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2021 | OriginalPaper | Chapter

A Comparative Analysis of Garbage Collectors and Their Suitability for Big Data Workloads

Authors : Advithi Nair, Aiswarya Sriram, Alka Simon, Subramaniam Kalambur, Dinkar Sitaram

Published in: Advances in Computing and Network Communications

Publisher: Springer Singapore

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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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Metadata
Title
A Comparative Analysis of Garbage Collectors and Their Suitability for Big Data Workloads
Authors
Advithi Nair
Aiswarya Sriram
Alka Simon
Subramaniam Kalambur
Dinkar Sitaram
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-6977-1_24