Adding upstream version 24.0.0.upstream/24.0.0

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

5 files changed, 0 insertions, 1569 deletions

diff --git a/tests/dataframe/integration/__init__.py b/tests/dataframe/integration/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/tests/dataframe/integration/__init__.py
+++ /dev/null
diff --git a/tests/dataframe/integration/dataframe_validator.py b/tests/dataframe/integration/dataframe_validator.py
deleted file mode 100644
index 22d4982..0000000
--- a/tests/dataframe/integration/dataframe_validator.py
+++ /dev/null
@@ -1,174 +0,0 @@
-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, dialect="spark")
-        sqlglot.schema.add_table("store", cls.sqlglot_store_schema, dialect="spark")
-        sqlglot.schema.add_table("district", cls.sqlglot_district_schema, dialect="spark")
-
-    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
deleted file mode 100644
index 702c6ee..0000000
--- a/tests/dataframe/integration/test_dataframe.py
+++ /dev/null
@@ -1,1281 +0,0 @@
-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"],
-            F.lit("literal_value"),
-        )
-        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"],
-            SF.lit("literal_value"),
-        )
-        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_store.store_id == self.df_spark_employee.store_id)
-            & (self.df_spark_store.num_sales == self.df_spark_employee.age),
-            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_store.store_id == self.df_sqlglot_employee.store_id)
-            & (self.df_sqlglot_store.num_sales == self.df_sqlglot_employee.age),
-            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_triple_join_no_select(self):
-        df = (
-            self.df_employee.join(
-                self.df_store,
-                on=self.df_employee["employee_id"] == self.df_store["store_id"],
-                how="left",
-            )
-            .join(
-                self.df_district,
-                on=self.df_store["store_id"] == self.df_district["district_id"],
-                how="left",
-            )
-            .orderBy(F.col("employee_id"))
-        )
-        dfs = (
-            self.dfs_employee.join(
-                self.dfs_store,
-                on=self.dfs_employee["employee_id"] == self.dfs_store["store_id"],
-                how="left",
-            )
-            .join(
-                self.dfs_district,
-                on=self.dfs_store["store_id"] == self.dfs_district["district_id"],
-                how="left",
-            )
-            .orderBy(SF.col("employee_id"))
-        )
-        self.compare_spark_with_sqlglot(df, dfs)
-
-    def test_triple_joins_filter(self):
-        df = (
-            self.df_employee.join(
-                self.df_store,
-                on=self.df_employee["employee_id"] == self.df_store["store_id"],
-                how="left",
-            ).join(
-                self.df_district,
-                on=self.df_store["store_id"] == self.df_district["district_id"],
-                how="left",
-            )
-        ).filter(F.coalesce(self.df_store["num_sales"], F.lit(0)) > 100)
-        dfs = (
-            self.dfs_employee.join(
-                self.dfs_store,
-                on=self.dfs_employee["employee_id"] == self.dfs_store["store_id"],
-                how="left",
-            ).join(
-                self.dfs_district,
-                on=self.dfs_store["store_id"] == self.dfs_district["district_id"],
-                how="left",
-            )
-        ).filter(SF.coalesce(self.dfs_store["num_sales"], SF.lit(0)) > 100)
-        self.compare_spark_with_sqlglot(df, dfs)
-
-    def test_triple_join_column_name_only(self):
-        df = (
-            self.df_employee.join(
-                self.df_store,
-                on=self.df_employee["employee_id"] == self.df_store["store_id"],
-                how="left",
-            )
-            .join(self.df_district, on="district_id", how="left")
-            .orderBy(F.col("employee_id"))
-        )
-        dfs = (
-            self.dfs_employee.join(
-                self.dfs_store,
-                on=self.dfs_employee["employee_id"] == self.dfs_store["store_id"],
-                how="left",
-            )
-            .join(self.dfs_district, on="district_id", how="left")
-            .orderBy(SF.col("employee_id"))
-        )
-        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))
-        self.assertEqual(
-            "'UnresolvedHint BROADCAST, ['a2]", self.get_explain_plan(dfs).split("\n")[1]
-        )
-
-    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))
-        self.assertEqual(
-            "'UnresolvedHint BROADCAST, ['a2]", self.get_explain_plan(dfs).split("\n")[1]
-        )
-
-    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
deleted file mode 100644
index 2768dda..0000000
--- a/tests/dataframe/integration/test_grouped_data.py
+++ /dev/null
@@ -1,71 +0,0 @@
-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
deleted file mode 100644
index 3bb3e20..0000000
--- a/tests/dataframe/integration/test_session.py
+++ /dev/null
@@ -1,43 +0,0 @@
-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)
-
-    def test_nameless_column(self):
-        query = "SELECT MAX(age) FROM employee"
-        df = self.spark.sql(query)
-        dfs = self.sqlglot.sql(query)
-        # Spark will alias the column to `max(age)` while sqlglot will alias to `_col_0` so their schemas will differ
-        self.compare_spark_with_sqlglot(df, dfs, skip_schema_compare=True)