Tuning Performance of Spark Programs
Hong Zhang, Zixia Liu, Liqiang Wang · 2018
Along with the explosive growth of data, there is a great demand to speedup the ability to process them. Although there are several platforms such as Spark that have made analysis easier to developers, the performance tuning for such platforms meanwhile becomes complex. In this paper, we propose an efficient performance optimization engine called Hedgehog to evaluate the performance based on "Law of Diminishing Marginal Utility" and give an optimal configuration setting. The initial experiments show that our optimization can gain 19.6% performance improvement compared to the naive configuration by tuning only 3 parameters.