Thanks for the comment, Forest. What I am asking is to make whatever DF repartition/coalesce functionalities available to SQL users.
Agree with you on that reducing the final number of output files by file size is very nice to have. Lukas indicated this is planned. On Wed, Jul 25, 2018 at 2:31 PM Forest Fang <forest.f...@outlook.com> wrote: > Sorry I see https://issues.apache.org/jira/browse/SPARK-6221 was > referenced in John's email. Can you elaborate how is your requirement > different? In my experience, it usually is driven by the need to decrease > the final output parallelism without compromising compute parallelism (i.e. > to prevent too many small files to be persisted on HDFS.) The requirement > in my experience is often pretty ballpark and does not require precise > number of partitions. Therefore setting the desired output size to say > 32-64mb usually gives a good enough result. I'm curious why 6221 was marked > as won't fix. > > On Wed, Jul 25, 2018 at 2:26 PM Forest Fang <forest.f...@outlook.com> > wrote: > >> Has there been any discussion to simply support Hive's merge small files >> configuration? It simply adds one additional stage to inspect size of each >> output file, recompute the desired parallelism to reach a target size, and >> runs a map-only coalesce before committing the final files. Since AFAIK >> SparkSQL already stages the final output commit, it seems feasible to >> respect this Hive config. >> >> >> https://community.hortonworks.com/questions/106987/hive-multiple-small-files.html >> >> >> On Wed, Jul 25, 2018 at 1:55 PM Mark Hamstra <m...@clearstorydata.com> >> wrote: >> >>> See some of the related discussion under >>> https://github.com/apache/spark/pull/21589 >>> >>> If feels to me like we need some kind of user code mechanism to signal >>> policy preferences to Spark. This could also include ways to signal >>> scheduling policy, which could include things like scheduling pool and/or >>> barrier scheduling. Some of those scheduling policies operate at inherently >>> different levels currently -- e.g. scheduling pools at the Job level >>> (really, the thread local level in the current implementation) and barrier >>> scheduling at the Stage level -- so it is not completely obvious how to >>> unify all of these policy options/preferences/mechanism, or whether it is >>> possible, but I think it is worth considering such things at a fairly high >>> level of abstraction and try to unify and simplify before making things >>> more complex with multiple policy mechanisms. >>> >>> On Wed, Jul 25, 2018 at 1:37 PM Reynold Xin <r...@databricks.com> wrote: >>> >>>> Seems like a good idea in general. Do other systems have similar >>>> concepts? In general it'd be easier if we can follow existing convention if >>>> there is any. >>>> >>>> >>>> On Wed, Jul 25, 2018 at 11:50 AM John Zhuge <jzh...@apache.org> wrote: >>>> >>>>> Hi all, >>>>> >>>>> Many Spark users in my company are asking for a way to control the >>>>> number of output files in Spark SQL. There are use cases to either reduce >>>>> or increase the number. The users prefer not to use function >>>>> *repartition*(n) or *coalesce*(n, shuffle) that require them to write >>>>> and deploy Scala/Java/Python code. >>>>> >>>>> Could we introduce a query hint for this purpose (similar to Broadcast >>>>> Join Hints)? >>>>> >>>>> /*+ *COALESCE*(n, shuffle) */ >>>>> >>>>> In general, is query hint is the best way to bring DF functionality to >>>>> SQL without extending SQL syntax? Any suggestion is highly appreciated. >>>>> >>>>> This requirement is not the same as SPARK-6221 that asked for >>>>> auto-merging output files. >>>>> >>>>> Thanks, >>>>> John Zhuge >>>>> >>>> -- John Zhuge
