? dCompaction: Speeding up Compaction of the LSM-Tree via Delayed Compaction
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (1) :41-54    DOI: 10.1007/s11390-017-1704-4
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dCompaction: Speeding up Compaction of the LSM-Tree via Delayed Compaction
Feng-Feng Pan1,2(潘锋烽), Student Member, CCF, ACM, IEEE, Yin-Liang Yue3(岳银亮), Member, CCF, ACM, IEEE, and Jin Xiong1,2(熊劲), Senior Member, CCF, Member, ACM, IEEE
1 State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

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Abstract Key-value (KV) stores have become a backbone of large-scale applications in today's data centers. Writeoptimized data structures like the Log-Structured Merge-tree (LSM-tree) and their variants are widely used in KV storage systems like BigTable and RocksDB. Conventional LSM-tree organizes KV items into multiple, successively larger components, and uses compaction to push KV items from one smaller component to another adjacent larger component until the KV items reach the largest component. Unfortunately, current compaction scheme incurs significant write amplification due to repeated KV item reads and writes, and then results in poor throughput. We propose a new compaction scheme, delayed compaction (dCompaction) that decreases write amplification. dCompaction postpones some compactions and gathers them into the following compaction. In this way, it avoids KV item reads and writes during compaction, and consequently improves the throughput of LSM-tree based KV stores. We implement dCompaction on RocksDB, and conduct extensive experiments. Validation using YCSB framework shows that compared with RocksDB, dCompaction has about 40% write performance improvements and also comparable read performance.
Articles by authors
Feng-Feng Pan
Yin-Liang Yue
Jin Xiong
Keywordskey-value store   Log-Structured Merge-tree (LSM-tree)   write amplification   delayed compaction     
Received 2016-08-01;
Fund:

This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000202 and the National Natural Science Foundation of China under Grant Nos. 61303056 and 61379042.

About author: Feng-Feng Pan received his B.S. degree in computer science and technology from Central South University, Changsha, in 2010. He is now a Ph.D. candidate in the Institute of Computing Technology, Chinese Academy of Sciences, Beijing. His research interests include big data storage and management, storage systems, and big data analysis.
Cite this article:   
Feng-Feng Pan, Yin-Liang Yue, Jin Xiong.dCompaction: Speeding up Compaction of the LSM-Tree via Delayed Compaction[J]  Journal of Computer Science and Technology, 2017,V32(1): 41-54
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