ueshin commented on code in PR #41606:
URL: https://github.com/apache/spark/pull/41606#discussion_r1253479475
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +233,71 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: Union[DataFrame, List[Row]], ignore_row_order:
bool = True
+):
+ if df is None and expected is None:
+ return True
+ elif df is None or expected is None:
+ return False
+
+ def compare(r1: Row, r2: Row):
+ if r1 is None and r2 is None:
+ return True
+ elif r1 is None or r2 is None:
+ return False
+
+ d1 = r1.asDict()
+ d2 = r2.asDict()
Review Comment:
We can't use `asDict()` here because `Row` can have same name fields.
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +233,71 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: Union[DataFrame, List[Row]], ignore_row_order:
bool = True
+):
+ if df is None and expected is None:
+ return True
+ elif df is None or expected is None:
+ return False
+
+ def compare(r1: Row, r2: Row):
+ if r1 is None and r2 is None:
+ return True
+ elif r1 is None or r2 is None:
+ return False
+
+ d1 = r1.asDict()
+ d2 = r2.asDict()
+
+ for key in d1.keys() & d2.keys():
+ if isinstance(d1[key], float) and isinstance(d2[key], float):
+ if abs(d1[key] - d2[key]) > 1e-5:
+ return False
+ else:
+ if d1[key] != d2[key]:
+ return False
+ return True
+
+ def assert_schema_equal(
+ df_schema: Optional[Union[AtomicType, StructType, str, List[str],
Tuple[str, ...]]],
+ expected_schema: Optional[Union[AtomicType, StructType, str,
List[str], Tuple[str, ...]]],
+ ):
+ if df_schema != expected_schema:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_SCHEMA",
+ message_parameters={"df_schema": df.schema, "expected_schema":
expected.schema},
+ )
+
+ def assert_rows_equal(rows1: Row, rows2: Row):
+ zipped = list(zip_longest(rows1, rows2))
+ rows_equal = True
+ error_table = PrettyTable(["df", "expected"])
+
+ for r1, r2 in zipped:
+ if compare(r1, r2):
+ error_table.add_row([blue(r1), blue(r2)])
+ else:
+ rows_equal = False
+ error_table.add_row([red(r1), red(r2)])
+
+ if not rows_equal:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_DATAFRAME",
+ message_parameters={"error_table": error_table.get_string()},
+ )
+
+ if ignore_row_order:
+ try:
+ df = df.sort(df.columns)
+ expected = expected.sort(expected.columns)
+ except:
+ raise PySparkAssertionError(
+ error_class="UNSUPPORTED_DTYPE_FOR_IGNORE_ROW_ORDER",
Review Comment:
Shall we use `DATATYPE` instead of `DTYPE`?
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +233,71 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: Union[DataFrame, List[Row]], ignore_row_order:
bool = True
+):
+ if df is None and expected is None:
+ return True
+ elif df is None or expected is None:
+ return False
+
+ def compare(r1: Row, r2: Row):
+ if r1 is None and r2 is None:
+ return True
+ elif r1 is None or r2 is None:
+ return False
+
+ d1 = r1.asDict()
+ d2 = r2.asDict()
+
+ for key in d1.keys() & d2.keys():
+ if isinstance(d1[key], float) and isinstance(d2[key], float):
+ if abs(d1[key] - d2[key]) > 1e-5:
+ return False
+ else:
+ if d1[key] != d2[key]:
+ return False
+ return True
+
+ def assert_schema_equal(
+ df_schema: Optional[Union[AtomicType, StructType, str, List[str],
Tuple[str, ...]]],
+ expected_schema: Optional[Union[AtomicType, StructType, str,
List[str], Tuple[str, ...]]],
+ ):
Review Comment:
Not that this is a private function in `assertDataFrameEqual`, we can expect
the schemas as `StructType` only?
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +233,71 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: Union[DataFrame, List[Row]], ignore_row_order:
bool = True
+):
+ if df is None and expected is None:
+ return True
+ elif df is None or expected is None:
+ return False
+
+ def compare(r1: Row, r2: Row):
+ if r1 is None and r2 is None:
+ return True
+ elif r1 is None or r2 is None:
+ return False
+
+ d1 = r1.asDict()
+ d2 = r2.asDict()
+
+ for key in d1.keys() & d2.keys():
+ if isinstance(d1[key], float) and isinstance(d2[key], float):
+ if abs(d1[key] - d2[key]) > 1e-5:
+ return False
+ else:
+ if d1[key] != d2[key]:
+ return False
+ return True
+
+ def assert_schema_equal(
+ df_schema: Optional[Union[AtomicType, StructType, str, List[str],
Tuple[str, ...]]],
+ expected_schema: Optional[Union[AtomicType, StructType, str,
List[str], Tuple[str, ...]]],
+ ):
+ if df_schema != expected_schema:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_SCHEMA",
+ message_parameters={"df_schema": df.schema, "expected_schema":
expected.schema},
+ )
+
+ def assert_rows_equal(rows1: Row, rows2: Row):
+ zipped = list(zip_longest(rows1, rows2))
+ rows_equal = True
+ error_table = PrettyTable(["df", "expected"])
+
+ for r1, r2 in zipped:
+ if compare(r1, r2):
+ error_table.add_row([blue(r1), blue(r2)])
+ else:
+ rows_equal = False
+ error_table.add_row([red(r1), red(r2)])
+
+ if not rows_equal:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_DATAFRAME",
+ message_parameters={"error_table": error_table.get_string()},
+ )
+
+ if ignore_row_order:
+ try:
+ df = df.sort(df.columns)
+ expected = expected.sort(expected.columns)
+ except:
Review Comment:
Shall we catch a specific exception class that could be caused by `df.sort`
only?
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +233,71 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: Union[DataFrame, List[Row]], ignore_row_order:
bool = True
+):
+ if df is None and expected is None:
+ return True
+ elif df is None or expected is None:
+ return False
+
+ def compare(r1: Row, r2: Row):
+ if r1 is None and r2 is None:
+ return True
+ elif r1 is None or r2 is None:
+ return False
+
+ d1 = r1.asDict()
+ d2 = r2.asDict()
+
+ for key in d1.keys() & d2.keys():
+ if isinstance(d1[key], float) and isinstance(d2[key], float):
+ if abs(d1[key] - d2[key]) > 1e-5:
+ return False
+ else:
+ if d1[key] != d2[key]:
+ return False
+ return True
+
+ def assert_schema_equal(
+ df_schema: Optional[Union[AtomicType, StructType, str, List[str],
Tuple[str, ...]]],
+ expected_schema: Optional[Union[AtomicType, StructType, str,
List[str], Tuple[str, ...]]],
+ ):
+ if df_schema != expected_schema:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_SCHEMA",
+ message_parameters={"df_schema": df.schema, "expected_schema":
expected.schema},
+ )
+
+ def assert_rows_equal(rows1: Row, rows2: Row):
+ zipped = list(zip_longest(rows1, rows2))
+ rows_equal = True
+ error_table = PrettyTable(["df", "expected"])
+
+ for r1, r2 in zipped:
+ if compare(r1, r2):
+ error_table.add_row([blue(r1), blue(r2)])
+ else:
+ rows_equal = False
+ error_table.add_row([red(r1), red(r2)])
+
+ if not rows_equal:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_DATAFRAME",
+ message_parameters={"error_table": error_table.get_string()},
+ )
+
+ if ignore_row_order:
+ try:
+ df = df.sort(df.columns)
+ expected = expected.sort(expected.columns)
Review Comment:
According to the type hint, `expected` can be `List[Row]`, and I don't think
this works in the case? Also at
https://github.com/apache/spark/pull/41606/files#diff-89e8f0a4413730d86ed98f11e10e3dfe259aa80378a2734eee659ea08bfff8b4R302-R303
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +233,71 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertDataFrameEqual(
+ df: DataFrame, expected: Union[DataFrame, List[Row]], ignore_row_order:
bool = True
+):
+ if df is None and expected is None:
+ return True
+ elif df is None or expected is None:
+ return False
+
+ def compare(r1: Row, r2: Row):
+ if r1 is None and r2 is None:
+ return True
+ elif r1 is None or r2 is None:
+ return False
+
+ d1 = r1.asDict()
+ d2 = r2.asDict()
+
+ for key in d1.keys() & d2.keys():
+ if isinstance(d1[key], float) and isinstance(d2[key], float):
+ if abs(d1[key] - d2[key]) > 1e-5:
+ return False
+ else:
+ if d1[key] != d2[key]:
+ return False
+ return True
+
+ def assert_schema_equal(
+ df_schema: Optional[Union[AtomicType, StructType, str, List[str],
Tuple[str, ...]]],
+ expected_schema: Optional[Union[AtomicType, StructType, str,
List[str], Tuple[str, ...]]],
+ ):
+ if df_schema != expected_schema:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_SCHEMA",
+ message_parameters={"df_schema": df.schema, "expected_schema":
expected.schema},
Review Comment:
nit: `df_schema` and `expected_schema`?
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