Big Stream Processing Systems: An Experimental Evaluation
Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE computer society
Abstract
As the world gets more instrumented and connected,
we are witnessing a flood of digital data generated from various
hardware (e.g., sensors) or software in the format of flowing
streams of data. Real-time processing for such massive amounts
of streaming data is a crucial requirement in several application
domains including financial markets, surveillance systems, man ufacturing, smart cities, and scalable monitoring infrastructure.
In the last few years, several big stream processing engines
have been introduced to tackle this challenge. In this article, we
present an extensive experimental study of five popular systems
in this domain, namely, Apache Storm, Apache Flink, Apache
Spark, Kafka Streams and Hazelcast Jet. We report and analyze
the performance characteristics of these systems. In addition,
we report a set of insights and important lessons that we have
learned from conducting our experiments.
Description
Keywords
Big Stream Processing, Benchmarking
Citation
Sakr, S. et al. (2019) “Big Stream Processing Systems: An Experimental Evaluation,” in 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pp. 53–60. .