  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.
