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Query optimizers are a performance-critical component in every databasesystem. Due to their complexity, optimizers take experts months to write andyears to refine. In this work, we demonstrate for the first time that learningto optimize queries without learning from an expert optimizer is both possibleand efficient. We present Balsa, a query optimizer built by deep reinforcementlearning. Balsa first learns basic knowledge from a simple,environment-agnostic simulator, followed by safe learning in real execution. Onthe Join Order Benchmark, Balsa matches the performance of two expert queryoptimizers, both open-source and commercial, with two hours of learning, andoutperforms them by up to 2.8$\times$ in workload runtime after a few morehours. Balsa thus opens the possibility of automatically learning to optimizein future compute environments where expert-designed optimizers do not exist.

Balsa: Learning a Query Optimizer Without Expert Demonstrations