Recent advances in machine learning based data structures, such as learned indexes, show that models can be trained to approximate the cumulative distribution of keys, thereby predicting key positions more efficiently than traditional indexes. While research prototypes have shown significant promise, so far there has been no comprehensive integration of learned indexes into a relational database system. In this paper, we explore the feasibility of learned indexes for relational systems by introducing PostLearn, an integration of the ALEX+ learned index as a native PostgreSQL index access method. We detail the design and the implementation challenges of embedding a model-based index within PostgreSQL's existing infrastructure and explore its potential performance benefits. While PostLearn can achieve up to 1.5x speedups for point lookups and small range scans compared to the built-in B+-Tree under selected workloads, end-to-end performance benefits are considerably lower than when tested in isolation.
Fuad et al. (Mon,) studied this question.