Towards Practical Learned Indexing
This work addresses inefficiencies in learned indexing for database systems, offering a more practical solution, though it appears incremental as it builds on existing methods like RadixSpline.
The paper tackles the problem of learned indexes having many hyperparameters, lacking error guarantees, and high build costs by introducing Practical Learned Index (PLEX), which uses a single hyperparameter for maximum prediction error and achieves a better trade-off between build and lookup time than state-of-the-art approaches.
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 $ε$ (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 $ε$ and then performs an ad-hoc analysis of the distribution of spline points to quickly tune the radix layer.