Why isn't Hadoop implemented using MPI?

Question

Correct me if I'm wrong, but my understanding is that Hadoop does not use MPI for communication between different nodes.

What are the technical reasons for this?

I could hazard a few guesses, but I do not know enough of how MPI is implemented "under the hood" to know whether or not I'm right.

Come to think of it, I'm not entirely familiar with Hadoop's internals either. I understand the framework at a conceptual level (map/combine/shuffle/reduce and how that works at a high level) but I don't know the nitty gritty implementation details. I've always assumed Hadoop was transmitting serialized data structures (perhaps GPBs) over a TCP connection, eg during the shuffle phase. Let me know if that's not true.

Answer 1

One of the big features of Hadoop/map-reduce is the fault tolerance. Fault tolerance is not supported in most (any?) current MPI implementations. It is being thought about for future versions of OpenMPI.

Sandia labs has a version of map-reduce which uses MPI, but it lacks fault tolerance.

Answer 2

MPI is Message Passing Interface. Right there in the name - there is no data locality. You send the data to another node for it to be computed on. Thus MPI is network-bound in terms of performance when working with large data.

MapReduce with the Hadoop Distributed File System duplicates data so that you can do your compute in local storage - streaming off the disk and straight to the processor. Thus MapReduce takes advantage of local storage to avoid the network bottleneck when working with large data.

This is not to say that MapReduce doesn't use the network... it does: and the shuffle is often the slowest part of a job! But it uses it as little, and as efficiently as possible.

To sum it up: Hadoop (and Google's stuff before it) did not use MPI because it could not have used MPI and worked. MapReduce systems were developed specifically to address MPI's shortcomings in light of trends in hardware: disk capacity exploding (and data with it), disk speed stagnant, networks slow, processor gigahertz peaked, multi-core taking over Moore's law.