Improving Read Performance of LSM-Tree Based KV Stores via Dual Grained Caches
- Resource Type
- Conference
- Authors
- Li, Xiang; Xu, Guangping; Fan, Hao; Lu, Hongli; Tang, Bo; Xue, Yanbing; Zong, Ziliang
- Source
- 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2019 IEEE 21st International Conference on. :841-848 Aug, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Compaction
Throughput
Conferences
Indexes
Computer science
Benchmark testing
Distributed databases
KV Stores
LSM trees
Caches
Performance Evaluation
- Language
Key-value (KV) stores based on the Log-Structure Merge tree (LSM-tree) have been widely used in modern storage systems (e.g. LevelDB and RocksDB) to achieve high write throughput. However, conventional LSM-tree design has high latency for random read operations, especially in the workloads with concurrent read and write operations. This paper proposes a dual-grained cache scheme to improve the performance of LSM-tree based KV stores, in which a coarse-grained cache improves read performance by leveraging spatial locality and a fine-grained cache tracks the frequently accessed KV pairs. We implement the dual-grained caching on a LSM-tree simulator and evaluate its performance using various Yahoo! Cloud Service Benchmark (YCSB) workloads. Our experimental results show that the proposed way of dual-grained caches can significantly improve the read performance of KV stores without sacrificing the advantages of the LSM-tree for write-intensive workloads.