HyukjinKwon commented on code in PR #41927:
URL: https://github.com/apache/spark/pull/41927#discussion_r1260475301
##########
python/pyspark/testing/utils.py:
##########
@@ -221,7 +221,130 @@ def check_error(
)
-def assertDataFrameEqual(df: DataFrame, expected: DataFrame, check_row_order:
bool = False):
+def assertSchemaEqual(
+ df_schema: StructType, expected_schema: StructType, ignore_nullable: bool
= False
+):
+ """
+ A util function to assert equality between DataFrame schemas `df_schema`
+ and `expected_schema`, with optional parameter `ignore_nullable`.
+
+ .. versionadded:: 3.5.0
+
+ Parameters
+ ----------
+ df_schema : StructType
+ The DataFrame schema that is being compared or tested.
+
+ expected_schema : StructType
+ The expected schema, for comparison with the actual schema.
+
+ ignore_nullable : bool, optional
+ A flag indicating whether the nullable flag should be ignored in
schema comparison.
+ If set to `False` (default), the nullable flag in the schemas is not
taken into account.
+ If set to `True`, the nullable flag will be checked during schema
comparison.
+
+ Examples
+ --------
+ >>> from pyspark.sql.types import StructType, StructField, ArrayType,
IntegerType, DoubleType
+ >>> s1 = StructType([StructField("names", ArrayType(DoubleType(), True),
True)])
+ >>> s2 = StructType([StructField("names", ArrayType(DoubleType(), True),
True)])
+ >>> assertSchemaEqual(s1, s2) # pass
+ >>> s1 = StructType([StructField("names", ArrayType(IntegerType(), True),
True)])
+ >>> s2 = StructType([StructField("names", ArrayType(DoubleType(), False),
True)])
+ >>> assertSchemaEqual(s1, s2) # fail # doctest: +IGNORE_EXCEPTION_DETAIL
+ Traceback (most recent call last):
+ ...
+ PySparkAssertionError: [DIFFERENT_SCHEMA] Schemas do not match:
+ [df]
+ StructField("names", ArrayType(IntegerType(), True), True)
+ <BLANKLINE>
+ [expected]
+ StructField("names", ArrayType(DoubleType(), False), True)
+ <BLANKLINE>
+ """
+
+ def compare_schemas_ignore_nullable(s1, s2):
+ if len(s1) != len(s2):
+ return False
+ zipped = zip_longest(s1, s2)
+ for sf1, sf2 in zipped:
+ if not compare_structfields_ignore_nullable(sf1, sf2):
+ return False
+ return True
+
+ def compare_structfields_ignore_nullable(df_structfield,
expected_structfield):
+ if ignore_nullable:
+ if df_structfield is None and expected_structfield is None:
+ return True
+ elif df_structfield is None or expected_structfield is None:
+ return False
+ if df_structfield.name != expected_structfield.name:
+ return False
+ else:
+ return compare_datatypes_ignore_nullable(
+ df_structfield.dataType, expected_structfield.dataType
+ )
+ else:
+ return df_structfield == expected_structfield
+
+ def compare_datatypes_ignore_nullable(dt1, dt2):
+ # checks datatype equality, using recursion to ignore nullable
+ if dt1.typeName() == dt2.typeName():
+ if dt1.typeName() == "array":
+ return compare_datatypes_ignore_nullable(dt1.elementType,
dt2.elementType)
+ elif dt1.typeName() == "struct":
+ return compare_schemas_ignore_nullable(dt1, dt2)
+ else:
+ return True
+ else:
+ return False
+
+ schemas_equal = True
+ error_msg = "Schemas do not match: \n"
+
+ if ignore_nullable:
+ if not compare_schemas_ignore_nullable(df_schema, expected_schema):
+ zipped = zip_longest(df_schema, expected_schema)
+ for df_structfield, expected_structfield in zipped:
+ if not compare_structfields_ignore_nullable(df_structfield,
expected_structfield):
+ schemas_equal = False
+ error_msg += (
+ "[df]"
+ + "\n"
+ + str(df_structfield)
+ + "\n\n"
+ + "[expected]"
+ + "\n"
+ + str(expected_structfield)
+ + "\n\n"
+ )
+ else:
+ if df_schema != expected_schema:
+ schemas_equal = False
+ zipped = zip_longest(df_schema, expected_schema)
+ for df_structfield, expected_structfield in zipped:
+ if df_structfield != expected_structfield:
+ error_msg += (
+ "[df]"
+ + "\n"
+ + str(df_structfield)
+ + "\n\n"
+ + "[expected]"
+ + "\n"
+ + str(expected_structfield)
+ + "\n\n"
+ )
+
+ if not schemas_equal:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_SCHEMA",
+ message_parameters={"error_msg": error_msg},
+ )
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: DataFrame, check_row_order: bool = False,
ignore_nullable: bool = False
Review Comment:
I am good with excluding nullability in this PR scope for now, but wonder if
we can have a good error message for deeply nested type. That's actually my
major concern.
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