Adding upstream version 9.0.1.upstream/9.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

5 files changed, 1351 insertions, 0 deletions

diff --git a/tests/dataframe/integration/__init__.py b/tests/dataframe/integration/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/tests/dataframe/integration/__init__.py
diff --git a/tests/dataframe/integration/dataframe_validator.py b/tests/dataframe/integration/dataframe_validator.py
new file mode 100644
index 0000000..6c4642f
--- /dev/null
+++ b/tests/dataframe/integration/dataframe_validator.py
@@ -0,0 +1,149 @@
+import typing as t
+import unittest
+import warnings
+
+import sqlglot
+from tests.helpers import SKIP_INTEGRATION
+
+if t.TYPE_CHECKING:
+    from pyspark.sql import DataFrame as SparkDataFrame
+
+
+@unittest.skipIf(SKIP_INTEGRATION, "Skipping Integration Tests since `SKIP_INTEGRATION` is set")
+class DataFrameValidator(unittest.TestCase):
+    spark = None
+    sqlglot = None
+    df_employee = None
+    df_store = None
+    df_district = None
+    spark_employee_schema = None
+    sqlglot_employee_schema = None
+    spark_store_schema = None
+    sqlglot_store_schema = None
+    spark_district_schema = None
+    sqlglot_district_schema = None
+
+    @classmethod
+    def setUpClass(cls):
+        from pyspark import SparkConf
+        from pyspark.sql import SparkSession, types
+
+        from sqlglot.dataframe.sql import types as sqlglotSparkTypes
+        from sqlglot.dataframe.sql.session import SparkSession as SqlglotSparkSession
+
+        # This is for test `test_branching_root_dataframes`
+        config = SparkConf().setAll([("spark.sql.analyzer.failAmbiguousSelfJoin", "false")])
+        cls.spark = SparkSession.builder.master("local[*]").appName("Unit-tests").config(conf=config).getOrCreate()
+        cls.spark.sparkContext.setLogLevel("ERROR")
+        cls.sqlglot = SqlglotSparkSession()
+        cls.spark_employee_schema = types.StructType(
+            [
+                types.StructField("employee_id", types.IntegerType(), False),
+                types.StructField("fname", types.StringType(), False),
+                types.StructField("lname", types.StringType(), False),
+                types.StructField("age", types.IntegerType(), False),
+                types.StructField("store_id", types.IntegerType(), False),
+            ]
+        )
+        cls.sqlglot_employee_schema = sqlglotSparkTypes.StructType(
+            [
+                sqlglotSparkTypes.StructField("employee_id", sqlglotSparkTypes.IntegerType(), False),
+                sqlglotSparkTypes.StructField("fname", sqlglotSparkTypes.StringType(), False),
+                sqlglotSparkTypes.StructField("lname", sqlglotSparkTypes.StringType(), False),
+                sqlglotSparkTypes.StructField("age", sqlglotSparkTypes.IntegerType(), False),
+                sqlglotSparkTypes.StructField("store_id", sqlglotSparkTypes.IntegerType(), False),
+            ]
+        )
+        employee_data = [
+            (1, "Jack", "Shephard", 37, 1),
+            (2, "John", "Locke", 65, 1),
+            (3, "Kate", "Austen", 37, 2),
+            (4, "Claire", "Littleton", 27, 2),
+            (5, "Hugo", "Reyes", 29, 100),
+        ]
+        cls.df_employee = cls.spark.createDataFrame(data=employee_data, schema=cls.spark_employee_schema)
+        cls.dfs_employee = cls.sqlglot.createDataFrame(data=employee_data, schema=cls.sqlglot_employee_schema)
+        cls.df_employee.createOrReplaceTempView("employee")
+
+        cls.spark_store_schema = types.StructType(
+            [
+                types.StructField("store_id", types.IntegerType(), False),
+                types.StructField("store_name", types.StringType(), False),
+                types.StructField("district_id", types.IntegerType(), False),
+                types.StructField("num_sales", types.IntegerType(), False),
+            ]
+        )
+        cls.sqlglot_store_schema = sqlglotSparkTypes.StructType(
+            [
+                sqlglotSparkTypes.StructField("store_id", sqlglotSparkTypes.IntegerType(), False),
+                sqlglotSparkTypes.StructField("store_name", sqlglotSparkTypes.StringType(), False),
+                sqlglotSparkTypes.StructField("district_id", sqlglotSparkTypes.IntegerType(), False),
+                sqlglotSparkTypes.StructField("num_sales", sqlglotSparkTypes.IntegerType(), False),
+            ]
+        )
+        store_data = [
+            (1, "Hydra", 1, 37),
+            (2, "Arrow", 2, 2000),
+        ]
+        cls.df_store = cls.spark.createDataFrame(data=store_data, schema=cls.spark_store_schema)
+        cls.dfs_store = cls.sqlglot.createDataFrame(data=store_data, schema=cls.sqlglot_store_schema)
+        cls.df_store.createOrReplaceTempView("store")
+
+        cls.spark_district_schema = types.StructType(
+            [
+                types.StructField("district_id", types.IntegerType(), False),
+                types.StructField("district_name", types.StringType(), False),
+                types.StructField("manager_name", types.StringType(), False),
+            ]
+        )
+        cls.sqlglot_district_schema = sqlglotSparkTypes.StructType(
+            [
+                sqlglotSparkTypes.StructField("district_id", sqlglotSparkTypes.IntegerType(), False),
+                sqlglotSparkTypes.StructField("district_name", sqlglotSparkTypes.StringType(), False),
+                sqlglotSparkTypes.StructField("manager_name", sqlglotSparkTypes.StringType(), False),
+            ]
+        )
+        district_data = [
+            (1, "Temple", "Dogen"),
+            (2, "Lighthouse", "Jacob"),
+        ]
+        cls.df_district = cls.spark.createDataFrame(data=district_data, schema=cls.spark_district_schema)
+        cls.dfs_district = cls.sqlglot.createDataFrame(data=district_data, schema=cls.sqlglot_district_schema)
+        cls.df_district.createOrReplaceTempView("district")
+        sqlglot.schema.add_table("employee", cls.sqlglot_employee_schema)
+        sqlglot.schema.add_table("store", cls.sqlglot_store_schema)
+        sqlglot.schema.add_table("district", cls.sqlglot_district_schema)
+
+    def setUp(self) -> None:
+        warnings.filterwarnings("ignore", category=ResourceWarning)
+        self.df_spark_store = self.df_store.alias("df_store")  # type: ignore
+        self.df_spark_employee = self.df_employee.alias("df_employee")  # type: ignore
+        self.df_spark_district = self.df_district.alias("df_district")  # type: ignore
+        self.df_sqlglot_store = self.dfs_store.alias("store")  # type: ignore
+        self.df_sqlglot_employee = self.dfs_employee.alias("employee")  # type: ignore
+        self.df_sqlglot_district = self.dfs_district.alias("district")  # type: ignore
+
+    def compare_spark_with_sqlglot(
+        self, df_spark, df_sqlglot, no_empty=True, skip_schema_compare=False
+    ) -> t.Tuple["SparkDataFrame", "SparkDataFrame"]:
+        def compare_schemas(schema_1, schema_2):
+            for schema in [schema_1, schema_2]:
+                for struct_field in schema.fields:
+                    struct_field.metadata = {}
+            self.assertEqual(schema_1, schema_2)
+
+        for statement in df_sqlglot.sql():
+            actual_df_sqlglot = self.spark.sql(statement)  # type: ignore
+        df_sqlglot_results = actual_df_sqlglot.collect()
+        df_spark_results = df_spark.collect()
+        if not skip_schema_compare:
+            compare_schemas(df_spark.schema, actual_df_sqlglot.schema)
+        self.assertEqual(df_spark_results, df_sqlglot_results)
+        if no_empty:
+            self.assertNotEqual(len(df_spark_results), 0)
+            self.assertNotEqual(len(df_sqlglot_results), 0)
+        return df_spark, actual_df_sqlglot
+
+    @classmethod
+    def get_explain_plan(cls, df: "SparkDataFrame", mode: str = "extended") -> str:
+        return df._sc._jvm.PythonSQLUtils.explainString(df._jdf.queryExecution(), mode)  # type: ignore
diff --git a/tests/dataframe/integration/test_dataframe.py b/tests/dataframe/integration/test_dataframe.py
new file mode 100644
index 0000000..c740bec
--- /dev/null
+++ b/tests/dataframe/integration/test_dataframe.py
@@ -0,0 +1,1103 @@
+from pyspark.sql import functions as F
+
+from sqlglot.dataframe.sql import functions as SF
+from tests.dataframe.integration.dataframe_validator import DataFrameValidator
+
+
+class TestDataframeFunc(DataFrameValidator):
+    def test_simple_select(self):
+        df_employee = self.df_spark_employee.select(F.col("employee_id"))
+        dfs_employee = self.df_sqlglot_employee.select(SF.col("employee_id"))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_simple_select_from_table(self):
+        df = self.df_spark_employee
+        dfs = self.sqlglot.read.table("employee")
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_simple_select_df_attribute(self):
+        df_employee = self.df_spark_employee.select(self.df_spark_employee.employee_id)
+        dfs_employee = self.df_sqlglot_employee.select(self.df_sqlglot_employee.employee_id)
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_simple_select_df_dict(self):
+        df_employee = self.df_spark_employee.select(self.df_spark_employee["employee_id"])
+        dfs_employee = self.df_sqlglot_employee.select(self.df_sqlglot_employee["employee_id"])
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_multiple_selects(self):
+        df_employee = self.df_spark_employee.select(
+            self.df_spark_employee["employee_id"], F.col("fname"), self.df_spark_employee.lname
+        )
+        dfs_employee = self.df_sqlglot_employee.select(
+            self.df_sqlglot_employee["employee_id"], SF.col("fname"), self.df_sqlglot_employee.lname
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_alias_no_op(self):
+        df_employee = self.df_spark_employee.alias("df_employee")
+        dfs_employee = self.df_sqlglot_employee.alias("dfs_employee")
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_alias_with_select(self):
+        df_employee = self.df_spark_employee.alias("df_employee").select(
+            self.df_spark_employee["employee_id"], F.col("df_employee.fname"), self.df_spark_employee.lname
+        )
+        dfs_employee = self.df_sqlglot_employee.alias("dfs_employee").select(
+            self.df_sqlglot_employee["employee_id"], SF.col("dfs_employee.fname"), self.df_sqlglot_employee.lname
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_case_when_otherwise(self):
+        df = self.df_spark_employee.select(
+            F.when((F.col("age") >= F.lit(40)) & (F.col("age") <= F.lit(60)), F.lit("between 40 and 60"))
+            .when(F.col("age") < F.lit(40), "less than 40")
+            .otherwise("greater than 60")
+        )
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.when((SF.col("age") >= SF.lit(40)) & (SF.col("age") <= SF.lit(60)), SF.lit("between 40 and 60"))
+            .when(SF.col("age") < SF.lit(40), "less than 40")
+            .otherwise("greater than 60")
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_case_when_no_otherwise(self):
+        df = self.df_spark_employee.select(
+            F.when((F.col("age") >= F.lit(40)) & (F.col("age") <= F.lit(60)), F.lit("between 40 and 60")).when(
+                F.col("age") < F.lit(40), "less than 40"
+            )
+        )
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.when((SF.col("age") >= SF.lit(40)) & (SF.col("age") <= SF.lit(60)), SF.lit("between 40 and 60")).when(
+                SF.col("age") < SF.lit(40), "less than 40"
+            )
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_where_clause_single(self):
+        df_employee = self.df_spark_employee.where(F.col("age") == F.lit(37))
+        dfs_employee = self.df_sqlglot_employee.where(SF.col("age") == SF.lit(37))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_where_clause_multiple_and(self):
+        df_employee = self.df_spark_employee.where((F.col("age") == F.lit(37)) & (F.col("fname") == F.lit("Jack")))
+        dfs_employee = self.df_sqlglot_employee.where(
+            (SF.col("age") == SF.lit(37)) & (SF.col("fname") == SF.lit("Jack"))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_where_many_and(self):
+        df_employee = self.df_spark_employee.where(
+            (F.col("age") == F.lit(37))
+            & (F.col("fname") == F.lit("Jack"))
+            & (F.col("lname") == F.lit("Shephard"))
+            & (F.col("employee_id") == F.lit(1))
+        )
+        dfs_employee = self.df_sqlglot_employee.where(
+            (SF.col("age") == SF.lit(37))
+            & (SF.col("fname") == SF.lit("Jack"))
+            & (SF.col("lname") == SF.lit("Shephard"))
+            & (SF.col("employee_id") == SF.lit(1))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_where_clause_multiple_or(self):
+        df_employee = self.df_spark_employee.where((F.col("age") == F.lit(37)) | (F.col("fname") == F.lit("Kate")))
+        dfs_employee = self.df_sqlglot_employee.where(
+            (SF.col("age") == SF.lit(37)) | (SF.col("fname") == SF.lit("Kate"))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_where_many_or(self):
+        df_employee = self.df_spark_employee.where(
+            (F.col("age") == F.lit(37))
+            | (F.col("fname") == F.lit("Kate"))
+            | (F.col("lname") == F.lit("Littleton"))
+            | (F.col("employee_id") == F.lit(2))
+        )
+        dfs_employee = self.df_sqlglot_employee.where(
+            (SF.col("age") == SF.lit(37))
+            | (SF.col("fname") == SF.lit("Kate"))
+            | (SF.col("lname") == SF.lit("Littleton"))
+            | (SF.col("employee_id") == SF.lit(2))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_where_mixed_and_or(self):
+        df_employee = self.df_spark_employee.where(
+            ((F.col("age") == F.lit(65)) & (F.col("fname") == F.lit("John")))
+            | ((F.col("lname") == F.lit("Shephard")) & (F.col("age") == F.lit(37)))
+        )
+        dfs_employee = self.df_sqlglot_employee.where(
+            ((SF.col("age") == SF.lit(65)) & (SF.col("fname") == SF.lit("John")))
+            | ((SF.col("lname") == SF.lit("Shephard")) & (SF.col("age") == SF.lit(37)))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_where_multiple_chained(self):
+        df_employee = self.df_spark_employee.where(F.col("age") == F.lit(37)).where(
+            self.df_spark_employee.fname == F.lit("Jack")
+        )
+        dfs_employee = self.df_sqlglot_employee.where(SF.col("age") == SF.lit(37)).where(
+            self.df_sqlglot_employee.fname == SF.lit("Jack")
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_operators(self):
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] < F.lit(50))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] < SF.lit(50))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] <= F.lit(37))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] <= SF.lit(37))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] > F.lit(50))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] > SF.lit(50))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] >= F.lit(37))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] >= SF.lit(37))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] != F.lit(50))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] != SF.lit(50))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] == F.lit(37))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] == SF.lit(37))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] % F.lit(5) == F.lit(0))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] % SF.lit(5) == SF.lit(0))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] + F.lit(5) > F.lit(28))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] + SF.lit(5) > SF.lit(28))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(self.df_spark_employee["age"] - F.lit(5) > F.lit(28))
+        dfs_employee = self.df_sqlglot_employee.where(self.df_sqlglot_employee["age"] - SF.lit(5) > SF.lit(28))
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+        df_employee = self.df_spark_employee.where(
+            self.df_spark_employee["age"] * F.lit(0.5) == self.df_spark_employee["age"] / F.lit(2)
+        )
+        dfs_employee = self.df_sqlglot_employee.where(
+            self.df_sqlglot_employee["age"] * SF.lit(0.5) == self.df_sqlglot_employee["age"] / SF.lit(2)
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_join_inner(self):
+        df_joined = self.df_spark_employee.join(self.df_spark_store, on=["store_id"], how="inner").select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            F.col("store_id"),
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(self.df_sqlglot_store, on=["store_id"], how="inner").select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            SF.col("store_id"),
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_join_inner_no_select(self):
+        df_joined = self.df_spark_employee.select(F.col("store_id"), F.col("fname"), F.col("lname")).join(
+            self.df_spark_store.select(F.col("store_id"), F.col("store_name")), on=["store_id"], how="inner"
+        )
+        dfs_joined = self.df_sqlglot_employee.select(SF.col("store_id"), SF.col("fname"), SF.col("lname")).join(
+            self.df_sqlglot_store.select(SF.col("store_id"), SF.col("store_name")), on=["store_id"], how="inner"
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_join_inner_equality_single(self):
+        df_joined = self.df_spark_employee.join(
+            self.df_spark_store, on=self.df_spark_employee.store_id == self.df_spark_store.store_id, how="inner"
+        ).select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            self.df_spark_employee.store_id,
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(
+            self.df_sqlglot_store, on=self.df_sqlglot_employee.store_id == self.df_sqlglot_store.store_id, how="inner"
+        ).select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            self.df_sqlglot_employee.store_id,
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_join_inner_equality_multiple(self):
+        df_joined = self.df_spark_employee.join(
+            self.df_spark_store,
+            on=[
+                self.df_spark_employee.store_id == self.df_spark_store.store_id,
+                self.df_spark_employee.age == self.df_spark_store.num_sales,
+            ],
+            how="inner",
+        ).select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            self.df_spark_employee.store_id,
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(
+            self.df_sqlglot_store,
+            on=[
+                self.df_sqlglot_employee.store_id == self.df_sqlglot_store.store_id,
+                self.df_sqlglot_employee.age == self.df_sqlglot_store.num_sales,
+            ],
+            how="inner",
+        ).select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            self.df_sqlglot_employee.store_id,
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_join_inner_equality_multiple_bitwise_and(self):
+        df_joined = self.df_spark_employee.join(
+            self.df_spark_store,
+            on=(self.df_spark_employee.store_id == self.df_spark_store.store_id)
+            & (self.df_spark_employee.age == self.df_spark_store.num_sales),
+            how="inner",
+        ).select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            self.df_spark_employee.store_id,
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(
+            self.df_sqlglot_store,
+            on=(self.df_sqlglot_employee.store_id == self.df_sqlglot_store.store_id)
+            & (self.df_sqlglot_employee.age == self.df_sqlglot_store.num_sales),
+            how="inner",
+        ).select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            self.df_sqlglot_employee.store_id,
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_join_left_outer(self):
+        df_joined = (
+            self.df_spark_employee.join(self.df_spark_store, on=["store_id"], how="left_outer")
+            .select(
+                self.df_spark_employee.employee_id,
+                self.df_spark_employee["fname"],
+                F.col("lname"),
+                F.col("age"),
+                F.col("store_id"),
+                self.df_spark_store.store_name,
+                self.df_spark_store["num_sales"],
+            )
+            .orderBy(F.col("employee_id"))
+        )
+        dfs_joined = (
+            self.df_sqlglot_employee.join(self.df_sqlglot_store, on=["store_id"], how="left_outer")
+            .select(
+                self.df_sqlglot_employee.employee_id,
+                self.df_sqlglot_employee["fname"],
+                SF.col("lname"),
+                SF.col("age"),
+                SF.col("store_id"),
+                self.df_sqlglot_store.store_name,
+                self.df_sqlglot_store["num_sales"],
+            )
+            .orderBy(SF.col("employee_id"))
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_join_full_outer(self):
+        df_joined = self.df_spark_employee.join(self.df_spark_store, on=["store_id"], how="full_outer").select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            F.col("store_id"),
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(self.df_sqlglot_store, on=["store_id"], how="full_outer").select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            SF.col("store_id"),
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_triple_join(self):
+        df = (
+            self.df_employee.join(self.df_store, on=self.df_employee.employee_id == self.df_store.store_id)
+            .join(self.df_district, on=self.df_store.store_id == self.df_district.district_id)
+            .select(
+                self.df_employee.employee_id,
+                self.df_store.store_id,
+                self.df_district.district_id,
+                self.df_employee.fname,
+                self.df_store.store_name,
+                self.df_district.district_name,
+            )
+        )
+        dfs = (
+            self.dfs_employee.join(self.dfs_store, on=self.dfs_employee.employee_id == self.dfs_store.store_id)
+            .join(self.dfs_district, on=self.dfs_store.store_id == self.dfs_district.district_id)
+            .select(
+                self.dfs_employee.employee_id,
+                self.dfs_store.store_id,
+                self.dfs_district.district_id,
+                self.dfs_employee.fname,
+                self.dfs_store.store_name,
+                self.dfs_district.district_name,
+            )
+        )
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_join_select_and_select_start(self):
+        df = self.df_spark_employee.select(F.col("fname"), F.col("lname"), F.col("age"), F.col("store_id")).join(
+            self.df_spark_store, "store_id", "inner"
+        )
+
+        dfs = self.df_sqlglot_employee.select(SF.col("fname"), SF.col("lname"), SF.col("age"), SF.col("store_id")).join(
+            self.df_sqlglot_store, "store_id", "inner"
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_branching_root_dataframes(self):
+        """
+        Test a pattern that has non-intuitive behavior in spark
+
+        Scenario: You do a self-join in a dataframe using an original dataframe and then a modified version
+        of it. You then reference the columns by the dataframe name instead of the column function.
+        Spark will use the root dataframe's column in the result.
+        """
+        df_hydra_employees_only = self.df_spark_employee.where(F.col("store_id") == F.lit(1))
+        df_joined = (
+            self.df_spark_employee.where(F.col("store_id") == F.lit(2))
+            .alias("df_arrow_employees_only")
+            .join(
+                df_hydra_employees_only.alias("df_hydra_employees_only"),
+                on=["store_id"],
+                how="full_outer",
+            )
+            .select(
+                self.df_spark_employee.fname,
+                F.col("df_arrow_employees_only.fname"),
+                df_hydra_employees_only.fname,
+                F.col("df_hydra_employees_only.fname"),
+            )
+        )
+
+        dfs_hydra_employees_only = self.df_sqlglot_employee.where(SF.col("store_id") == SF.lit(1))
+        dfs_joined = (
+            self.df_sqlglot_employee.where(SF.col("store_id") == SF.lit(2))
+            .alias("dfs_arrow_employees_only")
+            .join(
+                dfs_hydra_employees_only.alias("dfs_hydra_employees_only"),
+                on=["store_id"],
+                how="full_outer",
+            )
+            .select(
+                self.df_sqlglot_employee.fname,
+                SF.col("dfs_arrow_employees_only.fname"),
+                dfs_hydra_employees_only.fname,
+                SF.col("dfs_hydra_employees_only.fname"),
+            )
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_basic_union(self):
+        df_unioned = self.df_spark_employee.select(F.col("employee_id"), F.col("age")).union(
+            self.df_spark_store.select(F.col("store_id"), F.col("num_sales"))
+        )
+
+        dfs_unioned = self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("age")).union(
+            self.df_sqlglot_store.select(SF.col("store_id"), SF.col("num_sales"))
+        )
+        self.compare_spark_with_sqlglot(df_unioned, dfs_unioned)
+
+    def test_union_with_join(self):
+        df_joined = self.df_spark_employee.join(
+            self.df_spark_store,
+            on="store_id",
+            how="inner",
+        )
+        df_unioned = df_joined.select(F.col("store_id"), F.col("store_name")).union(
+            self.df_spark_district.select(F.col("district_id"), F.col("district_name"))
+        )
+
+        dfs_joined = self.df_sqlglot_employee.join(
+            self.df_sqlglot_store,
+            on="store_id",
+            how="inner",
+        )
+        dfs_unioned = dfs_joined.select(SF.col("store_id"), SF.col("store_name")).union(
+            self.df_sqlglot_district.select(SF.col("district_id"), SF.col("district_name"))
+        )
+
+        self.compare_spark_with_sqlglot(df_unioned, dfs_unioned)
+
+    def test_double_union_all(self):
+        df_unioned = (
+            self.df_spark_employee.select(F.col("employee_id"), F.col("fname"))
+            .unionAll(self.df_spark_store.select(F.col("store_id"), F.col("store_name")))
+            .unionAll(self.df_spark_district.select(F.col("district_id"), F.col("district_name")))
+        )
+
+        dfs_unioned = (
+            self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("fname"))
+            .unionAll(self.df_sqlglot_store.select(SF.col("store_id"), SF.col("store_name")))
+            .unionAll(self.df_sqlglot_district.select(SF.col("district_id"), SF.col("district_name")))
+        )
+
+        self.compare_spark_with_sqlglot(df_unioned, dfs_unioned)
+
+    def test_union_by_name(self):
+        df = self.df_spark_employee.select(F.col("employee_id"), F.col("fname"), F.col("lname")).unionByName(
+            self.df_spark_store.select(
+                F.col("store_name").alias("lname"),
+                F.col("store_id").alias("employee_id"),
+                F.col("store_name").alias("fname"),
+            )
+        )
+
+        dfs = self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("fname"), SF.col("lname")).unionByName(
+            self.df_sqlglot_store.select(
+                SF.col("store_name").alias("lname"),
+                SF.col("store_id").alias("employee_id"),
+                SF.col("store_name").alias("fname"),
+            )
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_union_by_name_allow_missing(self):
+        df = self.df_spark_employee.select(
+            F.col("age"), F.col("employee_id"), F.col("fname"), F.col("lname")
+        ).unionByName(
+            self.df_spark_store.select(
+                F.col("store_name").alias("lname"),
+                F.col("store_id").alias("employee_id"),
+                F.col("store_name").alias("fname"),
+                F.col("num_sales"),
+            ),
+            allowMissingColumns=True,
+        )
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.col("age"), SF.col("employee_id"), SF.col("fname"), SF.col("lname")
+        ).unionByName(
+            self.df_sqlglot_store.select(
+                SF.col("store_name").alias("lname"),
+                SF.col("store_id").alias("employee_id"),
+                SF.col("store_name").alias("fname"),
+                SF.col("num_sales"),
+            ),
+            allowMissingColumns=True,
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_order_by_default(self):
+        df = self.df_spark_store.groupBy(F.col("district_id")).agg(F.min("num_sales")).orderBy(F.col("district_id"))
+
+        dfs = (
+            self.df_sqlglot_store.groupBy(SF.col("district_id")).agg(SF.min("num_sales")).orderBy(SF.col("district_id"))
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_order_by_array_bool(self):
+        df = (
+            self.df_spark_store.groupBy(F.col("district_id"))
+            .agg(F.min("num_sales").alias("total_sales"))
+            .orderBy(F.col("total_sales"), F.col("district_id"), ascending=[1, 0])
+        )
+
+        dfs = (
+            self.df_sqlglot_store.groupBy(SF.col("district_id"))
+            .agg(SF.min("num_sales").alias("total_sales"))
+            .orderBy(SF.col("total_sales"), SF.col("district_id"), ascending=[1, 0])
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_order_by_single_bool(self):
+        df = (
+            self.df_spark_store.groupBy(F.col("district_id"))
+            .agg(F.min("num_sales").alias("total_sales"))
+            .orderBy(F.col("total_sales"), F.col("district_id"), ascending=False)
+        )
+
+        dfs = (
+            self.df_sqlglot_store.groupBy(SF.col("district_id"))
+            .agg(SF.min("num_sales").alias("total_sales"))
+            .orderBy(SF.col("total_sales"), SF.col("district_id"), ascending=False)
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_order_by_column_sort_method(self):
+        df = (
+            self.df_spark_store.groupBy(F.col("district_id"))
+            .agg(F.min("num_sales").alias("total_sales"))
+            .orderBy(F.col("total_sales").asc(), F.col("district_id").desc())
+        )
+
+        dfs = (
+            self.df_sqlglot_store.groupBy(SF.col("district_id"))
+            .agg(SF.min("num_sales").alias("total_sales"))
+            .orderBy(SF.col("total_sales").asc(), SF.col("district_id").desc())
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_order_by_column_sort_method_nulls_last(self):
+        df = (
+            self.df_spark_store.groupBy(F.col("district_id"))
+            .agg(F.min("num_sales").alias("total_sales"))
+            .orderBy(F.when(F.col("district_id") == F.lit(2), F.col("district_id")).asc_nulls_last())
+        )
+
+        dfs = (
+            self.df_sqlglot_store.groupBy(SF.col("district_id"))
+            .agg(SF.min("num_sales").alias("total_sales"))
+            .orderBy(SF.when(SF.col("district_id") == SF.lit(2), SF.col("district_id")).asc_nulls_last())
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_order_by_column_sort_method_nulls_first(self):
+        df = (
+            self.df_spark_store.groupBy(F.col("district_id"))
+            .agg(F.min("num_sales").alias("total_sales"))
+            .orderBy(F.when(F.col("district_id") == F.lit(1), F.col("district_id")).desc_nulls_first())
+        )
+
+        dfs = (
+            self.df_sqlglot_store.groupBy(SF.col("district_id"))
+            .agg(SF.min("num_sales").alias("total_sales"))
+            .orderBy(SF.when(SF.col("district_id") == SF.lit(1), SF.col("district_id")).desc_nulls_first())
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_intersect(self):
+        df_employee_duplicate = self.df_spark_employee.select(F.col("employee_id"), F.col("store_id")).union(
+            self.df_spark_employee.select(F.col("employee_id"), F.col("store_id"))
+        )
+
+        df_store_duplicate = self.df_spark_store.select(F.col("store_id"), F.col("district_id")).union(
+            self.df_spark_store.select(F.col("store_id"), F.col("district_id"))
+        )
+
+        df = df_employee_duplicate.intersect(df_store_duplicate)
+
+        dfs_employee_duplicate = self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("store_id")).union(
+            self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("store_id"))
+        )
+
+        dfs_store_duplicate = self.df_sqlglot_store.select(SF.col("store_id"), SF.col("district_id")).union(
+            self.df_sqlglot_store.select(SF.col("store_id"), SF.col("district_id"))
+        )
+
+        dfs = dfs_employee_duplicate.intersect(dfs_store_duplicate)
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_intersect_all(self):
+        df_employee_duplicate = self.df_spark_employee.select(F.col("employee_id"), F.col("store_id")).union(
+            self.df_spark_employee.select(F.col("employee_id"), F.col("store_id"))
+        )
+
+        df_store_duplicate = self.df_spark_store.select(F.col("store_id"), F.col("district_id")).union(
+            self.df_spark_store.select(F.col("store_id"), F.col("district_id"))
+        )
+
+        df = df_employee_duplicate.intersectAll(df_store_duplicate)
+
+        dfs_employee_duplicate = self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("store_id")).union(
+            self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("store_id"))
+        )
+
+        dfs_store_duplicate = self.df_sqlglot_store.select(SF.col("store_id"), SF.col("district_id")).union(
+            self.df_sqlglot_store.select(SF.col("store_id"), SF.col("district_id"))
+        )
+
+        dfs = dfs_employee_duplicate.intersectAll(dfs_store_duplicate)
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_except_all(self):
+        df_employee_duplicate = self.df_spark_employee.select(F.col("employee_id"), F.col("store_id")).union(
+            self.df_spark_employee.select(F.col("employee_id"), F.col("store_id"))
+        )
+
+        df_store_duplicate = self.df_spark_store.select(F.col("store_id"), F.col("district_id")).union(
+            self.df_spark_store.select(F.col("store_id"), F.col("district_id"))
+        )
+
+        df = df_employee_duplicate.exceptAll(df_store_duplicate)
+
+        dfs_employee_duplicate = self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("store_id")).union(
+            self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("store_id"))
+        )
+
+        dfs_store_duplicate = self.df_sqlglot_store.select(SF.col("store_id"), SF.col("district_id")).union(
+            self.df_sqlglot_store.select(SF.col("store_id"), SF.col("district_id"))
+        )
+
+        dfs = dfs_employee_duplicate.exceptAll(dfs_store_duplicate)
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_distinct(self):
+        df = self.df_spark_employee.select(F.col("age")).distinct()
+
+        dfs = self.df_sqlglot_employee.select(SF.col("age")).distinct()
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_union_distinct(self):
+        df_unioned = (
+            self.df_spark_employee.select(F.col("employee_id"), F.col("age"))
+            .union(self.df_spark_employee.select(F.col("employee_id"), F.col("age")))
+            .distinct()
+        )
+
+        dfs_unioned = (
+            self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("age"))
+            .union(self.df_sqlglot_employee.select(SF.col("employee_id"), SF.col("age")))
+            .distinct()
+        )
+        self.compare_spark_with_sqlglot(df_unioned, dfs_unioned)
+
+    def test_drop_duplicates_no_subset(self):
+        df = self.df_spark_employee.select("age").dropDuplicates()
+        dfs = self.df_sqlglot_employee.select("age").dropDuplicates()
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_drop_duplicates_subset(self):
+        df = self.df_spark_employee.dropDuplicates(["age"])
+        dfs = self.df_sqlglot_employee.dropDuplicates(["age"])
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_drop_na_default(self):
+        df = self.df_spark_employee.select(F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")).dropna()
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).dropna()
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_dropna_how(self):
+        df = self.df_spark_employee.select(
+            F.lit(None), F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")
+        ).dropna(how="all")
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.lit(None), SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).dropna(how="all")
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_dropna_thresh(self):
+        df = self.df_spark_employee.select(
+            F.lit(None), F.lit(1), F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")
+        ).dropna(how="any", thresh=2)
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.lit(None), SF.lit(1), SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).dropna(how="any", thresh=2)
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_dropna_subset(self):
+        df = self.df_spark_employee.select(
+            F.lit(None), F.lit(1), F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")
+        ).dropna(thresh=1, subset="the_age")
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.lit(None), SF.lit(1), SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).dropna(thresh=1, subset="the_age")
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_dropna_na_function(self):
+        df = self.df_spark_employee.select(F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")).na.drop()
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).na.drop()
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_fillna_default(self):
+        df = self.df_spark_employee.select(F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")).fillna(100)
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).fillna(100)
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_fillna_dict_replacement(self):
+        df = self.df_spark_employee.select(
+            F.col("fname"),
+            F.when(F.col("lname").startswith("L"), F.col("lname")).alias("l_lname"),
+            F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age"),
+        ).fillna({"fname": "Jacob", "l_lname": "NOT_LNAME"})
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.col("fname"),
+            SF.when(SF.col("lname").startswith("L"), SF.col("lname")).alias("l_lname"),
+            SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age"),
+        ).fillna({"fname": "Jacob", "l_lname": "NOT_LNAME"})
+
+        # For some reason the sqlglot results sets a column as nullable when it doesn't need to
+        # This seems to be a nuance in how spark dataframe from sql works so we can ignore
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_fillna_na_func(self):
+        df = self.df_spark_employee.select(F.when(F.col("age") < F.lit(50), F.col("age")).alias("the_age")).na.fill(100)
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.when(SF.col("age") < SF.lit(50), SF.col("age")).alias("the_age")
+        ).na.fill(100)
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_replace_basic(self):
+        df = self.df_spark_employee.select(F.col("age"), F.lit(37).alias("test_col")).replace(to_replace=37, value=100)
+
+        dfs = self.df_sqlglot_employee.select(SF.col("age"), SF.lit(37).alias("test_col")).replace(
+            to_replace=37, value=100
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_replace_basic_subset(self):
+        df = self.df_spark_employee.select(F.col("age"), F.lit(37).alias("test_col")).replace(
+            to_replace=37, value=100, subset="age"
+        )
+
+        dfs = self.df_sqlglot_employee.select(SF.col("age"), SF.lit(37).alias("test_col")).replace(
+            to_replace=37, value=100, subset="age"
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_replace_mapping(self):
+        df = self.df_spark_employee.select(F.col("age"), F.lit(37).alias("test_col")).replace({37: 100})
+
+        dfs = self.df_sqlglot_employee.select(SF.col("age"), SF.lit(37).alias("test_col")).replace({37: 100})
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_replace_mapping_subset(self):
+        df = self.df_spark_employee.select(
+            F.col("age"), F.lit(37).alias("test_col"), F.lit(50).alias("test_col_2")
+        ).replace({37: 100, 50: 1}, subset=["age", "test_col_2"])
+
+        dfs = self.df_sqlglot_employee.select(
+            SF.col("age"), SF.lit(37).alias("test_col"), SF.lit(50).alias("test_col_2")
+        ).replace({37: 100, 50: 1}, subset=["age", "test_col_2"])
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_replace_na_func_basic(self):
+        df = self.df_spark_employee.select(F.col("age"), F.lit(37).alias("test_col")).na.replace(
+            to_replace=37, value=100
+        )
+
+        dfs = self.df_sqlglot_employee.select(SF.col("age"), SF.lit(37).alias("test_col")).na.replace(
+            to_replace=37, value=100
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_with_column(self):
+        df = self.df_spark_employee.withColumn("test", F.col("age"))
+
+        dfs = self.df_sqlglot_employee.withColumn("test", SF.col("age"))
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_with_column_existing_name(self):
+        df = self.df_spark_employee.withColumn("fname", F.lit("blah"))
+
+        dfs = self.df_sqlglot_employee.withColumn("fname", SF.lit("blah"))
+
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)
+
+    def test_with_column_renamed(self):
+        df = self.df_spark_employee.withColumnRenamed("fname", "first_name")
+
+        dfs = self.df_sqlglot_employee.withColumnRenamed("fname", "first_name")
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_with_column_renamed_double(self):
+        df = self.df_spark_employee.select(F.col("fname").alias("first_name")).withColumnRenamed(
+            "first_name", "first_name_again"
+        )
+
+        dfs = self.df_sqlglot_employee.select(SF.col("fname").alias("first_name")).withColumnRenamed(
+            "first_name", "first_name_again"
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_drop_column_single(self):
+        df = self.df_spark_employee.select(F.col("fname"), F.col("lname"), F.col("age")).drop("age")
+
+        dfs = self.df_sqlglot_employee.select(SF.col("fname"), SF.col("lname"), SF.col("age")).drop("age")
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_drop_column_reference_join(self):
+        df_spark_employee_cols = self.df_spark_employee.select(
+            F.col("fname"), F.col("lname"), F.col("age"), F.col("store_id")
+        )
+        df_spark_store_cols = self.df_spark_store.select(F.col("store_id"), F.col("store_name"))
+        df = df_spark_employee_cols.join(df_spark_store_cols, on="store_id", how="inner").drop(
+            df_spark_employee_cols.age,
+        )
+
+        df_sqlglot_employee_cols = self.df_sqlglot_employee.select(
+            SF.col("fname"), SF.col("lname"), SF.col("age"), SF.col("store_id")
+        )
+        df_sqlglot_store_cols = self.df_sqlglot_store.select(SF.col("store_id"), SF.col("store_name"))
+        dfs = df_sqlglot_employee_cols.join(df_sqlglot_store_cols, on="store_id", how="inner").drop(
+            df_sqlglot_employee_cols.age,
+        )
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_limit(self):
+        df = self.df_spark_employee.limit(1)
+
+        dfs = self.df_sqlglot_employee.limit(1)
+
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_hint_broadcast_alias(self):
+        df_joined = self.df_spark_employee.join(
+            self.df_spark_store.alias("store").hint("broadcast", "store"),
+            on=self.df_spark_employee.store_id == self.df_spark_store.store_id,
+            how="inner",
+        ).select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            self.df_spark_employee.store_id,
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(
+            self.df_sqlglot_store.alias("store").hint("broadcast", "store"),
+            on=self.df_sqlglot_employee.store_id == self.df_sqlglot_store.store_id,
+            how="inner",
+        ).select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            self.df_sqlglot_employee.store_id,
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        df, dfs = self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+        self.assertIn("ResolvedHint (strategy=broadcast)", self.get_explain_plan(df))
+        self.assertIn("ResolvedHint (strategy=broadcast)", self.get_explain_plan(dfs))
+
+    def test_hint_broadcast_no_alias(self):
+        df_joined = self.df_spark_employee.join(
+            self.df_spark_store.hint("broadcast"),
+            on=self.df_spark_employee.store_id == self.df_spark_store.store_id,
+            how="inner",
+        ).select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            self.df_spark_employee.store_id,
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(
+            self.df_sqlglot_store.hint("broadcast"),
+            on=self.df_sqlglot_employee.store_id == self.df_sqlglot_store.store_id,
+            how="inner",
+        ).select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            self.df_sqlglot_employee.store_id,
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        df, dfs = self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+        self.assertIn("ResolvedHint (strategy=broadcast)", self.get_explain_plan(df))
+        self.assertIn("ResolvedHint (strategy=broadcast)", self.get_explain_plan(dfs))
+
+    # TODO: Add test to make sure with and without alias are the same once ids are deterministic
+
+    def test_broadcast_func(self):
+        df_joined = self.df_spark_employee.join(
+            F.broadcast(self.df_spark_store),
+            on=self.df_spark_employee.store_id == self.df_spark_store.store_id,
+            how="inner",
+        ).select(
+            self.df_spark_employee.employee_id,
+            self.df_spark_employee["fname"],
+            F.col("lname"),
+            F.col("age"),
+            self.df_spark_employee.store_id,
+            self.df_spark_store.store_name,
+            self.df_spark_store["num_sales"],
+        )
+        dfs_joined = self.df_sqlglot_employee.join(
+            SF.broadcast(self.df_sqlglot_store),
+            on=self.df_sqlglot_employee.store_id == self.df_sqlglot_store.store_id,
+            how="inner",
+        ).select(
+            self.df_sqlglot_employee.employee_id,
+            self.df_sqlglot_employee["fname"],
+            SF.col("lname"),
+            SF.col("age"),
+            self.df_sqlglot_employee.store_id,
+            self.df_sqlglot_store.store_name,
+            self.df_sqlglot_store["num_sales"],
+        )
+        df, dfs = self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+        self.assertIn("ResolvedHint (strategy=broadcast)", self.get_explain_plan(df))
+        self.assertIn("ResolvedHint (strategy=broadcast)", self.get_explain_plan(dfs))
+
+    def test_repartition_by_num(self):
+        """
+        The results are different when doing the repartition on a table created using VALUES in SQL.
+        So I just use the views instead for these tests
+        """
+        df = self.df_spark_employee.repartition(63)
+
+        dfs = self.sqlglot.read.table("employee").repartition(63)
+        df, dfs = self.compare_spark_with_sqlglot(df, dfs)
+        spark_num_partitions = df.rdd.getNumPartitions()
+        sqlglot_num_partitions = dfs.rdd.getNumPartitions()
+        self.assertEqual(spark_num_partitions, 63)
+        self.assertEqual(spark_num_partitions, sqlglot_num_partitions)
+
+    def test_repartition_name_only(self):
+        """
+        We use the view here to help ensure the explain plans are similar enough to compare
+        """
+        df = self.df_spark_employee.repartition("age")
+
+        dfs = self.sqlglot.read.table("employee").repartition("age")
+        df, dfs = self.compare_spark_with_sqlglot(df, dfs)
+        self.assertIn("RepartitionByExpression [age", self.get_explain_plan(df))
+        self.assertIn("RepartitionByExpression [age", self.get_explain_plan(dfs))
+
+    def test_repartition_num_and_multiple_names(self):
+        """
+        We use the view here to help ensure the explain plans are similar enough to compare
+        """
+        df = self.df_spark_employee.repartition(53, "age", "fname")
+
+        dfs = self.sqlglot.read.table("employee").repartition(53, "age", "fname")
+        df, dfs = self.compare_spark_with_sqlglot(df, dfs)
+        spark_num_partitions = df.rdd.getNumPartitions()
+        sqlglot_num_partitions = dfs.rdd.getNumPartitions()
+        self.assertEqual(spark_num_partitions, 53)
+        self.assertEqual(spark_num_partitions, sqlglot_num_partitions)
+        self.assertIn("RepartitionByExpression [age#3, fname#1], 53", self.get_explain_plan(df))
+        self.assertIn("RepartitionByExpression [age#3, fname#1], 53", self.get_explain_plan(dfs))
+
+    def test_coalesce(self):
+        df = self.df_spark_employee.coalesce(1)
+        dfs = self.df_sqlglot_employee.coalesce(1)
+        df, dfs = self.compare_spark_with_sqlglot(df, dfs)
+        spark_num_partitions = df.rdd.getNumPartitions()
+        sqlglot_num_partitions = dfs.rdd.getNumPartitions()
+        self.assertEqual(spark_num_partitions, 1)
+        self.assertEqual(spark_num_partitions, sqlglot_num_partitions)
+
+    def test_cache_select(self):
+        df_employee = (
+            self.df_spark_employee.groupBy("store_id")
+            .agg(F.countDistinct("employee_id").alias("num_employees"))
+            .cache()
+        )
+        df_joined = df_employee.join(self.df_spark_store, on="store_id").select(
+            self.df_spark_store.store_id, df_employee.num_employees
+        )
+        dfs_employee = (
+            self.df_sqlglot_employee.groupBy("store_id")
+            .agg(SF.countDistinct("employee_id").alias("num_employees"))
+            .cache()
+        )
+        dfs_joined = dfs_employee.join(self.df_sqlglot_store, on="store_id").select(
+            self.df_sqlglot_store.store_id, dfs_employee.num_employees
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
+
+    def test_persist_select(self):
+        df_employee = (
+            self.df_spark_employee.groupBy("store_id")
+            .agg(F.countDistinct("employee_id").alias("num_employees"))
+            .persist()
+        )
+        df_joined = df_employee.join(self.df_spark_store, on="store_id").select(
+            self.df_spark_store.store_id, df_employee.num_employees
+        )
+        dfs_employee = (
+            self.df_sqlglot_employee.groupBy("store_id")
+            .agg(SF.countDistinct("employee_id").alias("num_employees"))
+            .persist()
+        )
+        dfs_joined = dfs_employee.join(self.df_sqlglot_store, on="store_id").select(
+            self.df_sqlglot_store.store_id, dfs_employee.num_employees
+        )
+        self.compare_spark_with_sqlglot(df_joined, dfs_joined)
diff --git a/tests/dataframe/integration/test_grouped_data.py b/tests/dataframe/integration/test_grouped_data.py
new file mode 100644
index 0000000..2768dda
--- /dev/null
+++ b/tests/dataframe/integration/test_grouped_data.py
@@ -0,0 +1,71 @@
+from pyspark.sql import functions as F
+
+from sqlglot.dataframe.sql import functions as SF
+from tests.dataframe.integration.dataframe_validator import DataFrameValidator
+
+
+class TestDataframeFunc(DataFrameValidator):
+    def test_group_by(self):
+        df_employee = self.df_spark_employee.groupBy(self.df_spark_employee.age).agg(
+            F.min(self.df_spark_employee.employee_id)
+        )
+        dfs_employee = self.df_sqlglot_employee.groupBy(self.df_sqlglot_employee.age).agg(
+            SF.min(self.df_sqlglot_employee.employee_id)
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee, skip_schema_compare=True)
+
+    def test_group_by_where_non_aggregate(self):
+        df_employee = (
+            self.df_spark_employee.groupBy(self.df_spark_employee.age)
+            .agg(F.min(self.df_spark_employee.employee_id).alias("min_employee_id"))
+            .where(F.col("age") > F.lit(50))
+        )
+        dfs_employee = (
+            self.df_sqlglot_employee.groupBy(self.df_sqlglot_employee.age)
+            .agg(SF.min(self.df_sqlglot_employee.employee_id).alias("min_employee_id"))
+            .where(SF.col("age") > SF.lit(50))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_group_by_where_aggregate_like_having(self):
+        df_employee = (
+            self.df_spark_employee.groupBy(self.df_spark_employee.age)
+            .agg(F.min(self.df_spark_employee.employee_id).alias("min_employee_id"))
+            .where(F.col("min_employee_id") > F.lit(1))
+        )
+        dfs_employee = (
+            self.df_sqlglot_employee.groupBy(self.df_sqlglot_employee.age)
+            .agg(SF.min(self.df_sqlglot_employee.employee_id).alias("min_employee_id"))
+            .where(SF.col("min_employee_id") > SF.lit(1))
+        )
+        self.compare_spark_with_sqlglot(df_employee, dfs_employee)
+
+    def test_count(self):
+        df = self.df_spark_employee.groupBy(self.df_spark_employee.age).count()
+        dfs = self.df_sqlglot_employee.groupBy(self.df_sqlglot_employee.age).count()
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_mean(self):
+        df = self.df_spark_employee.groupBy().mean("age", "store_id")
+        dfs = self.df_sqlglot_employee.groupBy().mean("age", "store_id")
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_avg(self):
+        df = self.df_spark_employee.groupBy("age").avg("store_id")
+        dfs = self.df_sqlglot_employee.groupBy("age").avg("store_id")
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_max(self):
+        df = self.df_spark_employee.groupBy("age").max("store_id")
+        dfs = self.df_sqlglot_employee.groupBy("age").max("store_id")
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_min(self):
+        df = self.df_spark_employee.groupBy("age").min("store_id")
+        dfs = self.df_sqlglot_employee.groupBy("age").min("store_id")
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_sum(self):
+        df = self.df_spark_employee.groupBy("age").sum("store_id")
+        dfs = self.df_sqlglot_employee.groupBy("age").sum("store_id")
+        self.compare_spark_with_sqlglot(df, dfs)
diff --git a/tests/dataframe/integration/test_session.py b/tests/dataframe/integration/test_session.py
new file mode 100644
index 0000000..ff1477b
--- /dev/null
+++ b/tests/dataframe/integration/test_session.py
@@ -0,0 +1,28 @@
+from pyspark.sql import functions as F
+
+from sqlglot.dataframe.sql import functions as SF
+from tests.dataframe.integration.dataframe_validator import DataFrameValidator
+
+
+class TestSessionFunc(DataFrameValidator):
+    def test_sql_simple_select(self):
+        query = "SELECT fname, lname FROM employee"
+        df = self.spark.sql(query)
+        dfs = self.sqlglot.sql(query)
+        self.compare_spark_with_sqlglot(df, dfs)
+
+    def test_sql_with_join(self):
+        query = """
+        SELECT
+            e.employee_id
+            , s.store_id    
+        FROM
+            employee e
+            INNER JOIN
+            store s
+            ON
+                e.store_id = s.store_id
+        """
+        df = self.spark.sql(query).groupBy(F.col("store_id")).agg(F.countDistinct(F.col("employee_id")))
+        dfs = self.sqlglot.sql(query).groupBy(SF.col("store_id")).agg(SF.countDistinct(SF.col("employee_id")))
+        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)