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