A Big Data MapReduce Hadoop distribution architecture for processing input splits to solve the small data problem
R. Manjunath, Tejus, Channabasava R.K, S. Balaji · 2016
Hadoop deals with big data which is an open source Java framework. There are two core components in it namely: HDFS (Hadoop distributed file system) is the ability of a system to continue normal operation against hardware or software faults using inexpensive hardware and which stocks huge extent of data another one is MapReduce is a processing technique and programming model done in lateral and scattered manner. Hadoop does not perform well for short data because huge amount of short data could be greater task on the NameNode of HDFS which inturn its execution time is prolonged for which MapReduce is encountered. While dealing with great amount of short data as it is particularly designed to handle huge amount of data, hadoop experienced with a performance cost. This analysis permits the indetail description of HDFS, actual ways to deal with the problems along with proposed approach to handle short data files and short data file problems. In proposed approach, small files are merged using programming model on hadoop known as MapReduce. By this approach of Hadoop performance of handling small files which is larger than block size is improved. We also propose a Traffic analyzer with the combination of Hadoop and Map-Reduce paradigm. The joint of Hadoop and MapReduce programming tools makes it possible to provide batch analysis in minimum response time and in memory computing capacity in order to process log in a high available, efficient and stable way.