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We present FasCo, a simple yet effective learning-based estimator for thecost of executing a database query plan. FasCo uses significantly shortertraining time and a lower inference cost than the state-of-the-art approaches,while achieving higher estimation accuracy. The effectiveness of FasCo comesfrom embedding abundant explicit execution-plan-based features andincorporating a novel technique called cardinality calibration. Extensiveexperimental results show that FasCo achieves orders of magnitude higherefficiency than the state-of-the-art methods: on the JOB-M benchmark dataset,it cuts off training plans by 98\%, reducing training time from more than twodays to about eight minutes while entailing better accuracy. Furthermore, indynamic environments, FasCo can maintain satisfactory accuracy even withoutretraining, narrowing the gap between learning-based estimators and realsystems.

Less is More: Towards Lightweight Cost Estimator for Database Systems