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.

翻译：暂无翻译