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From "Cheng, Hao" <hao.ch...@intel.com>
Subject RE: Potential bug broadcastNestedLoopJoin or default value of spark.sql.autoBroadcastJoinThreshold
Date Wed, 12 Aug 2015 02:12:17 GMT
Firstly, spark.sql.autoBroadcastJoinThreshold only works for the EQUAL JOIN.

Currently, for the non-equal join, if the join type is the INNER join, then it will be done
by CartesianProduct join and BroadcastNestedLoopJoin works for the outer joins.

In the BroadcastnestedLoopJoin, the table with smaller estimate size will be broadcasted,
but if the smaller table is also a huge table, I don’t think Spark SQL can handle that right
now (OOM).

So, I am not sure how you created the df1 instance, but we’d better to reflect the real
size for the statistics of it, and let the framework decide what to do, hopefully Spark Sql
can support the non-equal join for large tables in the next release.

Hao

From: gen tang [mailto:gen.tang86@gmail.com]
Sent: Tuesday, August 11, 2015 10:12 PM
To: dev@spark.apache.org
Subject: Potential bug broadcastNestedLoopJoin or default value of spark.sql.autoBroadcastJoinThreshold

Hi,

Recently, I use spark sql to do join on non-equality condition, condition1 or condition2 for
example.

Spark will use broadcastNestedLoopJoin to do this. Assume that one of dataframe(df1) is not
created from hive table nor local collection and the other one is created from hivetable(df2).
For df1, spark will use defaultSizeInBytes * length of df1 to estimate the size of df1 and
use correct size for df2.

As the result, in most cases, spark will think df1 is bigger than df2 even df2 is really huge.
And spark will do df2.collect(), which will cause error or slowness of program.

Maybe we should just use defaultSizeInBytes for logicalRDD, not defaultSizeInBytes * length?

Hope this could be helpful
Thanks a lot in advance for your help and input.

Cheers
Gen

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