期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：8
页码：13635-13639
DOI：10.18535/ijecs/v4i8.02
出版社：IJECS
摘要：MapReduce is a well-known programming model and an implementation method for executing, processing andgenerating massive data sets. MapReduce algorithm consists of a map function that processes a key/value pair to produce a setof intermediate key/value pairs, and a reduce function which combine all these values related with the same intermediate key.MapReduce executes in parallel itself without implementing any parallel programming model and it is the most efficient wayto process unstructured data.In this research MapReduce algorithm is implemented on a cluster based machine using Hadoop distributed file system(HDFS) in order to perform a Pattern matching algorithm for different volumes of datasets. The quantitative performanceanalysis of MapReduce algorithm is done for the different volumes of data on the basis of execution time and number ofpatterns searched.So far relational databases are used for storing the data for the applications but now there is need to store huge amount ofdata to store and manage which cannot stored by relational databases. NoSQL technology over comes this problem. Thisresearch paper provides a brief introduction to NoSQL database working and comparative study between MongodB andCouchDB, Which are mostly used for big data application. The operations are performed to explore the results as distinguishbetween both NoSql databases. This paper shows the performance of Mongodb and CouchDB. Results proves that CouchDB ismore powerful than Mongodb to load and process on big data and processing very fast as compare to Mongodb. This paperdescribes the functionality of Mongodb and CouchDB over the large dataset
