HyukjinKwon commented on a change in pull request #35615:
URL: https://github.com/apache/spark/pull/35615#discussion_r812475306
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File path:
sql/core/src/test/scala/org/apache/spark/sql/IntegratedUDFTestUtils.scala
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@@ -31,15 +31,16 @@ import org.apache.spark.sql.catalyst.expressions.{Cast,
Expression, ExprId, Pyth
import org.apache.spark.sql.catalyst.plans.SQLHelper
import org.apache.spark.sql.execution.python.UserDefinedPythonFunction
import org.apache.spark.sql.expressions.SparkUserDefinedFunction
-import org.apache.spark.sql.types.{DataType, StringType}
+import org.apache.spark.sql.types.{DataType, IntegerType, StringType}
/**
- * This object targets to integrate various UDF test cases so that Scalar UDF,
Python UDF and
- * Scalar Pandas UDFs can be tested in SBT & Maven tests.
+ * This object targets to integrate various UDF test cases so that Scalar UDF,
Python UDF,
+ * Scalar Pandas UDF and Grouped Aggregate Pandas UDF can be tested in SBT &
Maven tests.
*
- * The available UDFs are special. It defines an UDF wrapped by cast. So, the
input column is
- * casted into string, UDF returns strings as are, and then output column is
casted back to
- * the input column. In this way, UDF is virtually no-op.
+ * The available UDFs are special. For Scalar UDF, Python UDF and Scalar
Pandas UDF,
+ * it defines an UDF wrapped by cast. So, the input column is casted into
string,
+ * UDF returns strings as are, and then output column is casted back to the
input column.
+ * In this way, UDF is virtually no-op.
Review comment:
I think we should mention that. grouped aggregtate `count`, not no-op.
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