[SPARK-42168][3.4][SQL][PYTHON][FOLLOW-UP] Test FlatMapCoGroupsInPand…

…as with Window function

### What changes were proposed in this pull request?
This ports tests from #39717 in branch-3.2 to branch-3.4. See #39752 (comment).

### Why are the changes needed?
To make sure this use case is tested.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
E2E test in `test_pandas_cogrouped_map.py` and analysis test in `EnsureRequirementsSuite.scala`.

Closes #39803 from EnricoMi/branch-3.4-cogroup-window-bug-test.

Authored-by: Enrico Minack <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

python/pyspark/sql/tests/pandas/test_pandas_cogrouped_map.py

@@ -18,8 +18,9 @@
 import unittest
 from typing import cast
 
-from pyspark.sql.functions import array, explode, col, lit, udf, pandas_udf
+from pyspark.sql.functions import array, explode, col, lit, udf, pandas_udf, sum
 from pyspark.sql.types import DoubleType, StructType, StructField, Row
+from pyspark.sql.window import Window
 from pyspark.errors import IllegalArgumentException, PythonException
 from pyspark.testing.sqlutils import (
     ReusedSQLTestCase,
@@ -365,6 +366,57 @@ def test_self_join(self):
 
         self.assertEqual(row.asDict(), Row(column=2, value=2).asDict())
 
+    def test_with_window_function(self):
+        # SPARK-42168: a window function with same partition keys but differing key order
+        ids = 2
+        days = 100
+        vals = 10000
+        parts = 10
+
+        id_df = self.spark.range(ids)
+        day_df = self.spark.range(days).withColumnRenamed("id", "day")
+        vals_df = self.spark.range(vals).withColumnRenamed("id", "value")
+        df = id_df.join(day_df).join(vals_df)
+
+        left_df = df.withColumnRenamed("value", "left").repartition(parts).cache()
+        # SPARK-42132: this bug requires us to alias all columns from df here
+        right_df = (
+            df.select(col("id").alias("id"), col("day").alias("day"), col("value").alias("right"))
+            .repartition(parts)
+            .cache()
+        )
+
+        # note the column order is different to the groupBy("id", "day") column order below
+        window = Window.partitionBy("day", "id")
+
+        left_grouped_df = left_df.groupBy("id", "day")
+        right_grouped_df = right_df.withColumn("day_sum", sum(col("day")).over(window)).groupBy(
+            "id", "day"
+        )
+
+        def cogroup(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
+            return pd.DataFrame(
+                [
+                    {
+                        "id": left["id"][0]
+                        if not left.empty
+                        else (right["id"][0] if not right.empty else None),
+                        "day": left["day"][0]
+                        if not left.empty
+                        else (right["day"][0] if not right.empty else None),
+                        "lefts": len(left.index),
+                        "rights": len(right.index),
+                    }
+                ]
+            )
+
+        df = left_grouped_df.cogroup(right_grouped_df).applyInPandas(
+            cogroup, schema="id long, day long, lefts integer, rights integer"
+        )
+
+        actual = df.orderBy("id", "day").take(days)
+        self.assertEqual(actual, [Row(0, day, vals, vals) for day in range(days)])
+
     @staticmethod
     def _test_with_key(left, right, isLeft):
         def right_assign_key(key, lft, rgt):

sql/core/src/test/scala/org/apache/spark/sql/execution/exchange/EnsureRequirementsSuite.scala

@@ -17,17 +17,22 @@
 
 package org.apache.spark.sql.execution.exchange
 
+import org.apache.spark.api.python.PythonEvalType
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.Sum
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.statsEstimation.StatsTestPlan
 import org.apache.spark.sql.connector.catalog.functions._
 import org.apache.spark.sql.execution.{DummySparkPlan, SortExec}
 import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.execution.python.FlatMapCoGroupsInPandasExec
+import org.apache.spark.sql.execution.window.WindowExec
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.internal.SQLConf.REQUIRE_ALL_CLUSTER_KEYS_FOR_CO_PARTITION
 import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 class EnsureRequirementsSuite extends SharedSparkSession {
   private val exprA = Literal(1)
@@ -1104,6 +1109,57 @@ class EnsureRequirementsSuite extends SharedSparkSession {
     }
   }
 
+  test("SPARK-42168: FlatMapCoGroupInPandas and Window function with differing key order") {
+    val lKey = AttributeReference("key", IntegerType)()
+    val lKey2 = AttributeReference("key2", IntegerType)()
+
+    val rKey = AttributeReference("key", IntegerType)()
+    val rKey2 = AttributeReference("key2", IntegerType)()
+    val rValue = AttributeReference("value", IntegerType)()
+
+    val left = DummySparkPlan()
+    val right = WindowExec(
+      Alias(
+        WindowExpression(
+          Sum(rValue).toAggregateExpression(),
+          WindowSpecDefinition(
+            Seq(rKey2, rKey),
+            Nil,
+            SpecifiedWindowFrame(RowFrame, UnboundedPreceding, UnboundedFollowing)
+          )
+        ), "sum")() :: Nil,
+      Seq(rKey2, rKey),
+      Nil,
+      DummySparkPlan()
+    )
+
+    val pythonUdf = PythonUDF("pyUDF", null,
+      StructType(Seq(StructField("value", IntegerType))),
+      Seq.empty,
+      PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF,
+      true)
+
+    val flapMapCoGroup = FlatMapCoGroupsInPandasExec(
+      Seq(lKey, lKey2),
+      Seq(rKey, rKey2),
+      pythonUdf,
+      AttributeReference("value", IntegerType)() :: Nil,
+      left,
+      right
+    )
+
+    val result = EnsureRequirements.apply(flapMapCoGroup)
+    result match {
+      case FlatMapCoGroupsInPandasExec(leftKeys, rightKeys, _, _,
+        SortExec(leftOrder, false, _, _), SortExec(rightOrder, false, _, _)) =>
+        assert(leftKeys === Seq(lKey, lKey2))
+        assert(rightKeys === Seq(rKey, rKey2))
+        assert(leftKeys.map(k => SortOrder(k, Ascending)) === leftOrder)
+        assert(rightKeys.map(k => SortOrder(k, Ascending)) === rightOrder)
+      case other => fail(other.toString)
+    }
+  }
+
   def bucket(numBuckets: Int, expr: Expression): TransformExpression = {
     TransformExpression(BucketFunction, Seq(expr), Some(numBuckets))
   }