  A groundswell of recent work has focused on improving data management systems
with learned components. Specifically, work on learned index structures has
proposed replacing traditional index structures, such as B-trees, with learned
models. Given the decades of research committed to improving index structures,
there is significant skepticism about whether learned indexes actually
outperform state-of-the-art implementations of traditional structures on
real-world data. To answer this question, we propose a new benchmarking
framework that comes with a variety of real-world datasets and baseline
implementations to compare against. We also show preliminary results for
selected index structures, and find that learned models indeed often outperform
state-of-the-art implementations, and are therefore a promising direction for
future research.
