  Latest research proposes to replace existing index structures with learned
models. However, current learned indexes tend to have many hyperparameters,
often do not provide any error guarantees, and are expensive to build. We
introduce Practical Learned Index (PLEX). PLEX only has a single hyperparameter
$\epsilon$ (maximum prediction error) and offers a better trade-off between
build and lookup time than state-of-the-art approaches. Similar to RadixSpline,
PLEX consists of a spline and a (multi-level) radix layer. It first builds a
spline satisfying the given $\epsilon$ and then performs an ad-hoc analysis of
the distribution of spline points to quickly tune the radix layer.
