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Query optimization is a pivotal part of every database management system(DBMS) since it determines the efficiency of query execution. Numerous workshave introduced Machine Learning (ML) techniques to cost modeling, cardinalityestimation, and end-to-end learned optimizer, but few of them are provenpractical due to long training time, lack of interpretability, and integrationcost. A recent study provides a practical method to optimize queries byrecommending per-query hints but it suffers from two inherited problems. First,it follows the regression framework to predict the absolute latency of eachquery plan, which is very challenging because the latencies of query plans fora certain query may span multiple orders of magnitude. Second, it requirestraining a model for each dataset, which restricts the application of thetrained models in practice. In this paper, we propose COOOL to predict CostOrders of query plans to cOOperate with DBMS by Learning-To-Rank. Instead ofestimating absolute costs, COOOL uses ranking-based approaches to computerelative ranking scores of the costs of query plans. We show that COOOL istheoretically valid to distinguish query plans with different latencies. Weimplement COOOL on PostgreSQL, and extensive experiments onjoin-order-benchmark and TPC-H data demonstrate that COOOL outperformsPostgreSQL and state-of-the-art methods on single-dataset tasks as well as aunified model for multiple-dataset tasks. Our experiments also shed some lighton why COOOL outperforms regression approaches from the representation learningperspective, which may guide future research.

COOOL: A Learning-To-Rank Approach for SQL Hint Recommendations