allisonwang-db commented on code in PR #47253:
URL: https://github.com/apache/spark/pull/47253#discussion_r1672808771
##########
common/utils/src/main/resources/error/error-conditions.json:
##########
@@ -954,6 +954,16 @@
"The input of <functionName> can't be <dataType> type data."
]
},
+ "UNSUPPORTED_UDF_INPUT_TYPE" : {
+ "message" : [
+ "UDFs do not support <dataType> type input data."
+ ]
+ },
+ "UNSUPPORTED_UDF_OUTPUT_TYPE" : {
+ "message" : [
+ "UDFs do not support <dataType> type output data."
Review Comment:
```suggestion
"UDFs do not support '<dataType>' as an output data type."
```
##########
python/pyspark/sql/types.py:
##########
@@ -194,16 +194,7 @@ def fromDDL(cls, ddl: str) -> "DataType":
>>> DataType.fromDDL("b: string, a: int")
StructType([StructField('b', StringType(), True), StructField('a',
IntegerType(), True)])
"""
- from pyspark.sql import SparkSession
- from pyspark.sql.functions import udf
-
- # Intentionally uses SparkSession so one implementation can be shared
with/without
- # Spark Connect.
- schema = (
- SparkSession.active().range(0).select(udf(lambda x: x,
returnType=ddl)("id")).schema
- )
- assert len(schema) == 1
- return schema[0].dataType
+ return _parse_datatype_string(ddl)
Review Comment:
Why is this change needed?
##########
common/utils/src/main/resources/error/error-conditions.json:
##########
@@ -954,6 +954,16 @@
"The input of <functionName> can't be <dataType> type data."
]
},
+ "UNSUPPORTED_UDF_INPUT_TYPE" : {
+ "message" : [
+ "UDFs do not support <dataType> type input data."
Review Comment:
```suggestion
"UDFs do not support '<dataType>' as an input data type."
```
##########
python/pyspark/sql/tests/pandas/test_pandas_udf_scalar.py:
##########
@@ -748,6 +750,31 @@ def check_vectorized_udf_return_scalar(self):
with self.assertRaisesRegex(Exception, "Return.*type.*Series"):
df.select(f(col("id"))).collect()
+ def test_udf_with_variant_input(self):
+ df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as
string)) v")
+ from pyspark.sql.functions import col
+
+ scalar_f = pandas_udf(lambda u: str(u), StringType())
+ iter_f = pandas_udf(
+ lambda it: map(lambda u: str(u), it), StringType(),
PandasUDFType.SCALAR_ITER
+ )
+ expectedErrorStr = 'UDFs do not support "VARIANT" type input data'
+ for f in [scalar_f, iter_f]:
+ with self.assertRaisesRegex(AnalysisException, expectedErrorStr):
+ df.select(f(col("v"))).collect()
Review Comment:
Can we check the error class instead of the error string here?
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/PythonUDF.scala:
##########
@@ -63,6 +63,32 @@ trait PythonFuncExpression extends NonSQLExpression with
UserDefinedExpression {
override def toString: String = s"$name(${children.mkString(",
")})#${resultId.id}$typeSuffix"
override def nullable: Boolean = true
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val check = super.checkInputDataTypes()
+ if (check.isFailure) {
+ check
+ } else {
+ val exprReturningVariant = children.collectFirst {
+ case e: Expression if typeContainsVariant(e.dataType) => e
+ }
+ exprReturningVariant match {
+ case Some(e) => TypeCheckResult.DataTypeMismatch(
+ errorSubClass = "UNSUPPORTED_UDF_INPUT_TYPE",
+ messageParameters = Map("dataType" -> s"\"${e.dataType.sql}\""))
+ case None => TypeCheckResult.TypeCheckSuccess
+ }
+ }
+ }
+
+ def typeContainsVariant(dt: DataType): Boolean = dt match {
Review Comment:
Do we have a helper function for this? This might be needed to check for
other expressions.
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