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Hadoop >> mail # user >> basic question about rack awareness and computation migration


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Re: basic question about rack awareness and computation migration
Thanks Harsh,

> Are your input lists big (for each compressed output)? And is the list
arbitrary or a defined list per goal?

I dictate what my inputs will look like.  If they need to be list of image
files, then I can do that.  If they need to be the images themselves as you
suggest, then I can do that too but I'm not exactly sure what that would
look like.  Basically, I will try to format my inputs in the way that makes
the most sense from a locality point of view.

Since all the keys must be writable, I explored the Writable interface and
found the interesting sub-classes:

   - FileSplit
   - BlockLocation
   - BytesWritable

These all look somewhat promising as they kind of reveal the location
information of the files.

I'm not exactly sure how I would use these to hint at the data locations.
 Since these chunks of the file appear to be somewhat arbitrary in size and
offset, I don't know how I could perform imagery operations on them.  For
example, if I knew that bytes 0x100-0x400 lie on node X, then that makes it
difficult for me to use that information to give to my image libraries -
does 0x100-0x400 correspond to some region/MBR within the image?  I'm not
sure how to make use of this information.

The responses I've gotten so far indicate to me that HDFS kind of does the
computation migration for me but that I have to give it enough information
to work with.  If someone could point to some detailed reading about this
subject that would be pretty helpful, as I just can't find the
documentation for it.

Thanks again,
-Julian

On Tue, Mar 5, 2013 at 5:39 PM, Harsh J <[EMAIL PROTECTED]> wrote:

> Your concern is correct: If your input is a list of files, rather than
> the files themselves, then the tasks would not be data-local - since
> the task input would just be the list of files, and the files' data
> may reside on any node/rack of the cluster.
>
> However, your job will still run as the HDFS reads do remote reads
> transparently without developer intervention and all will still work
> as you've written it to. If a block is found local to the DN, it is
> read locally as well - all of this is automatic.
>
> Are your input lists big (for each compressed output)? And is the list
> arbitrary or a defined list per goal?
>
> On Tue, Mar 5, 2013 at 5:19 PM, Julian Bui <[EMAIL PROTECTED]> wrote:
> > Hi hadoop users,
> >
> > I'm trying to find out if computation migration is something the
> developer
> > needs to worry about or if it's supposed to be hidden.
> >
> > I would like to use hadoop to take in a list of image paths in the hdfs
> and
> > then have each task compress these large, raw images into something much
> > smaller - say jpeg  files.
> >
> > Input: list of paths
> > Output: compressed jpeg
> >
> > Since I don't really need a reduce task (I'm more using hadoop for its
> > reliability and orchestration aspects), my mapper ought to just take the
> > list of image paths and then work on them.  As I understand it, each
> image
> > will likely be on multiple data nodes.
> >
> > My question is how will each mapper task "migrate the computation" to the
> > data nodes?  I recall reading that the namenode is supposed to deal with
> > this.  Is it hidden from the developer?  Or as the developer, do I need
> to
> > discover where the data lies and then migrate the task to that node?
>  Since
> > my input is just a list of paths, it seems like the namenode couldn't
> really
> > do this for me.
> >
> > Another question: Where can I find out more about this?  I've looked up
> > "rack awareness" and "computation migration" but haven't really found
> much
> > code relating to either one - leading me to believe I'm not supposed to
> have
> > to write code to deal with this.
> >
> > Anyway, could someone please help me out or set me straight on this?
> >
> > Thanks,
> > -Julian
>
>
>
> --
> Harsh J
>
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB