Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
SODA: an optimizing scheduler for large-scale stream-based distributed computer systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Scale-Up Strategies for Processing High-Rate Data Streams in System S
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Proceedings of the 18th ACM conference on Information and knowledge management
A Performance Study of Event Processing Systems
Performance Evaluation and Benchmarking
Scalable performance of system S for extract-transform-load processing
Proceedings of the 3rd Annual Haifa Experimental Systems Conference
Workload characterization for operator-based distributed stream processing applications
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Design principles for developing stream processing applications
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Event Processing in Action
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
ActiveMQ in Action
Scalable splitting of massive data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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Data stream processing systems have become popular due to their effectiveness in applications in large scale data stream processing scenarios. This paper compares and contrasts performance characteristics of three stream processing softwares System S, S4, and Esper. We study about which software aspects shape the characteristics of the workloads handled by these software. We use a micro benchmark and different real world stream applications on System S, S4, and Esper to construct 70 different application scenarios. We use job throughput, CPU, Memory consumption, and network utilization of each application scenario as performance metrics. We observed that S4's architectural aspect which instantiates a Processing Element (PE) for each keyed attribute is less efficient compared to the fixed number of PEs used by System S and Esper. Furthermore, all the Esper benchmarks produced more than 150% increased performance in single node compared to S4 benchmarks. S4 and Esper are more portable compared to System S and could be fine tuned for different application scenarios easily. In future we hope to widen our understanding of performance characteristics of these systems by investigating in to the code level profiling.