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	An Adaptive Skew Handling Join Algorithm for Large-scale Data Analysis
	Wu, Di ; Wang, Tengjiao ; Chen, Yuxin ; Li, Shun ; Li, Hongyan ; Lei, Kai
	2015
关键词	Skew handling join Adaptive Partitioning strategy
英文摘要	Join plays an essential role in large-scale data analysis, but the performance is severely degraded by data skew. Existing works can't adaptively handle data skew very well and reduce communication cost simultaneously. To address these problems, we firstly propose a mixed data structure comprising Bloom Filter and Histogram(BFH). Based on BFH, Bloom Filter and Histogram Join(BFHJ) is proposed to handle data skew adaptively. BFHJ can reduce communication cost by filtering unnecessary records. Furthermore, BFHJ adopts a heuristic partitioning strategies to balance workload. Experiments on TPC-H demonstrate that BFHJ outperforms the state-of-the-art methods in terms of communication cost, load balance and query time.; EI; CPCI-S(ISTP); wudi@sz.pku.edu.cn; tjwang@pku.edu.cn; chen.yuxin@pku.edu.cn; 433-437; 9098
语种	英语
出处	WEB-AGE INFORMATION MANAGEMENT (WAIM 2015)
DOI标识	10.1007/978-3-319-21042-1_35
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423544]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wu, Di,Wang, Tengjiao,Chen, Yuxin,et al. An Adaptive Skew Handling Join Algorithm for Large-scale Data Analysis. 2015-01-01.

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