Learned Index: A Comprehensive Experimental Evaluation

Abstract

Indexes can improve query-processing performance by avoiding full table scans. Although traditional indexes (e.g., B+-tree) have been widely used, learned indexes are proposed to adopt machine learning models to reduce the query latency and index size. However, existing learned indexes are (1) not thoroughly evaluated under the same experimental framework and are (2) not comprehensively compared with different settings (e.g., key lookup, key insert, concurrent operations, bulk loading). Moreover, it is hard to select appropriate learned indexes for practitioners in different settings. To address those problems, this paper detailedly reviews existing learned indexes and discusses the design choices of key components in learned indexes, including key lookup (position inference which predicts the position of a key, and position refinement which re-searches the position if the predicted position is incorrect), key insert, concurrency, and bulk loading. Moreover, we provide a testbed to facilitate the design and test of new learned indexes for researchers. We compare state-of-the-art learned indexes in the same experimental framework, and provide findings to select suitable learned indexes under various practical scenarios.