Since each hadoop tasks is isolated from others having more tmp
directories allows you to isolate that disk bandwidth as well. By
listing the disks you give more firepower to shuffle-sorting and
On Sun, Apr 22, 2012 at 10:02 AM, Jay Vyas <[EMAIL PROTECTED]> wrote:
> I don't understand why multiple disks would be particularly beneficial for
> a Map/Reduce job..... would I/O for a map/reduce job be i/o *as well as CPU
> bound* ? I would think that simply reading and parsing large files would
> still require dedicated CPU blocks. ?
> On Sun, Apr 22, 2012 at 3:14 AM, Harsh J <[EMAIL PROTECTED]> wrote:
>> You can use mapred.local.dir for this purpose. It accepts a list of
>> directories tasks may use, just like dfs.data.dir uses multiple disks
>> for block writes/reads.
>> On Sun, Apr 22, 2012 at 12:50 PM, mete <[EMAIL PROTECTED]> wrote:
>> > Hello folks,
>> > I have a job that processes text files from hdfs on local fs (temp
>> > directory) and then copies those back to hdfs.
>> > I added another drive to each server to have better io performance, but
>> > far as i could see hadoop.tmp.dir will not benefit from multiple
>> > if i setup two different folders on different disks. (dfs.data.dir works
>> > fine). As a result the disk with temp folder set is highy utilized, where
>> > the other one is a little bit idle.
>> > Does anyone have an idea on what to do? (i am using cdh3u3)
>> > Thanks in advance
>> > Mete
>> Harsh J
> Jay Vyas