When Learned Indexes Meet Persistent Memory: The Analysis and the Optimization

Abstract

The emerging persistent memory (PM) is increasingly being leveraged to construct high-performance and persistent indexes. By exploiting data distribution, recent learned indexes open up a new index design paradigm. Some prior studies try to refit the learned index according to the features of PM. However, they neglect to analyze the performance of existing learned index schemes on PM. In this paper, we provide a comprehensive analysis of learned indexes on PM and propose two optimization methods to improve the performance. In particular, we evaluate ALEX, PGM-index, and XIndex after converting them to persistent indexes. With appropriate modifications, some design choices of volatile learned index still show favorable performance on PM under workloads with simple data distribution. But they perform poorly when the data distribution becomes complex. According to the experiment results, we summarize some instructive insights and optimize persistent learned indexes for complex data distributions with two methods: 1) a cost-based insertion pattern selection to minimize PM writes and 2) recoverable internal nodes selective persistence to decrease the overhead of internal lookups. Our evaluations demonstrate the performance of optimized ALEX is 2.09x/1.53x of the original ALEX in insert/search. Meanwhile, it also outperforms the specific-designed persistent learned index.