[
https://issues.apache.org/jira/browse/SPARK-18642?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Mohit updated SPARK-18642:
--------------------------
Description:
When doing a left-join between two tables, say A and B, Catalyst has
information about the projection required for table B. Only the required
columns should be scanned.
Code snippet below explains the scenario:
scala> val dfA = sqlContext.read.parquet("/home/mohit/ruleA")
dfA: org.apache.spark.sql.DataFrame = [aid: int, aVal: string]
scala> val dfB = sqlContext.read.parquet("/home/mohit/ruleB")
dfB: org.apache.spark.sql.DataFrame = [bid: int, bVal: string]
scala> dfA.registerTempTable("A")
scala> dfB.registerTempTable("B")
scala> sqlContext.sql("select A.aid, B.bid from A left join B on A.aid=B.bid
where B.bid<2").explain
== Physical Plan ==
Project [aid#15,bid#17]
+- Filter (bid#17 < 2)
+- BroadcastHashOuterJoin [aid#15], [bid#17], LeftOuter, None
:- Scan ParquetRelation[aid#15,aVal#16] InputPaths: file:/home/mohit/ruleA
+- Scan ParquetRelation[bid#17,bVal#18] InputPaths: file:/home/mohit/ruleB
This is a watered-down example from a production issue which has a huge
performance impact.
External reference:
http://stackoverflow.com/questions/40783675/spark-sql-catalyst-is-scanning-undesired-columns
was:
When doing a left-join between two tables, say A and B, Catalyst has
information about the projection required for table B. Only the required
columns should be scanned.
Code snippet below explains the scenario:
scala> val dfA = sqlContext.read.parquet("/home/mohit/ruleA")
dfA: org.apache.spark.sql.DataFrame = [aid: int, aVal: string]
scala> val dfB = sqlContext.read.parquet("/home/mohit/ruleB")
dfB: org.apache.spark.sql.DataFrame = [bid: int, bVal: string]
scala> dfA.registerTempTable("A")
scala> dfB.registerTempTable("B")
scala> sqlContext.sql("select A.aid, B.bid from A left join B on A.aid=B.bid
where B.bid<2").explain
== Physical Plan ==
Project [aid#15,bid#17]
+- Filter (bid#17 < 2)
+- BroadcastHashOuterJoin [aid#15], [bid#17], LeftOuter, None
:- Scan ParquetRelation[aid#15,aVal#16] InputPaths: file:/home/mohit/ruleA
+- Scan ParquetRelation[bid#17,bVal#18] InputPaths: file:/home/mohit/ruleB
This is a watered-down example from a production issue which has a huge
performance impact.
> Spark SQL: Catalyst is scanning undesired columns
> -------------------------------------------------
>
> Key: SPARK-18642
> URL: https://issues.apache.org/jira/browse/SPARK-18642
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.2
> Environment: Ubuntu 14.04
> Spark: Local Mode
> Reporter: Mohit
> Labels: performance
>
> When doing a left-join between two tables, say A and B, Catalyst has
> information about the projection required for table B. Only the required
> columns should be scanned.
> Code snippet below explains the scenario:
> scala> val dfA = sqlContext.read.parquet("/home/mohit/ruleA")
> dfA: org.apache.spark.sql.DataFrame = [aid: int, aVal: string]
> scala> val dfB = sqlContext.read.parquet("/home/mohit/ruleB")
> dfB: org.apache.spark.sql.DataFrame = [bid: int, bVal: string]
> scala> dfA.registerTempTable("A")
> scala> dfB.registerTempTable("B")
> scala> sqlContext.sql("select A.aid, B.bid from A left join B on A.aid=B.bid
> where B.bid<2").explain
> == Physical Plan ==
> Project [aid#15,bid#17]
> +- Filter (bid#17 < 2)
> +- BroadcastHashOuterJoin [aid#15], [bid#17], LeftOuter, None
> :- Scan ParquetRelation[aid#15,aVal#16] InputPaths:
> file:/home/mohit/ruleA
> +- Scan ParquetRelation[bid#17,bVal#18] InputPaths:
> file:/home/mohit/ruleB
> This is a watered-down example from a production issue which has a huge
> performance impact.
> External reference:
> http://stackoverflow.com/questions/40783675/spark-sql-catalyst-is-scanning-undesired-columns
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
