Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/16928#discussion_r102660814
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/UnivocityParser.scala
---
@@ -202,21 +212,41 @@ private[csv] class UnivocityParser(
}
numMalformedRecords += 1
None
- } else if (options.failFast && schema.length != tokens.length) {
+ } else if (options.failFast && dataSchema.length != tokens.length) {
throw new RuntimeException(s"Malformed line in FAILFAST mode: " +
s"${tokens.mkString(options.delimiter.toString)}")
} else {
- val checkedTokens = if (options.permissive && schema.length >
tokens.length) {
- tokens ++ new Array[String](schema.length - tokens.length)
- } else if (options.permissive && schema.length < tokens.length) {
- tokens.take(schema.length)
+ val checkedTokens = if (options.permissive) {
+ // If a length of parsed tokens is not equal to expected one, it
makes the length the same
+ // with the expected. If the length is shorter, it adds extra
tokens in the tail.
+ // If longer, it drops extra tokens.
--- End diff --
Yup, I agree in a way but I guess "it is pretty common that CSV is
malformed in this way" (said by the analysis team in my company). Could we
leave it as is for now here?
Let me try to raise a different JIRA after checking R's `read.csv` or other
libraries.
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