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From "Don Drake (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-17384) SQL - Running query with outer join from 1.6 fails
Date Fri, 02 Sep 2016 20:22:20 GMT
Don Drake created SPARK-17384:
---------------------------------

             Summary: SQL - Running query with outer join from 1.6 fails
                 Key: SPARK-17384
                 URL: https://issues.apache.org/jira/browse/SPARK-17384
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.0.0
            Reporter: Don Drake


I have some complex (10-table joins) SQL queries that utilize outer joins that work fine in
Spark 1.6.2, but fail under Spark 2.0.  I was able to duplicate the problem using a simple
test case.

Here's the code for Spark 2.0 that doesn't run (this runs fine in Spark 1.6.2):

{code}
case class C1(f1: String, f2: String, f3: String, f4: String)
case class C2(g1: String, g2: String, g3: String, g4: String)
case class C3(h1: String, h2: String, h3: String, h4: String)

val sqlContext = spark.sqlContext 

val c1 = sc.parallelize(Seq(
  C1("h1", "c1a1", "c1b1", "c1c1"),
  C1("h2", "c1a2", "c1b2", "c1c2"),
  C1(null, "c1a3", "c1b3", "c1c3")
  )).toDF
c1.createOrReplaceTempView("c1")

val c2 = sc.parallelize(Seq(
  C2("h1", "c2a1", "c2b1", "c2c1"),
  C2("h2", "c2a2", "c2b2", "c2c2"),
  C2(null, "c2a3", "c2b3", "c2c3"),
  C2(null, "c2a4", "c2b4", "c2c4"),
  C2("h333", "c2a333", "c2b333", "c2c333")
  )).toDF
c2.createOrReplaceTempView("c2")

val c3 = sc.parallelize(Seq(
  C3("h1", "c3a1", "c3b1", "c3c1"),
  C3("h2", "c3a2", "c3b2", "c3c2"),
  C3(null, "c3a3", "c3b3", "c3c3")
  )).toDF
c3.createOrReplaceTempView("c3")

// doesn't work in Spark 2.0, works in Spark 1.6
val bad_df = sqlContext.sql("""
  select * 
  from c1, c3
  left outer join c2 on (c1.f1 = c2.g1)
  where c1.f1 = c3.h1
""").show()

// works in both
val works_df = sqlContext.sql("""
  select * 
  from c1
  left outer join c2 on (c1.f1 = c2.g1), 
  c3
  where c1.f1 = c3.h1
""").show()
{code}

Here's the output after running bad_df in Spark 2.0:

{code}
scala> val bad_df = sqlContext.sql("""
     |   select *
     |   from c1, c3
     |   left outer join c2 on (c1.f1 = c2.g1)
     |   where c1.f1 = c3.h1
     | """).show()
org.apache.spark.sql.AnalysisException: cannot resolve '`c1.f1`' given input columns: [h3,
g3, h4, g2, g4, h2, h1, g1]; line 4 pos 25
  at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:190)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:201)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$5.apply(QueryPlan.scala:209)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:209)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:125)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
  at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
  at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:582)
  at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:682)
  ... 53 elided

scala>
{code}

I confirmed this fails on the Spark 2.0 nightly build as well.  This runs just fine in Spark
1.6.2.




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