LSM-tree-based key-value stores (KV stores) convert random-write requests to sequence-write ones to achieve high I/O performance. Meanwhile, compaction operations in KV stores update SSTables in forms of reorganizing low-level data components to high-level ones, thereby guaranteeing an orderly data layout in each component. Repeated writes caused by compaction (a.k.a. write amplification) impacts I/O bandwidth and overall system performance. Near-data processing (NDP) is one of the effective approaches to addressing this write-amplification issue. Most NDP-based techniques adopt synchronous parallel schemes to perform a compaction task on both the host and its NDP-enabled device. In synchronous parallel compaction schemes, the execution time of compaction is determined by a subsystem that has lower compaction performance coupled by under-utilized computing resources in a NDP framework. To solve this problem, we propose an asynchronous parallel scheme named PStore to improve the compaction performance in KV stores. In PStore, we designed a multi-tasks queue and three priority-based scheduling methods. PStore elects proper compaction tasks to be offloaded in host- and device-side compaction modules. Our proposed cross-leveled compaction mechanism mitigates write amplification induced by asynchronous compaction. PStore featured with the asynchronous compaction mechanism fully utilizes computing resources in both host- and device-side subsystems. Compared with the two popular synchronous compaction modes based on KV stores (TStore and LevelDB), our PStore immensely improves the throughput by up to a factor of 14 and 10.52 with an average of a factor of 2.09 and 1.73, respectively.

Key-value (KV) stores based on log-structured merge tree (LSM-tree) have been extensively studied and deployed in major information technology infrastructures. Because this type of systems is catered for KV store accessing disks, a limited disk bandwidth increases the difficulty of serving online data requests. One solution involves using a large DRAM such that frequent KV pairs are buffered and accessed from the main memory – and this solution exposes a major design drawback of the KV store: its lack of support for integrated data management in memory and on disks. For example, data in the most popular LSM-tree implementation – RocksDB – may reside in a small write buffer (MemTable) that organizes KV pairs for disk writes, a buffer cache for disk blocks, a write-ahead log on the disk for data persistence, and in various LSM levels on the disk. Without the integrated management of indexes, data, and their persistence in a hierarchical memory/disk architecture, memory is under-utilized along with missed performance optimization opportunities. We propose a KV store, TrieKV, which holistically incorporates DRAM, persistent memory (PMem), and disk with certain desired features: (1) fast in-memory access, (2) accurate identification of hot/cold data at an adaptable granularity, (3) customized memory space allocation for minimized fragmentation, (4) hotness-aware data placement across the storage hierarchy, (5) in-place data persistence in the PMem, and (6) hotness-aware LSM-tree compaction. TrieKV employs a single, integrated trie-structured index for all KV pairs in memory, where access hotness can be consistently discovered. Accordingly, the KV placement is dynamically determined according to the hotness and persistence needs of the storage hierarchy spanning the DRAM, PMem, and solid-state drive. In the experiment, we demonstrate that the 99th latency of RocksDB and NoveLSM is 38x and 6x higher than that of TrieKV, respectively. In addition, TrieKV outperforms RocksDB and NoveLSM by a factor of 5.6 and 1.7in terms of throughput, respectively.