I think this can be useful. The only thing is that we are slowly migrating to the Dataset/DataFrame API, and leave RDD mostly as is as a lower level API. Maybe we should do both? In either case it would be great to discuss the API on a pull request. Cheers.
On Wed, Feb 24, 2016 at 2:08 PM, Nezih Yigitbasi < nyigitb...@netflix.com.invalid> wrote: > Hi Spark devs, > > I have sent an email about my problem some time ago where I want to merge > a large number of small files with Spark. Currently I am using Hive with > the CombineHiveInputFormat and I can control the size of the output files > with the max split size parameter (which is used for coalescing the input > splits by the CombineHiveInputFormat). My first attempt was to use > coalesce(), but since coalesce only considers the target number of > partitions the output file sizes were varying wildly. > > What I think can be useful is to have an optional PartitionCoalescer > parameter (a new interface) in the coalesce() method (or maybe we can add > a new method ?) that the callers can implement for custom coalescing > strategies — for my use case I have already implemented a > SizeBasedPartitionCoalescer that coalesces partitions by looking at their > sizes and by using a max split size parameter, similar to the > CombineHiveInputFormat (I also had to expose HadoopRDD to get access to > the individual split sizes etc.). > > What do you guys think about such a change, can it be useful to other > users as well? Or do you think that there is an easier way to accomplish > the same merge logic? If you think it may be useful, I already have an > implementation and I will be happy to work with the community to contribute > it. > > Thanks, > Nezih > >
