  HTAP systems are designed to handle transactional and analytical workloads.
Besides a mixed workload at any given time, the workload can also change over
time. A popular kind of continuously changing workload is one that oscillates
between being write-heavy and being read-heavy. These oscillating workloads can
be observed in many applications. Indexes, e.g., the B+-tree and the LSM-Tree
cannot perform equally well all the time. Conventional adaptive indexing does
not solve this issue either as it focuses on adapting in one direction. This
paper investigates how to support oscillating workloads with adaptive indexes
that adapt the underlying index structures in both directions. With the
observation that real-world datasets are skewed, we focus on optimizing the
indexes within the hotspot regions. We encapsulate the adaptation techniques
into the Adaptive Hotspot-Aware Tree adaptive index. We compare the indexes and
discuss the insights of each adaptation technique. Our investigation highlights
the trade-offs of AHA-tree as well as the pros and cons of each design choice.
AHA-tree can behave competitively as compared to an LSM-tree for write-heavy
transactional workloads. Upon switching to a read-heavy analytical workload,
and after some transient adaptation period, AHA-tree can behave as a B+-tree
and can match the B+-trees read performance.
