Technical Evolution and Performance Analysis of MapReduce in Modern Distributed Systems
DOI:
https://doi.org/10.32628/CSEIT25111206Keywords:
MapReduce, Distributed Computing, Big Data Processing, Parallel Computing, Data AnalyticsAbstract
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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