START Self Tuning Adaptive Radix Tree
structures like the Adaptive Radix Tree (ART) are a central part of in memory database systems. However, we found that radix nodes that index a single byte are not optimal for read heavy workloads. In this work, we introduce START, a self tuning variant of ART that uses nodes spanning multiple keybytes. To determine where to introduce these new node types, we propose a cost model and an optimizer. These components allow us to fine tune an existing ART, reducing its overall height, and improving performance. As a result, START performs on average 85 faster than a regular ART on a wide variety of read only workloads and 45% faster for read mostly workloads.
2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW)