2015
DOI: 10.15514/ispras-2015-27(5)-3
|View full text |Cite
|
Sign up to set email alerts
|

Implementing Apache Spark jobs execution and Apache Spark cluster creation for Openstack Sahara

Abstract: In this paper the problem of creating virtual clusters in clouds for big data analysis with Apache Hadoop and Apache Spark is discussed. Both clouds and MapReduce models are popular nowadays for a bunch of reasons: cheapness and efficient big data analysis respectively. For these thoughts, having an open source solution for building clusters is important. The article gives an overview on existing methods for Apache Spark cluster creation in clouds. We consider two open source cloud engines OpenStack and Eucaly… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2017
2017
2020
2020

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 0 publications
0
1
0
Order By: Relevance
“…We show a specific example 'Word count' which is one of the most common data analytics in table II. The system proceeds the analytics' tasks related to map procedure with 'map' and 'flatMap' API supported by the framework and reduce procedure with 'reduceByKey' and 'collect' API [16]. # Save and load model clusters.save(sc,"target/org/apache/spark/PythonKMeans Example/KMeansModel") sameModel = KMeansModel.load(sc, "target/org/apache/spark/ PythonKMeansExample/KMeansModel") Table III shows an example 'K-means' which is more complex than 'Word count'.…”
Section: ) Execution Procedures Of Big Data Analyticsmentioning
confidence: 99%
“…We show a specific example 'Word count' which is one of the most common data analytics in table II. The system proceeds the analytics' tasks related to map procedure with 'map' and 'flatMap' API supported by the framework and reduce procedure with 'reduceByKey' and 'collect' API [16]. # Save and load model clusters.save(sc,"target/org/apache/spark/PythonKMeans Example/KMeansModel") sameModel = KMeansModel.load(sc, "target/org/apache/spark/ PythonKMeansExample/KMeansModel") Table III shows an example 'K-means' which is more complex than 'Word count'.…”
Section: ) Execution Procedures Of Big Data Analyticsmentioning
confidence: 99%