Performance Optimization System for Hadoop and Spark Frameworks

Hrachya Astsatryan; Aram Kocharyan; Daniel Hagimont; Arthur Lalayan

doi:10.2478/cait-2020-0056

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Performance Optimization System for Hadoop and Spark Frameworks

Cybernetics and Information Technologies

Volume 20 (2020): Issue 6 (December 2020)

By: Hrachya Astsatryan, Aram Kocharyan, Daniel Hagimont and Arthur Lalayan

Open Access

|Dec 2020

Abstract

The optimization of large-scale data sets depends on the technologies and methods used. The MapReduce model, implemented on Apache Hadoop or Spark, allows splitting large data sets into a set of blocks distributed on several machines. Data compression reduces data size and transfer time between disks and memory but requires additional processing. Therefore, finding an optimal tradeoff is a challenge, as a high compression factor may underload Input/Output but overload the processor. The paper aims to present a system enabling the selection of the compression tools and tuning the compression factor to reach the best performance in Apache Hadoop and Spark infrastructures based on simulation analyzes.

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Articles in this issue

DOI: https://doi.org/10.2478/cait-2020-0056 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

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Language: English

Page range: 5 - 17

Submitted on: Jul 6, 2020

Accepted on: Sep 25, 2020

Published on: Dec 31, 2020

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Hadoop,

Spark,

data compression,

CPU/IO tradeoff,

performance optimization

Related subjects:

Computer sciences,

Information technology

© 2020 Hrachya Astsatryan, Aram Kocharyan, Daniel Hagimont, Arthur Lalayan, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 20 (2020): Issue 6 (December 2020)