Here's a great tool for handling exactly that case: https://github.com/edwardcapriolo/filecrush
On Wed, Jul 31, 2013 at 2:40 AM, Something Something <mailinglist...@gmail.com> wrote: > Each bz2 file after merging is about 50Megs. The reducers take about 9 > minutes. > > Note: 'getmerge' is not an option. There isn't enough disk space to do a > getmerge on the local production box. Plus we need a scalable solution as > these files will get a lot bigger soon. > > On Tue, Jul 30, 2013 at 10:34 PM, Ben Juhn <benjij...@gmail.com> wrote: > >> How big are your 50 files? How long are the reducers taking? >> >> On Jul 30, 2013, at 10:26 PM, Something Something < >> mailinglist...@gmail.com> wrote: >> >> > Hello, >> > >> > One of our pig scripts creates over 500 small part files. To save on >> > namespace, we need to cut down the # of files, so instead of saving 500 >> > small files we need to merge them into 50. We tried the following: >> > >> > 1) When we set parallel number to 50, the Pig script takes a long time - >> > for obvious reasons. >> > 2) If we use Hadoop Streaming, it puts some garbage values into the key >> > field. >> > 3) We wrote our own Map Reducer program that reads these 500 small part >> > files & uses 50 reducers. Basically, the Mappers simply write the line & >> > reducers loop thru values & write them out. We set >> > job.setOutputKeyClass(NullWritable.class) so that the key is not written >> to >> > the output file. This is performing better than Pig. Actually Mappers >> run >> > very fast, but Reducers take some time to complete, but this approach >> seems >> > to be working well. >> > >> > Is there a better way to do this? What strategy can you think of to >> > increase speed of reducers. >> > >> > Any help in this regard will be greatly appreciated. Thanks. >> >>
