feat: add cube support

src/gateway/converter/spark_to_substrait.py

@@ -7,6 +7,7 @@
 import pathlib
 import re
 from enum import Enum
+from itertools import combinations
 
 import pyarrow as pa
 import pyspark.sql.connect.proto.base_pb2 as spark_pb2
@@ -19,7 +20,11 @@
 from substrait.gen.proto.extensions import extensions_pb2
 
 from gateway.converter.conversion_options import ConversionOptions
-from gateway.converter.spark_functions import ExtensionFunction, FunctionType, lookup_spark_function
+from gateway.converter.spark_functions import (
+    ExtensionFunction,
+    FunctionType,
+    lookup_spark_function,
+)
 from gateway.converter.substrait_builder import (
     add_function,
     aggregate_relation,
@@ -1260,29 +1265,8 @@ def convert_aggregate_relation(self, rel: spark_relations_pb2.Aggregate) -> alge
         self._next_under_aggregation_reference_id = 0
         self._under_aggregation_projects = []
 
-        # TODO -- Deal with mixed groupings and measures.
-        grouping_expression_list = []
-        for idx, grouping in enumerate(rel.grouping_expressions):
-            grouping_expression_list.append(self.convert_expression(grouping))
-            symbol.generated_fields.append(self.determine_name_for_grouping(grouping))
-            self._top_level_projects.append(field_reference(idx))
-        aggregate.groupings.append(
-            algebra_pb2.AggregateRel.Grouping(grouping_expressions=grouping_expression_list)
-        )
-
-        self._expression_processing_mode = ExpressionProcessingMode.AGGR_TOP_LEVEL
-
-        for expr in rel.aggregate_expressions:
-            result = self.convert_expression(expr)
-            if result:
-                self._top_level_projects.append(result)
-            symbol.generated_fields.append(self.determine_expression_name(expr))
-        symbol.output_fields.clear()
-        symbol.output_fields.extend(symbol.generated_fields)
-        if len(rel.grouping_expressions) > 1:
-            # Hide the grouping source from the downstream relations.
-            for i in range(len(rel.grouping_expressions) + len(rel.aggregate_expressions)):
-                aggregate.common.emit.output_mapping.append(i)
+        # Handle different group by types
+        self.handle_grouping_and_measures(rel, aggregate, symbol)
 
         self._expression_processing_mode = ExpressionProcessingMode.NORMAL
 
@@ -1312,6 +1296,66 @@ def convert_aggregate_relation(self, rel: spark_relations_pb2.Aggregate) -> alge
 
         return algebra_pb2.Rel(aggregate=aggregate)
 
+    def handle_grouping_and_measures(self, rel: spark_relations_pb2.Aggregate,
+                                     aggregate: algebra_pb2.AggregateRel,
+                                     symbol):
+        """Handle group by aggregations."""
+        grouping_expression_list = []
+        rel_grouping_expressions = rel.grouping_expressions
+        for idx, grouping in enumerate(rel_grouping_expressions):
+            grouping_expression_list.append(self.convert_expression(grouping))
+            symbol.generated_fields.append(self.determine_name_for_grouping(grouping))
+            self._top_level_projects.append(field_reference(idx))
+
+        match rel.group_type:
+            case spark_relations_pb2.Aggregate.GroupType.GROUP_TYPE_GROUPBY:
+                aggregate.groupings.append(
+                    algebra_pb2.AggregateRel.Grouping(
+                        grouping_expressions=grouping_expression_list)
+                )
+            case spark_relations_pb2.Aggregate.GroupType.GROUP_TYPE_CUBE:
+                # Generate and add all groupings required for CUBE
+                cube_groupings = self.create_cube_groupings(rel_grouping_expressions)
+                aggregate.groupings.extend(cube_groupings)
+            case _:
+                raise NotImplementedError(
+                    "Only GROUPBY and CUBE group types are currently supported."
+                )
+
+        self._expression_processing_mode = ExpressionProcessingMode.AGGR_TOP_LEVEL
+
+        for expr in rel.aggregate_expressions:
+            result = self.convert_expression(expr)
+            if result:
+                self._top_level_projects.append(result)
+            symbol.generated_fields.append(self.determine_expression_name(expr))
+        symbol.output_fields.clear()
+        symbol.output_fields.extend(symbol.generated_fields)
+        if len(rel.grouping_expressions) > 1:
+            # Hide the grouping source from the downstream relations.
+            for i in range(len(rel.grouping_expressions) + len(rel.aggregate_expressions)):
+                aggregate.common.emit.output_mapping.append(i)
+
+    def create_cube_groupings(self, grouping_expressions):
+        """Create all combinations of grouping expressions."""
+        num_expressions = len(grouping_expressions)
+        cube_groupings = []
+
+        # Generate all possible combinations of grouping expressions
+        for i in range(num_expressions + 1):
+            for combination in combinations(range(num_expressions), i):
+                # Create a list of the current combination of grouping expressions
+                converted_expressions = []
+                for j in combination:
+                    converted_expression = self.convert_expression(grouping_expressions[j])
+                    converted_expressions.append(converted_expression)
+                # Add the grouping for this combination
+                cube_groupings.append(
+                    algebra_pb2.AggregateRel.Grouping(grouping_expressions=converted_expressions)
+                )
+
+        return cube_groupings
+
     # pylint: disable=too-many-locals,pointless-string-statement
     def convert_show_string_relation(self, rel: spark_relations_pb2.ShowString) -> algebra_pb2.Rel:
         """Convert a show string relation into a Substrait subplan."""

src/gateway/tests/test_dataframe_api.py

@@ -64,7 +64,7 @@
     ucase,
     upper,
 )
-from pyspark.sql.types import DoubleType, StructField, StructType
+from pyspark.sql.types import DoubleType, IntegerType, StringType, StructField, StructType
 from pyspark.sql.window import Window
 from pyspark.testing import assertDataFrameEqual
 
@@ -154,6 +154,13 @@ def mark_dataframe_tests_as_xfail(request):
         pytest.skip(reason="inf vs -inf difference")
     if source == "gateway-over-duckdb" and originalname in ["test_union", "test_unionall"]:
         pytest.skip(reason="distinct not handled properly")
+    if source == "gateway-over-datafusion" and originalname == "test_rollup":
+        pytest.skip(reason="rollup aggregation not yet implemented in gateway")
+    if source == "gateway-over-duckdb" and originalname == "test_rollup":
+        pytest.skip(reason="rollup aggregation not yet implemented in gateway")
+    if source == "gateway-over-duckdb" and originalname == "test_cube":
+        pytest.skip(reason="cube aggregation not yet implemented in DuckDB")
+
 
 # ruff: noqa: E712
 class TestDataFrameAPI:
@@ -689,14 +696,14 @@ def test_exceptall(self, register_tpch_dataset, spark_session, caplog):
                 n_name="RUSSIA",
                 n_regionkey=3,
                 n_comment="uctions. furiously unusual instructions sleep furiously ironic "
-                "packages. slyly ",
+                          "packages. slyly ",
             ),
             Row(
                 n_nationkey=22,
                 n_name="RUSSIA",
                 n_regionkey=3,
                 n_comment="uctions. furiously unusual instructions sleep furiously ironic "
-                "packages. slyly ",
+                          "packages. slyly ",
             ),
             Row(
                 n_nationkey=23,
@@ -709,14 +716,14 @@ def test_exceptall(self, register_tpch_dataset, spark_session, caplog):
                 n_name="UNITED STATES",
                 n_regionkey=1,
                 n_comment="ly ironic requests along the slyly bold ideas hang after the "
-                "blithely special notornis; blithely even accounts",
+                          "blithely special notornis; blithely even accounts",
             ),
             Row(
                 n_nationkey=24,
                 n_name="UNITED STATES",
                 n_regionkey=1,
                 n_comment="ly ironic requests along the slyly bold ideas hang after the "
-                "blithely special notornis; blithely even accounts",
+                          "blithely special notornis; blithely even accounts",
             ),
         ]
 
@@ -802,14 +809,14 @@ def test_subtract(self, register_tpch_dataset, spark_session):
                 n_name="RUSSIA",
                 n_regionkey=3,
                 n_comment="uctions. furiously unusual instructions sleep furiously "
-                "ironic packages. slyly ",
+                          "ironic packages. slyly ",
             ),
             Row(
                 n_nationkey=24,
                 n_name="UNITED STATES",
                 n_regionkey=1,
                 n_comment="ly ironic requests along the slyly bold ideas hang after "
-                "the blithely special notornis; blithely even accounts",
+                          "the blithely special notornis; blithely even accounts",
             ),
         ]
 
@@ -2657,7 +2664,7 @@ def test_computation_with_two_aggregations(self, register_tpch_dataset, spark_se
         assertDataFrameEqual(outcome, expected)
 
     def test_computation_with_two_aggregations_and_internal_calculation(
-        self, register_tpch_dataset, spark_session
+            self, register_tpch_dataset, spark_session
     ):
         expected = [
             Row(l_suppkey=1, a=Decimal("3903113211864.3000")),
@@ -2762,3 +2769,76 @@ def test_row_number(self, users_dataframe):
                 "row_number").limit(3)
 
         assertDataFrameEqual(outcome, expected)
+
+
+@pytest.fixture(scope="class")
+def userage_dataframe(spark_session_for_setup):
+    data = [
+        [1, "Alice"],
+        [2, "Bob"],
+        [3, "Alice"]
+    ]
+
+    schema = StructType(
+        [
+            StructField("age", IntegerType(), True),
+            StructField("name", StringType(), True),
+        ]
+    )
+
+    test_df = spark_session_for_setup.createDataFrame(data, schema)
+
+    test_df.createOrReplaceTempView("userage")
+    return spark_session_for_setup.table("userage")
+
+
+class TestDataFrameDecisionSupport:
+    """Tests data science methods of the dataframe side of SparkConnect."""
+
+    def test_groupby(self, userage_dataframe):
+        expected = [
+            Row(name='Alice', age=1, count=1),
+            Row(name='Alice', age=3, count=1),
+            Row(name='Bob', age=2, count=1)
+        ]
+
+        with utilizes_valid_plans(userage_dataframe):
+            outcome = userage_dataframe.groupby("name", "age").count().orderBy("name",
+                                                                               "age").collect()
+
+        assertDataFrameEqual(outcome, expected)
+
+    def test_rollup(self, userage_dataframe):
+        expected = [
+            Row(name='Alice', age=1, count=1),
+            Row(name='Alice', age=3, count=1),
+            Row(name='Alice', age=None, count=2),
+            Row(name='Bob', age=2, count=1),
+            Row(name='Bob', age=None, count=1),
+            Row(name=None, age=None, count=3)
+        ]
+
+        with utilizes_valid_plans(userage_dataframe):
+            outcome = userage_dataframe.rollup("name", "age").count().orderBy("name",
+                                                                              "age").collect()
+
+        assertDataFrameEqual(outcome, expected)
+
+    def test_cube(self, userage_dataframe):
+        expected = [
+            Row(name='Alice', age=1, count=1),
+            Row(name='Alice', age=3, count=1),
+            Row(name='Alice', age=None, count=2),
+            Row(name='Bob', age=2, count=1),
+            Row(name='Bob', age=None, count=1),
+            Row(name=None, age=1, count=1),
+            Row(name=None, age=2, count=1),
+            Row(name=None, age=3, count=1),
+            Row(name=None, age=None, count=3)
+        ]
+
+        with utilizes_valid_plans(userage_dataframe):
+            outcome = userage_dataframe.cube("name", "age").count().orderBy("name",
+                                                                            "age").collect()
+
+        assertDataFrameEqual(outcome, expected)