A review on hadoop — HDFS infrastructure extensions

Apache's Hadoop
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as of now is pretty good but there are scopes of extensions and enhancements. A large number of improvements are proposed to Hadoop which is an open source implementation of Google's Map/Reduce framework. It enables distributed, data intensive and parallel applications by decomposing a massive job into smaller tasks and a massive data set into smaller partitions such that each task processes a different partition in parallel. Hadoop uses Hadoop distributed File System (HDFS) which is an open source implementation of the Google File System (GFS) for storing data. Map/Reduce application mainly uses HDFS for storing data. HDFS is a very large distributed file system that uses commodity hardware and provides high throughput as well as fault tolerance. Many big enterprises believe that within a few years more than half of the world's data will be stored in Hadoop. HDFS stores files as a series of blocks and are replicated for fault tolerance. Strategic data partitioning, processing, layouts, replication and placement of data blocks will increase the performance of Hadoop and a lot of research is going on in this area. This paper reviews some of the major enhancements suggested to Hadoop especially in data storage, processing and placement.