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From Herman van Hövell tot Westerflier <>
Subject Re: Join Strategies
Date Mon, 15 Jan 2018 19:22:08 GMT
Hey Marco,

A Cartesian product is an inner join by definition :). The current
cartesian product operator does not support outer joins, so we use the only
operator that does: BroadcastNestedLoopJoinExec. This is far from great,
and it does have the potential to OOM, there are some safety nets in the
driver that should start complaining before you actually OOM though.

An outer non-equi join is pretty hard to do in a distributed setting. This
is caused by two things:

   - There is no way to partition the data in such a way that you can
   exploit some locality (know that all the same keys are in one partition),
   unless you use only one partition or use some clever index.
   - You need to keep track of records that do not match the join condition
   if you are doing a full join or a join in which the stream side does not
   match the join side. This is the number one source of complexity in the
   current join implementations. If you can partition your data then you can
   track and emit unmatched rows as part of processing the partition. If you
   cannot (and you have more than 1 partition) then you need to send the
   unmatched rows (in some form) back to the driver and figure out which
   records actually have not been matched (see BroadcastNestedLoopJoinExec for

It is definitely doable to implement a such a join, however I have not seen
many JIRA's or user requests for this.



On Sat, Jan 13, 2018 at 6:41 AM, Marco Gaido <> wrote:

> Hi dev,
> I have a question about how join strategies are defined.
> I see that CartesianProductExec is used only for InnerJoin, while for
> other kind of joins BroadcastNestedLoopJoinExec is used.
> For reference:
> d06080a0f7044a0c3/sql/core/src/main/scala/org/apache/spark/sql/execution/
> SparkStrategies.scala#L260
> May you kindly explain me why this is done? It doesn't seem a great choice
> to me, since BroadcastNestedLoopJoinExec can fail with OOM.
> Thanks,
> Marco

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