[GitHub] [spark] dtenedor commented on a diff in pull request #42595: [SPARK-44901][SQL] Add API in Python UDTF 'analyze' method to return partitioning/ordering expressions

["dtenedor (via GitHub)" <gi...@apache.org>]

dtenedor commented on code in PR #42595:
URL: https://github.com/apache/spark/pull/42595#discussion_r1309301713


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sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala:
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@@ -643,11 +643,17 @@ class SQLQueryTestSuite extends QueryTest with SharedSparkSession with SQLHelper
             s"$testCaseName - ${udf.prettyName}", absPath, resultFile, udf)
         }
       } else if (file.getAbsolutePath.startsWith(s"$inputFilePath${File.separator}udtf")) {
-        Seq(TestPythonUDTF("udtf")).map { udtf =>
-          UDTFTestCase(
-            s"$testCaseName - ${udtf.prettyName}", absPath, resultFile, udtf
-          )
-        }
+        val udtfs = Seq(
+          TestPythonUDTF("udtf"),
+          TestPythonUDTFCountSumLast,
+          TestPythonUDTFWithSinglePartition,
+          TestPythonUDTFPartitionBy,
+          TestPythonUDTFInvalidPartitionByAndWithSinglePartition,
+          TestPythonUDTFInvalidOrderByWithoutPartitionBy
+        )
+        Seq(UDTFTestCase(
+          s"$testCaseName - Python UDTFs", absPath, resultFile, udtfs
+        ))

Review Comment:
   Thanks, I found it was necessary to create multiple UDTF instances in order to exercise different combinations of behaviors for functions that exported required partitioning properties from the 'analyze' method vs. calls to those functions with explicit PARTITION BY clauses. To make this more consistent with how the other test cases worked, I created a wrapping class 'TestUDTFSet' and renamed the test case type to 'UDTFSetTestCase', which now accepts the former as a single value instead.



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