| # |
| # 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 time |
| import unittest |
| import datetime |
| from decimal import Decimal |
| from typing import Iterator, Tuple |
| |
| from pyspark.util import PythonEvalType |
| |
| from pyspark.sql.functions import arrow_udf, ArrowUDFType |
| from pyspark.sql import functions as F |
| from pyspark.sql.types import ( |
| IntegerType, |
| ByteType, |
| StructType, |
| ShortType, |
| BooleanType, |
| LongType, |
| FloatType, |
| DoubleType, |
| DecimalType, |
| StringType, |
| ArrayType, |
| StructField, |
| Row, |
| MapType, |
| BinaryType, |
| YearMonthIntervalType, |
| ) |
| from pyspark.errors import AnalysisException, PythonException |
| from pyspark.testing.utils import ( |
| have_numpy, |
| numpy_requirement_message, |
| have_pyarrow, |
| pyarrow_requirement_message, |
| ) |
| from pyspark.testing.sqlutils import ReusedSQLTestCase |
| |
| |
| @unittest.skipIf(not have_pyarrow, pyarrow_requirement_message) |
| class ScalarArrowUDFTestsMixin: |
| @property |
| def nondeterministic_arrow_udf(self): |
| import pyarrow as pa |
| import numpy as np |
| |
| @arrow_udf("double") |
| def random_udf(v): |
| return pa.array(np.random.random(len(v))) |
| |
| return random_udf.asNondeterministic() |
| |
| @property |
| def nondeterministic_arrow_iter_udf(self): |
| import pyarrow as pa |
| import numpy as np |
| |
| @arrow_udf("double", ArrowUDFType.SCALAR_ITER) |
| def random_udf(it): |
| for v in it: |
| yield pa.array(np.random.random(len(v))) |
| |
| return random_udf.asNondeterministic() |
| |
| def test_arrow_udf_tokenize(self): |
| import pyarrow as pa |
| |
| df = self.spark.createDataFrame([("hi boo",), ("bye boo",)], ["vals"]) |
| |
| tokenize = arrow_udf( |
| lambda s: pa.compute.ascii_split_whitespace(s), |
| ArrayType(StringType()), |
| ) |
| |
| result = df.select(tokenize("vals").alias("hi")) |
| self.assertEqual(tokenize.returnType, ArrayType(StringType())) |
| self.assertEqual([Row(hi=["hi", "boo"]), Row(hi=["bye", "boo"])], result.collect()) |
| |
| def test_arrow_udf_output_nested_arrays(self): |
| import pyarrow as pa |
| |
| df = self.spark.createDataFrame([("hi boo",), ("bye boo",)], ["vals"]) |
| |
| tokenize = arrow_udf( |
| lambda s: pa.array([[v] for v in pa.compute.ascii_split_whitespace(s).to_pylist()]), |
| ArrayType(ArrayType(StringType())), |
| ) |
| |
| result = df.select(tokenize("vals").alias("hi")) |
| self.assertEqual(tokenize.returnType, ArrayType(ArrayType(StringType()))) |
| self.assertEqual([Row(hi=[["hi", "boo"]]), Row(hi=[["bye", "boo"]])], result.collect()) |
| |
| def test_arrow_udf_output_structs(self): |
| import pyarrow as pa |
| |
| df = self.spark.range(10).select("id", F.lit("foo").alias("name")) |
| |
| create_struct = arrow_udf( |
| lambda x, y: pa.StructArray.from_arrays([x, y], names=["c1", "c2"]), |
| "struct<c1:long, c2:string>", |
| ) |
| |
| self.assertEqual( |
| df.select(create_struct("id", "name").alias("res")).first(), |
| Row(res=Row(c1=0, c2="foo")), |
| ) |
| |
| def test_arrow_udf_output_nested_structs(self): |
| import pyarrow as pa |
| |
| df = self.spark.range(10).select("id", F.lit("foo").alias("name")) |
| |
| create_struct = arrow_udf( |
| lambda x, y: pa.StructArray.from_arrays( |
| [x, pa.StructArray.from_arrays([x, y], names=["c3", "c4"])], names=["c1", "c2"] |
| ), |
| "struct<c1:long, c2:struct<c3:long, c4:string>>", |
| ) |
| |
| self.assertEqual( |
| df.select(create_struct("id", "name").alias("res")).first(), |
| Row(res=Row(c1=0, c2=Row(c3=0, c4="foo"))), |
| ) |
| |
| def test_arrow_udf_input_output_nested_structs(self): |
| # root |
| # |-- s: struct (nullable = false) |
| # | |-- a: integer (nullable = false) |
| # | |-- y: struct (nullable = false) |
| # | | |-- b: integer (nullable = false) |
| # | | |-- x: struct (nullable = false) |
| # | | | |-- c: integer (nullable = false) |
| # | | | |-- d: integer (nullable = false) |
| df = self.spark.sql( |
| """ |
| SELECT STRUCT(a, STRUCT(b, STRUCT(c, d) AS x) AS y) AS s |
| FROM VALUES |
| (1, 2, 3, 4), |
| (-1, -2, -3, -4) |
| AS tab(a, b, c, d) |
| """ |
| ) |
| |
| schema = StructType( |
| [ |
| StructField("a", IntegerType(), False), |
| StructField( |
| "y", |
| StructType( |
| [ |
| StructField("b", IntegerType(), False), |
| StructField( |
| "x", |
| StructType( |
| [ |
| StructField("c", IntegerType(), False), |
| StructField("d", IntegerType(), False), |
| ] |
| ), |
| False, |
| ), |
| ] |
| ), |
| False, |
| ), |
| ] |
| ) |
| |
| extract_y = arrow_udf(lambda s: s.field("y"), schema["y"].dataType) |
| result = df.select(extract_y("s").alias("y")) |
| |
| self.assertEqual( |
| [Row(y=Row(b=2, x=Row(c=3, d=4))), Row(y=Row(b=-2, x=Row(c=-3, d=-4)))], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_nested_maps(self): |
| import pyarrow as pa |
| |
| schema = StructType( |
| [ |
| StructField("id", StringType(), True), |
| StructField( |
| "attributes", MapType(StringType(), MapType(StringType(), StringType())), True |
| ), |
| ] |
| ) |
| data = [("1", {"personal": {"name": "John", "city": "New York"}})] |
| # root |
| # |-- id: string (nullable = true) |
| # |-- attributes: map (nullable = true) |
| # | |-- key: string |
| # | |-- value: map (valueContainsNull = true) |
| # | | |-- key: string |
| # | | |-- value: string (valueContainsNull = true) |
| df = self.spark.createDataFrame(data, schema) |
| |
| str_repr = arrow_udf(lambda s: pa.array(str(x.as_py()) for x in s), StringType()) |
| result = df.select(str_repr("attributes").alias("s")) |
| |
| self.assertEqual( |
| [Row(s="[('personal', [('name', 'John'), ('city', 'New York')])]")], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_nested_arrays(self): |
| import pyarrow as pa |
| |
| # root |
| # |-- a: array (nullable = false) |
| # | |-- element: array (containsNull = false) |
| # | | |-- element: integer (containsNull = true) |
| df = self.spark.sql( |
| """ |
| SELECT ARRAY(ARRAY(1,2,3),ARRAY(4,NULL),ARRAY(5,6,NULL)) AS arr |
| """ |
| ) |
| |
| str_repr = arrow_udf(lambda s: pa.array(str(x.as_py()) for x in s), StringType()) |
| result = df.select(str_repr("arr").alias("s")) |
| |
| self.assertEqual( |
| [Row(s="[[1, 2, 3], [4, None], [5, 6, None]]")], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_arrow_array_struct(self): |
| import pyarrow as pa |
| |
| df = self.spark.createDataFrame( |
| [[[("a", 2, 3.0), ("a", 2, 3.0)]], [[("b", 5, 6.0), ("b", 5, 6.0)]]], |
| "array_struct_col array<struct<col1:string, col2:long, col3:double>>", |
| ) |
| |
| @arrow_udf("array<struct<col1:string, col2:long, col3:double>>") |
| def return_cols(cols): |
| assert isinstance(cols, pa.Array) |
| return cols |
| |
| result = df.select(return_cols("array_struct_col")) |
| |
| self.assertEqual( |
| [ |
| Row(output=[Row(col1="a", col2=2, col3=3.0), Row(col1="a", col2=2, col3=3.0)]), |
| Row(output=[Row(col1="b", col2=5, col3=6.0), Row(col1="b", col2=5, col3=6.0)]), |
| ], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_dates(self): |
| import pyarrow as pa |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (1, DATE('2022-02-22')), |
| (2, DATE('2023-02-22')), |
| (3, DATE('2024-02-22')) |
| AS tab(i, date) |
| """ |
| ) |
| |
| @arrow_udf("int") |
| def extract_year(d): |
| assert isinstance(d, pa.Array) |
| assert isinstance(d, pa.Date32Array) |
| return pa.array([x.as_py().year for x in d], pa.int32()) |
| |
| result = df.select(extract_year("date").alias("year")) |
| self.assertEqual( |
| [Row(year=2022), Row(year=2023), Row(year=2024)], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_output_dates(self): |
| import pyarrow as pa |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (2022, 1, 5), |
| (2023, 2, 6), |
| (2024, 3, 7) |
| AS tab(y, m, d) |
| """ |
| ) |
| |
| @arrow_udf("date") |
| def build_date(y, m, d): |
| assert all(isinstance(x, pa.Array) for x in [y, m, d]) |
| dates = [ |
| datetime.date(int(y[i].as_py()), int(m[i].as_py()), int(d[i].as_py())) |
| for i in range(len(y)) |
| ] |
| return pa.array(dates, pa.date32()) |
| |
| result = df.select(build_date("y", "m", "d").alias("date")) |
| self.assertEqual( |
| [ |
| Row(date=datetime.date(2022, 1, 5)), |
| Row(date=datetime.date(2023, 2, 6)), |
| Row(date=datetime.date(2024, 3, 7)), |
| ], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_timestamps(self): |
| import pyarrow as pa |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (1, TIMESTAMP('2019-04-12 15:50:01')), |
| (2, TIMESTAMP('2020-04-12 15:50:02')), |
| (3, TIMESTAMP('2021-04-12 15:50:03')) |
| AS tab(i, ts) |
| """ |
| ) |
| |
| @arrow_udf("int") |
| def extract_second(d): |
| assert isinstance(d, pa.Array) |
| assert isinstance(d, pa.TimestampArray) |
| return pa.array([x.as_py().second for x in d], pa.int32()) |
| |
| result = df.select(extract_second("ts").alias("second")) |
| self.assertEqual( |
| [Row(second=1), Row(second=2), Row(second=3)], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_output_timestamps_ltz(self): |
| from zoneinfo import ZoneInfo |
| import pyarrow as pa |
| |
| tz = self.spark.conf.get("spark.sql.session.timeZone") |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (2022, 1, 5, 15, 0, 1), |
| (2023, 2, 6, 16, 1, 2), |
| (2024, 3, 7, 17, 2, 3) |
| AS tab(y, m, d, h, mi, s) |
| """ |
| ) |
| |
| @arrow_udf("timestamp") |
| def build_ts(y, m, d, h, mi, s): |
| assert all(isinstance(x, pa.Array) for x in [y, m, d, h, mi, s]) |
| dates = [ |
| datetime.datetime( |
| int(y[i].as_py()), |
| int(m[i].as_py()), |
| int(d[i].as_py()), |
| int(h[i].as_py()), |
| int(mi[i].as_py()), |
| int(s[i].as_py()), |
| tzinfo=ZoneInfo(tz), |
| ).astimezone(datetime.timezone.utc) |
| for i in range(len(y)) |
| ] |
| return pa.array(dates, pa.timestamp("us", "UTC")) |
| |
| result = df.select(build_ts("y", "m", "d", "h", "mi", "s").alias("ts")) |
| self.assertEqual( |
| [ |
| Row(ts=datetime.datetime(2022, 1, 5, 15, 0, 1)), |
| Row(ts=datetime.datetime(2023, 2, 6, 16, 1, 2)), |
| Row(ts=datetime.datetime(2024, 3, 7, 17, 2, 3)), |
| ], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_output_timestamps_ntz(self): |
| import pyarrow as pa |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (2022, 1, 5, 15, 0, 1), |
| (2023, 2, 6, 16, 1, 2), |
| (2024, 3, 7, 17, 2, 3) |
| AS tab(y, m, d, h, mi, s) |
| """ |
| ) |
| |
| @arrow_udf("timestamp_ntz") |
| def build_ts(y, m, d, h, mi, s): |
| assert all(isinstance(x, pa.Array) for x in [y, m, d, h, mi, s]) |
| dates = [ |
| datetime.datetime( |
| int(y[i].as_py()), |
| int(m[i].as_py()), |
| int(d[i].as_py()), |
| int(h[i].as_py()), |
| int(mi[i].as_py()), |
| int(s[i].as_py()), |
| ) |
| for i in range(len(y)) |
| ] |
| return pa.array(dates, pa.timestamp("us")) |
| |
| result = df.select(build_ts("y", "m", "d", "h", "mi", "s").alias("ts")) |
| self.assertEqual( |
| [ |
| Row(ts=datetime.datetime(2022, 1, 5, 15, 0, 1)), |
| Row(ts=datetime.datetime(2023, 2, 6, 16, 1, 2)), |
| Row(ts=datetime.datetime(2024, 3, 7, 17, 2, 3)), |
| ], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_times(self): |
| import pyarrow as pa |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (1, TIME '12:34:56'), |
| (2, TIME '1:2:3'), |
| (3, TIME '0:58:59') |
| AS tab(i, ts) |
| """ |
| ) |
| |
| @arrow_udf("int") |
| def extract_second(v): |
| assert isinstance(v, pa.Array) |
| assert isinstance(v, pa.Time64Array), type(v) |
| return pa.array([t.as_py().second for t in v], pa.int32()) |
| |
| result = df.select(extract_second("ts").alias("sec")) |
| self.assertEqual( |
| [ |
| Row(sec=56), |
| Row(sec=3), |
| Row(sec=59), |
| ], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_output_times(self): |
| import pyarrow as pa |
| |
| df = self.spark.sql( |
| """ |
| SELECT * FROM VALUES |
| (12, 34, 56), |
| (1, 2, 3), |
| (0, 58, 59) |
| AS tab(h, mi, s) |
| """ |
| ) |
| |
| @arrow_udf("time") |
| def build_time(h, mi, s): |
| assert all(isinstance(x, pa.Array) for x in [h, mi, s]) |
| dates = [ |
| datetime.time( |
| int(h[i].as_py()), |
| int(mi[i].as_py()), |
| int(s[i].as_py()), |
| ) |
| for i in range(len(h)) |
| ] |
| return pa.array(dates, pa.time64("ns")) |
| |
| result = df.select(build_time("h", "mi", "s").alias("t")) |
| self.assertEqual( |
| [ |
| Row(t=datetime.time(12, 34, 56)), |
| Row(t=datetime.time(1, 2, 3)), |
| Row(t=datetime.time(0, 58, 59)), |
| ], |
| result.collect(), |
| ) |
| |
| def test_arrow_udf_input_variant(self): |
| import pyarrow as pa |
| |
| @arrow_udf("int") |
| def scalar_f(v: pa.Array) -> pa.Array: |
| assert isinstance(v, pa.Array) |
| assert isinstance(v, pa.StructArray) |
| assert isinstance(v.field("metadata"), pa.BinaryArray) |
| assert isinstance(v.field("value"), pa.BinaryArray) |
| return pa.compute.binary_length(v.field("value")) |
| |
| @arrow_udf("int") |
| def iter_f(it: Iterator[pa.Array]) -> Iterator[pa.Array]: |
| for v in it: |
| assert isinstance(v, pa.Array) |
| assert isinstance(v, pa.StructArray) |
| assert isinstance(v.field("metadata"), pa.BinaryArray) |
| assert isinstance(v.field("value"), pa.BinaryArray) |
| yield pa.compute.binary_length(v.field("value")) |
| |
| df = self.spark.range(0, 10).selectExpr("parse_json(cast(id as string)) v") |
| expected = [Row(l=2) for i in range(10)] |
| |
| for f in [scalar_f, iter_f]: |
| result = df.select(f("v").alias("l")).collect() |
| self.assertEqual(result, expected) |
| |
| def test_arrow_udf_output_variant(self): |
| # referring to test_udf_with_variant_output in test_pandas_udf_scalar |
| import pyarrow as pa |
| |
| # referring to_arrow_type in to pyspark.sql.pandas.types |
| fields = [ |
| pa.field("value", pa.binary(), nullable=False), |
| pa.field("metadata", pa.binary(), nullable=False, metadata={b"variant": b"true"}), |
| ] |
| |
| @arrow_udf("variant") |
| def scalar_f(v: pa.Array) -> pa.Array: |
| assert isinstance(v, pa.Array) |
| v = pa.array([bytes([12, i.as_py()]) for i in v], pa.binary()) |
| m = pa.array([bytes([1, 0, 0]) for i in v], pa.binary()) |
| return pa.StructArray.from_arrays([v, m], fields=fields) |
| |
| @arrow_udf("variant") |
| def iter_f(it: Iterator[pa.Array]) -> Iterator[pa.Array]: |
| for v in it: |
| assert isinstance(v, pa.Array) |
| v = pa.array([bytes([12, i.as_py()]) for i in v]) |
| m = pa.array([bytes([1, 0, 0]) for i in v]) |
| yield pa.StructArray.from_arrays([v, m], fields=fields) |
| |
| df = self.spark.range(0, 10) |
| expected = [Row(l=i) for i in range(10)] |
| |
| for f in [scalar_f, iter_f]: |
| result = df.select(f("id").cast("int").alias("l")).collect() |
| self.assertEqual(result, expected) |
| |
| def test_arrow_udf_null_boolean(self): |
| data = [(True,), (True,), (None,), (False,)] |
| schema = StructType().add("bool", BooleanType()) |
| df = self.spark.createDataFrame(data, schema) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| bool_f = arrow_udf(lambda x: x, BooleanType(), udf_type) |
| res = df.select(bool_f(F.col("bool"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_udf_null_byte(self): |
| data = [(None,), (2,), (3,), (4,)] |
| schema = StructType().add("byte", ByteType()) |
| df = self.spark.createDataFrame(data, schema) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| byte_f = arrow_udf(lambda x: x, ByteType(), udf_type) |
| res = df.select(byte_f(F.col("byte"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_udf_null_short(self): |
| data = [(None,), (2,), (3,), (4,)] |
| schema = StructType().add("short", ShortType()) |
| df = self.spark.createDataFrame(data, schema) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| short_f = arrow_udf(lambda x: x, ShortType(), udf_type) |
| res = df.select(short_f(F.col("short"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_udf_null_int(self): |
| data = [(None,), (2,), (3,), (4,)] |
| schema = StructType().add("int", IntegerType()) |
| df = self.spark.createDataFrame(data, schema) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| int_f = arrow_udf(lambda x: x, IntegerType(), udf_type) |
| res = df.select(int_f(F.col("int"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_udf_null_long(self): |
| data = [(None,), (2,), (3,), (4,)] |
| schema = StructType().add("long", LongType()) |
| df = self.spark.createDataFrame(data, schema) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| long_f = arrow_udf(lambda x: x, LongType(), udf_type) |
| res = df.select(long_f(F.col("long"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_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 [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| float_f = arrow_udf(lambda x: x, FloatType(), udf_type) |
| res = df.select(float_f(F.col("float"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_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 [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| double_f = arrow_udf(lambda x: x, DoubleType(), udf_type) |
| res = df.select(double_f(F.col("double"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_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 [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| decimal_f = arrow_udf(lambda x: x, DecimalType(38, 18), udf_type) |
| res = df.select(decimal_f(F.col("decimal"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_udf_null_string(self): |
| data = [("foo",), (None,), ("bar",), ("bar",)] |
| schema = StructType().add("str", StringType()) |
| df = self.spark.createDataFrame(data, schema) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| str_f = arrow_udf(lambda x: x, StringType(), udf_type) |
| res = df.select(str_f(F.col("str"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_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 [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| binary_f = arrow_udf(lambda x: x, BinaryType(), udf_type) |
| res = df.select(binary_f(F.col("binary"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_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 [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| array_f = arrow_udf(lambda x: x, ArrayType(IntegerType()), udf_type) |
| result = df.select(array_f(F.col("array"))) |
| self.assertEqual(df.collect(), result.collect()) |
| |
| def test_arrow_udf_empty_partition(self): |
| df = self.spark.createDataFrame([Row(id=1)]).repartition(2) |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| f = arrow_udf(lambda x: x, LongType(), udf_type) |
| res = df.select(f(F.col("id"))) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_arrow_udf_datatype_string(self): |
| df = self.spark.range(10).select( |
| F.col("id").cast("string").alias("str"), |
| F.col("id").cast("int").alias("int"), |
| F.col("id").alias("long"), |
| F.col("id").cast("float").alias("float"), |
| F.col("id").cast("double").alias("double"), |
| F.col("id").cast("decimal").alias("decimal1"), |
| F.col("id").cast("decimal(10, 0)").alias("decimal2"), |
| F.col("id").cast("decimal(38, 18)").alias("decimal3"), |
| F.col("id").cast("boolean").alias("bool"), |
| ) |
| |
| def f(x): |
| return x |
| |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| str_f = arrow_udf(f, "string", udf_type) |
| int_f = arrow_udf(f, "integer", udf_type) |
| long_f = arrow_udf(f, "long", udf_type) |
| float_f = arrow_udf(f, "float", udf_type) |
| double_f = arrow_udf(f, "double", udf_type) |
| decimal1_f = arrow_udf(f, "decimal", udf_type) |
| decimal2_f = arrow_udf(f, "decimal(10, 0)", udf_type) |
| decimal3_f = arrow_udf(f, "decimal(38, 18)", udf_type) |
| bool_f = arrow_udf(f, "boolean", udf_type) |
| res = df.select( |
| str_f(F.col("str")), |
| int_f(F.col("int")), |
| long_f(F.col("long")), |
| float_f(F.col("float")), |
| double_f(F.col("double")), |
| decimal1_f("decimal1"), |
| decimal2_f("decimal2"), |
| decimal3_f("decimal3"), |
| bool_f(F.col("bool")), |
| ) |
| self.assertEqual(df.collect(), res.collect()) |
| |
| def test_udf_register_arrow_udf_basic(self): |
| import pyarrow as pa |
| |
| scalar_original_add = arrow_udf( |
| lambda x, y: pa.compute.add(x, y).cast(pa.int32()), IntegerType() |
| ) |
| self.assertEqual(scalar_original_add.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF) |
| self.assertEqual(scalar_original_add.deterministic, True) |
| |
| with self.temp_func("add1"): |
| new_add = self.spark.udf.register("add1", scalar_original_add) |
| |
| self.assertEqual(new_add.deterministic, True) |
| self.assertEqual(new_add.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF) |
| |
| df = self.spark.range(10).select( |
| F.col("id").cast("int").alias("a"), F.col("id").cast("int").alias("b") |
| ) |
| res1 = df.select(new_add(F.col("a"), F.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(F.expr("a + b")) |
| self.assertEqual(expected.collect(), res1.collect()) |
| self.assertEqual(expected.collect(), res2.collect()) |
| |
| @arrow_udf(LongType()) |
| def scalar_iter_add(it: Iterator[Tuple[pa.Array, pa.Array]]) -> Iterator[pa.Array]: |
| for a, b in it: |
| yield pa.compute.add(a, b) |
| |
| with self.temp_func("add1"): |
| new_add = self.spark.udf.register("add1", scalar_iter_add) |
| |
| res3 = df.select(new_add(F.col("a"), F.col("b"))) |
| res4 = self.spark.sql( |
| "SELECT add1(t.a, t.b) FROM (SELECT id as a, id as b FROM range(10)) t" |
| ) |
| expected = df.select(F.expr("a + b")) |
| self.assertEqual(expected.collect(), res3.collect()) |
| self.assertEqual(expected.collect(), res4.collect()) |
| |
| def test_catalog_register_arrow_udf_basic(self): |
| import pyarrow as pa |
| |
| scalar_original_add = arrow_udf( |
| lambda x, y: pa.compute.add(x, y).cast(pa.int32()), IntegerType() |
| ) |
| self.assertEqual(scalar_original_add.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF) |
| self.assertEqual(scalar_original_add.deterministic, True) |
| |
| with self.temp_func("add1"): |
| new_add = self.spark.catalog.registerFunction("add1", scalar_original_add) |
| |
| self.assertEqual(new_add.deterministic, True) |
| self.assertEqual(new_add.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF) |
| |
| df = self.spark.range(10).select( |
| F.col("id").cast("int").alias("a"), F.col("id").cast("int").alias("b") |
| ) |
| res1 = df.select(new_add(F.col("a"), F.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(F.expr("a + b")) |
| self.assertEqual(expected.collect(), res1.collect()) |
| self.assertEqual(expected.collect(), res2.collect()) |
| |
| @arrow_udf(LongType()) |
| def scalar_iter_add(it: Iterator[Tuple[pa.Array, pa.Array]]) -> Iterator[pa.Array]: |
| for a, b in it: |
| yield pa.compute.add(a, b) |
| |
| with self.temp_func("add1"): |
| new_add = self.spark.catalog.registerFunction("add1", scalar_iter_add) |
| |
| res3 = df.select(new_add(F.col("a"), F.col("b"))) |
| res4 = self.spark.sql( |
| "SELECT add1(t.a, t.b) FROM (SELECT id as a, id as b FROM range(10)) t" |
| ) |
| expected = df.select(F.expr("a + b")) |
| self.assertEqual(expected.collect(), res3.collect()) |
| self.assertEqual(expected.collect(), res4.collect()) |
| |
| def test_udf_register_nondeterministic_arrow_udf(self): |
| import pyarrow as pa |
| |
| random_arrow_udf = arrow_udf( |
| lambda x: pa.compute.add(x, random.randint(6, 6)), LongType() |
| ).asNondeterministic() |
| self.assertEqual(random_arrow_udf.deterministic, False) |
| self.assertEqual(random_arrow_udf.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF) |
| |
| with self.temp_func("randomArrowUDF"): |
| nondeterministic_arrow_udf = self.spark.udf.register("randomArrowUDF", random_arrow_udf) |
| |
| self.assertEqual(nondeterministic_arrow_udf.deterministic, False) |
| self.assertEqual( |
| nondeterministic_arrow_udf.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF |
| ) |
| [row] = self.spark.sql("SELECT randomArrowUDF(1)").collect() |
| self.assertEqual(row[0], 7) |
| |
| def test_catalog_register_nondeterministic_arrow_udf(self): |
| import pyarrow as pa |
| |
| random_arrow_udf = arrow_udf( |
| lambda x: pa.compute.add(x, random.randint(6, 6)), LongType() |
| ).asNondeterministic() |
| self.assertEqual(random_arrow_udf.deterministic, False) |
| self.assertEqual(random_arrow_udf.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF) |
| |
| with self.temp_func("randomArrowUDF"): |
| nondeterministic_arrow_udf = self.spark.catalog.registerFunction( |
| "randomArrowUDF", random_arrow_udf |
| ) |
| |
| self.assertEqual(nondeterministic_arrow_udf.deterministic, False) |
| self.assertEqual( |
| nondeterministic_arrow_udf.evalType, PythonEvalType.SQL_SCALAR_ARROW_UDF |
| ) |
| [row] = self.spark.sql("SELECT randomArrowUDF(1)").collect() |
| self.assertEqual(row[0], 7) |
| |
| @unittest.skipIf(not have_numpy, numpy_requirement_message) |
| def test_nondeterministic_arrow_udf(self): |
| import pyarrow as pa |
| |
| # Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations |
| @arrow_udf("double") |
| def scalar_plus_ten(v): |
| return pa.compute.add(v, 10) |
| |
| @arrow_udf("double", ArrowUDFType.SCALAR_ITER) |
| def iter_plus_ten(it): |
| for v in it: |
| yield pa.compute.add(v, 10) |
| |
| for plus_ten in [scalar_plus_ten, iter_plus_ten]: |
| random_udf = self.nondeterministic_arrow_udf |
| |
| df = self.spark.range(10).withColumn("rand", random_udf("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)) |
| |
| @unittest.skipIf(not have_numpy, numpy_requirement_message) |
| def test_nondeterministic_arrow_udf_in_aggregate(self): |
| with self.quiet(): |
| df = self.spark.range(10) |
| for random_udf in [ |
| self.nondeterministic_arrow_udf, |
| self.nondeterministic_arrow_iter_udf, |
| ]: |
| with self.assertRaisesRegex(AnalysisException, "Non-deterministic"): |
| df.groupby("id").agg(F.sum(random_udf("id"))).collect() |
| with self.assertRaisesRegex(AnalysisException, "Non-deterministic"): |
| df.agg(F.sum(random_udf("id"))).collect() |
| |
| def test_arrow_udf_chained(self): |
| import pyarrow.compute as pc |
| |
| scalar_f = arrow_udf(lambda x: pc.add(x, 1), LongType()) |
| scalar_g = arrow_udf(lambda x: pc.subtract(x, 1), LongType()) |
| |
| iter_f = arrow_udf( |
| lambda it: map(lambda x: pc.add(x, 1), it), |
| LongType(), |
| ArrowUDFType.SCALAR_ITER, |
| ) |
| iter_g = arrow_udf( |
| lambda it: map(lambda x: pc.subtract(x, 1), it), |
| LongType(), |
| ArrowUDFType.SCALAR_ITER, |
| ) |
| |
| df = self.spark.range(10) |
| expected = df.select(F.col("id").alias("res")).collect() |
| |
| for f, g in [ |
| (scalar_f, scalar_g), |
| (iter_f, iter_g), |
| (scalar_f, iter_g), |
| (iter_f, scalar_g), |
| ]: |
| res = df.select(g(f(F.col("id"))).alias("res")) |
| self.assertEqual(expected, res.collect()) |
| |
| def test_arrow_udf_chained_ii(self): |
| import pyarrow.compute as pc |
| |
| scalar_f = arrow_udf(lambda x: pc.add(x, 1), LongType()) |
| |
| iter_f = arrow_udf( |
| lambda it: map(lambda x: pc.add(x, 1), it), |
| LongType(), |
| ArrowUDFType.SCALAR_ITER, |
| ) |
| |
| df = self.spark.range(10) |
| expected = df.select((F.col("id") + 3).alias("res")).collect() |
| |
| for f1, f2, f3 in [ |
| (scalar_f, scalar_f, scalar_f), |
| (iter_f, iter_f, iter_f), |
| (scalar_f, iter_f, scalar_f), |
| (iter_f, scalar_f, scalar_f), |
| (iter_f, iter_f, iter_f), |
| ]: |
| res = df.select(f1(f2(f3(F.col("id")))).alias("res")) |
| self.assertEqual(expected, res.collect()) |
| |
| def test_arrow_udf_chained_iii(self): |
| import pyarrow as pa |
| |
| scalar_f = arrow_udf(lambda x: pa.compute.add(x, 1), LongType()) |
| scalar_g = arrow_udf(lambda x: pa.compute.subtract(x, 1), LongType()) |
| scalar_m = arrow_udf(lambda x, y: pa.compute.multiply(x, y), LongType()) |
| |
| iter_f = arrow_udf( |
| lambda it: map(lambda x: pa.compute.add(x, 1), it), |
| LongType(), |
| ArrowUDFType.SCALAR_ITER, |
| ) |
| iter_g = arrow_udf( |
| lambda it: map(lambda x: pa.compute.subtract(x, 1), it), |
| LongType(), |
| ArrowUDFType.SCALAR_ITER, |
| ) |
| |
| @arrow_udf(LongType()) |
| def iter_m(it: Iterator[Tuple[pa.Array, pa.Array]]) -> Iterator[pa.Array]: |
| for a, b in it: |
| yield pa.compute.multiply(a, b) |
| |
| df = self.spark.range(10) |
| expected = df.select(((F.col("id") + 1) * (F.col("id") - 1)).alias("res")).collect() |
| |
| for f, g, m in [ |
| (scalar_f, scalar_g, scalar_m), |
| (iter_f, iter_g, iter_m), |
| (scalar_f, iter_g, scalar_m), |
| (iter_f, scalar_g, scalar_m), |
| (iter_f, scalar_g, iter_m), |
| ]: |
| res = df.select(m(f(F.col("id")), g(F.col("id"))).alias("res")) |
| self.assertEqual(expected, res.collect()) |
| |
| def test_arrow_udf_chained_struct_type(self): |
| import pyarrow as pa |
| |
| return_type = StructType([StructField("id", LongType()), StructField("str", StringType())]) |
| |
| @arrow_udf(return_type) |
| def scalar_f(id): |
| return pa.StructArray.from_arrays([id, id.cast(pa.string())], names=["id", "str"]) |
| |
| @arrow_udf(return_type) |
| def iter_f(it: Iterator[pa.Array]) -> Iterator[pa.Array]: |
| for id in it: |
| yield pa.StructArray.from_arrays([id, id.cast(pa.string())], names=["id", "str"]) |
| |
| scalar_g = arrow_udf(lambda x: x, return_type) |
| iter_g = arrow_udf(lambda x: x, return_type, ArrowUDFType.SCALAR_ITER) |
| |
| df = self.spark.range(10) |
| expected = df.select( |
| F.struct(F.col("id"), F.col("id").cast("string").alias("str")).alias("res") |
| ).collect() |
| |
| for f, g in [ |
| (scalar_f, scalar_g), |
| (iter_f, iter_g), |
| (scalar_f, iter_g), |
| (iter_f, scalar_g), |
| ]: |
| res = df.select(g(f(F.col("id"))).alias("res")) |
| self.assertEqual(expected, res.collect()) |
| |
| def test_arrow_udf_named_arguments(self): |
| import pyarrow as pa |
| |
| @arrow_udf("int") |
| def test_udf(a, b): |
| return pa.compute.add(a, pa.compute.multiply(b, 10)).cast(pa.int32()) |
| |
| with self.temp_func("test_udf"): |
| self.spark.udf.register("test_udf", test_udf) |
| |
| expected = [Row(0), Row(101)] |
| for i, df in enumerate( |
| [ |
| self.spark.range(2).select(test_udf(F.col("id"), b=F.col("id") * 10)), |
| self.spark.range(2).select(test_udf(a=F.col("id"), b=F.col("id") * 10)), |
| self.spark.range(2).select(test_udf(b=F.col("id") * 10, a=F.col("id"))), |
| self.spark.sql("SELECT test_udf(id, b => id * 10) FROM range(2)"), |
| self.spark.sql("SELECT test_udf(a => id, b => id * 10) FROM range(2)"), |
| self.spark.sql("SELECT test_udf(b => id * 10, a => id) FROM range(2)"), |
| ] |
| ): |
| with self.subTest(query_no=i): |
| self.assertEqual(expected, df.collect()) |
| |
| def test_arrow_udf_named_arguments_negative(self): |
| import pyarrow as pa |
| |
| @arrow_udf("int") |
| def test_udf(a, b): |
| return pa.compute.add(a, b).cast(pa.int32()) |
| |
| with self.temp_func("test_udf"): |
| self.spark.udf.register("test_udf", test_udf) |
| |
| with self.assertRaisesRegex( |
| AnalysisException, |
| "DUPLICATE_ROUTINE_PARAMETER_ASSIGNMENT.DOUBLE_NAMED_ARGUMENT_REFERENCE", |
| ): |
| self.spark.sql("SELECT test_udf(a => id, a => id * 10) FROM range(2)").show() |
| |
| with self.assertRaisesRegex(AnalysisException, "UNEXPECTED_POSITIONAL_ARGUMENT"): |
| self.spark.sql("SELECT test_udf(a => id, id * 10) FROM range(2)").show() |
| |
| with self.assertRaisesRegex( |
| PythonException, r"test_udf\(\) got an unexpected keyword argument 'c'" |
| ): |
| self.spark.sql("SELECT test_udf(c => 'x') FROM range(2)").show() |
| |
| def test_arrow_udf_named_arguments_and_defaults(self): |
| import pyarrow as pa |
| |
| @arrow_udf("int") |
| def test_udf(a, b=0): |
| return pa.compute.add(a, pa.compute.multiply(b, 10)).cast(pa.int32()) |
| |
| with self.temp_func("test_udf"): |
| self.spark.udf.register("test_udf", test_udf) |
| |
| # without "b" |
| expected = [Row(0), Row(1)] |
| for i, df in enumerate( |
| [ |
| self.spark.range(2).select(test_udf(F.col("id"))), |
| self.spark.range(2).select(test_udf(a=F.col("id"))), |
| self.spark.sql("SELECT test_udf(id) FROM range(2)"), |
| self.spark.sql("SELECT test_udf(a => id) FROM range(2)"), |
| ] |
| ): |
| with self.subTest(with_b=False, query_no=i): |
| self.assertEqual(expected, df.collect()) |
| |
| # with "b" |
| expected = [Row(0), Row(101)] |
| for i, df in enumerate( |
| [ |
| self.spark.range(2).select(test_udf(F.col("id"), b=F.col("id") * 10)), |
| self.spark.range(2).select(test_udf(a=F.col("id"), b=F.col("id") * 10)), |
| self.spark.range(2).select(test_udf(b=F.col("id") * 10, a=F.col("id"))), |
| self.spark.sql("SELECT test_udf(id, b => id * 10) FROM range(2)"), |
| self.spark.sql("SELECT test_udf(a => id, b => id * 10) FROM range(2)"), |
| self.spark.sql("SELECT test_udf(b => id * 10, a => id) FROM range(2)"), |
| ] |
| ): |
| with self.subTest(with_b=True, query_no=i): |
| self.assertEqual(expected, df.collect()) |
| |
| def test_arrow_udf_kwargs(self): |
| import pyarrow as pa |
| |
| @arrow_udf("int") |
| def test_udf(a, **kwargs): |
| return pa.compute.add(a, pa.compute.multiply(kwargs["b"], 10)).cast(pa.int32()) |
| |
| with self.temp_func("test_udf"): |
| self.spark.udf.register("test_udf", test_udf) |
| |
| expected = [Row(0), Row(101)] |
| for i, df in enumerate( |
| [ |
| self.spark.range(2).select(test_udf(a=F.col("id"), b=F.col("id") * 10)), |
| self.spark.range(2).select(test_udf(b=F.col("id") * 10, a=F.col("id"))), |
| self.spark.sql("SELECT test_udf(a => id, b => id * 10) FROM range(2)"), |
| self.spark.sql("SELECT test_udf(b => id * 10, a => id) FROM range(2)"), |
| ] |
| ): |
| with self.subTest(query_no=i): |
| self.assertEqual(expected, df.collect()) |
| |
| def test_arrow_iter_udf_single_column(self): |
| import pyarrow as pa |
| |
| @arrow_udf(LongType()) |
| def add_one(it: Iterator[pa.Array]) -> Iterator[pa.Array]: |
| for s in it: |
| yield pa.compute.add(s, 1) |
| |
| df = self.spark.range(10) |
| expected = df.select((F.col("id") + 1).alias("res")).collect() |
| |
| result = df.select(add_one("id").alias("res")) |
| self.assertEqual(expected, result.collect()) |
| |
| def test_arrow_iter_udf_two_columns(self): |
| import pyarrow as pa |
| |
| @arrow_udf(LongType()) |
| def multiple(it: Iterator[Tuple[pa.Array, pa.Array]]) -> Iterator[pa.Array]: |
| for a, b in it: |
| yield pa.compute.multiply(a, b) |
| |
| df = self.spark.range(10).select( |
| F.col("id").alias("a"), |
| (F.col("id") + 1).alias("b"), |
| ) |
| |
| expected = df.select((F.col("a") * F.col("b")).alias("res")).collect() |
| |
| result = df.select(multiple("a", "b").alias("res")) |
| self.assertEqual(expected, result.collect()) |
| |
| def test_arrow_iter_udf_three_columns(self): |
| import pyarrow as pa |
| |
| @arrow_udf(LongType()) |
| def multiple(it: Iterator[Tuple[pa.Array, pa.Array, pa.Array]]) -> Iterator[pa.Array]: |
| for a, b, c in it: |
| yield pa.compute.multiply(pa.compute.multiply(a, b), c) |
| |
| df = self.spark.range(10).select( |
| F.col("id").alias("a"), |
| (F.col("id") + 1).alias("b"), |
| (F.col("id") + 2).alias("c"), |
| ) |
| |
| expected = df.select((F.col("a") * F.col("b") * F.col("c")).alias("res")).collect() |
| |
| result = df.select(multiple("a", "b", "c").alias("res")) |
| self.assertEqual(expected, result.collect()) |
| |
| def test_return_type_coercion(self): |
| import pyarrow as pa |
| |
| df = self.spark.range(10) |
| |
| scalar_long = arrow_udf(lambda x: pa.compute.add(x, 1), LongType()) |
| result1 = df.select(scalar_long("id").alias("res")) |
| self.assertEqual(10, len(result1.collect())) |
| |
| # long -> int coercion |
| scalar_int1 = arrow_udf(lambda x: pa.compute.add(x, 1), IntegerType()) |
| result2 = df.select(scalar_int1("id").alias("res")) |
| self.assertEqual(10, len(result2.collect())) |
| |
| # long -> int coercion, overflow |
| scalar_int2 = arrow_udf(lambda x: pa.compute.add(x, 2147483647), IntegerType()) |
| result3 = df.select(scalar_int2("id").alias("res")) |
| with self.assertRaises(Exception): |
| # pyarrow.lib.ArrowInvalid: |
| # Integer value 2147483652 not in range: -2147483648 to 2147483647 |
| result3.collect() |
| |
| def test_unsupported_return_types(self): |
| import pyarrow as pa |
| |
| with self.quiet(): |
| for udf_type in [ArrowUDFType.SCALAR, ArrowUDFType.SCALAR_ITER]: |
| with self.assertRaisesRegex( |
| NotImplementedError, |
| "Invalid return type.*scalar Arrow UDF.*ArrayType.*YearMonthIntervalType", |
| ): |
| arrow_udf(lambda x: x, ArrayType(YearMonthIntervalType()), udf_type) |
| |
| with self.assertRaisesRegex( |
| NotImplementedError, |
| "Invalid return type.*scalar Arrow UDF.*ArrayType.*YearMonthIntervalType", |
| ): |
| |
| @arrow_udf(ArrayType(YearMonthIntervalType())) |
| def func_a(a: pa.Array) -> pa.Array: |
| return a |
| |
| |
| class ScalarArrowUDFTests(ScalarArrowUDFTestsMixin, 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() |
| |
| |
| if __name__ == "__main__": |
| from pyspark.sql.tests.arrow.test_arrow_udf_scalar import * # noqa: F401 |
| |
| try: |
| import xmlrunner |
| |
| testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) |
| except ImportError: |
| testRunner = None |
| unittest.main(testRunner=testRunner, verbosity=2) |
