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https://issues.apache.org/jira/browse/SPARK-21109?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16073066#comment-16073066
]
Jerry Lam commented on SPARK-21109:
-----------------------------------
Does the order of the columns part of the schema? It actually depends on which
database you are talking about. Some databases reorder the fields regardless on
the order you define them. IF schema includes positions of the field for spark,
then this applies for spark. That's why I explained what schema means to me. If
you understand the problem, I'm not arguing the schema at all. I'm talking
about "Dataset[my_case]" not "Dataset[Row]"
You haven't answer my question regarding how one implements the method:
{code}
def unionTwoDatasets(data1: Dataset[my_case], data2: Dataset[my_case]) = {
....
}
{code}
Your suggestion is to assume data1 and data2 are dataframe and check the schema
of both. but I think this is why Spark develops Dataset?
> 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}
> Note that both data1 and data2 are the same type Dataset[my_case]
> 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}
> This bug is very obscure if you are implementing an interface with 2 input
> arguments of Dataset[A]. If you need to union two datasets for the
> implementation, some datasets will work and some don't. Or some will work but
> WRONG.
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