Co-KV：一种使用近数据处理改进LSM树压缩的协作键值存储
Co-KV: A Collaborative Key-Value Store Using Near-Data Processing to Improve Compaction for the LSM-tree
摘要：
基于日志结构合并树的键值存储在大规模存储系统中得到了广泛的应用。在键值存储的压缩中，SSTables与重叠的键范围合并，并为数据查询进行排序。然而，这会导致写入放大，从而降低系统性能，特别是在更新密集型工作负载下。目前的优化主要集中在减少主机中压缩的过载，但很少充分利用设备中的计算。为了解决这些问题，我们提出了Co-KV，一种主机和近数据处理之间的协作键值存储（即，NDP）模型的SSD，以提高压缩。Co-KV提供三个好处：（1）通过主机和设备之间的压缩卸载方案来减少写入放大;（2）减轻主机中的压缩的过载并基于NDP模型利用SSD中的计算;以及（3）在更新密集型工作负载下提高基于LSM树的键值存储的性能。大量的db_bench实验表明，Co-KV在很大程度上实现了2.0倍的整体吞吐量提高，并将写入放大率降低了高达36.0%，超过了最先进的LevelDB。在YCSB工作负载下，Co-KV将吞吐量提高了1.7 - 2.4倍，同时将写入放大率和平均延迟分别降低了30.0%和43.0%。
英文摘要：
Log-structured merge tree (LSM-tree) based key-value stores are widely employed in large-scale storage systems. In the compaction of the key-value store, SSTables are merged with overlapping key ranges and sorted for data queries. This, however, incurs write amplification and thus degrades system performance, especially under update-intensive workloads. Current optimization focuses mostly on the reduction of the overload of compaction in the host, but rarely makes full use of computation in the device. To address these issues, we propose Co-KV, a Collaborative Key-Value store between the host and a near-data processing ( i.e., NDP) model based SSD to improve compaction. Co-KV offers three benefits: (1) reducing write amplification by a compaction offloading scheme between host and device; (2) relieving the overload of compaction in the host and leveraging computation in the SSD based on the NDP model; and (3) improving the performance of LSM-tree based key-value stores under update-intensive workloads. Extensive db_bench experiment show that Co-KV largely achieves a 2.0x overall throughput improvement, and a write amplification reduction by up to 36.0% over the state-of-the-art LevelDB. Under YCSB workloads, Co-KV increases the throughput by 1.7x - 2.4x while decreases the write amplification and average latency by up to 30.0% and 43.0%, respectively.
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