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_r251279489
##########
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:
Yea, that's safer approach if possible but to do that, we should manually
check the input like the current PR. It doesn't looks a good idea to me to
check input outside of `JacksonParser`.
The problem is, we cannot distinguish the cases below without parsing:
```
[{...}, {...}]
```
```
[]
```
```
{...}
```
```
# empty string
```
One line (`input: IN`) can be, 0 record, 1 record and multiple records.
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