  We present FasCo, a simple yet effective learning-based estimator for the
cost of executing a database query plan. FasCo uses significantly shorter
training time and a lower inference cost than the state-of-the-art approaches,
while achieving higher estimation accuracy. The effectiveness of FasCo comes
from embedding abundant explicit execution-plan-based features and
incorporating a novel technique called cardinality calibration. Extensive
experimental results show that FasCo achieves orders of magnitude higher
efficiency 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 two
days to about eight minutes while entailing better accuracy. Furthermore, in
dynamic environments, FasCo can maintain satisfactory accuracy even without
retraining, narrowing the gap between learning-based estimators and real
systems.
