Benchmarking the Scalability of Distributed Stream Processing Engines in Case of Load Peaks
Year: 2022
Type: dissertation
Abstract: Traditional databases and batch processing systems are not able to handle the loads experienced by many modern applications in real-time. Consequently, in order to process the increasing amounts of data in real-time, Distributed Stream Processing Engines (DSPEs) are used to process real-time loads. Furthermore, increasing data rates make scalability an important quality attribute of a DSPEs. On top, productive environments are encounter fluctuating loads and load peaks. \nIn order to tackle these challenges, we develop a model of load and empirically evaluate the scalability of the DSPEs Kafka Streams and Apache Flink in case of load peaks, in this thesis. For this purpose, we benchmark the impact of different loads. Consequently, we use the Theodolite benchmarking method and extend the Theodolite framework. Moreover, in order to express and compare different loads, we develop a model capable of expressing variable loads over time. Furthermore, we model two different classes of load profiles with load peaks in frequency dimension and describe how to scale these load profiles. Geared towards using these load profiles, we implement a method to generate loads with variable frequency. For the evaluation, we develop a new Service Level Objective (SLO) which can be used to evaluate whether a System Under Tests (SUTs) can process a load profile. Finally, we benchmark Kafka Streams and Flink with three different load profiles and compare the results to similar benchmarks. \nThe results of our benchmarks indicate that load peaks have a significant impact on the scalability of stream processing engines. Subsequently, the handling of load peaks should be considered when deploying applications in cloud environments. Furthermore, our results show that both Kafka Streams and Flink can be considered as scalable in case of load peaks for the evaluated benchmarks. Depending on the task sample, either Kafka Streams or Flink has a higher resource demand. In direct comparison to other benchmarks conducted with Theodolite with similar configurations, we observed deviations in the results. Additionally, the results of our benchmarks indicate that scaling the amount of keys of a load has a greater impact on the scalability as scaling the height of the peak of a load.
Author: Boguhn, Lorenz
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6980189711