Title: An adaptive memory tuning strategy with high performance for Spark

Authors: Di Chen; Haopeng Chen; Zhipeng Jiang; Yao Zhao

Addresses: School of Software, Shanghai Jiao Tong University, Shanghai, China ' School of Software, Shanghai Jiao Tong University, Shanghai, China ' School of Software, Shanghai Jiao Tong University, Shanghai, China ' School of Software, Shanghai Jiao Tong University, Shanghai, China

Abstract: With the rapid development of internet, people put more and more focus on data, which contains much information and are of great value. To gain better performance in data analysis, in-memory computing has been more and more popular. Spark (Zaharia et al., 2010) is a successful example of improving computing performance through in-memory computing. However, how to make full use of memory resource is still a problem for Spark. In this paper, we presented an adaptive memory tuning strategy for Spark, which enables dynamic data compression and serialisation selecting strategy to use less resource usage and obtain faster data process. We derived the strategy of selecting the optimal data compression and serialisation mathematically. It chooses proper memory tuning strategy according to resource usage and can obtain good performance in applications, which persist data frequently.

Keywords: Spark; in-memory computing; data persisting; data caching; memory tuning.

DOI: 10.1504/IJBDI.2017.086970

International Journal of Big Data Intelligence, 2017 Vol.4 No.4, pp.276 - 286

Received: 14 Apr 2016
Accepted: 02 Nov 2016
Published online: 03 Oct 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article