So you are saying, we will first do a 'hadoop count' to get the total # of bytes for all files. Let's say that comes to: 1538684305
Default Block Size is: 128M So, total # of blocks needed: 1538684305 / 131072 = 11740 Max file blocks = 11740 / 50 (# of output files) = 234 Does this calculation look right? On Wed, Jul 31, 2013 at 10:28 AM, John Meagher <john.meag...@gmail.com>wrote: > It is file size based, not file count based. For fewer files up the > max-file-blocks setting. > > On Wed, Jul 31, 2013 at 12:21 PM, Something Something > <mailinglist...@gmail.com> wrote: > > Thanks, John. But I don't see an option to specify the # of output > files. > > How does Crush decide how many files to create? Is it only based on > file > > sizes? > > > > On Wed, Jul 31, 2013 at 6:28 AM, John Meagher <john.meag...@gmail.com > >wrote: > > > >> 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. > >> >> > >> >> > >> >
