International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org
Call for Paper, Published Articles, Indexing Infromation
Title: |
A SURVEY ON A NOVEL SPARK ON HADOOP YARN FRAMEWORK BASED IN-MEMORY PARALLEL PROCESSING FOR EFFECTIVE PERFORMANCE
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Author Name: |
V.Sreedevi, J.Swami Naik |
Abstract: |
Abstract
A Novel spark is extends the popular MapReduce model to
efficiently support more types of computations, including
interactive queries and streaming. Nowadays speed is important
in processing huge datasets, as it means the difference between
exploring data interactively and waiting minutes or hours. One
of the main features Spark offers for speed is the ability to run
computations in-memory, but the system is also more efficient
than MapReduce for complex applications running on disk. In
this paper we are facilitate implementation and assure high
performance of spark based algorithms in a complex cloud
computing environment, for a parallel programming model is
used. By incorporating RS data with Resilient Distributed
Datasets (RDDs) of spark, all level parallel RS algorithms can
be easily expressed with transformations and actions. And also
to improve the performance Data-intensive multitasking
algorithms and iteration-intensive algorithms were evaluated on
Hadoop YARN framework. By supporting these workloads in
the same engine, Spark makes it easy and inexpensive to
combine different processing types, which is often necessary in
production data analysis pipelines. In addition it reduces the
management burden of maintaining separate tools.
Keywords: Apache Spark, big data, Hadoop yet
anotherresource negotiator (YARN), parallel processing, remote
sensing (RS). |
Cite this article: |
V.Sreedevi, J.Swami Naik , "
A SURVEY ON A NOVEL SPARK ON HADOOP YARN FRAMEWORK BASED IN-MEMORY PARALLEL PROCESSING FOR EFFECTIVE PERFORMANCE " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS),
Volume 6, Issue 5, September - October 2017 , pp.
001-008 , ISSN 2278-6856.
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