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Existing learned indexes (e.g., RMI, ALEX, PGM) optimize the internalregressor of each node, not the overall structure such as index height, thesize of each layer, etc. In this paper, we share our recent findings that wecan achieve significantly faster lookup speed by optimizing the structure aswell as internal regressors. Specifically, our approach (called AirIndex)expresses the end-to-end lookup time as a novel objective function, andsearches for optimal design decisions using a purpose-built optimizer. In ourexperiments with state-of-the-art methods, AirIndex achieves 3.3x-7.7x fasterlookup for the data stored on local SSD, and 1.4x-3.0x faster lookup for thedata on Azure Cloud Storage.

Automatically Finding Optimal Index Structure