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The current boom of learned query optimizers (LQO) can be explained not onlyby the general continuous improvement of deep learning (DL) methods but also bythe straightforward formulation of a query optimization problem (QOP) as amachine learning (ML) one. The idea is often to replace dynamic programmingapproaches, widespread for solving QOP, with more powerful methods such asreinforcement learning. However, such a rapid "game change" in the field of QOPcould not pass without consequences - other parts of the ML pipeline, exceptfor predictive model development, have large improvement potential. Forinstance, different LQOs introduce their own restrictions on training datageneration from queries, use an arbitrary train/validation approach, andevaluate on a voluntary split of benchmark queries.  In this paper, we attempt to standardize the ML pipeline for evaluating LQOsby introducing a new end-to-end benchmarking framework. Additionally, we guidethe reader through each data science stage in the ML pipeline and provide novelinsights from the machine learning perspective, considering the specifics ofQOP. Finally, we perform a rigorous evaluation of existing LQOs, showing thatPostgreSQL outperforms these LQOs in almost all experiments depending on thetrain/test splits.

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