Advocating for Key-Value Stores with Workload Pattern Aware Dynamic Compaction
Heejin Yoon, Jin Yang, Juyoung Bang, Sam H. Noh, Young-ri Choi · 2024
In real life, the ratio of write and read operations of key-value (KV) store workloads usually changes over time. In this paper, we present a Dynamic wOrkload Pattern Aware LSM-based KV store (DOPA-DB), which supports dynamic compaction strategies depending on the workload pattern. In particular, DOPA-DB is a tiered LSM-based KV store with multiple key ranges, which enables varying compaction sizes. For write-intensive workloads, DOPA-DB can minimize write stalls while minimizing compaction overhead, and for read-intensive workloads, it can aggressively perform compaction to reduce the number of file accesses. Our preliminary experimental results show the potential benefits of dynamic compaction and provide insight into research directions for dynamic compaction strategies.