title: What are main reasons of input splitting in MapReduce?
body: <p>In <a href="http://www.usenix.org/event/osdi04/tech/full_papers/dean/dean.pdf" rel="nofollow">MapReduce papers</a> is described that input files are partitioned in M input splits. I know the HDFS in Hadoop makes partitioning automatically to blocks of 64 MB (default) and then replicate these blocks to few other nodes in cluster for providing fault tolerance. I'd like to know if this partitioning of files in HDFS means the input splitting described in mentioned MapReduce papers. Is fault tolerance single reason of this splitting or are there more important reasons? </p>
  
  <p>And what if I have MapReduce over cluster of nodes without distributed file system (data only on local disks with common file sytem)? Do I need to split input files on local disk before map phase?</p>
  
  <p>Thank you for your answers.</p>