In-memory Key-Value (KV) stores are widely deployed in modern data centers. These systems generally use DRAM as their storage medium, causing huge hardware costs. The emerging persistent memory (PMEM) is a potential substitute for DRAM, which has a lower price and larger capacity, but lower access speed and bandwidth. Many prior studies strive to build hybrid memory systems to retain both the advantages of DRAM and PMEM. However, they are either application agnostic or simply take DRAM as a cache, which are both not efficient for in-memory KV stores. In this paper, we propose SwapKV, a well-designed in-memory KV store for hybrid DRAM-PMEM system. SwapKV has several promising properties. First, SwapKV combines DRAM and PMEM to a uniform memory pool and only stores one copy of data, which maximizes capacity utilization. Second, SwapKV maps all writing operations to DRAM and migrates data to PMEM with large blocks asynchronously, which mitigates the intrinsic inefficiency of PMEM for writing operations. Third, SwapKV maintains the hot data in DRAM through an efficient hotness filtering and data swapping mechanism, which ensures high system throughput and responsiveness. We implement SwapKV and evaluate it under various workload patterns. The results demonstrate that SwapKV improves the throughput by 11\%$\sim$41\% compared to the state-of-the-art alternatives.

The Log-Structured Merge Tree (LSM-Tree) is widely used in key-value (KV) stores because of its excwrite performance. But LSM-Tree-based KV stores still have the overhead of write-ahead log and write stall caused by slow L 0 flush and L 0 - L 1 compaction. New byte-addressable, persistent memory (PM) devices bring an opportunity to improve the write performance of LSM-Tree. Previous studies on PM-based LSM-Tree have not fully exploited PM’s “dual role” of main memory and external storage. In this article, we analyze two strategies of memtables based on PM and the reasons write stall problems occur in the first place. Inspired by the analysis result, we propose FlatLSM, a specially designed flat LSM-Tree for non-volatile memory based KV stores. First, we propose PMTable with separated index and data. The PM Log utilizes the Buffer Log to store KVs of size less than 256B. Second, to solve the write stall problem, FlatLSM merges the volatile memtables and the persistent L 0 into large PMTables, which can reduce the depth of LSM-Tree and concentrate I/O bandwidth on L 0 - L 1 compaction. To mitigate write stall caused by flushing large PMTables to SSD, we propose a parallel flush/compaction algorithm based on KV separation. We implemented FlatLSM based on RocksDB and evaluated its performance on Intel’s latest PM device, the Intel Optane DC PMM with the state-of-the-art PM-based LSM-Tree KV stores, FlatLSM improves the throughput 5.2× on random write workload and 2.55× on YCSB-A.