[jira] [Created] (SPARK-18058) AnalysisException may be thrown when union two DFs whose struct fields have different nullability

["Cheng Lian (JIRA)" <ji...@apache.org>]

Cheng Lian created SPARK-18058:
----------------------------------

             Summary: AnalysisException may be thrown when union two DFs whose struct fields have different nullability
                 Key: SPARK-18058
                 URL: https://issues.apache.org/jira/browse/SPARK-18058
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.0.1, 1.6.2
            Reporter: Cheng Lian


The following Spark shell snippet reproduces this issue:
{code}
spark.range(10).createOrReplaceTempView("t1")
spark.range(10).map(i => i: java.lang.Long).toDF("id").createOrReplaceTempView("t2")
sql("SELECT struct(id) FROM t1 UNION ALL SELECT struct(id) FROM t2")
{code}

{noformat}
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. StructType(StructField(id,LongType,true)) <> StructType(StructField(id,LongType,false)) at the first column of the second table;
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:57)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$11$$anonfun$apply$12.apply(CheckAnalysis.scala:291)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$11$$anonfun$apply$12.apply(CheckAnalysis.scala:289)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$11.apply(CheckAnalysis.scala:289)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$11.apply(CheckAnalysis.scala:278)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:278)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:132)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
  at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:61)
  at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:573)
  ... 50 elided
{noformat}

The reason is that we treat two {{StructType}} incompatible even if their only differ from each other in field nullability.



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