HyukjinKwon commented on code in PR #41606:
URL: https://github.com/apache/spark/pull/41606#discussion_r1250057360
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +220,45 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertSparkSchemaEquality(
+ s1: Optional[Union[AtomicType, StructType, str, List[str], Tuple[str,
...]]],
+ s2: Optional[Union[AtomicType, StructType, str, List[str], Tuple[str,
...]]],
+):
+ if s1 != s2:
+ msg = "Schemas are different"
+ raise AssertionError(msg)
+
+
+def assertSparkDFEquality(
+ left: PySparkDataFrame, right: PySparkDataFrame,
+):
+ def assert_rows_equality(rows1, rows2):
+ if rows1 != rows2:
+ msg = "Dataframes are different"
+ raise AssertionError(msg)
+
+ left = left.sort(left.columns)
+ right = right.sort(right.columns)
+
+ assertSparkSchemaEquality(left.schema, right.schema)
Review Comment:
Column order is ignored here, which I don't think it's correct. Spark does
not guarantee the order of results so you might need to collected results
instead, but not the schema.
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