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An experimental analysis of limitations of MapReduce for iterative algorithms on Spark

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Abstract

MapReduce is the most popular framework for distributed processing. Recently, the scalability of data mining and machine learning algorithms has significantly improved with help from MapReduce. However, MapReduce does not handle iterative algorithms very efficiently. The problem is that many data mining and machine learning algorithms are iterative by nature. In order to overcome the limitations of MapReduce, many advanced distributed systems have been developed, including HaLoop, iMapReduce, Twister, and Spark. In this paper, we identify and categorize the limitations of MapReduce in handling iterative algorithms, and then, experimentally investigate the consequences of these limitations by using the most flexible and stable distributed system, Spark. According to our experiment results, the network I/O overhead was the primary factor that affected system performance the most. The disk I/O overhead also affected system performance, but it was less significant than the network I/O overhead. For the synchronization overhead, it affected system performance only when the static data was not cached.

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Acknowledgements

This research, “Geospatial Big Data Management, Analysis and Service Platform Technology Development”, was supported by the MOLIT(The Ministry of Land, Infrastructure and Transport), Korea, under the national spatial information research program supervised by the KAIA(Korea Agency for Infrastructure Technology Advancement)”(17NSIP-B081011-04). In addition, this project was supported by Microsoft Research through “Azure for Research” global RFP program.

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  1. Graduate School of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea

    Minseo Kang & Jae-Gil Lee

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  1. Minseo Kang
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  2. Jae-Gil Lee
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Correspondence to Jae-Gil Lee.

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This paper is a revised and expanded version of a paper entitled ‘A Comparative Analysis of Iterative MapReduce Systems’ The paper proudly received the Runner-Up Paper Award. Presented at the 6th International Conference on Emerging Databases: Technologies, Applications, and Theory (EDB 2016), 17–19 October 2016, Jeju Island, Korea.

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Kang, M., Lee, JG. An experimental analysis of limitations of MapReduce for iterative algorithms on Spark. Cluster Comput 20, 3593–3604 (2017). https://doi.org/10.1007/s10586-017-1167-y

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