Emmanuel Leroy created SPARK-13913:
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Summary: DataFrame.withColumn fails when trying to replace
existing column with dot in name
Key: SPARK-13913
URL: https://issues.apache.org/jira/browse/SPARK-13913
Project: Spark
Issue Type: Bug
Affects Versions: 1.6.0
Reporter: Emmanuel Leroy
http://stackoverflow.com/questions/36000147/spark-1-6-apply-function-to-column-with-dot-in-name-how-to-properly-escape-coln/36005334#36005334
if I do (column name exists already and has dot in it, but is not a nested
column):
scala> df = df.withColumn("raw.hourOfDay", df.col("`raw.hourOfDay`"))
scala> df = df.withColumn("raw.hourOfDay", df.col("`raw.hourOfDay`"))
org.apache.spark.sql.AnalysisException: cannot resolve 'raw.minOfDay' given
input columns raw.hourOfDay_2, raw.dayOfWeek, raw.sensor2, raw.hourOfDay,
raw.minOfDay;
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:60)
at
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)
at
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)
at
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)
at
org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:318)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:107)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:117)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:121)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at
org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:121)
at
org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:125)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
but if I do:
scala> df = df.withColumn("raw.hourOfDay_2", df.col("`raw.hourOfDay`"))
scala> df.printSchema
root
|-- raw.hourOfDay: long (nullable = true)
|-- raw.minOfDay: long (nullable = true)
|-- raw.dayOfWeek: long (nullable = true)
|-- raw.sensor2: long (nullable = true)
|-- raw.hourOfDay_2: long (nullable = true)
it works fine (i.e. new column is created with dot in ColName).
The only difference is that the name "raw.hourOfDay_2" does not exist yet, and
is properly created as a colName with dot, not as a nested column.
The documentation however says that if the column exists it will replace it,
but it seems there is a miss-interpretation of the column name as a nested
column
def withColumn(colName: String, col: Column): DataFrame
Returns a new DataFrame by adding a column or replacing the existing column
that has the same name.
Replacing a column without a dot in it works fine.
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