  The recent proposal of learned index structures opens up a new perspective on
how traditional range indexes can be optimized. However, the current learned
indexes assume the data distribution is relatively static and the access
pattern is uniform, while real-world scenarios consist of skew query
distribution and evolving data. In this paper, we demonstrate that the missing
consideration of access patterns and dynamic data distribution notably hinders
the applicability of learned indexes. To this end, we propose solutions for
learned indexes for dynamic workloads (called Doraemon). To improve the latency
for skew queries, Doraemon augments the training data with access frequencies.
To address the slow model re-training when data distribution shifts, Doraemon
caches the previously-trained models and incrementally fine-tunes them for
similar access patterns and data distribution. Our preliminary result shows
that, Doraemon improves the query latency by 45.1% and reduces the model
re-training time to 1/20.
