Are Updatable Learned Indexes Ready?

TL;DR

This paper provides a comprehensive evaluation of updatable learned indexes, comparing their performance, memory efficiency, and robustness against traditional indexes using real datasets and workloads.

Contribution

It is the first extensive study assessing how updatable learned indexes perform in realistic scenarios, guiding future development and deployment.

Findings

Learned indexes outperform traditional ones in memory efficiency.

Performance varies significantly across datasets and workloads.

Robustness of learned indexes depends on data dynamics.

Abstract

Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against the traditional ones under realistic workloads with changing data distributions and concurrency levels. This makes practitioners still wary about how these new indexes would actually behave in practice. To fill this gap, this paper conducts the first comprehensive evaluation on updatable learned indexes. Our evaluation uses ten real datasets and various workloads to challenge learned indexes in three aspects: performance, memory space efficiency and robustness. Based on the results, we give a series of takeaways that can guide the future development and deployment of learned indexes.