iKnowFirst: An Efficient DPU-Assisted Compaction for LSM-Tree-Based Key-Value Stores
Jiahong Chen, Shengzhe Wang, Zhihao Zhang, Suzhen Wu, Bo Mao · 2023
In scenarios with write-intensive workloads, LSM-tree-based key-value stores, such as RocksDB, suffer from compaction-induced performance degradation. RocksDB provides configurable compaction options to mitigate the severe read/write amplification problems associated with compaction. The advent of the Data Processing Unit (DPU) allows us to better utilize the configurable options of RocksDB to guide the key-value store system in choosing a suitable compaction strategy with prior knowledge of the workload characteristics. This paper proposes iKnowFirst, an efficient DPU-assisted key-value store. iKnowFirst (1) sets a data buffer on the DPU and separates hot-cold data to relieve the pressure of subsequent LSM-tree compaction, (2) senses the characteristics of the workloads in advance, and dynamically guides RocksDB to choose different compaction modes or enable/disable compaction when the workloads change, to cope with the scenario of write outbreak, and (3) implements an auto-selecting interface for compaction strategies selection. Our prototype implementation and experimental results show that iKnowFirst achieves 3.2× improvement compared to the original RocksDB on write-intensive and highly skewed workloads while showing acceptable performance under read-intensive workloads.