panbingkun commented on code in PR #44665:
URL: https://github.com/apache/spark/pull/44665#discussion_r1519273344
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/csvExpressions.scala:
##########
@@ -260,16 +263,33 @@ case class StructsToCsv(
child = child,
timeZoneId = None)
+ override def checkInputDataTypes(): TypeCheckResult = {
+ child.dataType match {
+ case schema: StructType if schema.map(_.dataType).forall(
+ dt => isSupportedDataType(dt)) => TypeCheckSuccess
+ case _ => DataTypeMismatch(
+ errorSubClass = "UNSUPPORTED_INPUT_TYPE",
Review Comment:
Using `UNSUPPORTED_INPUT_TYPE` instead of `UNEXPECTED_INPUT_TYPE` here will
make the prompt information more `clear` to the user, for example:
```
val rows = new java.util.ArrayList[Row]()
rows.add(Row(1L, Row(2L, "Alice",
Map("math" -> 100L, "english" -> 200L, "science" -> null))))
val valueSchema = StructType(Seq(
StructField("age", LongType),
StructField("name", StringType),
StructField("scores", MapType(StringType, LongType))))
val schema = StructType(Seq(
StructField("key", LongType),
StructField("value", valueSchema)))
val df = spark.createDataFrame(rows, schema)
df.select(to_csv($"value")).collect()
```
Before(Last review):
Users may feel confused about prompt: `requires the "STRUCT" type, however
"value" has the type "STRUCT...`
```
[DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE] Cannot resolve "to_csv(value)" due
to data type mismatch: The 1 parameter requires the "STRUCT" type, however
"value" has the type "STRUCT<age: BIGINT, name: STRING, scores: MAP<STRING,
BIGINT>>". SQLSTATE: 42K09;
```
After(Right now):
```
[DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE] Cannot resolve "to_csv(value)"
due to data type mismatch: The input of `to_csv` can't be "STRUCT<age: BIGINT,
name: STRING, scores: MAP<STRING, BIGINT>>" type data. SQLSTATE: 42K09;
```
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