PLEX: 走向实际经验指数化 (PLEX: Towards Practical Learned Indexing)

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.

翻译：最新的研究提议用学习模型取代现有的指数结构。但是,目前学习的指数往往有许多超参数,往往不提供任何错误保证,而且建造费用昂贵。我们引入了实际学习指数(PLEX ) 。 PLEX 只有一个超参数 $\ epsilon$( 最大预测错误 ), 并且提供了比最先进的方法更好的构建和查找时间的权衡。与RadixSpline 类似, PLEX 包含一个样条和一个( 多层次)的辐射层。它首先建立一个满足给定的 $\ epsilon$的样条, 然后对样条点的分布进行特别分析, 以快速调节辐射层。