[jira] [Assigned] (SPARK-34417) org.apache.spark.sql.DataFrameNaFunctions.fillMap(values: Seq[(String, Any)]) fails for column name having a dot

["Apache Spark (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-34417?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-34417:
------------------------------------

    Assignee:     (was: Apache Spark)

> org.apache.spark.sql.DataFrameNaFunctions.fillMap(values: Seq[(String, Any)]) fails for column name having a dot
> ----------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-34417
>                 URL: https://issues.apache.org/jira/browse/SPARK-34417
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 3.0.1
>         Environment: Spark version - 3.0.1
> OS - macOS 10.15.7
>            Reporter: Amandeep Sharma
>            Priority: Major
>
> Code to reproduce the issue:
> {code:java}
> import org.apache.spark.sql.SparkSession
> object ColumnNameWithDot {
>   def main(args: Array[String]): Unit = {
>     val spark = SparkSession.builder.appName("Simple Application")
>       .config("spark.master", "local").getOrCreate()
>     spark.sparkContext.setLogLevel("OFF")
>     import spark.implicits._
>     val df = Seq(("abc", 23), ("def", 44), (null, 0)).toDF("ColWith.Dot", "Col")
>     df.na.fill(Map("`ColWith.Dot`" -> "na"))
>       .show()
>   }
> }
> {code}
> *Analysis*
> *------------------------------PART-I-----------------------------------*
> Debugged the spark code. It is due to a bug in the spark-catalyst code at
> [https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala#L266%23L268]
> Function in question resolves the column as per the code-comments in the following order until a match is found.
>  * Consider pattern dbName.tableName.columnName
>  * Consider tableName.columnName
>  * Consider everything as columnName
> But implementation considers only the first part for the resolution in the third step. It should join all parts using dot(.).
> *------------------------------PART-II-----------------------------------*
> If we don’t use column name with back-tick them it fails at [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala#L400]
>  If it is quoted, the condition at [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala#L413] becomes false as *k* has value quoted with back-tick whereas *f.name* is not. Then it fails at [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala#L422]
> It is failing due to the reason mentioned in the PART-I.
> *Solution*
> Make changes in [https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/package.scala#L266%23L268] as below:
> {color:#ff0000}-val name = nameParts.head{color}
>  {color:#00875a}+ val name = nameParts.mkString(".") // join all part using .{color}
>  val attributes = collectMatches(name, direct.get(name.toLowerCase(Locale.ROOT)))
>  {color:#ff0000}- (attributes, nameParts.tail){color}
>  {color:#00875a}+ (attributes, Seq.empty){color}
> *{color:#172b4d}Workaround{color}*
> {color:#172b4d}We can make change in [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala#L396]{color}
> {color:#172b4d}While we are resolving the input columns, create a new map with the name of the resolved column and replace value as below.{color}
> {color:#172b4d}Idea is to use resolved named instead of input name while filling null values.{color}
> {code:java}
> private def fillMap(values: Seq[(String, Any)]): DataFrame = {
>   // Error handling
>   var resolved: Map[String, Any] = Map()
>   values.foreach { case (colName, replaceValue) =>
>     // Check column name exists
>     val resolvedColumn = df.resolve(colName)
>     // Check data type
>     replaceValue match {
>       case _: jl.Double | _: jl.Float | _: jl.Integer | _: jl.Long | _: jl.Boolean | _: String =>
>       // This is good
>       case _ => throw new IllegalArgumentException(
>         s"Unsupported value type ${replaceValue.getClass.getName} ($replaceValue).")
>     }
>     resolved += (resolvedColumn.name -> replaceValue)
>   }
>   val columnEquals = df.sparkSession.sessionState.analyzer.resolver
>   val projections = df.schema.fields.map { f =>
>     resolved.find { case (k, _) => columnEquals(k, f.name) }.map { case (_, v) =>
>       v match {
>         case v: jl.Float => fillCol[Float](f, v)
>         case v: jl.Double => fillCol[Double](f, v)
>         case v: jl.Long => fillCol[Long](f, v)
>         case v: jl.Integer => fillCol[Integer](f, v)
>         case v: jl.Boolean => fillCol[Boolean](f, v.booleanValue())
>         case v: String => fillCol[String](f, v)
>       }
>     }.getOrElse(df.col(f.name))
>   }
>   df.select(projections : _*)
> }
> {code}



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