Jerry Lam created SPARK-21109:
---------------------------------
Summary: union two dataset[A] don't work as expected if one of the
datasets is originated from a dataframe
Key: SPARK-21109
URL: https://issues.apache.org/jira/browse/SPARK-21109
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.1.1
Reporter: Jerry Lam
To reproduce the issue:
{code}
case class my_case(id0: Long, id1: Int, id2: Int, id3: String)
val data1 = Seq(my_case(0L, 0, 0, "0")).toDS
val data2 = Seq(("1", 1, 1, 1L)).toDF("id3", "id1", "id2", "id0").as[my_case]
data1.show
+---+---+---+---+
|id0|id1|id2|id3|
+---+---+---+---+
| 0| 0| 0| 0|
+---+---+---+---+
data2.show
+---+---+---+---+
|id3|id1|id2|id0|
+---+---+---+---+
| 1| 1| 1| 1|
+---+---+---+---+
data1.union(data2).show
org.apache.spark.sql.AnalysisException: Cannot up cast `id0` from string to
bigint as it may truncate
The type path of the target object is:
- field (class: "scala.Long", name: "id0")
- root class: "my_case"
You can either add an explicit cast to the input data or choose a higher
precision type of the field in the target object;
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveUpCast$$fail(Analyzer.scala:2123)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34$$anonfun$applyOrElse$14.applyOrElse(Analyzer.scala:2153)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34$$anonfun$applyOrElse$14.applyOrElse(Analyzer.scala:2140)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:268)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:268)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:267)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4$$anonfun$apply$11.apply(TreeNode.scala:336)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:334)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:273)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:245)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:245)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:266)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:276)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$6.apply(QueryPlan.scala:285)
at
org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:285)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:245)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:236)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34.applyOrElse(Analyzer.scala:2140)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$34.applyOrElse(Analyzer.scala:2136)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.apply(Analyzer.scala:2136)
at
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.apply(Analyzer.scala:2121)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
at
scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381)
at
org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolveAndBind(ExpressionEncoder.scala:258)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:209)
at org.apache.spark.sql.Dataset.<init>(Dataset.scala:167)
at org.apache.spark.sql.Dataset$.apply(Dataset.scala:58)
at org.apache.spark.sql.Dataset.withSetOperator(Dataset.scala:2859)
at org.apache.spark.sql.Dataset.union(Dataset.scala:1632)
{code}
A hacky way to fix the above is:
{code}
data1.union(data2.map{a=>a}).show
+---+---+---+---+
|id0|id1|id2|id3|
+---+---+---+---+
| 0| 0| 0| 0|
| 1| 1| 1| 1|
+---+---+---+---+
{code}
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