Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/16928#discussion_r101894489 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala --- @@ -45,24 +46,35 @@ private[csv] class UnivocityParser( // A `ValueConverter` is responsible for converting the given value to a desired type. private type ValueConverter = String => Any + private val inputSchema = columnNameOfCorruptRecord.map { fn => + StructType(schema.filter(_.name != fn)) + }.getOrElse(schema) + private val valueConverters = - schema.map(f => makeConverter(f.name, f.dataType, f.nullable, options)).toArray + inputSchema.map(f => makeConverter(f.name, f.dataType, f.nullable, options)).toArray private val parser = new CsvParser(options.asParserSettings) private var numMalformedRecords = 0 private val row = new GenericInternalRow(requiredSchema.length) - private val indexArr: Array[Int] = { + private val shouldHandleCorruptRecord = columnNameOfCorruptRecord.isDefined + private val corruptIndex = columnNameOfCorruptRecord.flatMap { fn => + requiredSchema.getFieldIndex(fn) + }.getOrElse(-1) + + private val indexArr: Array[(Int, Int)] = { val fields = if (options.dropMalformed) { // If `dropMalformed` is enabled, then it needs to parse all the values // so that we can decide which row is malformed. requiredSchema ++ schema.filterNot(requiredSchema.contains(_)) } else { requiredSchema } - fields.map(schema.indexOf(_: StructField)).toArray + fields.zipWithIndex.filter { case (_, i) => i != corruptIndex }.map { case (f, i) => + (inputSchema.indexOf(f), i) + }.toArray --- End diff -- Let me checkout this PR and check if this bit can be cleaner but it might be even nicer if you could double check please.
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