Antony Rowstron, Dushyanth Narayanan, Austin Donnelly, Greg O'Shea, and Andrew Douglas
10 April 2012
The norm for data analytics is now to run them on commodity
clusters with MapReduce-like abstractions. One only
needs to read the popular blogs to see the evidence of this.
We believe that we could now say that "nobody ever got fired
for using Hadoop on a cluster"!
We completely agree that Hadoop on a cluster is the
right solution for jobs where the input data is multi-terabyte
or larger. However, in this position paper we ask if this is
the right path for general purpose data analytics? Evidence
suggests that many MapReduce-like jobs process relatively
small input data sets (less than 14 GB). Memory has reached
a GB/$ ratio such that it is now technically and financially
feasible to have servers with 100s GB of DRAM. We therefore
ask, should we be scaling by using single machines with
very large memories rather than clusters? We conjecture that,
in terms of hardware and programmer time, this may be a
better option for the majority of data processing jobs.
|Published in||1st International Workshop on Hot Topics in Cloud Data Processing (HotCDP 2012)|
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