SpacKV: A Pmem-Aware Key-Value Separation Store Based on LSM-Tree.

NPC(2022)

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摘要
Key-value (KV) stores based on persistent memories such as Intel Optane Pmem can deliver higher throughput and lower latency, compared to traditional SSD/HDD. Many KV stores adopt LSM-tree as the bone index structure. However, LSM-tree suffers from severe write amplification, which degrades the system's performance and exacerbates the wearout of persistent memory. In this paper, we propose SpacKV, a hybrid DRAM-Pmem KV store, which applies a KV separation scheme and exploits Pmem's device characteristics to achieve high throughput. We design a dedicated value storage structure to maintain localized order of values for efficient range queries and a compaction-triggered garbage collection mechanism to minimize intermediate I/O overhead. Moreover, we leverage Pmem's key features: byte-addressability, access unit of 256 bytes and specific persistence instructions to further mitigate the write amplification effect. The experimental results show that SpacKV achieves 1.4-10.8x, 4.7-9.7x, and 6.7-13.5x in terms of write, read, and range query performance over three state-of-the-art LSM-tree based KV stores: LevelDB-Pmem, RocksDB-Pmem, and MatrixKV, respectively.
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关键词
Persistent memory, LSM-tree, KV separation, Garbage collection, Byte-addressability
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