  While in-memory learned indexes have shown promising performance as compared
to B+-tree, most widely used databases in real applications still rely on
disk-based operations. Based on our experiments, we observe that directly
applying the existing learned indexes on disk suffers from several drawbacks
and cannot outperform a standard B+-tree in most cases. Therefore, in this work
we make the first attempt to show how the idea of learned index can benefit the
on-disk index by proposing AULID, a fully on-disk updatable learned index that
can achieve state-of-the-art performance across multiple workload types. The
AULID approach combines the benefits from both traditional indexing techniques
and the learned indexes to reduce the I/O cost, the main overhead under disk
setting. Specifically, three aspects are taken into consideration in reducing
I/O costs: (1) reduce the overhead in updating the index structure; (2) induce
shorter paths from root to leaf node; (3) achieve better locality to minimize
the number of block reads required to complete a scan. Five principles are
proposed to guide the design of AULID which shows remarkable performance gains
and meanwhile is easy to implement. Our evaluation shows that AULID has
comparable storage costs to a B+-tree and is much smaller than other learned
indexes, and AULID is up to 2.11x, 8.63x, 1.72x, 5.51x, and 8.02x more
efficient than FITing-tree, PGM, B+-tree, ALEX, and LIPP.
