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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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Venu Palvai updated SPARK-13219:
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Nick,
We addressed this issue by creating a custom hive context to modify query
plans generated by Spark SQL. We have implemented in such a way that values
for = and IN operators get pushed against joining tables. Looking forward,
it should also work for pushing range scans down to data source.
In addition to the optimisations mentioned above, we have made several
other improvements to augment query plans.
We can share our code and thoughts for Spark community is interested in
learning about our approach.
We have implemented a data warehouse/analytics platform using dimensional
data model approach on Spark+Cassandra by using our customizations.
thanks,
Venu Palvai
On Tue, Feb 14, 2017 at 12:59 PM, Nick Dimiduk (JIRA) <[email protected]>
> 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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