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, > Anil > > On Mar 20, 2012, at 11:07 PM, Jane Wayne <[email protected]> wrote: > > > 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 mappers > > 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, > and > > 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) > to > > 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 > the > > matrix. for example, mapper 1 can work on the first 500 rows, mapper 2 > can > > 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 > size > > 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 > the > > chance of having multiple mappers work simultaneously on the data/matrix? > > if i can increase the number of mappers, i think (pretty sure) my > > implementation will improve in speed linearly. > > > > any help is appreciated. >
