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Benchmarking Distributed Stream Data Processing Systems
Citation key KarimovRKSHM2018
Author Jeyhun Karimov, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen, Volker Markl
Title of Book 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16-19, 2018
Pages 1507-1518
Year 2018
ISBN 978-1-5386-5520-7
DOI 10.1109/ICDE.2018.00169
Journal 2018 IEEE 34th International Conference on Data Engineering (ICDE)
Editor IEEE Computer Society
Organization IEEE
Abstract The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to compare the systems for simple workloads, there is a clear gap of detailed analyses of the systems’ performance characteristics. In this paper, we propose a framework for benchmarking distributed stream processing engines. We use our suite to evaluate the performance of three widely used SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our evaluation focuses in particular on measuring the throughput and latency of windowed operations, which are the basic type of operations in stream analytics. For this benchmark, we design workloads based on real-life, industrial use-cases inspired by the online gaming industry. The contribution of our work is threefold. First, we give a definition of latency and throughput for stateful operators. Second, we carefully separate the system under test and driver, in order to correctly represent the open world model of typical stream processing deployments and can, therefore, measure system performance under realistic conditions. Third, we build the first benchmarking framework to define and test the sustainable performance of streaming systems. Our detailed evaluation highlights the individual characteristics and use-cases of each system.
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