Standard versus uniform binary search and their variants in learned static indexing: The case of the searching on sorted data benchmarking software platform

Learned Indexes use a model to restrict the search of a sorted table to a smaller interval. Typically, a final binary search is done using the lower_bound routine of the Standard C++ library. Recent studies have shown that on current processors other search approaches (such as k-ary search) can be more efficient in some applications. Using the SOSD learned indexing benchmarking software, we extend these results to show that k-ary search is indeed a better choice when using learned indexes. We highlight how such a choice may be dependent on the computer architecture used, for example, Intel I7 or Apple M1, and provide guidelines for the selection of the Search routine within the learned indexing framework.

Automated summary

Learned Indexes use a model to restrict the search range of a sorted table, and using the SOSD benchmarking software, this study demonstrates that k-ary search is more efficient in certain computer architectures. This research provides guidelines for selecting the search routine within the learned indexing framework.