RangeKV: An Efficient Key Value Store Based on Hybrid DRAM NVM SSD Storage Structure
Persistent key value (KV) stores are an integral part of storage infrastructure in data centers. Emerging non volatile memory (NVM) technologies are potential alternatives for future memory architecture design. In this study, we use NVM to optimize the KV store and propose RangeKV, an LSM tree based persistent KV store built on a heterogeneous storage architecture. RangeKV uses RangeTab in NVM to manage L0 data and increases L0 capacity to reduce the number of LSM tree levels and system compactions. RangeKV pre constructs the hash index of RangeTab data to reduce NVM access times and adopts a double buffer structure to reduce LSM tree write amplification due to compactions. We implement RangeKV based on RocksDB and conduct a comparative test and performance evaluation with RocksDB and NoveLSM. The test results show that the overall random write throughput is improved by <inline formula> <tex math notation="LaTeX"$4.5\times /tex math>/inline formula> to <inline formula> <tex math notation="LaTeX"$5.7\times /tex math>/inline formula> compared to RocksDB. In addition, RangeKV has a significant performance advantage over NoveLSM.
IEEE Access