RadixSpline: A Single-Pass Learned Index
This addresses the implementation and build speed bottlenecks for database systems using learned indexes, though it appears incremental as it builds on existing learned index concepts.
The paper tackles the problem of slow build times in learned index structures by introducing RadixSpline, a learned index that can be built in a single pass over data while achieving competitive size and lookup performance with state-of-the-art models like RMI, as demonstrated on the SOSD benchmark.
Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.