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