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spark-instrumented-optimizer/python/pyspark/sql/tests/test_udf.py
Takuya UESHIN 032d17933b [SPARK-31945][SQL][PYSPARK] Enable cache for the same Python function 
### What changes were proposed in this pull request?

This PR proposes to make `PythonFunction` holds `Seq[Byte]` instead of `Array[Byte]` to be able to compare if the byte array has the same values for the cache manager.

### Why are the changes needed?

Currently the cache manager doesn't use the cache for `udf` if the `udf` is created again even if the functions is the same.

```py
>>> func = lambda x: x

>>> df = spark.range(1)
>>> df.select(udf(func)("id")).cache()
```
```py
>>> df.select(udf(func)("id")).explain()
== Physical Plan ==
*(2) Project [pythonUDF0#14 AS <lambda>(id)#12]
+- BatchEvalPython [<lambda>(id#0L)], [pythonUDF0#14]
 +- *(1) Range (0, 1, step=1, splits=12)
```

This is because `PythonFunction` holds `Array[Byte]`, and `equals` method of array equals only when the both array is the same instance.

### Does this PR introduce _any_ user-facing change?

Yes, if the user reuse the Python function for the UDF, the cache manager will detect the same function and use the cache for it.

### How was this patch tested?

I added a test case and manually.

```py
>>> df.select(udf(func)("id")).explain()
== Physical Plan ==
InMemoryTableScan [<lambda>(id)#12]
   +- InMemoryRelation [<lambda>(id)#12], StorageLevel(disk, memory, deserialized, 1 replicas)
         +- *(2) Project [pythonUDF0#5 AS <lambda>(id)#3]
            +- BatchEvalPython [<lambda>(id#0L)], [pythonUDF0#5]
               +- *(1) Range (0, 1, step=1, splits=12)
```

Closes #28774 from ueshin/issues/SPARK-31945/udf_cache.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-06-10 16:38:59 +09:00

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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
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# http://www.apache.org/licenses/LICENSE-2.0
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# 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 functools
import pydoc
import shutil
import tempfile
import unittest
from pyspark import SparkContext
from pyspark.sql import SparkSession, Column, Row
from pyspark.sql.functions import UserDefinedFunction, udf
from pyspark.sql.types import *
from pyspark.sql.utils import AnalysisException
from pyspark.testing.sqlutils import ReusedSQLTestCase, test_compiled, test_not_compiled_message
from pyspark.testing.utils import QuietTest
class UDFTests(ReusedSQLTestCase):
def test_udf_with_callable(self):
d = [Row(number=i, squared=i**2) for i in range(10)]
rdd = self.sc.parallelize(d)
data = self.spark.createDataFrame(rdd)
class PlusFour:
def __call__(self, col):
if col is not None:
return col + 4
call = PlusFour()
pudf = UserDefinedFunction(call, LongType())
res = data.select(pudf(data['number']).alias('plus_four'))
self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85)
def test_udf_with_partial_function(self):
d = [Row(number=i, squared=i**2) for i in range(10)]
rdd = self.sc.parallelize(d)
data = self.spark.createDataFrame(rdd)
def some_func(col, param):
if col is not None:
return col + param
pfunc = functools.partial(some_func, param=4)
pudf = UserDefinedFunction(pfunc, LongType())
res = data.select(pudf(data['number']).alias('plus_four'))
self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85)
def test_udf(self):
self.spark.catalog.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
[row] = self.spark.sql("SELECT twoArgs('test', 1)").collect()
self.assertEqual(row[0], 5)
# This is to check if a deprecated 'SQLContext.registerFunction' can call its alias.
sqlContext = self.spark._wrapped
sqlContext.registerFunction("oneArg", lambda x: len(x), IntegerType())
[row] = sqlContext.sql("SELECT oneArg('test')").collect()
self.assertEqual(row[0], 4)
def test_udf2(self):
with self.tempView("test"):
self.spark.catalog.registerFunction("strlen", lambda string: len(string), IntegerType())
self.spark.createDataFrame(self.sc.parallelize([Row(a="test")]))\
.createOrReplaceTempView("test")
[res] = self.spark.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect()
self.assertEqual(4, res[0])
def test_udf3(self):
two_args = self.spark.catalog.registerFunction(
"twoArgs", UserDefinedFunction(lambda x, y: len(x) + y))
self.assertEqual(two_args.deterministic, True)
[row] = self.spark.sql("SELECT twoArgs('test', 1)").collect()
self.assertEqual(row[0], u'5')
def test_udf_registration_return_type_none(self):
two_args = self.spark.catalog.registerFunction(
"twoArgs", UserDefinedFunction(lambda x, y: len(x) + y, "integer"), None)
self.assertEqual(two_args.deterministic, True)
[row] = self.spark.sql("SELECT twoArgs('test', 1)").collect()
self.assertEqual(row[0], 5)
def test_udf_registration_return_type_not_none(self):
with QuietTest(self.sc):
with self.assertRaisesRegexp(TypeError, "Invalid return type"):
self.spark.catalog.registerFunction(
"f", UserDefinedFunction(lambda x, y: len(x) + y, StringType()), StringType())
def test_nondeterministic_udf(self):
# Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations
import random
udf_random_col = udf(lambda: int(100 * random.random()), IntegerType()).asNondeterministic()
self.assertEqual(udf_random_col.deterministic, False)
df = self.spark.createDataFrame([Row(1)]).select(udf_random_col().alias('RAND'))
udf_add_ten = udf(lambda rand: rand + 10, IntegerType())
[row] = df.withColumn('RAND_PLUS_TEN', udf_add_ten('RAND')).collect()
self.assertEqual(row[0] + 10, row[1])
def test_nondeterministic_udf2(self):
import random
random_udf = udf(lambda: random.randint(6, 6), IntegerType()).asNondeterministic()
self.assertEqual(random_udf.deterministic, False)
random_udf1 = self.spark.catalog.registerFunction("randInt", random_udf)
self.assertEqual(random_udf1.deterministic, False)
[row] = self.spark.sql("SELECT randInt()").collect()
self.assertEqual(row[0], 6)
[row] = self.spark.range(1).select(random_udf1()).collect()
self.assertEqual(row[0], 6)
[row] = self.spark.range(1).select(random_udf()).collect()
self.assertEqual(row[0], 6)
# render_doc() reproduces the help() exception without printing output
pydoc.render_doc(udf(lambda: random.randint(6, 6), IntegerType()))
pydoc.render_doc(random_udf)
pydoc.render_doc(random_udf1)
pydoc.render_doc(udf(lambda x: x).asNondeterministic)
def test_nondeterministic_udf3(self):
# regression test for SPARK-23233
f = udf(lambda x: x)
# Here we cache the JVM UDF instance.
self.spark.range(1).select(f("id"))
# This should reset the cache to set the deterministic status correctly.
f = f.asNondeterministic()
# Check the deterministic status of udf.
df = self.spark.range(1).select(f("id"))
deterministic = df._jdf.logicalPlan().projectList().head().deterministic()
self.assertFalse(deterministic)
def test_nondeterministic_udf_in_aggregate(self):
from pyspark.sql.functions import sum
import random
udf_random_col = udf(lambda: int(100 * random.random()), 'int').asNondeterministic()
df = self.spark.range(10)
with QuietTest(self.sc):
with self.assertRaisesRegexp(AnalysisException, "nondeterministic"):
df.groupby('id').agg(sum(udf_random_col())).collect()
with self.assertRaisesRegexp(AnalysisException, "nondeterministic"):
df.agg(sum(udf_random_col())).collect()
def test_chained_udf(self):
self.spark.catalog.registerFunction("double", lambda x: x + x, IntegerType())
[row] = self.spark.sql("SELECT double(1)").collect()
self.assertEqual(row[0], 2)
[row] = self.spark.sql("SELECT double(double(1))").collect()
self.assertEqual(row[0], 4)
[row] = self.spark.sql("SELECT double(double(1) + 1)").collect()
self.assertEqual(row[0], 6)
def test_single_udf_with_repeated_argument(self):
# regression test for SPARK-20685
self.spark.catalog.registerFunction("add", lambda x, y: x + y, IntegerType())
row = self.spark.sql("SELECT add(1, 1)").first()
self.assertEqual(tuple(row), (2, ))
def test_multiple_udfs(self):
self.spark.catalog.registerFunction("double", lambda x: x * 2, IntegerType())
[row] = self.spark.sql("SELECT double(1), double(2)").collect()
self.assertEqual(tuple(row), (2, 4))
[row] = self.spark.sql("SELECT double(double(1)), double(double(2) + 2)").collect()
self.assertEqual(tuple(row), (4, 12))
self.spark.catalog.registerFunction("add", lambda x, y: x + y, IntegerType())
[row] = self.spark.sql("SELECT double(add(1, 2)), add(double(2), 1)").collect()
self.assertEqual(tuple(row), (6, 5))
def test_udf_in_filter_on_top_of_outer_join(self):
left = self.spark.createDataFrame([Row(a=1)])
right = self.spark.createDataFrame([Row(a=1)])
df = left.join(right, on='a', how='left_outer')
df = df.withColumn('b', udf(lambda x: 'x')(df.a))
self.assertEqual(df.filter('b = "x"').collect(), [Row(a=1, b='x')])
def test_udf_in_filter_on_top_of_join(self):
# regression test for SPARK-18589
left = self.spark.createDataFrame([Row(a=1)])
right = self.spark.createDataFrame([Row(b=1)])
f = udf(lambda a, b: a == b, BooleanType())
df = left.crossJoin(right).filter(f("a", "b"))
self.assertEqual(df.collect(), [Row(a=1, b=1)])
def test_udf_in_join_condition(self):
# regression test for SPARK-25314
left = self.spark.createDataFrame([Row(a=1)])
right = self.spark.createDataFrame([Row(b=1)])
f = udf(lambda a, b: a == b, BooleanType())
# The udf uses attributes from both sides of join, so it is pulled out as Filter +
# Cross join.
df = left.join(right, f("a", "b"))
with self.sql_conf({"spark.sql.crossJoin.enabled": False}):
with self.assertRaisesRegexp(AnalysisException, 'Detected implicit cartesian product'):
df.collect()
with self.sql_conf({"spark.sql.crossJoin.enabled": True}):
self.assertEqual(df.collect(), [Row(a=1, b=1)])
def test_udf_in_left_outer_join_condition(self):
# regression test for SPARK-26147
from pyspark.sql.functions import col
left = self.spark.createDataFrame([Row(a=1)])
right = self.spark.createDataFrame([Row(b=1)])
f = udf(lambda a: str(a), StringType())
# The join condition can't be pushed down, as it refers to attributes from both sides.
# The Python UDF only refer to attributes from one side, so it's evaluable.
df = left.join(right, f("a") == col("b").cast("string"), how="left_outer")
with self.sql_conf({"spark.sql.crossJoin.enabled": True}):
self.assertEqual(df.collect(), [Row(a=1, b=1)])
def test_udf_and_common_filter_in_join_condition(self):
# regression test for SPARK-25314
# test the complex scenario with both udf and common filter
left = self.spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
right = self.spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
f = udf(lambda a, b: a == b, BooleanType())
df = left.join(right, [f("a", "b"), left.a1 == right.b1])
# do not need spark.sql.crossJoin.enabled=true for udf is not the only join condition.
self.assertEqual(df.collect(), [Row(a=1, a1=1, a2=1, b=1, b1=1, b2=1)])
def test_udf_not_supported_in_join_condition(self):
# regression test for SPARK-25314
# test python udf is not supported in join type except inner join.
left = self.spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
right = self.spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
f = udf(lambda a, b: a == b, BooleanType())
def runWithJoinType(join_type, type_string):
with self.assertRaisesRegexp(
AnalysisException,
'Using PythonUDF.*%s is not supported.' % type_string):
left.join(right, [f("a", "b"), left.a1 == right.b1], join_type).collect()
runWithJoinType("full", "FullOuter")
runWithJoinType("left", "LeftOuter")
runWithJoinType("right", "RightOuter")
runWithJoinType("leftanti", "LeftAnti")
runWithJoinType("leftsemi", "LeftSemi")
def test_udf_as_join_condition(self):
left = self.spark.createDataFrame([Row(a=1, a1=1, a2=1), Row(a=2, a1=2, a2=2)])
right = self.spark.createDataFrame([Row(b=1, b1=1, b2=1), Row(b=1, b1=3, b2=1)])
f = udf(lambda a: a, IntegerType())
df = left.join(right, [f("a") == f("b"), left.a1 == right.b1])
self.assertEqual(df.collect(), [Row(a=1, a1=1, a2=1, b=1, b1=1, b2=1)])
def test_udf_without_arguments(self):
self.spark.catalog.registerFunction("foo", lambda: "bar")
[row] = self.spark.sql("SELECT foo()").collect()
self.assertEqual(row[0], "bar")
def test_udf_with_array_type(self):
with self.tempView("test"):
d = [Row(l=list(range(3)), d={"key": list(range(5))})]
rdd = self.sc.parallelize(d)
self.spark.createDataFrame(rdd).createOrReplaceTempView("test")
self.spark.catalog.registerFunction(
"copylist", lambda l: list(l), ArrayType(IntegerType()))
self.spark.catalog.registerFunction("maplen", lambda d: len(d), IntegerType())
[(l1, l2)] = self.spark.sql("select copylist(l), maplen(d) from test").collect()
self.assertEqual(list(range(3)), l1)
self.assertEqual(1, l2)
def test_broadcast_in_udf(self):
bar = {"a": "aa", "b": "bb", "c": "abc"}
foo = self.sc.broadcast(bar)
self.spark.catalog.registerFunction("MYUDF", lambda x: foo.value[x] if x else '')
[res] = self.spark.sql("SELECT MYUDF('c')").collect()
self.assertEqual("abc", res[0])
[res] = self.spark.sql("SELECT MYUDF('')").collect()
self.assertEqual("", res[0])
def test_udf_with_filter_function(self):
df = self.spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
from pyspark.sql.functions import col
from pyspark.sql.types import BooleanType
my_filter = udf(lambda a: a < 2, BooleanType())
sel = df.select(col("key"), col("value")).filter((my_filter(col("key"))) & (df.value < "2"))
self.assertEqual(sel.collect(), [Row(key=1, value='1')])
def test_udf_with_aggregate_function(self):
df = self.spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
from pyspark.sql.functions import col, sum
from pyspark.sql.types import BooleanType
my_filter = udf(lambda a: a == 1, BooleanType())
sel = df.select(col("key")).distinct().filter(my_filter(col("key")))
self.assertEqual(sel.collect(), [Row(key=1)])
my_copy = udf(lambda x: x, IntegerType())
my_add = udf(lambda a, b: int(a + b), IntegerType())
my_strlen = udf(lambda x: len(x), IntegerType())
sel = df.groupBy(my_copy(col("key")).alias("k"))\
.agg(sum(my_strlen(col("value"))).alias("s"))\
.select(my_add(col("k"), col("s")).alias("t"))
self.assertEqual(sel.collect(), [Row(t=4), Row(t=3)])
def test_udf_in_generate(self):
from pyspark.sql.functions import explode
df = self.spark.range(5)
f = udf(lambda x: list(range(x)), ArrayType(LongType()))
row = df.select(explode(f(*df))).groupBy().sum().first()
self.assertEqual(row[0], 10)
df = self.spark.range(3)
res = df.select("id", explode(f(df.id))).collect()
self.assertEqual(res[0][0], 1)
self.assertEqual(res[0][1], 0)
self.assertEqual(res[1][0], 2)
self.assertEqual(res[1][1], 0)
self.assertEqual(res[2][0], 2)
self.assertEqual(res[2][1], 1)
range_udf = udf(lambda value: list(range(value - 1, value + 1)), ArrayType(IntegerType()))
res = df.select("id", explode(range_udf(df.id))).collect()
self.assertEqual(res[0][0], 0)
self.assertEqual(res[0][1], -1)
self.assertEqual(res[1][0], 0)
self.assertEqual(res[1][1], 0)
self.assertEqual(res[2][0], 1)
self.assertEqual(res[2][1], 0)
self.assertEqual(res[3][0], 1)
self.assertEqual(res[3][1], 1)
def test_udf_with_order_by_and_limit(self):
my_copy = udf(lambda x: x, IntegerType())
df = self.spark.range(10).orderBy("id")
res = df.select(df.id, my_copy(df.id).alias("copy")).limit(1)
self.assertEqual(res.collect(), [Row(id=0, copy=0)])
def test_udf_registration_returns_udf(self):
df = self.spark.range(10)
add_three = self.spark.udf.register("add_three", lambda x: x + 3, IntegerType())
self.assertListEqual(
df.selectExpr("add_three(id) AS plus_three").collect(),
df.select(add_three("id").alias("plus_three")).collect()
)
# This is to check if a 'SQLContext.udf' can call its alias.
sqlContext = self.spark._wrapped
add_four = sqlContext.udf.register("add_four", lambda x: x + 4, IntegerType())
self.assertListEqual(
df.selectExpr("add_four(id) AS plus_four").collect(),
df.select(add_four("id").alias("plus_four")).collect()
)
def test_non_existed_udf(self):
spark = self.spark
self.assertRaisesRegexp(AnalysisException, "Can not load class non_existed_udf",
lambda: spark.udf.registerJavaFunction("udf1", "non_existed_udf"))
# This is to check if a deprecated 'SQLContext.registerJavaFunction' can call its alias.
sqlContext = spark._wrapped
self.assertRaisesRegexp(AnalysisException, "Can not load class non_existed_udf",
lambda: sqlContext.registerJavaFunction("udf1", "non_existed_udf"))
def test_non_existed_udaf(self):
spark = self.spark
self.assertRaisesRegexp(AnalysisException, "Can not load class non_existed_udaf",
lambda: spark.udf.registerJavaUDAF("udaf1", "non_existed_udaf"))
def test_udf_with_input_file_name(self):
from pyspark.sql.functions import input_file_name
sourceFile = udf(lambda path: path, StringType())
filePath = "python/test_support/sql/people1.json"
row = self.spark.read.json(filePath).select(sourceFile(input_file_name())).first()
self.assertTrue(row[0].find("people1.json") != -1)
def test_udf_with_input_file_name_for_hadooprdd(self):
from pyspark.sql.functions import input_file_name
def filename(path):
return path
sameText = udf(filename, StringType())
rdd = self.sc.textFile('python/test_support/sql/people.json')
df = self.spark.read.json(rdd).select(input_file_name().alias('file'))
row = df.select(sameText(df['file'])).first()
self.assertTrue(row[0].find("people.json") != -1)
rdd2 = self.sc.newAPIHadoopFile(
'python/test_support/sql/people.json',
'org.apache.hadoop.mapreduce.lib.input.TextInputFormat',
'org.apache.hadoop.io.LongWritable',
'org.apache.hadoop.io.Text')
df2 = self.spark.read.json(rdd2).select(input_file_name().alias('file'))
row2 = df2.select(sameText(df2['file'])).first()
self.assertTrue(row2[0].find("people.json") != -1)
def test_udf_defers_judf_initialization(self):
# This is separate of UDFInitializationTests
# to avoid context initialization
# when udf is called
f = UserDefinedFunction(lambda x: x, StringType())
self.assertIsNone(
f._judf_placeholder,
"judf should not be initialized before the first call."
)
self.assertIsInstance(f("foo"), Column, "UDF call should return a Column.")
self.assertIsNotNone(
f._judf_placeholder,
"judf should be initialized after UDF has been called."
)
def test_udf_with_string_return_type(self):
add_one = UserDefinedFunction(lambda x: x + 1, "integer")
make_pair = UserDefinedFunction(lambda x: (-x, x), "struct<x:integer,y:integer>")
make_array = UserDefinedFunction(
lambda x: [float(x) for x in range(x, x + 3)], "array<double>")
expected = (2, Row(x=-1, y=1), [1.0, 2.0, 3.0])
actual = (self.spark.range(1, 2).toDF("x")
.select(add_one("x"), make_pair("x"), make_array("x"))
.first())
self.assertTupleEqual(expected, actual)
def test_udf_shouldnt_accept_noncallable_object(self):
non_callable = None
self.assertRaises(TypeError, UserDefinedFunction, non_callable, StringType())
def test_udf_with_decorator(self):
from pyspark.sql.functions import lit
from pyspark.sql.types import IntegerType, DoubleType
@udf(IntegerType())
def add_one(x):
if x is not None:
return x + 1
@udf(returnType=DoubleType())
def add_two(x):
if x is not None:
return float(x + 2)
@udf
def to_upper(x):
if x is not None:
return x.upper()
@udf()
def to_lower(x):
if x is not None:
return x.lower()
@udf
def substr(x, start, end):
if x is not None:
return x[start:end]
@udf("long")
def trunc(x):
return int(x)
@udf(returnType="double")
def as_double(x):
return float(x)
df = (
self.spark
.createDataFrame(
[(1, "Foo", "foobar", 3.0)], ("one", "Foo", "foobar", "float"))
.select(
add_one("one"), add_two("one"),
to_upper("Foo"), to_lower("Foo"),
substr("foobar", lit(0), lit(3)),
trunc("float"), as_double("one")))
self.assertListEqual(
[tpe for _, tpe in df.dtypes],
["int", "double", "string", "string", "string", "bigint", "double"]
)
self.assertListEqual(
list(df.first()),
[2, 3.0, "FOO", "foo", "foo", 3, 1.0]
)
def test_udf_wrapper(self):
from pyspark.sql.types import IntegerType
def f(x):
"""Identity"""
return x
return_type = IntegerType()
f_ = udf(f, return_type)
self.assertTrue(f.__doc__ in f_.__doc__)
self.assertEqual(f, f_.func)
self.assertEqual(return_type, f_.returnType)
class F(object):
"""Identity"""
def __call__(self, x):
return x
f = F()
return_type = IntegerType()
f_ = udf(f, return_type)
self.assertTrue(f.__doc__ in f_.__doc__)
self.assertEqual(f, f_.func)
self.assertEqual(return_type, f_.returnType)
f = functools.partial(f, x=1)
return_type = IntegerType()
f_ = udf(f, return_type)
self.assertTrue(f.__doc__ in f_.__doc__)
self.assertEqual(f, f_.func)
self.assertEqual(return_type, f_.returnType)
def test_nonparam_udf_with_aggregate(self):
import pyspark.sql.functions as f
df = self.spark.createDataFrame([(1, 2), (1, 2)])
f_udf = f.udf(lambda: "const_str")
rows = df.distinct().withColumn("a", f_udf()).collect()
self.assertEqual(rows, [Row(_1=1, _2=2, a=u'const_str')])
# SPARK-24721
@unittest.skipIf(not test_compiled, test_not_compiled_message)
def test_datasource_with_udf(self):
from pyspark.sql.functions import lit, col
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 = udf(lambda x: x + 1, 'int')(lit(1))
c2 = udf(lambda x: x + 1, 'int')(col('i'))
f1 = udf(lambda x: False, 'boolean')(lit(1))
f2 = udf(lambda x: False, '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.assertEquals(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.assertEquals(expected.collect(), result.collect())
for df in [filesource_df, datasource_df, datasource_v2_df]:
for f in [f1, f2]:
result = df.filter(f)
self.assertEquals(0, result.count())
finally:
shutil.rmtree(path)
# SPARK-25591
def test_same_accumulator_in_udfs(self):
data_schema = StructType([StructField("a", IntegerType(), True),
StructField("b", IntegerType(), True)])
data = self.spark.createDataFrame([[1, 2]], schema=data_schema)
test_accum = self.sc.accumulator(0)
def first_udf(x):
test_accum.add(1)
return x
def second_udf(x):
test_accum.add(100)
return x
func_udf = udf(first_udf, IntegerType())
func_udf2 = udf(second_udf, IntegerType())
data = data.withColumn("out1", func_udf(data["a"]))
data = data.withColumn("out2", func_udf2(data["b"]))
data.collect()
self.assertEqual(test_accum.value, 101)
# SPARK-26293
def test_udf_in_subquery(self):
f = udf(lambda x: x, "long")
with self.tempView("v"):
self.spark.range(1).filter(f("id") >= 0).createTempView("v")
sql = self.spark.sql
result = sql("select i from values(0L) as data(i) where i in (select id from v)")
self.assertEqual(result.collect(), [Row(i=0)])
def test_udf_globals_not_overwritten(self):
@udf('string')
def f():
assert "itertools" not in str(map)
self.spark.range(1).select(f()).collect()
def test_worker_original_stdin_closed(self):
# Test if it closes the original standard input of worker inherited from the daemon,
# and replaces it with '/dev/null'. See SPARK-26175.
def task(iterator):
import sys
res = sys.stdin.read()
# Because the standard input is '/dev/null', it reaches to EOF.
assert res == '', "Expect read EOF from stdin."
return iterator
self.sc.parallelize(range(1), 1).mapPartitions(task).count()
def test_udf_with_256_args(self):
N = 256
data = [["data-%d" % i for i in range(N)]] * 5
df = self.spark.createDataFrame(data)
def f(*a):
return "success"
fUdf = udf(f, StringType())
r = df.select(fUdf(*df.columns))
self.assertEqual(r.first()[0], "success")
def test_udf_cache(self):
func = lambda x: x
df = self.spark.range(1)
df.select(udf(func)("id")).cache()
self.assertEqual(df.select(udf(func)("id"))._jdf.queryExecution()
.withCachedData().getClass().getSimpleName(), 'InMemoryRelation')
class UDFInitializationTests(unittest.TestCase):
def tearDown(self):
if SparkSession._instantiatedSession is not None:
SparkSession._instantiatedSession.stop()
if SparkContext._active_spark_context is not None:
SparkContext._active_spark_context.stop()
def test_udf_init_shouldnt_initialize_context(self):
UserDefinedFunction(lambda x: x, StringType())
self.assertIsNone(
SparkContext._active_spark_context,
"SparkContext shouldn't be initialized when UserDefinedFunction is created."
)
self.assertIsNone(
SparkSession._instantiatedSession,
"SparkSession shouldn't be initialized when UserDefinedFunction is created."
)
if __name__ == "__main__":
from pyspark.sql.tests.test_udf import *
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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