OLAP and OLTP query plans in HTAP systems, proposing a framework that uses LLMs with RAG to generate clear explanations, demonstrating potential for database optimization.
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.