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From Emmanuel <ele...@msn.com>
Subject bug? using withColumn with colName with dot can't replace column
Date Tue, 15 Mar 2016 17:51:15 GMT

In Spark 1.6
if I do (column name has dot in it, but is not a nested column):
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:
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. column is created).
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

defwithColumn(colName: String, col: Column): DataFrameReturns a new DataFrame by adding a
column or replacing the existing column that has the same name.


Any thoughts on why the different behavior when the column exists?

Thanks
 		 	   		  
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