Large language model (LLM) has marked a pivotal moment in the field of
machine learning and deep learning. Recently its capability for query planning
has been investigated, including both single-modal and multi-modal queries.
However, there is no work on the query optimization capability of LLM. As a
critical (or could even be the most important) step that significantly impacts
the execution performance of the query plan, such analysis and attempts should
not be missed. From another aspect, existing query optimizers are usually
rule-based or rule-based + cost-based, i.e., they are dependent on manually
created rules to complete the query plan rewrite/transformation. Given the fact
that modern optimizers include hundreds to thousands of rules, designing a
multi-modal query optimizer following a similar way is significantly
time-consuming since we will have to enumerate as many multi-modal optimization
rules as possible, which has not been well addressed today. In this paper, we
investigate the query optimization ability of LLM and use LLM to design LaPuda,
a novel LLM and Policy based multi-modal query optimizer. Instead of
enumerating specific and detailed rules, LaPuda only needs a few abstract
policies to guide LLM in the optimization, by which much time and human effort
are saved. Furthermore, to prevent LLM from making mistakes or negative
optimization, we borrow the idea of gradient descent and propose a guided cost
descent (GCD) algorithm to perform the optimization, such that the optimization
can be kept in the correct direction. In our evaluation, our methods
consistently outperform the baselines in most cases. For example, the optimized
plans generated by our methods result in 1~3x higher execution speed than those
by the baselines.

本文研究了大型语言模型（LLM）在查询优化方面的能力，并使用 LLM 设计了 LaPuda，一种新颖的基于 LLM 和策略的多模态查询优化器，通过几个抽象策略指导 LLM 进行优化，从而节省了大量时间和人力资源。此外，为了避免 LLM 产生错误的优化，我们借鉴了梯度下降的思想，提出了一种引导成本下降（GCD）算法来执行优化，从而保持优化在正确的方向上。通过评估，我们的方法在大多数情况下都优于基准方法，例如，我们的方法生成的优化计划的执行速度比基准方法快 1~3 倍。