Comparative Analysis of Apache Spark and Hadoop MapReduce Using Various Parameters and Execution Time

被引：1

作者：

Meena, Bhagavathula ^{[1
]}

Sarwani, I. S. L. ^{[2
]}

Archana, M. ^{[3
]}

Supriya, P. ^{[4
]}

机构：

[1] Raghu Engn Coll, CSE Dept, Visakhapatnam, Andhra Pradesh, India

[2] ANITS, Visakhapatnam, Andhra Pradesh, India

[3] CVR Coll Engn, Hyderabad, Telangana, India

[4] Raghu Engn Coll, Visakhapatnam, Andhra Pradesh, India

来源：

INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019 | 2020年 / 1034卷

关键词：

Hadoop; Apache Spark; Big Data; HDFS; MapReduce;

D O I：

10.1007/978-981-15-1084-7_70

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Due to rapid growth in Information technology there is a lot of advancement in Electronics and communication. Every hour lot of data with various medium is getting generated which is referred as big data. Big Data and Hadoop are the trending terms nowadays. Storage and analysis of such a large data is becoming one of the challenges for computer science and Information Technology devotee throughout the world in the most recent couple of the years. As Apache Spark and Hadoop are the frameworks used for analyzing big data, our paper discusses a comparison of both the frame works by choosing different sizes of datasets and in terms of time comparison also. This comparison is made using word count algorithm. Although both the resources are relayed on an idea of significantly varying Big Data performance. This paper shows an analysis on both frameworks for word count algorithm over Hadoop MapReduce and Apache spark environment

引用

页码：719 / 725

页数：7

共 50 条

[21] MapReduce Based Analysis of Sample Applications Using Hadoop
Ghazi, Mohd Rehan
Raghava, N. S.
[J]. APPLICATIONS OF COMPUTING AND COMMUNICATION TECHNOLOGIES, ICACCT 2018, 2018, 899 : 34 - 44
[22] Sentiment analysis using semantic similarity and Hadoop MapReduce
Madani, Youness
Erritali, Mohammed
Bengourram, Jamaa
[J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2019, 59 (02) : 413 - 436
[23] Sentiment analysis using semantic similarity and Hadoop MapReduce
Youness Madani
Mohammed Erritali
Jamaa Bengourram
[J]. Knowledge and Information Systems, 2019, 59 : 413 - 436
[24] Combiner to Reduce the Time of Processing in Trend Analysis using Hadoop's MapReduce Framework
Pinto, Vivek Francis
[J]. 2017 2ND INTERNATIONAL CONFERENCE ON COMPUTATIONAL SYSTEMS AND INFORMATION TECHNOLOGY FOR SUSTAINABLE SOLUTION (CSITSS-2017), 2017, : 166 - 169
[25] Time Estimation and Resource Minimization Scheme for Apache Spark and Hadoop Big Data Systems With Failures
Lee, Jinbae
Kim, Bobae
Chung, Jong-Moon
[J]. IEEE ACCESS, 2019, 7 : 9658 - 9666
[26] Big data classification using heterogeneous ensemble classifiers in Apache Spark based on MapReduce paradigm
Kadkhodaei, Hamidreza
Moghadam, Amir Masoud Eftekhari
Dehghan, Mehdi
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2021, 183
[27] Reduced Time Compression in Big Data Using MapReduce Approach and Hadoop
Meena, K.
Sujatha, J.
[J]. JOURNAL OF MEDICAL SYSTEMS, 2019, 43 (08)
[28] Reduced Time Compression in Big Data Using MapReduce Approach and Hadoop
K. Meena
J. Sujatha
[J]. Journal of Medical Systems, 2019, 43
[29] Real-time digital forensic triaging for cloud data analysis using MapReduce on Hadoop framework
Povar, Digambar
Saibharath
Geethakumari, G.
[J]. INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS, 2015, 7 (02) : 119 - 133
[30] An Efficient Approach to Extract and Store Big Semantic Web Data Using Hadoop and Apache Spark GraphX
Mohammed, Wria Mohammed Salih
Maa, Alaa Khalil Ju
[J]. ADCAIJ-ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 2024, 13

← 1 2 3 4 5 →