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Performance Optimization System for Hadoop and Spark Frameworks
- Source :
- Cybernetics and Information Technologies; December 2020, Vol. 20 Issue: 6 p5-17, 13p
- Publication Year :
- 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.
Details
- Language :
- English
- ISSN :
- 13119702 and 13144081
- Volume :
- 20
- Issue :
- 6
- Database :
- Supplemental Index
- Journal :
- Cybernetics and Information Technologies
- Publication Type :
- Periodical
- Accession number :
- ejs55124500
- Full Text :
- https://doi.org/10.2478/cait-2020-0056