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Adding upstream version 10.0.1.

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Signed-off-by: Daniel Baumann <daniel@debian.org>
This commit is contained in:
Daniel Baumann 2025-02-13 14:52:26 +01:00
parent 24752785d9
commit 1e860cc299
Signed by: daniel
GPG key ID: FBB4F0E80A80222F
98 changed files with 4080 additions and 1666 deletions
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52
tests/dataframe/integration/dataframe_validator.py
View file

@ -1,9 +1,9 @@
import sys
import typing as t
import unittest
import warnings
import sqlglot
from sqlglot.helper import PYTHON_VERSION
from tests.helpers import SKIP_INTEGRATION
if t.TYPE_CHECKING:
@ -11,7 +11,8 @@ if t.TYPE_CHECKING:
@unittest.skipIf(
SKIP_INTEGRATION or sys.version_info[:2] > (3, 10), "Skipping Integration Tests since `SKIP_INTEGRATION` is set"
SKIP_INTEGRATION or PYTHON_VERSION > (3, 10),
"Skipping Integration Tests since `SKIP_INTEGRATION` is set",
)
class DataFrameValidator(unittest.TestCase):
spark = None
@ -36,7 +37,12 @@ class DataFrameValidator(unittest.TestCase):
# 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 = (
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(
@ -50,7 +56,9 @@ class DataFrameValidator(unittest.TestCase):
)
cls.sqlglot_employee_schema = sqlglotSparkTypes.StructType(
[
sqlglotSparkTypes.StructField("employee_id", sqlglotSparkTypes.IntegerType(), False),
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),
@ -64,8 +72,12 @@ class DataFrameValidator(unittest.TestCase):
(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 = 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(
@ -80,7 +92,9 @@ class DataFrameValidator(unittest.TestCase):
[
sqlglotSparkTypes.StructField("store_id", sqlglotSparkTypes.IntegerType(), False),
sqlglotSparkTypes.StructField("store_name", sqlglotSparkTypes.StringType(), False),
sqlglotSparkTypes.StructField("district_id", sqlglotSparkTypes.IntegerType(), False),
sqlglotSparkTypes.StructField(
"district_id", sqlglotSparkTypes.IntegerType(), False
),
sqlglotSparkTypes.StructField("num_sales", sqlglotSparkTypes.IntegerType(), False),
]
)
@ -89,7 +103,9 @@ class DataFrameValidator(unittest.TestCase):
(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.dfs_store = cls.sqlglot.createDataFrame(
data=store_data, schema=cls.sqlglot_store_schema
)
cls.df_store.createOrReplaceTempView("store")
cls.spark_district_schema = types.StructType(
@ -101,17 +117,27 @@ class DataFrameValidator(unittest.TestCase):
)
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),
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 = 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)