When you read both ‘a’ and ‘b', can you try repartitioning both by column ‘id’?
If you .cache() and .count() to force a shuffle, it'll push the records that
will be joined to the same executors.
So;
a = spark.read.parquet(‘path_to_table_a’).repartition(‘id’).cache()
a.count()
b = spark.read.parquet(‘path_to_table_b').repartition(‘id’).cache()
b.count()
And then join..
> On Aug 8, 2016, at 8:17 PM, Ashic Mahtab <as...@live.com> wrote:
>
> Hello,
> We have two parquet inputs of the following form:
>
> a: id:String, Name:String (1.5TB)
> b: id:String, Number:Int (1.3GB)
>
> We need to join these two to get (id, Number, Name). We've tried two
> approaches:
>
> a.join(b, Seq("id"), "right_outer")
>
> where a and b are dataframes. We also tried taking the rdds, mapping them to
> pair rdds with id as the key, and then joining. What we're seeing is that
> temp file usage is increasing on the join stage, and filling up our disks,
> causing the job to crash. Is there a way to join these two data sets without
> well...crashing?
>
> Note, the ids are unique, and there's a one to one mapping between the two
> datasets.
>
> Any help would be appreciated.
>
> -Ashic.
