电气设施中利用物联网技术实现实时大数据智能平台的 Hadoop 和 Spark 性能对比分析

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Maratbek T. Gabdullin, Yerulan Suinullayev, Yelikbay Kabi, Jeong Won Kang, Assel Mukasheva
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引用次数: 0

摘要

随着物联网技术在电力系统中的加速应用以及对改进海量数据处理方法的需求,实时大数据智能平台必须满足物联网集成电力系统中日益增长的数据处理需求。因此,在本研究中,我们评估了 Hadoop 和 Spark 在迭代计算和实时数据处理应用中的性能。我们的评估基于执行时间、资源利用率和可扩展性等指标,尤其是在数据量不断增加的情况下。比较旨在为研究人员、从业人员和企业家提供指导,帮助他们根据具体要求选择平台。研究确定了两个平台的优缺点,为优化大数据应用程序的性能提供了宝贵的见解。研究使用文本文档和图表对 Word Count 和 PageRank 任务进行了比较,并在不同规模的数据集上进行了性能测试。结果表明,由于 Spark 使用了内存计算,因此在大多数应用中,特别是在迭代计算和实时数据处理方面,Spark 的性能优于 Hadoop。不过,Hadoop 最适合需要多个步骤的批处理操作。它可以在多个集群节点上并行执行这些操作,从而实现对大量数据的快速处理。Hadoop 和 Spark 在迭代计算和实时数据处理应用中的全面性能比较为研究人员、从业人员和企业提供了使用这些大数据平台的利弊权衡方面的宝贵信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparative Analysis of Hadoop and Spark Performance for Real-time Big Data Smart Platforms Utilizing IoT Technology in Electrical Facilities

Comparative Analysis of Hadoop and Spark Performance for Real-time Big Data Smart Platforms Utilizing IoT Technology in Electrical Facilities

As the adoption of IoT technology in power systems accelerates and the need for improved methods to handle large volumes of data emerges, real-time big data smart platforms must address the growing data processing demands in IoT integrated power systems. Therefore, in this study, we assess the performance of Hadoop and Spark for iterative computing and real-time data processing applications. Our evaluation is based on metrics such as execution time, resource utilization, and scalability, particularly with increasing data volume. The comparison aims to provide guidance to researchers, practitioners and entrepreneurs on platform selection depending on their specific requirements. The study identified the strengths and weaknesses of both platforms and provided valuable insights into optimizing the performance of big data applications. Text documents and charts for Word Count and PageRank tasks were used for comparison, and performance testing was performed on datasets of different sizes. The results showed that Spark outperforms Hadoop in most applications, especially in iterative computation and real-time data processing, due to its use of in-memory computation. However, Hadoop is best suited for batch processing operations that require multiple steps. It can perform these operations in parallel across multiple cluster nodes, enabling fast processing of large amounts of data. This comprehensive performance comparison of Hadoop and Spark in iterative computing and real-time data processing applications provides valuable information for researchers, practitioners, and enterprises on the trade-offs and benefits of using these big data platforms.

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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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