HyukjinKwon commented on a change in pull request #23665: [SPARK-26745][SQL]
Skip empty lines in JSON-derived DataFrames when skipParsing optimization in
effect
URL: https://github.com/apache/spark/pull/23665#discussion_r251482039
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/FailureSafeParser.scala
##########
@@ -55,11 +56,15 @@ class FailureSafeParser[IN](
def parse(input: IN): Iterator[InternalRow] = {
try {
- if (skipParsing) {
- Iterator.single(InternalRow.empty)
- } else {
- rawParser.apply(input).toIterator.map(row => toResultRow(Some(row), ()
=> null))
- }
+ if (skipParsing) {
+ if (unparsedRecordIsNonEmpty(input)) {
Review comment:
Like, how are we going to explain this to users? When you count, the record
is separated by newlines but other operations recognise JSON array and empty
strings .. , and count is a special case? CSV doesn't do that fwiw - it filters
empty strings. I think we shouldn't do reasoning in particular for such basic
operations.
If I were a user, I would think it's counter initiative. Is it me alone
thinking in this way? This case specifically doesn't look straightforward. I
know there are issues about parsing stuff and we should define the behaviour
but this case looks not.
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