if anyone is facing the same problem, here's what i did. i took anil's
advice to use NLineInputFormat (because that approach would scale out my
however, i am using the new mapreduce package/API in hadoop v0.20.2. i
notice that you cannot use NLineInputFormat from the old package/API
when i took a look at hadoop v1.0.1, there is a NLineInputFormat class for
the new API. i simply copied and pasted this file into my project. i got 4
errors associated with import statements and annotations. when i removed
the 2 import statements and corresponding 2 annotations, the class compiled
successfully. after this modification, running NLineInputFormat of v1.0.1
on a cluster based on v0.20.2, works.
one mini-problem solved, many more to go.
thanks for the help.
On Wed, Mar 21, 2012 at 3:33 AM, Jane Wayne <[EMAIL PROTECTED]>wrote:
> as i understand, that class does not exist for new API in hadoop v0.20.2
> (which is what i am using). if i am mistaken, where is it?
> i am looking at hadoop v1.0.1, and there is a NLineInputFormat class. i
> wonder if i can simply copy/paste this into my project.
> On Wed, Mar 21, 2012 at 2:37 AM, Anil Gupta <[EMAIL PROTECTED]> wrote:
>> Have a look at NLineInputFormat class in Hadoop. That class will solve
>> your purpose.
>> Best Regards,
>> On Mar 20, 2012, at 11:07 PM, Jane Wayne <[EMAIL PROTECTED]>
>> > i have a matrix that i am performing operations on. it is 10,000 rows by
>> > 5,000 columns. the total size of the file is just under 30 MB. my HDFS
>> > block size is set to 64 MB. from what i understand, the number of
>> > is roughly equal to the number of HDFS blocks used in the input. i.e.
>> if my
>> > input data spans 1 block, then only 1 mapper is created, if my data
>> spans 2
>> > blocks, then 2 mappers will be created, etc...
>> > so, with my 1 matrix file of 15 MB, this won't fill up a block of data,
>> > being as such, only 1 mapper will be called upon the data. is this
>> > understanding correct?
>> > if so, what i want to happen is for more than one mapper (let's say 10)
>> > work on the data, even though it remains on 1 block. my analysis (or
>> > map/reduce job) is such that +1 mappers can work on different parts of
>> > matrix. for example, mapper 1 can work on the first 500 rows, mapper 2
>> > work on the next 500 rows, etc... how can i set up multiple mappers (+1
>> > mapper) to work on a file that resides only one block (or a file whose
>> > is smaller than the HDFS block size).
>> > can i split the matrix into (let's say) 10 files? that will mean 30 MB
>> / 10
>> > = 3 MB per file. then put each 3 MB file onto HDFS ? will this increase
>> > chance of having multiple mappers work simultaneously on the
>> > if i can increase the number of mappers, i think (pretty sure) my
>> > implementation will improve in speed linearly.
>> > any help is appreciated.