Coverage for pyspark/sql/tests/test_pandas_udf

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# Licensed to the Apache Software Foundation (ASF) under one or more

# contributor license agreements. See the NOTICE file distributed with

# this work for additional information regarding copyright ownership.

# The ASF licenses this file to You under the Apache License, Version 2.0

# (the "License"); you may not use this file except in compliance with

# the License. You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import os

import random

import shutil

import tempfile

import time

import unittest

from datetime import date, datetime

from decimal import Decimal

from distutils.version import LooseVersion

from pyspark import TaskContext

from pyspark.rdd import PythonEvalType

from pyspark.sql import Column

from pyspark.sql.functions import array, col, expr, lit, sum, struct, udf, pandas_udf, \

PandasUDFType

from pyspark.sql.types import IntegerType, ByteType, StructType, ShortType, BooleanType, \

LongType, FloatType, DoubleType, DecimalType, StringType, ArrayType, StructField, \

Row, TimestampType, MapType, DateType, BinaryType

from pyspark.sql.utils import AnalysisException

from pyspark.testing.sqlutils import ReusedSQLTestCase, test_compiled,\

test_not_compiled_message, have_pandas, have_pyarrow, pandas_requirement_message, \

pyarrow_requirement_message

from pyspark.testing.utils import QuietTest

if have_pandas:

import pandas as pd

44 ↛ 45line 44 didn't jump to line 45, because the condition on line 44 was never trueif have_pyarrow:

import pyarrow as pa # noqa: F401

@unittest.skipIf(

not have_pandas or not have_pyarrow,

pandas_requirement_message or pyarrow_requirement_message) # type: ignore

class ScalarPandasUDFTests(ReusedSQLTestCase):

@classmethod

def setUpClass(cls):

ReusedSQLTestCase.setUpClass()

# Synchronize default timezone between Python and Java

cls.tz_prev = os.environ.get("TZ", None) # save current tz if set

tz = "America/Los_Angeles"

os.environ["TZ"] = tz

time.tzset()

cls.sc.environment["TZ"] = tz

cls.spark.conf.set("spark.sql.session.timeZone", tz)

@classmethod

def tearDownClass(cls):

del os.environ["TZ"]

if cls.tz_prev is not None:

os.environ["TZ"] = cls.tz_prev

time.tzset()

ReusedSQLTestCase.tearDownClass()

@property

def nondeterministic_vectorized_udf(self):

import numpy as np

@pandas_udf('double')

def random_udf(v):

return pd.Series(np.random.random(len(v)))

random_udf = random_udf.asNondeterministic()

return random_udf

@property

def nondeterministic_vectorized_iter_udf(self):

import numpy as np

@pandas_udf('double', PandasUDFType.SCALAR_ITER)

def random_udf(it):

for v in it:

yield pd.Series(np.random.random(len(v)))

random_udf = random_udf.asNondeterministic()

return random_udf

def test_pandas_udf_tokenize(self):

tokenize = pandas_udf(lambda s: s.apply(lambda str: str.split(' ')),

ArrayType(StringType()))

self.assertEqual(tokenize.returnType, ArrayType(StringType()))

df = self.spark.createDataFrame([("hi boo",), ("bye boo",)], ["vals"])

result = df.select(tokenize("vals").alias("hi"))

self.assertEqual([Row(hi=[u'hi', u'boo']), Row(hi=[u'bye', u'boo'])], result.collect())

def test_pandas_udf_nested_arrays(self):

tokenize = pandas_udf(lambda s: s.apply(lambda str: [str.split(' ')]),

ArrayType(ArrayType(StringType())))

self.assertEqual(tokenize.returnType, ArrayType(ArrayType(StringType())))

df = self.spark.createDataFrame([("hi boo",), ("bye boo",)], ["vals"])

result = df.select(tokenize("vals").alias("hi"))

self.assertEqual([Row(hi=[[u'hi', u'boo']]), Row(hi=[[u'bye', u'boo']])], result.collect())

def test_vectorized_udf_basic(self):

df = self.spark.range(10).select(

col('id').cast('string').alias('str'),

col('id').cast('int').alias('int'),

col('id').alias('long'),

col('id').cast('float').alias('float'),

col('id').cast('double').alias('double'),

col('id').cast('decimal').alias('decimal'),

col('id').cast('boolean').alias('bool'),

array(col('id')).alias('array_long'))

f = lambda x: x

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

str_f = pandas_udf(f, StringType(), udf_type)

int_f = pandas_udf(f, IntegerType(), udf_type)

long_f = pandas_udf(f, LongType(), udf_type)

float_f = pandas_udf(f, FloatType(), udf_type)

double_f = pandas_udf(f, DoubleType(), udf_type)

decimal_f = pandas_udf(f, DecimalType(), udf_type)

bool_f = pandas_udf(f, BooleanType(), udf_type)

array_long_f = pandas_udf(f, ArrayType(LongType()), udf_type)

res = df.select(str_f(col('str')), int_f(col('int')),

long_f(col('long')), float_f(col('float')),

double_f(col('double')), decimal_f('decimal'),

bool_f(col('bool')), array_long_f('array_long'))

self.assertEqual(df.collect(), res.collect())

def test_register_nondeterministic_vectorized_udf_basic(self):

random_pandas_udf = pandas_udf(

lambda x: random.randint(6, 6) + x, IntegerType()).asNondeterministic()

self.assertEqual(random_pandas_udf.deterministic, False)

self.assertEqual(random_pandas_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)

nondeterministic_pandas_udf = self.spark.catalog.registerFunction(

"randomPandasUDF", random_pandas_udf)

self.assertEqual(nondeterministic_pandas_udf.deterministic, False)

self.assertEqual(nondeterministic_pandas_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)

[row] = self.spark.sql("SELECT randomPandasUDF(1)").collect()

self.assertEqual(row[0], 7)

def random_iter_udf(it):

for i in it:

yield random.randint(6, 6) + i

random_pandas_iter_udf = pandas_udf(

random_iter_udf, IntegerType(), PandasUDFType.SCALAR_ITER).asNondeterministic()

self.assertEqual(random_pandas_iter_udf.deterministic, False)

self.assertEqual(random_pandas_iter_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF)

nondeterministic_pandas_iter_udf = self.spark.catalog.registerFunction(

"randomPandasIterUDF", random_pandas_iter_udf)

self.assertEqual(nondeterministic_pandas_iter_udf.deterministic, False)

self.assertEqual(nondeterministic_pandas_iter_udf.evalType,

PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF)

[row] = self.spark.sql("SELECT randomPandasIterUDF(1)").collect()

self.assertEqual(row[0], 7)

def test_vectorized_udf_null_boolean(self):

data = [(True,), (True,), (None,), (False,)]

schema = StructType().add("bool", BooleanType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

bool_f = pandas_udf(lambda x: x, BooleanType(), udf_type)

res = df.select(bool_f(col('bool')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_byte(self):

data = [(None,), (2,), (3,), (4,)]

schema = StructType().add("byte", ByteType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

byte_f = pandas_udf(lambda x: x, ByteType(), udf_type)

res = df.select(byte_f(col('byte')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_short(self):

data = [(None,), (2,), (3,), (4,)]

schema = StructType().add("short", ShortType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

short_f = pandas_udf(lambda x: x, ShortType(), udf_type)

res = df.select(short_f(col('short')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_int(self):

data = [(None,), (2,), (3,), (4,)]

schema = StructType().add("int", IntegerType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

int_f = pandas_udf(lambda x: x, IntegerType(), udf_type)

res = df.select(int_f(col('int')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_long(self):

data = [(None,), (2,), (3,), (4,)]

schema = StructType().add("long", LongType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

long_f = pandas_udf(lambda x: x, LongType(), udf_type)

res = df.select(long_f(col('long')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_float(self):

data = [(3.0,), (5.0,), (-1.0,), (None,)]

schema = StructType().add("float", FloatType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

float_f = pandas_udf(lambda x: x, FloatType(), udf_type)

res = df.select(float_f(col('float')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_double(self):

data = [(3.0,), (5.0,), (-1.0,), (None,)]

schema = StructType().add("double", DoubleType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

double_f = pandas_udf(lambda x: x, DoubleType(), udf_type)

res = df.select(double_f(col('double')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_decimal(self):

data = [(Decimal(3.0),), (Decimal(5.0),), (Decimal(-1.0),), (None,)]

schema = StructType().add("decimal", DecimalType(38, 18))

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

decimal_f = pandas_udf(lambda x: x, DecimalType(38, 18), udf_type)

res = df.select(decimal_f(col('decimal')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_string(self):

data = [("foo",), (None,), ("bar",), ("bar",)]

schema = StructType().add("str", StringType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

str_f = pandas_udf(lambda x: x, StringType(), udf_type)

res = df.select(str_f(col('str')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_string_in_udf(self):

df = self.spark.range(10)

scalar_f = lambda x: pd.Series(map(str, x))

def iter_f(it):

for i in it:

yield scalar_f(i)

for f, udf_type in [(scalar_f, PandasUDFType.SCALAR), (iter_f, PandasUDFType.SCALAR_ITER)]:

str_f = pandas_udf(f, StringType(), udf_type)

actual = df.select(str_f(col('id')))

expected = df.select(col('id').cast('string'))

self.assertEqual(expected.collect(), actual.collect())

def test_vectorized_udf_datatype_string(self):

df = self.spark.range(10).select(

col('id').cast('string').alias('str'),

col('id').cast('int').alias('int'),

col('id').alias('long'),

col('id').cast('float').alias('float'),

col('id').cast('double').alias('double'),

col('id').cast('decimal').alias('decimal'),

col('id').cast('boolean').alias('bool'))

f = lambda x: x

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

str_f = pandas_udf(f, 'string', udf_type)

int_f = pandas_udf(f, 'integer', udf_type)

long_f = pandas_udf(f, 'long', udf_type)

float_f = pandas_udf(f, 'float', udf_type)

double_f = pandas_udf(f, 'double', udf_type)

decimal_f = pandas_udf(f, 'decimal(38, 18)', udf_type)

bool_f = pandas_udf(f, 'boolean', udf_type)

res = df.select(str_f(col('str')), int_f(col('int')),

long_f(col('long')), float_f(col('float')),

double_f(col('double')), decimal_f('decimal'),

bool_f(col('bool')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_null_binary(self):

data = [(bytearray(b"a"),), (None,), (bytearray(b"bb"),), (bytearray(b"ccc"),)]

schema = StructType().add("binary", BinaryType())

df = self.spark.createDataFrame(data, schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

str_f = pandas_udf(lambda x: x, BinaryType(), udf_type)

res = df.select(str_f(col('binary')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_array_type(self):

data = [([1, 2],), ([3, 4],)]

array_schema = StructType([StructField("array", ArrayType(IntegerType()))])

df = self.spark.createDataFrame(data, schema=array_schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

array_f = pandas_udf(lambda x: x, ArrayType(IntegerType()), udf_type)

result = df.select(array_f(col('array')))

self.assertEqual(df.collect(), result.collect())

def test_vectorized_udf_null_array(self):

data = [([1, 2],), (None,), (None,), ([3, 4],), (None,)]

array_schema = StructType([StructField("array", ArrayType(IntegerType()))])

df = self.spark.createDataFrame(data, schema=array_schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

array_f = pandas_udf(lambda x: x, ArrayType(IntegerType()), udf_type)

result = df.select(array_f(col('array')))

self.assertEqual(df.collect(), result.collect())

def test_vectorized_udf_struct_type(self):

df = self.spark.range(10)

return_type = StructType([

StructField('id', LongType()),

StructField('str', StringType())])

def scalar_func(id):

return pd.DataFrame({'id': id, 'str': id.apply(str)})

def iter_func(it):

for id in it:

yield scalar_func(id)

for func, udf_type in [(scalar_func, PandasUDFType.SCALAR),

(iter_func, PandasUDFType.SCALAR_ITER)]:

f = pandas_udf(func, returnType=return_type, functionType=udf_type)

expected = df.select(struct(col('id'), col('id').cast('string').alias('str'))

.alias('struct')).collect()

actual = df.select(f(col('id')).alias('struct')).collect()

self.assertEqual(expected, actual)

g = pandas_udf(func, 'id: long, str: string', functionType=udf_type)

actual = df.select(g(col('id')).alias('struct')).collect()

self.assertEqual(expected, actual)

struct_f = pandas_udf(lambda x: x, return_type, functionType=udf_type)

actual = df.select(struct_f(struct(col('id'), col('id').cast('string').alias('str'))))

self.assertEqual(expected, actual.collect())

def test_vectorized_udf_struct_complex(self):

df = self.spark.range(10)

return_type = StructType([

StructField('ts', TimestampType()),

StructField('arr', ArrayType(LongType()))])

def _scalar_f(id):

return pd.DataFrame({'ts': id.apply(lambda i: pd.Timestamp(i)),

'arr': id.apply(lambda i: [i, i + 1])})

scalar_f = pandas_udf(_scalar_f, returnType=return_type)

@pandas_udf(returnType=return_type, functionType=PandasUDFType.SCALAR_ITER)

def iter_f(it):

for id in it:

yield _scalar_f(id)

for f, udf_type in [(scalar_f, PandasUDFType.SCALAR), (iter_f, PandasUDFType.SCALAR_ITER)]:

actual = df.withColumn('f', f(col('id'))).collect()

for i, row in enumerate(actual):

id, f = row

self.assertEqual(i, id)

self.assertEqual(pd.Timestamp(i).to_pydatetime(), f[0])

self.assertListEqual([i, i + 1], f[1])

def test_vectorized_udf_nested_struct(self):

nested_type = StructType([

StructField('id', IntegerType()),

StructField('nested', StructType([

StructField('foo', StringType()),

StructField('bar', FloatType())

]))

])

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

with QuietTest(self.sc):

with self.assertRaisesRegex(

Exception,

'Invalid return type with scalar Pandas UDFs'):

pandas_udf(lambda x: x, returnType=nested_type, functionType=udf_type)

def test_vectorized_udf_map_type(self):

data = [({},), ({"a": 1},), ({"a": 1, "b": 2},), ({"a": 1, "b": 2, "c": 3},)]

schema = StructType([StructField("map", MapType(StringType(), LongType()))])

df = self.spark.createDataFrame(data, schema=schema)

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):

with QuietTest(self.sc):

with self.assertRaisesRegex(Exception, "MapType.*not supported"):

pandas_udf(lambda x: x, MapType(StringType(), LongType()), udf_type)

else:

map_f = pandas_udf(lambda x: x, MapType(StringType(), LongType()), udf_type)

result = df.select(map_f(col('map')))

self.assertEqual(df.collect(), result.collect())

def test_vectorized_udf_complex(self):

df = self.spark.range(10).select(

col('id').cast('int').alias('a'),

col('id').cast('int').alias('b'),

col('id').cast('double').alias('c'))

scalar_add = pandas_udf(lambda x, y: x + y, IntegerType())

scalar_power2 = pandas_udf(lambda x: 2 ** x, IntegerType())

scalar_mul = pandas_udf(lambda x, y: x * y, DoubleType())

@pandas_udf(IntegerType(), PandasUDFType.SCALAR_ITER)

def iter_add(it):

for x, y in it:

yield x + y

@pandas_udf(IntegerType(), PandasUDFType.SCALAR_ITER)

def iter_power2(it):

for x in it:

yield 2 ** x

@pandas_udf(DoubleType(), PandasUDFType.SCALAR_ITER)

def iter_mul(it):

for x, y in it:

yield x * y

for add, power2, mul in [(scalar_add, scalar_power2, scalar_mul),

(iter_add, iter_power2, iter_mul)]:

res = df.select(add(col('a'), col('b')), power2(col('a')), mul(col('b'), col('c')))

expected = df.select(expr('a + b'), expr('power(2, a)'), expr('b * c'))

self.assertEqual(expected.collect(), res.collect())

def test_vectorized_udf_exception(self):

df = self.spark.range(10)

scalar_raise_exception = pandas_udf(lambda x: x * (1 / 0), LongType())

@pandas_udf(LongType(), PandasUDFType.SCALAR_ITER)

def iter_raise_exception(it):

for x in it:

yield x * (1 / 0)

for raise_exception in [scalar_raise_exception, iter_raise_exception]:

with QuietTest(self.sc):

with self.assertRaisesRegex(Exception, 'division( or modulo)? by zero'):

df.select(raise_exception(col('id'))).collect()

def test_vectorized_udf_invalid_length(self):

df = self.spark.range(10)

raise_exception = pandas_udf(lambda _: pd.Series(1), LongType())

with QuietTest(self.sc):

with self.assertRaisesRegex(

Exception,

'Result vector from pandas_udf was not the required length'):

df.select(raise_exception(col('id'))).collect()

@pandas_udf(LongType(), PandasUDFType.SCALAR_ITER)

def iter_udf_wong_output_size(it):

for _ in it:

yield pd.Series(1)

with QuietTest(self.sc):

with self.assertRaisesRegex(

Exception,

"The length of output in Scalar iterator.*"

"the length of output was 1"):

df.select(iter_udf_wong_output_size(col('id'))).collect()

@pandas_udf(LongType(), PandasUDFType.SCALAR_ITER)

def iter_udf_not_reading_all_input(it):

for batch in it:

batch_len = len(batch)

yield pd.Series([1] * batch_len)

break

with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 3}):

df1 = self.spark.range(10).repartition(1)

with QuietTest(self.sc):

with self.assertRaisesRegex(

Exception,

"pandas iterator UDF should exhaust"):

df1.select(iter_udf_not_reading_all_input(col('id'))).collect()

def test_vectorized_udf_chained(self):

df = self.spark.range(10)

scalar_f = pandas_udf(lambda x: x + 1, LongType())

scalar_g = pandas_udf(lambda x: x - 1, LongType())

iter_f = pandas_udf(lambda it: map(lambda x: x + 1, it), LongType(),

PandasUDFType.SCALAR_ITER)

iter_g = pandas_udf(lambda it: map(lambda x: x - 1, it), LongType(),

PandasUDFType.SCALAR_ITER)

for f, g in [(scalar_f, scalar_g), (iter_f, iter_g)]:

res = df.select(g(f(col('id'))))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_chained_struct_type(self):

df = self.spark.range(10)

return_type = StructType([

StructField('id', LongType()),

StructField('str', StringType())])

@pandas_udf(return_type)

def scalar_f(id):

return pd.DataFrame({'id': id, 'str': id.apply(str)})

scalar_g = pandas_udf(lambda x: x, return_type)

@pandas_udf(return_type, PandasUDFType.SCALAR_ITER)

def iter_f(it):

for id in it:

yield pd.DataFrame({'id': id, 'str': id.apply(str)})

iter_g = pandas_udf(lambda x: x, return_type, PandasUDFType.SCALAR_ITER)

expected = df.select(struct(col('id'), col('id').cast('string').alias('str'))

.alias('struct')).collect()

for f, g in [(scalar_f, scalar_g), (iter_f, iter_g)]:

actual = df.select(g(f(col('id'))).alias('struct')).collect()

self.assertEqual(expected, actual)

def test_vectorized_udf_wrong_return_type(self):

with QuietTest(self.sc):

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

with self.assertRaisesRegex(

NotImplementedError,

'Invalid return type.*scalar Pandas UDF.*ArrayType.*TimestampType'):

pandas_udf(lambda x: x, ArrayType(TimestampType()), udf_type)

def test_vectorized_udf_return_scalar(self):

df = self.spark.range(10)

scalar_f = pandas_udf(lambda x: 1.0, DoubleType())

iter_f = pandas_udf(lambda it: map(lambda x: 1.0, it), DoubleType(),

PandasUDFType.SCALAR_ITER)

for f in [scalar_f, iter_f]:

with QuietTest(self.sc):

with self.assertRaisesRegex(Exception, 'Return.*type.*Series'):

df.select(f(col('id'))).collect()

def test_vectorized_udf_decorator(self):

df = self.spark.range(10)

@pandas_udf(returnType=LongType())

def scalar_identity(x):

return x

@pandas_udf(returnType=LongType(), functionType=PandasUDFType.SCALAR_ITER)

def iter_identity(x):

return x

for identity in [scalar_identity, iter_identity]:

res = df.select(identity(col('id')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_empty_partition(self):

df = self.spark.createDataFrame(self.sc.parallelize([Row(id=1)], 2))

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

f = pandas_udf(lambda x: x, LongType(), udf_type)

res = df.select(f(col('id')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_struct_with_empty_partition(self):

df = self.spark.createDataFrame(self.sc.parallelize([Row(id=1)], 2))\

.withColumn('name', lit('John Doe'))

@pandas_udf("first string, last string")

def scalar_split_expand(n):

return n.str.split(expand=True)

@pandas_udf("first string, last string", PandasUDFType.SCALAR_ITER)

def iter_split_expand(it):

for n in it:

yield n.str.split(expand=True)

for split_expand in [scalar_split_expand, iter_split_expand]:

result = df.select(split_expand('name')).collect()

self.assertEqual(1, len(result))

row = result[0]

self.assertEqual('John', row[0]['first'])

self.assertEqual('Doe', row[0]['last'])

def test_vectorized_udf_varargs(self):

df = self.spark.createDataFrame(self.sc.parallelize([Row(id=1)], 2))

scalar_f = pandas_udf(lambda *v: v[0], LongType())

@pandas_udf(LongType(), PandasUDFType.SCALAR_ITER)

def iter_f(it):

for v in it:

yield v[0]

for f in [scalar_f, iter_f]:

res = df.select(f(col('id'), col('id')))

self.assertEqual(df.collect(), res.collect())

def test_vectorized_udf_unsupported_types(self):

with QuietTest(self.sc):

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

with self.assertRaisesRegex(

NotImplementedError,

'Invalid return type.*scalar Pandas UDF.*ArrayType.*TimestampType'):

pandas_udf(lambda x: x, ArrayType(TimestampType()), udf_type)

with self.assertRaisesRegex(

NotImplementedError,

'Invalid return type.*scalar Pandas UDF.*ArrayType.StructType'):

pandas_udf(lambda x: x,

ArrayType(StructType([StructField('a', IntegerType())])), udf_type)

def test_vectorized_udf_dates(self):

schema = StructType().add("idx", LongType()).add("date", DateType())

data = [(0, date(1969, 1, 1),),

(1, date(2012, 2, 2),),

(2, None,),

(3, date(2100, 4, 4),),

(4, date(2262, 4, 12),)]

df = self.spark.createDataFrame(data, schema=schema)

def scalar_check_data(idx, date, date_copy):

msgs = []

is_equal = date.isnull()

for i in range(len(idx)):

if (is_equal[i] and data[idx[i]][1] is None) or \

date[i] == data[idx[i]][1]:

msgs.append(None)

else:

msgs.append(

"date values are not equal (date='%s': data[%d][1]='%s')"

% (date[i], idx[i], data[idx[i]][1]))

return pd.Series(msgs)

def iter_check_data(it):

for idx, date, date_copy in it:

yield scalar_check_data(idx, date, date_copy)

pandas_scalar_check_data = pandas_udf(scalar_check_data, StringType())

pandas_iter_check_data = pandas_udf(iter_check_data, StringType(),

PandasUDFType.SCALAR_ITER)

for check_data, udf_type in [(pandas_scalar_check_data, PandasUDFType.SCALAR),

(pandas_iter_check_data, PandasUDFType.SCALAR_ITER)]:

date_copy = pandas_udf(lambda t: t, returnType=DateType(), functionType=udf_type)

df = df.withColumn("date_copy", date_copy(col("date")))

result = df.withColumn("check_data",

check_data(col("idx"), col("date"), col("date_copy"))).collect()

self.assertEqual(len(data), len(result))

for i in range(len(result)):

self.assertEqual(data[i][1], result[i][1]) # "date" col

self.assertEqual(data[i][1], result[i][2]) # "date_copy" col

self.assertIsNone(result[i][3]) # "check_data" col

def test_vectorized_udf_timestamps(self):

schema = StructType([

StructField("idx", LongType(), True),

StructField("timestamp", TimestampType(), True)])

data = [(0, datetime(1969, 1, 1, 1, 1, 1)),

(1, datetime(2012, 2, 2, 2, 2, 2)),

(2, None),

(3, datetime(2100, 3, 3, 3, 3, 3))]

df = self.spark.createDataFrame(data, schema=schema)

def scalar_check_data(idx, timestamp, timestamp_copy):

msgs = []

is_equal = timestamp.isnull() # use this array to check values are equal

for i in range(len(idx)):

# Check that timestamps are as expected in the UDF

if (is_equal[i] and data[idx[i]][1] is None) or \

timestamp[i].to_pydatetime() == data[idx[i]][1]:

msgs.append(None)

else:

msgs.append(

"timestamp values are not equal (timestamp='%s': data[%d][1]='%s')"

% (timestamp[i], idx[i], data[idx[i]][1]))

return pd.Series(msgs)

def iter_check_data(it):

for idx, timestamp, timestamp_copy in it:

yield scalar_check_data(idx, timestamp, timestamp_copy)

pandas_scalar_check_data = pandas_udf(scalar_check_data, StringType())

pandas_iter_check_data = pandas_udf(iter_check_data, StringType(),

PandasUDFType.SCALAR_ITER)

for check_data, udf_type in [(pandas_scalar_check_data, PandasUDFType.SCALAR),

(pandas_iter_check_data, PandasUDFType.SCALAR_ITER)]:

# Check that a timestamp passed through a pandas_udf will not be altered by timezone

# calc

f_timestamp_copy = pandas_udf(lambda t: t,

returnType=TimestampType(), functionType=udf_type)

df = df.withColumn("timestamp_copy", f_timestamp_copy(col("timestamp")))

result = df.withColumn("check_data", check_data(col("idx"), col("timestamp"),

col("timestamp_copy"))).collect()

# Check that collection values are correct

self.assertEqual(len(data), len(result))

for i in range(len(result)):

self.assertEqual(data[i][1], result[i][1]) # "timestamp" col

self.assertEqual(data[i][1], result[i][2]) # "timestamp_copy" col

self.assertIsNone(result[i][3]) # "check_data" col

def test_vectorized_udf_return_timestamp_tz(self):

df = self.spark.range(10)

@pandas_udf(returnType=TimestampType())

def scalar_gen_timestamps(id):

ts = [pd.Timestamp(i, unit='D', tz='America/Los_Angeles') for i in id]

return pd.Series(ts)

@pandas_udf(returnType=TimestampType(), functionType=PandasUDFType.SCALAR_ITER)

def iter_gen_timestamps(it):

for id in it:

ts = [pd.Timestamp(i, unit='D', tz='America/Los_Angeles') for i in id]

yield pd.Series(ts)

for gen_timestamps in [scalar_gen_timestamps, iter_gen_timestamps]:

result = df.withColumn("ts", gen_timestamps(col("id"))).collect()

spark_ts_t = TimestampType()

for r in result:

i, ts = r

ts_tz = pd.Timestamp(i, unit='D', tz='America/Los_Angeles').to_pydatetime()

expected = spark_ts_t.fromInternal(spark_ts_t.toInternal(ts_tz))

self.assertEqual(expected, ts)

def test_vectorized_udf_check_config(self):

with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 3}):

df = self.spark.range(10, numPartitions=1)

@pandas_udf(returnType=LongType())

def scalar_check_records_per_batch(x):

return pd.Series(x.size).repeat(x.size)

@pandas_udf(returnType=LongType(), functionType=PandasUDFType.SCALAR_ITER)

def iter_check_records_per_batch(it):

for x in it:

yield pd.Series(x.size).repeat(x.size)

for check_records_per_batch in [scalar_check_records_per_batch,

iter_check_records_per_batch]:

result = df.select(check_records_per_batch(col("id"))).collect()

for (r,) in result:

self.assertTrue(r <= 3)

def test_vectorized_udf_timestamps_respect_session_timezone(self):

schema = StructType([

StructField("idx", LongType(), True),

StructField("timestamp", TimestampType(), True)])

data = [(1, datetime(1969, 1, 1, 1, 1, 1)),

(2, datetime(2012, 2, 2, 2, 2, 2)),

(3, None),

(4, datetime(2100, 3, 3, 3, 3, 3))]

df = self.spark.createDataFrame(data, schema=schema)

scalar_internal_value = pandas_udf(

lambda ts: ts.apply(lambda ts: ts.value if ts is not pd.NaT else None), LongType())

@pandas_udf(LongType(), PandasUDFType.SCALAR_ITER)

def iter_internal_value(it):

for ts in it:

yield ts.apply(lambda ts: ts.value if ts is not pd.NaT else None)

for internal_value, udf_type in [(scalar_internal_value, PandasUDFType.SCALAR),

(iter_internal_value, PandasUDFType.SCALAR_ITER)]:

f_timestamp_copy = pandas_udf(lambda ts: ts, TimestampType(), udf_type)

timezone = "America/Los_Angeles"

with self.sql_conf({"spark.sql.session.timeZone": timezone}):

df_la = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \

.withColumn("internal_value", internal_value(col("timestamp")))

result_la = df_la.select(col("idx"), col("internal_value")).collect()

# Correct result_la by adjusting 3 hours difference between Los Angeles and New York

diff = 3 * 60 * 60 * 1000 * 1000 * 1000

result_la_corrected = \

df_la.select(col("idx"), col("tscopy"), col("internal_value") + diff).collect()

timezone = "America/New_York"

with self.sql_conf({"spark.sql.session.timeZone": timezone}):

df_ny = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \

.withColumn("internal_value", internal_value(col("timestamp")))

result_ny = df_ny.select(col("idx"), col("tscopy"), col("internal_value")).collect()

self.assertNotEqual(result_ny, result_la)

self.assertEqual(result_ny, result_la_corrected)

def test_nondeterministic_vectorized_udf(self):

# Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations

@pandas_udf('double')

def scalar_plus_ten(v):

return v + 10

@pandas_udf('double', PandasUDFType.SCALAR_ITER)

def iter_plus_ten(it):

for v in it:

yield v + 10

for plus_ten in [scalar_plus_ten, iter_plus_ten]:

random_udf = self.nondeterministic_vectorized_udf

df = self.spark.range(10).withColumn('rand', random_udf(col('id')))

result1 = df.withColumn('plus_ten(rand)', plus_ten(df['rand'])).toPandas()

self.assertEqual(random_udf.deterministic, False)

self.assertTrue(result1['plus_ten(rand)'].equals(result1['rand'] + 10))

def test_nondeterministic_vectorized_udf_in_aggregate(self):

df = self.spark.range(10)

for random_udf in [self.nondeterministic_vectorized_udf,

self.nondeterministic_vectorized_iter_udf]:

with QuietTest(self.sc):

with self.assertRaisesRegex(AnalysisException, 'nondeterministic'):

df.groupby(df.id).agg(sum(random_udf(df.id))).collect()

with self.assertRaisesRegex(AnalysisException, 'nondeterministic'):

df.agg(sum(random_udf(df.id))).collect()

def test_register_vectorized_udf_basic(self):

df = self.spark.range(10).select(

col('id').cast('int').alias('a'),

col('id').cast('int').alias('b'))

scalar_original_add = pandas_udf(lambda x, y: x + y, IntegerType())

self.assertEqual(scalar_original_add.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)

@pandas_udf(IntegerType(), PandasUDFType.SCALAR_ITER)

def iter_original_add(it):

for x, y in it:

yield x + y

self.assertEqual(iter_original_add.evalType, PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF)

for original_add in [scalar_original_add, iter_original_add]:

self.assertEqual(original_add.deterministic, True)

new_add = self.spark.catalog.registerFunction("add1", original_add)

res1 = df.select(new_add(col('a'), col('b')))

res2 = self.spark.sql(

"SELECT add1(t.a, t.b) FROM (SELECT id as a, id as b FROM range(10)) t")

expected = df.select(expr('a + b'))

self.assertEqual(expected.collect(), res1.collect())

self.assertEqual(expected.collect(), res2.collect())

def test_scalar_iter_udf_init(self):

import numpy as np

@pandas_udf('int', PandasUDFType.SCALAR_ITER)

def rng(batch_iter):

context = TaskContext.get()

part = context.partitionId()

np.random.seed(part)

for batch in batch_iter:

yield pd.Series(np.random.randint(100, size=len(batch)))

with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 2}):

df = self.spark.range(10, numPartitions=2).select(rng(col("id").alias("v")))

result1 = df.collect()

result2 = df.collect()

self.assertEqual(result1, result2,

"SCALAR ITER UDF can initialize state and produce deterministic RNG")

def test_scalar_iter_udf_close(self):

@pandas_udf('int', PandasUDFType.SCALAR_ITER)

def test_close(batch_iter):

try:

for batch in batch_iter:

yield batch

finally:

raise RuntimeError("reached finally block")

with QuietTest(self.sc):

with self.assertRaisesRegex(Exception, "reached finally block"):

self.spark.range(1).select(test_close(col("id"))).collect()

def test_scalar_iter_udf_close_early(self):

tmp_dir = tempfile.mkdtemp()

try:

tmp_file = tmp_dir + '/reach_finally_block'

@pandas_udf('int', PandasUDFType.SCALAR_ITER)

def test_close(batch_iter):

generator_exit_caught = False

try:

for batch in batch_iter:

yield batch

time.sleep(1.0) # avoid the function finish too fast.

except GeneratorExit as ge:

generator_exit_caught = True

raise ge

finally:

assert generator_exit_caught, "Generator exit exception was not caught."

open(tmp_file, 'a').close()

with QuietTest(self.sc):

with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 1,

"spark.sql.execution.pandas.udf.buffer.size": 4}):

self.spark.range(10).repartition(1) \

.select(test_close(col("id"))).limit(2).collect()

# wait here because python udf worker will take some time to detect

# jvm side socket closed and then will trigger `GenerateExit` raised.

# wait timeout is 10s.

for i in range(100):

time.sleep(0.1)

if os.path.exists(tmp_file):

break

assert os.path.exists(tmp_file), "finally block not reached."

finally:

shutil.rmtree(tmp_dir)

# Regression test for SPARK-23314

def test_timestamp_dst(self):

# Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am

dt = [datetime(2015, 11, 1, 0, 30),

datetime(2015, 11, 1, 1, 30),

datetime(2015, 11, 1, 2, 30)]

df = self.spark.createDataFrame(dt, 'timestamp').toDF('time')

for udf_type in [PandasUDFType.SCALAR, PandasUDFType.SCALAR_ITER]:

foo_udf = pandas_udf(lambda x: x, 'timestamp', udf_type)

result = df.withColumn('time', foo_udf(df.time))

self.assertEqual(df.collect(), result.collect())

def test_udf_category_type(self):

@pandas_udf('string')

def to_category_func(x):

return x.astype('category')

pdf = pd.DataFrame({"A": [u"a", u"b", u"c", u"a"]})

df = self.spark.createDataFrame(pdf)

df = df.withColumn("B", to_category_func(df['A']))

result_spark = df.toPandas()

spark_type = df.dtypes[1][1]

# spark data frame and arrow execution mode enabled data frame type must match pandas

self.assertEqual(spark_type, 'string')

# Check result of column 'B' must be equal to column 'A' in type and values

pd.testing.assert_series_equal(result_spark["A"], result_spark["B"], check_names=False)

def test_type_annotation(self):

# Regression test to check if type hints can be used. See SPARK-23569.

def noop(col: pd.Series) -> pd.Series:

return col

df = self.spark.range(1).select(pandas_udf(f=noop, returnType='bigint')('id'))

self.assertEqual(df.first()[0], 0)

def test_mixed_udf(self):

df = self.spark.range(0, 1).toDF('v')

# Test mixture of multiple UDFs and Pandas UDFs.

@udf('int')

def f1(x):

assert type(x) == int

return x + 1

@pandas_udf('int')

def f2_scalar(x):

assert type(x) == pd.Series

return x + 10

@pandas_udf('int', PandasUDFType.SCALAR_ITER)

def f2_iter(it):

for x in it:

assert type(x) == pd.Series

yield x + 10

@udf('int')

def f3(x):

assert type(x) == int

return x + 100

@pandas_udf('int')

def f4_scalar(x):

assert type(x) == pd.Series

return x + 1000

@pandas_udf('int', PandasUDFType.SCALAR_ITER)

def f4_iter(it):

for x in it:

assert type(x) == pd.Series

yield x + 1000

expected_chained_1 = df.withColumn('f2_f1', df['v'] + 11).collect()

expected_chained_2 = df.withColumn('f3_f2_f1', df['v'] + 111).collect()

expected_chained_3 = df.withColumn('f4_f3_f2_f1', df['v'] + 1111).collect()

expected_chained_4 = df.withColumn('f4_f2_f1', df['v'] + 1011).collect()

expected_chained_5 = df.withColumn('f4_f3_f1', df['v'] + 1101).collect()

expected_multi = df \

.withColumn('f1', df['v'] + 1) \

.withColumn('f2', df['v'] + 10) \

.withColumn('f3', df['v'] + 100) \

.withColumn('f4', df['v'] + 1000) \

.withColumn('f2_f1', df['v'] + 11) \

.withColumn('f3_f1', df['v'] + 101) \

.withColumn('f4_f1', df['v'] + 1001) \

.withColumn('f3_f2', df['v'] + 110) \

.withColumn('f4_f2', df['v'] + 1010) \

.withColumn('f4_f3', df['v'] + 1100) \

.withColumn('f3_f2_f1', df['v'] + 111) \

.withColumn('f4_f2_f1', df['v'] + 1011) \

.withColumn('f4_f3_f1', df['v'] + 1101) \

.withColumn('f4_f3_f2', df['v'] + 1110) \

.withColumn('f4_f3_f2_f1', df['v'] + 1111) \

.collect()

for f2, f4 in [(f2_scalar, f4_scalar), (f2_scalar, f4_iter),

(f2_iter, f4_scalar), (f2_iter, f4_iter)]:

# Test single expression with chained UDFs

df_chained_1 = df.withColumn('f2_f1', f2(f1(df['v'])))

df_chained_2 = df.withColumn('f3_f2_f1', f3(f2(f1(df['v']))))

df_chained_3 = df.withColumn('f4_f3_f2_f1', f4(f3(f2(f1(df['v'])))))

df_chained_4 = df.withColumn('f4_f2_f1', f4(f2(f1(df['v']))))

df_chained_5 = df.withColumn('f4_f3_f1', f4(f3(f1(df['v']))))

self.assertEqual(expected_chained_1, df_chained_1.collect())

self.assertEqual(expected_chained_2, df_chained_2.collect())

self.assertEqual(expected_chained_3, df_chained_3.collect())

self.assertEqual(expected_chained_4, df_chained_4.collect())

self.assertEqual(expected_chained_5, df_chained_5.collect())

# Test multiple mixed UDF expressions in a single projection

df_multi_1 = df \

.withColumn('f1', f1(col('v'))) \

.withColumn('f2', f2(col('v'))) \

.withColumn('f3', f3(col('v'))) \

.withColumn('f4', f4(col('v'))) \

.withColumn('f2_f1', f2(col('f1'))) \

.withColumn('f3_f1', f3(col('f1'))) \

.withColumn('f4_f1', f4(col('f1'))) \

.withColumn('f3_f2', f3(col('f2'))) \

.withColumn('f4_f2', f4(col('f2'))) \

.withColumn('f4_f3', f4(col('f3'))) \

.withColumn('f3_f2_f1', f3(col('f2_f1'))) \

.withColumn('f4_f2_f1', f4(col('f2_f1'))) \

.withColumn('f4_f3_f1', f4(col('f3_f1'))) \

.withColumn('f4_f3_f2', f4(col('f3_f2'))) \

.withColumn('f4_f3_f2_f1', f4(col('f3_f2_f1')))

# Test mixed udfs in a single expression

df_multi_2 = df \

.withColumn('f1', f1(col('v'))) \

.withColumn('f2', f2(col('v'))) \

.withColumn('f3', f3(col('v'))) \

.withColumn('f4', f4(col('v'))) \

.withColumn('f2_f1', f2(f1(col('v')))) \

.withColumn('f3_f1', f3(f1(col('v')))) \

.withColumn('f4_f1', f4(f1(col('v')))) \

.withColumn('f3_f2', f3(f2(col('v')))) \

.withColumn('f4_f2', f4(f2(col('v')))) \

.withColumn('f4_f3', f4(f3(col('v')))) \

.withColumn('f3_f2_f1', f3(f2(f1(col('v'))))) \

.withColumn('f4_f2_f1', f4(f2(f1(col('v'))))) \

.withColumn('f4_f3_f1', f4(f3(f1(col('v'))))) \

.withColumn('f4_f3_f2', f4(f3(f2(col('v'))))) \

.withColumn('f4_f3_f2_f1', f4(f3(f2(f1(col('v'))))))

self.assertEqual(expected_multi, df_multi_1.collect())

self.assertEqual(expected_multi, df_multi_2.collect())

def test_mixed_udf_and_sql(self):

df = self.spark.range(0, 1).toDF('v')

# Test mixture of UDFs, Pandas UDFs and SQL expression.

@udf('int')

def f1(x):

assert type(x) == int

return x + 1

def f2(x):

assert type(x) == Column

return x + 10

@pandas_udf('int')

def f3s(x):

assert type(x) == pd.Series

return x + 100

@pandas_udf('int', PandasUDFType.SCALAR_ITER)

def f3i(it):

for x in it:

assert type(x) == pd.Series

yield x + 100

expected = df.withColumn('f1', df['v'] + 1) \

.withColumn('f2', df['v'] + 10) \

.withColumn('f3', df['v'] + 100) \

.withColumn('f1_f2', df['v'] + 11) \

.withColumn('f1_f3', df['v'] + 101) \

.withColumn('f2_f1', df['v'] + 11) \

.withColumn('f2_f3', df['v'] + 110) \

.withColumn('f3_f1', df['v'] + 101) \

.withColumn('f3_f2', df['v'] + 110) \

.withColumn('f1_f2_f3', df['v'] + 111) \

.withColumn('f1_f3_f2', df['v'] + 111) \

.withColumn('f2_f1_f3', df['v'] + 111) \

.withColumn('f2_f3_f1', df['v'] + 111) \

.withColumn('f3_f1_f2', df['v'] + 111) \

.withColumn('f3_f2_f1', df['v'] + 111) \

.collect()

for f3 in [f3s, f3i]:

df1 = df.withColumn('f1', f1(df['v'])) \

.withColumn('f2', f2(df['v'])) \

.withColumn('f3', f3(df['v'])) \

.withColumn('f1_f2', f1(f2(df['v']))) \

.withColumn('f1_f3', f1(f3(df['v']))) \

.withColumn('f2_f1', f2(f1(df['v']))) \

.withColumn('f2_f3', f2(f3(df['v']))) \

.withColumn('f3_f1', f3(f1(df['v']))) \

.withColumn('f3_f2', f3(f2(df['v']))) \

.withColumn('f1_f2_f3', f1(f2(f3(df['v'])))) \

.withColumn('f1_f3_f2', f1(f3(f2(df['v'])))) \

.withColumn('f2_f1_f3', f2(f1(f3(df['v'])))) \

.withColumn('f2_f3_f1', f2(f3(f1(df['v'])))) \

.withColumn('f3_f1_f2', f3(f1(f2(df['v'])))) \

.withColumn('f3_f2_f1', f3(f2(f1(df['v']))))

self.assertEqual(expected, df1.collect())

# SPARK-24721

@unittest.skipIf(not test_compiled, test_not_compiled_message) # type: ignore

def test_datasource_with_udf(self):

# Same as SQLTests.test_datasource_with_udf, but with Pandas UDF

# This needs to a separate test because Arrow dependency is optional

import numpy as np

path = tempfile.mkdtemp()

shutil.rmtree(path)

try:

self.spark.range(1).write.mode("overwrite").format('csv').save(path)

filesource_df = self.spark.read.option('inferSchema', True).csv(path).toDF('i')

datasource_df = self.spark.read \

.format("org.apache.spark.sql.sources.SimpleScanSource") \

.option('from', 0).option('to', 1).load().toDF('i')

datasource_v2_df = self.spark.read \

.format("org.apache.spark.sql.connector.SimpleDataSourceV2") \

.load().toDF('i', 'j')

c1 = pandas_udf(lambda x: x + 1, 'int')(lit(1))

c2 = pandas_udf(lambda x: x + 1, 'int')(col('i'))

f1 = pandas_udf(lambda x: pd.Series(np.repeat(False, len(x))), 'boolean')(lit(1))

f2 = pandas_udf(lambda x: pd.Series(np.repeat(False, len(x))), 'boolean')(col('i'))

for df in [filesource_df, datasource_df, datasource_v2_df]:

result = df.withColumn('c', c1)

expected = df.withColumn('c', lit(2))

self.assertEqual(expected.collect(), result.collect())

for df in [filesource_df, datasource_df, datasource_v2_df]:

result = df.withColumn('c', c2)

expected = df.withColumn('c', col('i') + 1)

self.assertEqual(expected.collect(), result.collect())

for df in [filesource_df, datasource_df, datasource_v2_df]:

for f in [f1, f2]:

result = df.filter(f)

self.assertEqual(0, result.count())

finally:

shutil.rmtree(path)

if __name__ == "__main__":

from pyspark.sql.tests.test_pandas_udf_scalar import * # noqa: F401

try:

import xmlrunner # type: ignore[import]

testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)

except ImportError:

testRunner = None

unittest.main(testRunner=testRunner, verbosity=2)

Coverage for pyspark/sql/tests/test_pandas_udf_scalar.py : 9%

702 statements 85 run 617 missing 0 excluded 1 partial