Scalability and State: A Critical Assessment of Throughput Obtainable on Big Data Streaming Frameworks for Applications With and Without State Information

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10659)


Emerging Big Data streaming applications are facing unbounded (infinite) data sets at a scale of millions of events per second. The information captured in a single event, e.g., GPS position information of mobile phone users, loses value (perishes) over time and requires sub-second latency responses. Conventional Cloud-based batch-processing platforms are inadequate to meet these constraints.

Existing streaming engines exhibit low throughput and are thus equally ill-suited for emerging Big Data streaming applications. To validate this claim, we evaluated the Yahoo streaming benchmark and our own real-time trend detector on three state-of-the-art streaming engines: Apache Storm, Apache Flink and Spark Streaming. We adapted the Kieker dynamic profiling framework to gather accurate profiling information on the throughput and CPU utilization exhibited by the two benchmarks on the Google Compute Engine.

To estimate the performance overhead incurred by current streaming engines, we re-implemented our Java-based trend detector as a multi-threaded, shared-memory application in Open image in new window . The achieved throughput of 3.2 million events per second on a stand-alone 2 CPU (44 cores) Intel Xeon E5-2699 v4 server is 44 times higher than the maximum throughput achieved with the Apache Storm version of the trend detector deployed on 30 virtual machines (nodes) in the Cloud. Our experiment suggests vertical scaling as a viable alternative to horizontal scaling, especially if shared state has to be maintained in a streaming application. For reproducibility, we have open-sourced our framework configurations on GitHub [1].


Streaming Engine Spark Streaming Trend Detection Apache Storm Google Compute Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Research supported by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning under grant NRF2015M3C4A7065522.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

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