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In this paper, we show that all mainstream LSM-tree based key-value stores in the literature and in industry are suboptimal with respect to how they trade off among the I/O costs of updates, point lookups, range lookups, as well as the cost of storage, measured as space-amplification. The reason is that they perform expensive merge operations in order to (1) bound the number of runs that a lookup has to probe, and to (2) remove obsolete entries to reclaim space. However, most of these merge operations reduce point lookup cost, long range lookup cost, and space-amplification by a negligible amount.
Dayan et al. (Fri,) studied this question.