Abstract
Big data applications tend to be memory intensive, and many of them are written in memory managed languages like Java/Scala. The efficiency of the garbage collector (GC) plays an important role in the performance of these applications. In our paper, we perform a comparative analysis of Java garbage collectors for three commonly used big data workloads to check the choice of the garbage collector for each of the workloads. The garbage collectors under scrutiny are garbage first, parallel and ConcurrentMarkSweep. We demonstrate (a) the relative difference between existing Java workloads that are used to study garbage collectors and big data workloads and (b) the selection of the right garbage collector for a given workload. We find that the garbage first collector gives a performance uplift of up to 15% in certain workloads.
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Acknowledgements
The authors wish to thank Dr. Prakash Raghavendra from AMD India Pvt. Ltd. for providing intellectual assistance throughout the study.
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Nair, A., Sriram, A., Simon, A., Kalambur, S., Sitaram, D. (2021). A Comparative Analysis of Garbage Collectors and Their Suitability for Big Data Workloads. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 735. Springer, Singapore. https://doi.org/10.1007/978-981-33-6977-1_24
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