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The performance of worst-case optimal join algorithms depends on the order inwhich the join attributes are processed. Selecting good orders before queryexecution is hard, due to the large space of possible orders and unreliableexecution cost estimates in case of data skew or data correlation. We proposeADOPT, a query engine that combines adaptive query processing with a worst-caseoptimal join algorithm, which uses an order on the join attributes instead of ajoin order on relations. ADOPT divides query execution into episodes in whichdifferent attribute orders are tried. Based on run time feedback on attributeorder performance, ADOPT converges quickly to near-optimal orders. It avoidsredundant work across different orders via a novel data structure, keepingtrack of parts of the join input that have been successfully processed. Itselects attribute orders to try via reinforcement learning, balancing the needfor exploring new orders with the desire to exploit promising orders. Inexperiments with various data sets and queries, it outperforms baselines,including commercial and open-source systems using worst-case optimal joinalgorithms, whenever queries become complex and therefore difficult tooptimize.

ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning