Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/17142#discussion_r104063545
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
---
@@ -54,39 +54,77 @@ private[csv] class UnivocityParser(
private val dataSchema = StructType(schema.filter(_.name !=
options.columnNameOfCorruptRecord))
- private val valueConverters =
- dataSchema.map(f => makeConverter(f.name, f.dataType, f.nullable,
options)).toArray
-
private val tokenizer = new CsvParser(options.asParserSettings)
private var numMalformedRecords = 0
private val row = new GenericInternalRow(requiredSchema.length)
- // This gets the raw input that is parsed lately.
+ // In `PERMISSIVE` parse mode, we should be able to put the raw
malformed row into the field
+ // specified in `columnNameOfCorruptRecord`. The raw input is retrieved
by this method.
private def getCurrentInput(): String =
tokenizer.getContext.currentParsedContent().stripLineEnd
- // This parser loads an `indexArr._1`-th position value in input tokens,
- // then put the value in `row(indexArr._2)`.
- 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
- }
- // TODO: Revisit this; we need to clean up code here for readability.
- // See an URL below for related discussions:
- // https://github.com/apache/spark/pull/16928#discussion_r102636720
- val fieldsWithIndexes = fields.zipWithIndex
- corruptFieldIndex.map { case corrFieldIndex =>
- fieldsWithIndexes.filter { case (_, i) => i != corrFieldIndex }
- }.getOrElse {
- fieldsWithIndexes
- }.map { case (f, i) =>
- (dataSchema.indexOf(f), i)
- }.toArray
--- End diff --
It is just a little bit for codes.. actually :). I hope the comment makes
reading this code easier and not look too verbose.
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