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The knobs of modern database management systems have significant impact onthe performance of the systems. With the development of cloud databases, anestimation service for knobs is urgently needed to improve the performance ofdatabase. Unfortunately, few attentions have been paid to estimate theperformance of certain knob configurations. To fill this gap, we propose IWEK,an interpretable & transferable what-if estimator for database knobs. Toachieve interpretable estimation, we propose linear estimator based on therandom forest for database knobs for the explicit and trustable evaluationresults. Due to its interpretability, our estimator capture the directrelationships between knob configuration and its performance, to guarantee thehigh availability of database. We design a two-stage transfer algorithm toleverage historical experiences to efficiently build the knob estimator for newscenarios. Due to its lightweight design, our method can largely reduce theoverhead of collecting training data and could achieve cold start knobestimation for new scenarios. Extensive experiments on YCSB and TPCC show thatour method performs well in interpretable and transferable knob estimation withlimited training data. Further, our method could achieve efficient estimatortransfer with only 10 samples in TPCC and YSCB.

IWEK: An Interpretable What-If Estimator for Database Knobs