Hisoka-X opened a new pull request, #43243:
URL: https://github.com/apache/spark/pull/43243
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### What changes were proposed in this pull request?
This PR fix CSV/JSON schema inference when timestamps do not match specified
timestampFormat will report error.
```scala
//eg
val csv = spark.read.option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss")
.option("inferSchema", true).csv(Seq("2884-06-24T02:45:51.138").toDS())
csv.show()
//error
Caused by: java.time.format.DateTimeParseException: Text
'2884-06-24T02:45:51.138' could not be parsed, unparsed text found at index 19
```
This bug only happend when partition had one row. The data type should be
`StringType` not `TimestampType` because the value not match `timestampFormat`.
Use csv as eg, in `CSVInferSchema::tryParseTimestampNTZ`, first, use
`timestampNTZFormatter.parseWithoutTimeZoneOptional` to inferring return
`TimestampType`, if same partition had another row, it will use
`tryParseTimestamp` to parse row with user defined `timestampFormat`, then
found it can't be convert to timestamp with `timestampFormat`. Finally return
`StringType`. But when only one row, we use
`timestampNTZFormatter.parseWithoutTimeZoneOptional` to parse normally
timestamp not right. We should only parse it when `spark.sql.timestampType` is
`TIMESTAMP_NTZ`. If `spark.sql.timestampType` is `TIMESTAMP_LTZ`, we should
directly parse it use `tryParseTimestamp`. To avoid return `TimestampType` when
timestamps do not match specified timestampFormat.
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### Why are the changes needed?
Fix schema inference bug.
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### Does this PR introduce _any_ user-facing change?
No
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### How was this patch tested?
add new test.
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### Was this patch authored or co-authored using generative AI tooling?
No
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