Near-Data Processing-Enabled and Time-Aware Compaction Optimization for LSM-tree-based Key-Value Stores

Abstract

With the growing volume of storage systems, the traditional relational databases cannot reach the high performance required by big-data applications. As high-throughput alternatives to relational databases, LSM-tree-based key-value stores (KV stores in short) are confronted with degraded write performance during compaction under update-intensive workloads. To address this issue, we design and implement a time-aware compaction optimization framework for KV stores called TStore. TStore explores the near-data processing (i.e., NDP) model. It dynamically partitions compaction tasks into both host and NDP-enabled device to minimize the total time of compaction. The partitioned compaction tasks are conducted by the host and the device in parallel. The NDP-based devices exhibit low-latency, high-performance and high-bandwidth capability, thus facilitating key-value stores. TStore can not only accomplish compaction for KV stores, but also improve overall performance by removing bottleneck in compaction. Results show that the TStore with an NDP framework can achieve 3.8x and 1.9x performance improvement over LevelDB and Co-KV under the db_bench workload. In addition, the TStore-enabled KV store outperforms LevelDB and Co-KV by a factor of 3.6x and 1.9x in throughput and 72.0% and 48.9% in latency, respectively, under realistic workloads generated by YCSB.