allisonwang-db commented on code in PR #41606:
URL: https://github.com/apache/spark/pull/41606#discussion_r1251258342
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +216,50 @@ 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 assertSparkDFEqual(left: PySparkDataFrame, right: PySparkDataFrame):
+ def assert_rows_equality(rows1, rows2):
+ if rows1 != rows2:
Review Comment:
Let's add some tests for
1. None/Null
2. Complex types (array, map, struct)
3. Floating points
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +216,50 @@ 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 assertSparkDFEqual(left: PySparkDataFrame, right: PySparkDataFrame):
+ def assert_rows_equality(rows1, rows2):
+ if rows1 != rows2:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_DATAFRAME",
+ message_parameters={},
+ )
+
+ left = left.sort(left.columns)
Review Comment:
We don't always want the rows to be sorted, e.g. when a query has a "ORDER
BY" clause. We can check the logical plan of the dataframe to see if there are
any `Sort`.
```
val isSorted = df.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty
```
I am not sure if this works for Spark Connect tests.
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +216,50 @@ def check_error(
self.assertEqual(
expected, actual, f"Expected message parameters was '{expected}',
got '{actual}'"
)
+
+
+def assertSparkSchemaEquality(
Review Comment:
assertSchemaEqual
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +216,50 @@ 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 assertSparkDFEqual(left: PySparkDataFrame, right: PySparkDataFrame):
Review Comment:
Maybe we can have this:
assertDataFrameEqual(df: DataFrame, expected: Union[DataFrame, List[Row]])
We can use the `checkAnswer` from the Scala side for reference.
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +216,50 @@ 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 assertSparkDFEqual(left: PySparkDataFrame, right: PySparkDataFrame):
+ def assert_rows_equality(rows1, rows2):
+ if rows1 != rows2:
+ raise PySparkAssertionError(
+ error_class="DIFFERENT_DATAFRAME",
+ message_parameters={},
+ )
+
+ left = left.sort(left.columns)
Review Comment:
If checking the logical plan is not doable, we can probably have a param
`ignore_row_order=True` for users to specify whether they want the rows sorted.
##########
python/pyspark/testing/utils.py:
##########
@@ -209,3 +216,50 @@ 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 assertSparkDFEqual(left: PySparkDataFrame, right: PySparkDataFrame):
+ def assert_rows_equality(rows1, rows2):
+ if rows1 != rows2:
+ raise PySparkAssertionError(
Review Comment:
Do we need to use error classes here?
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