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Towards Practical Learned Indexing
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abstract
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.
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Cited by 1 Pith paper
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Evaluating Learned Indexes in LSM-tree Systems: Benchmarks,Insights and Design Choices
A unified benchmark shows learned indexes beat fence pointers on memory-latency tradeoff in LSM-trees, with position boundary and SSTable granularity as the key tuning knobs.
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