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A COMPARATIVE STUDY OF BIG DATA STREAM PROCESSING FRAMEWORKS: SPARK, STORM, AND FLINK

BROWSE_DETAIL_CREATION_DATE: 22-08-2016

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Alayyoub, Mohammed (Author),

BROWSE_DETAIL_CONTRIBUTERS: Yazıcı, Ali (Advisor),

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Stream Processing, Stream data, Spark, Storm, Flink, Big Data


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This thesis reviews a comparative study of several Big Data stream processing frameworks including Apache Spark, Flink, and Storm. Additionally, this study evaluates these frameworks’ performance under different considerations and scenarios with optimizing each to their ideal potential. Also it measures resource usage and performance scalability of the frameworks within different cluster sizes. The findings from the Chapter “Comparison of Stream Processing Frameworks” indicates that Flink outperforms both Storm and Spark under equal considerations. However, Spark can be optimized to provide higher throughput than Flink with the cost of higher latency.


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