Paper Info
Title
Optimization Factor Analysis Of Large-Scale Join Queries On Different Platforms
Abstract
Popular big data computing platforms, such as Spark, provide new computing paradigm for traditional database operations, such as queries. Except for the management ability of large-scale data, big data platforms earn the reputation for their simple programming interface and good performance of scaling out. But traditional databases have intrinsic optimization mechanisms for fundamental operators, which supports efficient and flexible data processing. It is very valuable to give a comprehensive view of these two kinds of platforms on data processing performance. In this paper, we focus on join operation, a primary and frequently used operator for both databases and big data analysis, design and conduct extensive experiments to test the performance of the two classic platforms under unified datasets and hardware, which will disclose the performance influence on computing schema, storage media, etc. Based on the experimental analysis, we also put forwards our advice on computing platform onsideration for different application scenarios.
Year
DOI
Venue
2017
10.1007/978-3-319-55705-2_3
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2017)
Keywords
Field
DocType
Join query, Large-scale data, Performance analysis
Data processing,Spark (mathematics),Computer science,Sort-merge join,Operator (computer programming),Big data,Schema (psychology),Database,Reputation
Conference
Volume
ISSN
Citations 
10179
0302-9743
0
PageRank 
References 
Authors
0.34
10
6
Name
Order
Citations
PageRank
Chao Yang121146.97
Qian Wang211.02
Qing Yang34825.86
Huibing Zhang446.14
Jingwei Zhang5217.15
Ya Zhou6108.62