[GitHub] [spark] ueshin commented on a diff in pull request #41924: [SPARK-44364] [PYTHON] Add support for List[Row] data type for expected

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

ueshin commented on code in PR #41924:
URL: https://github.com/apache/spark/pull/41924#discussion_r1260128512


##########
python/pyspark/testing/utils.py:
##########
@@ -382,18 +388,23 @@ def assert_rows_equal(rows1: List[Row], rows2: List[Row]):
         try:
             # rename duplicate columns for sorting
             renamed_df = df.toDF(*[f"_{i}" for i in range(len(df.columns))])
-            renamed_expected = expected.toDF(*[f"_{i}" for i in range(len(expected.columns))])
-
             df = renamed_df.sort(renamed_df.columns).toDF(*df.columns)
-            expected = renamed_expected.sort(renamed_expected.columns).toDF(*expected.columns)
+            if isinstance(expected, List):
+                expected.sort()

Review Comment:
   ```py
   expected = sorted(expected)
   ```
   
   to avoid updating the input.



##########
python/pyspark/testing/utils.py:
##########
@@ -382,18 +388,23 @@ def assert_rows_equal(rows1: List[Row], rows2: List[Row]):
         try:
             # rename duplicate columns for sorting
             renamed_df = df.toDF(*[f"_{i}" for i in range(len(df.columns))])
-            renamed_expected = expected.toDF(*[f"_{i}" for i in range(len(expected.columns))])
-
             df = renamed_df.sort(renamed_df.columns).toDF(*df.columns)
-            expected = renamed_expected.sort(renamed_expected.columns).toDF(*expected.columns)
+            if isinstance(expected, List):
+                expected.sort()

Review Comment:
   Btw, I'm wondering Python's `sort` or `sorted` works as same as `df.sort()`. 
   We might want to ask users to make it already sorted when they specify a list here.
   cc @allisonwang-db 



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