Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective

TL;DR

This paper introduces a standardized benchmarking framework for learned query optimizers, evaluates existing methods, and finds PostgreSQL often outperforms learned optimizers across various tests.

Contribution

It proposes a comprehensive ML pipeline for evaluating learned query optimizers and provides a rigorous comparison showing PostgreSQL's strong performance.

Findings

PostgreSQL outperforms learned query optimizers in most tests.

Standardized benchmarking framework for LQOs is introduced.

Insights into ML pipeline stages specific to query optimization.

Abstract

The current boom of learned query optimizers (LQO) can be explained not only by the general continuous improvement of deep learning (DL) methods but also by the straightforward formulation of a query optimization problem (QOP) as a machine learning (ML) one. The idea is often to replace dynamic programming approaches, widespread for solving QOP, with more powerful methods such as reinforcement learning. However, such a rapid "game change" in the field of QOP could not pass without consequences - other parts of the ML pipeline, except for predictive model development, have large improvement potential. For instance, different LQOs introduce their own restrictions on training data generation from queries, use an arbitrary train/validation approach, and evaluate on a voluntary split of benchmark queries. In this paper, we attempt to standardize the ML pipeline for evaluating LQOs by introducing a new end-to-end benchmarking framework. Additionally, we guide the reader through each data science stage in the ML pipeline and provide novel insights from the machine learning perspective, considering the specifics of QOP. Finally, we perform a rigorous evaluation of existing LQOs, showing that PostgreSQL outperforms these LQOs in almost all experiments depending on the train/test splits.