[jira] [Resolved] (SPARK-24859) Predicates pushdown on outer joins

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-24859?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-24859.
----------------------------------
    Resolution: Cannot Reproduce

Seems fixed in the master branch. Please let me know if you have a JIRA fixing this. Let me leave this resolved for now.

> Predicates pushdown on outer joins
> ----------------------------------
>
>                 Key: SPARK-24859
>                 URL: https://issues.apache.org/jira/browse/SPARK-24859
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, SQL
>    Affects Versions: 2.2.0
>         Environment: Cloudera CDH 5.13.1
>            Reporter: Johannes Mayer
>            Priority: Major
>
> I have two AVRO tables in Hive called FAct and DIm. Both are partitioned by a common column called part_col. Now I want to join both tables on their id but only for some of partitions.
> If I use an inner join, everything works well:
>  
> {code:java}
> select *
> from FA f
> join DI d
> on(f.id = d.id and f.part_col = d.part_col)
> where f.part_col = 'xyz'
> {code}
>  
> In the sql explain plan I can see, that the predicate part_col = 'xyz' is also used in the DIm HiveTableScan.
>  
> When I execute the same query using a left join the full dim table is scanned. There are some workarounds for this issue, but I wanted to report this as a bug, since it works on an inner join, and i think the behaviour should be the same for an outer join.
> Here is a self contained example (created in Zeppelin):
>  
> {code:java}
> val fact = Seq((1, 100), (2, 200), (3,100), (4,200)).toDF("id", "part_col")
> val dim = Seq((1, 100), (2, 200)).toDF("id", "part_col")
> fact.repartition($"part_col").write.mode("overwrite").partitionBy("part_col").format("com.databricks.spark.avro").save("/tmp/jira/fact")
> dim.repartition($"part_col").write.mode("overwrite").partitionBy("part_col").format("com.databricks.spark.avro").save("/tmp/jira/dim")
>  
> spark.sqlContext.sql("create table if not exists fact(id int) partitioned by (part_col int) stored as avro location '/tmp/jira/fact'")
> spark.sqlContext.sql("msck repair table fact") spark.sqlContext.sql("create table if not exists dim(id int) partitioned by (part_col int) stored as avro location '/tmp/jira/dim'")
> spark.sqlContext.sql("msck repair table dim"){code}
>  
>   
>   
>  *Inner join example:*
> {code:java}
> select * from fact f
> join dim d
> on (f.id = d.id
> and f.part_col = d.part_col)
> where f.part_col = 100{code}
> Excerpt from Spark-SQL physical explain plan: 
> {code:java}
> HiveTableScan [id#411, part_col#412], CatalogRelation `default`.`fact`, org.apache.hadoop.hive.serde2.avro.AvroSerDe, [id#411], [part_col#412], [isnotnull(part_col#412), (part_col#412 = 100)] 
> HiveTableScan [id#413, part_col#414], CatalogRelation `default`.`dim`, org.apache.hadoop.hive.serde2.avro.AvroSerDe, [id#413], [part_col#414], [isnotnull(part_col#414), (part_col#414 = 100)]{code}
>  
>  *Outer join example:*
> {code:java}
> select * from fact f
> left join dim d
> on (f.id = d.id
> and f.part_col = d.part_col)
> where f.part_col = 100{code}
>  
>  Excerpt from Spark-SQL physical explain plan:
>   
> {code:java}
> HiveTableScan [id#426, part_col#427], CatalogRelation `default`.`fact`, org.apache.hadoop.hive.serde2.avro.AvroSerDe, [id#426], [part_col#427], [isnotnull(part_col#427), (part_col#427 = 100)]   
> HiveTableScan [id#428, part_col#429], CatalogRelation `default`.`dim`, org.apache.hadoop.hive.serde2.avro.AvroSerDe, [id#428], [part_col#429] {code}
>  
>   
> As you can see the predicate is not pushed down to the HiveTableScan of the dim table on the outer join.
>  



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