Technical Evolution and Performance Analysis of MapReduce in Modern Distributed Systems

Authors

  • Shailin Saraiya Roku Inc., USA Author

DOI:

https://doi.org/10.32628/CSEIT25111206

Keywords:

MapReduce, Distributed Computing, Big Data Processing, Parallel Computing, Data Analytics

Abstract

MapReduce has emerged as a cornerstone technology in the big data ecosystem, fundamentally transforming how organizations process and analyze massive datasets. This article provides a detailed examination of MapReduce's architecture, exploring its evolution from Google's original implementation to its current role in modern distributed computing systems. This article classifies into the three key phases of MapReduce—Map, Shuffle, Sort, and Reduce—analyzing how each contributes to efficient parallel data processing. This article demonstrates MapReduce's versatility and impact on real-world applications through practical examples from social media analytics, e-commerce, and search engine technology. The discussion encompasses critical implementation aspects, including hardware requirements, software frameworks, and performance optimization strategies, while addressing common challenges and limitations. By examining current applications and future trends, this article serves as a comprehensive guide for understanding how MapReduce continues to power the big data revolution, offering insights for technical practitioners and decision-makers in data-driven organizations.

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References

J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," UV Homepage, 2008. Available: https://courses.cs.washington.edu/courses/cse547/17sp/content/Downloads/p107-dean.pdf

The Apache Software Foundation, "ASF FY2024 Annual Report," Apache Software Foundation, 2024. Available: https://apache.org/foundation/docs/FY2024AnnualReport.pdf

University of California, "Hadoop Distributed File System (HDFS)." Available: https://www.cs.ucr.edu/~eldawy/21SCS167/slides/CS167-03-HDFS.pdf

B. Calder et al., "Windows Azure Storage: A Highly Available Cloud Storage Service with Strong Consistency," Microsoft Research, 2011. Available: https://www.cs.purdue.edu/homes/csjgwang/CloudNativeDB/AzureStorageSOSP11.pdf

Zhenhua Guo and Geoffrey Charles Fox, "Improving MapReduce Performance in Heterogeneous Network Environments and Resource Utilization," ResearchGate, May 2012. Available: https://www.researchgate.net/publication/254038645_Improving_MapReduce_Performance_in_Heterogeneous_Network_Environments_and_Resource_Utilization

D. Borthakur et al., "Apache Hadoop Goes Realtime at Facebook," ResearchGate, June 2011. Available: https://www.researchgate.net/publication/221214019_Apache_Hadoop_goes_realtime_at_Facebook

M. Chowdhury et al., "Managing Data Transfers in Computer Clusters with Orchestra," Mosharaf Chowdhury 2011. Available: https://www.mosharaf.com/wp-content/uploads/orchestra-sigcomm11.pdf

Peter Bajcsy et al., "Terabyte-sized Image Computations on Hadoop Cluster Platforms," NIST. Available: https://isg.nist.gov/deepzoomweb/resources/nist/paper/template_v9.pdf

K. Morton, M. Balazinska, and D. Grossman, "ParaTimer: A Progress Indicator for MapReduce DAGs," UW Homepage. Available: https://homes.cs.washington.edu/~magda/papers/morton-sigmod10.pdf

Seyednima Khezr and Nima Jafari Navimipour, "MapReduce and Its Application in Optimization Algorithms: A Comprehensive Study," ResearchGate, August 2015. Available: https://www.researchgate.net/publication/303286828_MapReduce_and_Its_Application_in_Optimization_Algorithms_A_Comprehensive_Study

C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch, "Heterogeneity and Dynamicity of Clouds at Scale: Google Trace Analysis," ResearchGate, October 2012. Available: https://www.researchgate.net/publication/262326398_Heterogeneity_and_dynamicity_of_clouds_at_scale_Google_trace_analysis

V. K. Vavilapalli et al., "Apache Hadoop YARN: Yet Another Resource Negotiator," Department of Computer Science and Engineering - HKUST, 2013. Available: https://www.cse.ust.hk/~weiwa/teaching/Fall15-COMP6611B/reading_list/YARN.pdf

J. Lin and A. Kolcz, "Large-Scale Machine Learning at Twitter," AI Chat for Scientific PDFs, Twitter, Inc. Available: https://typeset.io/pdf/large-scale-machine-learning-at-twitter-3l667gv2k6.pdf

A. Thusoo et al., "Data Warehousing and Analytics Infrastructure at Facebook," ResearchGate, June 2010. Available: https://www.researchgate.net/publication/221213095_Data_warehousing_and_analytics_infrastructure_at_facebook

G. Ananthanarayanan et al., "Real-time Video Analytics: The Killer App for Edge Computing," IEEE Xplore, 2017. Available: https://ieeexplore.ieee.org/document/8057318

Dhruba Borthakur, "The Hadoop Distributed File System Design," Apache Hadoop Documentation, 2005. Available: https://web.mit.edu/mriap/hadoop/hadoop-0.13.1/docs/hdfs_design.pdf

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Published

03-01-2025

Issue

Section

Research Articles

How to Cite

Technical Evolution and Performance Analysis of MapReduce in Modern Distributed Systems. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 29-35. https://doi.org/10.32628/CSEIT25111206