Proceedings of the VLDB Endowment, Vol. 16, No. 11.
Yunjia Zhang*, Yannis Chronis*, Jignesh M. Patel, Theodoros Rekatsinas*
Carnegie Mellon University
* University of Wisconsin-Madison
There have been many decades of work on optimizing query processing in database management systems. Recently, modern machine learning (ML), and specifically reinforcement learning (RL), has gained increased attention as a means to develop a query optimizer (QO). In this work, we take a closer look at two recent state-of-the-art (SOTA) RL-based QO methods to better understand their behavior.We find that these RL-based methods do not generalize as well as it seems at first glance. Thus, we ask a simple question: How do SOTA RL-based QOs compare to a simple, modern, adaptive query processing approach? To answer this question, we choose two simple adaptive query processing techniques and implemented them in PostgreSQL. The first adapts an individual join operation on-the-fly and switches between a Nested Loop Join algorithm and a Hash Join algorithm to avoid sub-optimal join algorithm decisions. The second is a technique called Lookahead Information Passing (LIP), in which adaptive semijoin techniques are used to make a pipeline of join operations execute efficiently. To our surprise, we find that this simple adaptive query processing approach is not only competitive to the SOTA RL-based approaches but, in some cases, outperforms the RL-based approaches. The adaptive approach is also appealing because it does not require an expensive training step, and it is fully interpretable compared to the RL-based QO approaches. Further, the adaptive method works across complex query constructs that RL-based QO methods currently cannot optimize.
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