Github user HyukjinKwon commented on a diff in the pull request: https://github.com/apache/spark/pull/11724#discussion_r56785216 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala --- @@ -108,14 +109,38 @@ private[csv] object CSVInferSchema { } private def tryParseDouble(field: String): DataType = { - if ((allCatch opt field.toDouble).isDefined) { + val doubleTry = allCatch opt field.toDouble --- End diff -- I see. There is a problem here. - The maximum precision supported in Spark `DecimalType` is 38. - In Spark `DecimalType`, `scale` cannot be more than `precision`, meaning there should not be such a value, `0.xxx..` for Spark `DecimalType`. Now I cannot think of more than three options below: - Try `DecimalType` first. In this case, some basic number with fractions such as `1.1` will be inferred as `DecimalType`. - Try `DecimalType` first and let `DecimalType` do not treat numbers with fractions by checking `scale`. - Try `DoubleType` first and check the precision loss. - If it loses, try `DecimalType` - If it fails to be parsed as `DecimalType` (due to both conditions in `DecimalType` above), then infer this as `DoubleType` allowing the precision loss. If none of them is preferable, then I will close this as I cannot come up with a better idea.
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