xiaonanyang-db commented on code in PR #37933:
URL: https://github.com/apache/spark/pull/37933#discussion_r977079883
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala:
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@@ -233,7 +238,39 @@ class CSVInferSchema(val options: CSVOptions) extends
Serializable {
* is compatible with both input data types.
*/
private def compatibleType(t1: DataType, t2: DataType): Option[DataType] = {
- TypeCoercion.findTightestCommonType(t1,
t2).orElse(findCompatibleTypeForCSV(t1, t2))
+ (t1, t2) match {
+ case (DateType, TimestampType) | (DateType, TimestampNTZType) |
+ (TimestampNTZType, DateType) | (TimestampType, DateType) =>
+ // For a column containing a mixture of dates and timestamps
+ // infer it as timestamp type if its dates can be inferred as
timestamp type
+ // otherwise infer it as StringType
Review Comment:
We want to have consistent behavior when timestamp format is not specified.
When `prefersDate=false`, a column with mixed date and timestamp could be
inferred as timestamp if possible.
Thus, we added the additional handling here to have similar behavior as
above when `prefersDate=true`.
Does this make sense?
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