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https://issues.apache.org/jira/browse/SPARK-27227?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Song Jun updated SPARK-27227:
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Summary: Spark Runtime Filter (was: Dynamic Partition Prune in Spark)
> Spark Runtime Filter
> --------------------
>
> Key: SPARK-27227
> URL: https://issues.apache.org/jira/browse/SPARK-27227
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Song Jun
> Priority: Major
>
> When we equi-join one big table with a smaller table, we can collect some
> statistics from the smaller table side, and use it to the scan of big table
> to do partition prune or data filter before execute the join.
> This can significantly improve SQL perfermance.
> For a simple example:
> select * from A, B where A.a = B.b
> A is big table ,B is small table.
> There are two scenarios:
> 1. A.a is a partition column of table A
> we can collect all the values of B.b, and send it to table A to do
> partition prune on A.a.
> 2. A.a is not a partition column of table A
> we can collect real-time some statistics(such as min/max/bloomfilter) of
> B.b by execute extra sql(select max(b),min(b),bbf(b) from B), and send it to
> table A to do filter on A.a.
> Addititionaly, if a more complex query select * from A join (select * from
> B where B.c = 1) X on A.a = B.b, then we collect real-time statistics(such as
> min/max/bloomfilter) of X by execute extra sql(select max(b),min(b),bbf(b)
> from X)
> Above two scenarios, we can filter out lots of data by partition prune or
> data filter, thus we can imporve perfermance.
> 10TB TPC-DS gain about 35% improvement in our test.
> I will submit a SPIP later.
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