What do you mean by 'real-time' though? While HDFS has pretty good
contiguous data read speeds (and you get N x replicas to read from),
if you're looking to "cache" frequently accessed files into memory
then HDFS does not natively have support for that. Otherwise, I agree
with Brock, seems like you could make it work with HDFS (sans
MapReduce - no need to run it if you don't need it).
The presence of NameNode audit logging will help your file access
On Tue, Oct 16, 2012 at 1:17 AM, Matt Painter <[EMAIL PROTECTED]> wrote:
> I am a new Hadoop user, and would really appreciate your opinions on whether
> Hadoop is the right tool for what I'm thinking of using it for.
> I am investigating options for scaling an archive of around 100Tb of image
> data. These images are typically TIFF files of around 50-100Mb each and need
> to be made available online in realtime. Access to the files will be
> sporadic and occasional, but writing the files will be a daily activity.
> Speed of write is not particularly important.
> Our previous solution was a monolithic, expensive - and very full - SAN so I
> am excited by Hadoop's distributed, extensible, redundant architecture.
> My concern is that a lot of the discussion on and use cases for Hadoop is
> regarding data processing with MapReduce and - from what I understand -
> using HDFS for the purpose of input for MapReduce jobs. My other concern is
> vague indication that it's not a 'real-time' system. We may be using
> MapReduce in small components of the application, but it will most likely be
> in file access analysis rather than any processing on the files themselves.
> In other words, what I really want is a distributed, resilient, scalable
> Is Hadoop suitable if we just use this facility, or would I be misusing it
> and inviting grief?