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Liang-Chi Hsieh commented on SPARK-22283: ----------------------------------------- When joined result has duplicate column name, you can't select any of the ambiguous columns by just name. Doesn't {{withColumn}} current behavior simply follow it? > withColumn should replace multiple instances with a single one > -------------------------------------------------------------- > > Key: SPARK-22283 > URL: https://issues.apache.org/jira/browse/SPARK-22283 > Project: Spark > Issue Type: Bug > Components: Spark Core > Affects Versions: 2.2.0 > Reporter: Albert Meltzer > > Currently, {{withColumn}} claims to do the following: _"adding a column or > replacing the existing column that has the same name."_ > Unfortunately, if multiple existing columns have the same name (which is a > normal occurrence after a join), this results in multiple replaced -- and > retained -- > columns (with the same value), and messages about an ambiguous column. > The current implementation of {{withColumn}} contains this: > {noformat} > def withColumn(colName: String, col: Column): DataFrame = { > val resolver = sparkSession.sessionState.analyzer.resolver > val output = queryExecution.analyzed.output > val shouldReplace = output.exists(f => resolver(f.name, colName)) > if (shouldReplace) { > val columns = output.map { field => > if (resolver(field.name, colName)) { > col.as(colName) > } else { > Column(field) > } > } > select(columns : _*) > } else { > select(Column("*"), col.as(colName)) > } > } > {noformat} > Instead, suggest something like this (which replaces all matching fields with > a single instance of the new one): > {noformat} > def withColumn(colName: String, col: Column): DataFrame = { > val resolver = sparkSession.sessionState.analyzer.resolver > val output = queryExecution.analyzed.output > val existing = output.filterNot(f => resolver(f.name, colName)).map(new > Column(_)) > select(existing :+ col.as(colName): _*) > } > {noformat} -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org