CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm

Learned indexes, which use machine learning models to replace traditional index structures, have shown promising results in recent studies. Existing learned indexes use heuristic rules to construct index structures, which are often suboptimal and sensitive to data distribution. In this paper, we argue that upper-level RMI nodes should focus on data partitioning instead of model fitting, and show that it leads to much better results in real-world datasets. We introduce entropy as a metric to quantify and characterize the models in learned indexes, which provides a new theoretical basis for subsequent works. Moreover, we propose a new memory layout design with a fixed node size throughout the tree structure, which allows the type of each node to be flexibly chosen at runtime. We propose CARMI, a new efficient and updatable cache-aware RMI framework. To reduce reliance on the expertise of database administrators, CARMI uses a hybrid construction algorithm to automatically construct the index structures under various datasets and workloads without any manual tuning. Our experimental study shows that CARMI performs better and is more robust compared to baselines, achieving an average of 2.37x/1.98x speedup compared to B+ Tree/ALEX, while using only about 0.70x memory space of B+ Tree. In the SOSD platform, CARMI outperforms all the baselines over the real-world datasets, with an average speedup of 1.21x over the nearest competitor. We believe that our theoretical analysis and proposed framework can help learned indexes to get closer to practical deployment.