Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/17142#discussion_r104107028
--- 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
+ // This parser loads an `tokenIndexArr`-th position value in input
tokens,
+ // then put the value in `row(rowIndexArr)`.
+ //
+ // For example, let's say there is CSV data as below:
+ //
+ // a,b,c
+ // 1,2,A
+ //
+ // Also, let's say `columnNameOfCorruptRecord` is set to "_unparsed",
`header` is `true`
+ // by user and the user selects "c", "b", "_unparsed" and "a" fields. In
this case, we need
+ // to map those values below:
+ //
+ // required schema - ["c", "b", "_unparsed", "a"]
+ // CSV data schema - ["a", "b", "c"]
+ // required CSV data schema - ["c", "b", "a"]
+ //
+ // with the input tokens,
+ //
+ // input tokens - [1, 2, "A"]
+ //
+ // Each input token is placed in each output row's position by mapping
these. In this case,
+ //
+ // output row - ["A", 2, null, 1]
+ //
+ // In more details,
+ // - `valueConverters`, input tokens - CSV data schema
+ // `valueConverters` keeps the positions of input token indices (by
its index) to each
+ // value's converter (by its value) in an order of CSV data schema. In
this case,
+ // [string->int, string->int, string->string].
+ //
+ // - `tokenIndexArr`, input tokens - required CSV data schema
--- End diff --
ditto; `tokenIndexArr` is an index array in tokens corresponding to
`requiredSchema`?
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