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: 
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,

On Tue, Mar 5, 2013 at 5:39 PM, Harsh J <> 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 <> 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