  There is great excitement about learned index structures, but understandable
skepticism about the practicality of a new method uprooting decades of research
on B-Trees. In this paper, we work to remove some of that uncertainty by
demonstrating how a learned index can be integrated in a distributed,
disk-based database system: Google's Bigtable. We detail several design
decisions we made to integrate learned indexes in Bigtable. Our results show
that integrating learned index significantly improves the end-to-end read
latency and throughput for Bigtable.
