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MapReduce: Hadoop and Cloud MapReduce

Ricky Ho has two great articles on how MapReduce is implemented by Hadoop and Cloud MapReduce:

Cloud MapReduce enjoys the inherit scalability and resiliency, which greatly simplifies its architecture.

  1. Cloud MapReduce doesn’t need to design a central coordinator components (like the NameNode and JobTracker in the Hadoop environment). They simply store the job progress status information in the distributed metadata store (SimpleDB).
  2. Cloud MapReduce doesn’t need to worry about scalability in the communication path and how data can be moved efficiently between nodes, all is taken care by the underlying CloudOS
  3. Cloud MapReduce doesn’t need to worry about disk I/O issue because all storage is effectively remote and being taken care by the Cloud OS.

Cloud MapReduce implementation is detailed in this ☞ paper (PDF).

These are very interesting details on how to build a scalable (probably also fault tolerant) solution.