Abstract
It has been observed that there has been a great interest in computing experiments which has been useful on shared nothing computers and commodity machines. We need multiple systems running in parallel working closely together towards the same goal. Frequently it has been experienced and observed that the distributed execution engine named MapReduce handles the primary input-output workload for such clusters. There are numerous distributed file systems around viz. Tinyfs, Glusterfs, Lustre, Cephfs, zfs and HDFS (Hadoop Distributed File System), we studied them and implemented MapReduce on few distributed file systems. It has been studied that distributed file systems (DFS) work very well on many small files but some do not generate expected output on large files. We implemented MapReduce algorithms onto three nodes of each distributed files systems for small and large files, and the analysis is been put forward in this paper. Even we came across the various implementation issues of various DFS, they have also been mentioned in this paper.
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