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https://issues.apache.org/jira/browse/SPARK-13219?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Evan Chan updated SPARK-13219:
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Hi Gagan,
That is an interesting optimization but not the same one that Venu speaks of (I
worked on those optimizations). Basically those optimizations are for where
the column name in the WHERE clause are present in both tables, and my
impression is this is what this fix is for as well.
Your case would be very useful too. You can do it in two steps though, first
do the lookup of postal codes from location, then translate your select from
address into an IN condition.
Of course it’s better if Spark does this so that the results don’t have to be
passed back through the driver.
> Pushdown predicate propagation in SparkSQL with join
> ----------------------------------------------------
>
> Key: SPARK-13219
> URL: https://issues.apache.org/jira/browse/SPARK-13219
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.4.1, 1.5.2, 1.6.0
> Environment: Spark 1.4
> Datastax Spark connector 1.4
> Cassandra. 2.1.12
> Centos 6.6
> Reporter: Abhinav Chawade
>
> When 2 or more tables are joined in SparkSQL and there is an equality clause
> in query on attributes used to perform the join, it is useful to apply that
> clause on scans for both table. If this is not done, one of the tables
> results in full scan which can reduce the query dramatically. Consider
> following example with 2 tables being joined.
> {code}
> CREATE TABLE assets (
> assetid int PRIMARY KEY,
> address text,
> propertyname text
> )
> CREATE TABLE tenants (
> assetid int PRIMARY KEY,
> name text
> )
> spark-sql> explain select t.name from tenants t, assets a where a.assetid =
> t.assetid and t.assetid='1201';
> WARN 2016-02-05 23:05:19 org.apache.hadoop.util.NativeCodeLoader: Unable to
> load native-hadoop library for your platform... using builtin-java classes
> where applicable
> == Physical Plan ==
> Project [name#14]
> ShuffledHashJoin [assetid#13], [assetid#15], BuildRight
> Exchange (HashPartitioning 200)
> Filter (CAST(assetid#13, DoubleType) = 1201.0)
> HiveTableScan [assetid#13,name#14], (MetastoreRelation element, tenants,
> Some(t)), None
> Exchange (HashPartitioning 200)
> HiveTableScan [assetid#15], (MetastoreRelation element, assets, Some(a)),
> None
> Time taken: 1.354 seconds, Fetched 8 row(s)
> {code}
> The simple workaround is to add another equality condition for each table but
> it becomes cumbersome. It will be helpful if the query planner could improve
> filter propagation.
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