Caching the partitioned_df <- this one, but you have to do the
partitioning using something like sql("SELECT * FROM ... CLUSTER BY a") as
there is no such operation exposed on dataframes.
2) Here is the JIRA: https://issues.apache.org/jira/browse/SPARK-5354
Thanks Michael for your input.
By 1) do you mean:
- Caching the partitioned_rdd
- Caching the partitioned_df
- *Or* just caching unpartitioned_df without the need of creating
the partitioned_rdd
variable?
Can you expand a little bit more 2)
Thanks!
On Wed, Oct 14, 2015 at 12:11
This won't help as for two reasons:
1) Its all still just creating lineage since you aren't caching the
partitioned data. It will still fetch the shuffled blocks for each query.
2) The query optimizer is not aware of RDD level partitioning since its
mostly a blackbox.
1) could be fixed by addin
My current version of spark is 1.3.0 and my question is the next:
I have large data frames where the main field is an user id. I need to do
many group by's and joins using that field. Do the performance will
increase if before doing any group by or join operation I first convert to
rdd to partitio
