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Design and Implementation of a High-Performance Stream-Oriented Big Data Processing System

机译:面向流的高性能大数据处理系统的设计与实现

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While the real-time processing applications are ubiquitous, which lead to the sprout of many distributed real-time stream processing systems. In this paper, we design a high-performance streamed-oriented big data processing system, including a messaging queue on Kafka and a Spark Streaming-based processing engine, and we evaluate the effectiveness of our system through a real-time traffic statistics application. Then we focus on the relationship between the parameters of Kafka and Spark Streaming and the performance of our system. In the last, we discuss the scalability of the optimized system of Kafka and Spark Streaming.
机译:尽管实时处理应用无处不在,但是这导致了许多分布式实时流处理系统的兴起。在本文中,我们设计了一个高性能的面向流的大数据处理系统,包括Kafka上的消息队列和基于Spark Streaming的处理引擎,并且我们通过实时流量统计应用程序评估了系统的有效性。然后,我们重点研究Kafka和Spark Streaming的参数与系统性能之间的关系。最后,我们讨论了Kafka和Spark Streaming优化系统的可伸缩性。

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