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Comparing Distributed Online Stream Processing Systems Considering Fault Tolerance Issues

André Leon Sampaio Gradvohl1, Hermes Senger2, Luciana Arantes3, and Pierre Sens3
1. School of Technology / University of Campinas, Brazil
2. Department of Computer Science / Federal University of São Carlos, Brazil
3. Laboratoire d’Informatique de Paris 6 / Université Pierre et Marie Curie, France
Abstract—This paper presents an analysis of four online stream processing systems (MillWheel, S4, Spark Streaming and Storm) regarding the strategies they use for fault tolerance. We use this sort of system for processing of data streams that can come from different sources such as web sites, sensors, mobile phones or any set of devices that provide real-time high-speed data. Typically, these systems are concerned more with the throughput in data processing than on fault tolerance. However, depending on the type of application, we should consider fault tolerance as an important a feature. The work describes some of the main strategies for fault tolerance – replication components, upstream backup, checkpoint and recovery – and shows how each of the four systems uses these strategies. In the end, the paper discusses the advantages and disadvantages of the combination of the strategies for fault tolerance in these systems. 
Index Terms—fault-tolerance, distributed systems, system applications, online stream processing
Cite: André Leon Sampaio Gradvohl, Hermes Senger, Luciana Arantes, and Pierre Sens, "Comparing Distributed Online Stream Processing Systems Considering Fault Tolerance Issues," Journal of Emerging Technologies in Web Intelligence, Vol. 6, No. 2, pp. 174-179, May 2014. doi:10.4304/jetwi.6.2.174-179
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