aeb3649fb9
### What changes were proposed in this pull request? This replaces deprecated API usage in PySpark tests with the preferred APIs. These have been deprecated for some time and usage is not consistent within tests. - https://docs.python.org/3/library/unittest.html#deprecated-aliases ### Why are the changes needed? For consistency and eventual removal of deprecated APIs. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existing tests Closes #30557 from BryanCutler/replace-deprecated-apis-in-tests. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
568 lines
26 KiB
Python
568 lines
26 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import datetime
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import os
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import threading
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import time
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import unittest
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import warnings
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from distutils.version import LooseVersion
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from pyspark import SparkContext, SparkConf
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from pyspark.sql import Row, SparkSession
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from pyspark.sql.functions import udf
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from pyspark.sql.types import StructType, StringType, IntegerType, LongType, \
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FloatType, DoubleType, DecimalType, DateType, TimestampType, BinaryType, StructField, ArrayType
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from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
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pandas_requirement_message, pyarrow_requirement_message
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from pyspark.testing.utils import QuietTest
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if have_pandas:
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import pandas as pd
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from pandas.testing import assert_frame_equal
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if have_pyarrow:
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import pyarrow as pa # noqa: F401
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@unittest.skipIf(
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not have_pandas or not have_pyarrow,
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pandas_requirement_message or pyarrow_requirement_message) # type: ignore
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class ArrowTests(ReusedSQLTestCase):
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@classmethod
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def setUpClass(cls):
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from datetime import date, datetime
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from decimal import Decimal
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super(ArrowTests, cls).setUpClass()
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cls.warnings_lock = threading.Lock()
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# Synchronize default timezone between Python and Java
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cls.tz_prev = os.environ.get("TZ", None) # save current tz if set
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tz = "America/Los_Angeles"
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os.environ["TZ"] = tz
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time.tzset()
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cls.spark.conf.set("spark.sql.session.timeZone", tz)
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# Test fallback
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cls.spark.conf.set("spark.sql.execution.arrow.enabled", "false")
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assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.enabled") == "false"
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cls.spark.conf.set("spark.sql.execution.arrow.enabled", "true")
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assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.enabled") == "true"
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cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled", "true")
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assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.fallback.enabled") == "true"
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cls.spark.conf.set("spark.sql.execution.arrow.fallback.enabled", "false")
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assert cls.spark.conf.get("spark.sql.execution.arrow.pyspark.fallback.enabled") == "false"
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# Enable Arrow optimization in this tests.
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cls.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
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# Disable fallback by default to easily detect the failures.
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cls.spark.conf.set("spark.sql.execution.arrow.pyspark.fallback.enabled", "false")
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cls.schema = StructType([
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StructField("1_str_t", StringType(), True),
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StructField("2_int_t", IntegerType(), True),
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StructField("3_long_t", LongType(), True),
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StructField("4_float_t", FloatType(), True),
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StructField("5_double_t", DoubleType(), True),
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StructField("6_decimal_t", DecimalType(38, 18), True),
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StructField("7_date_t", DateType(), True),
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StructField("8_timestamp_t", TimestampType(), True),
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StructField("9_binary_t", BinaryType(), True)])
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cls.data = [(u"a", 1, 10, 0.2, 2.0, Decimal("2.0"),
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date(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1), bytearray(b"a")),
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(u"b", 2, 20, 0.4, 4.0, Decimal("4.0"),
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date(2012, 2, 2), datetime(2012, 2, 2, 2, 2, 2), bytearray(b"bb")),
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(u"c", 3, 30, 0.8, 6.0, Decimal("6.0"),
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date(2100, 3, 3), datetime(2100, 3, 3, 3, 3, 3), bytearray(b"ccc")),
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(u"d", 4, 40, 1.0, 8.0, Decimal("8.0"),
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date(2262, 4, 12), datetime(2262, 3, 3, 3, 3, 3), bytearray(b"dddd"))]
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@classmethod
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def tearDownClass(cls):
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del os.environ["TZ"]
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if cls.tz_prev is not None:
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os.environ["TZ"] = cls.tz_prev
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time.tzset()
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super(ArrowTests, cls).tearDownClass()
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def create_pandas_data_frame(self):
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import numpy as np
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data_dict = {}
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for j, name in enumerate(self.schema.names):
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data_dict[name] = [self.data[i][j] for i in range(len(self.data))]
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# need to convert these to numpy types first
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data_dict["2_int_t"] = np.int32(data_dict["2_int_t"])
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data_dict["4_float_t"] = np.float32(data_dict["4_float_t"])
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return pd.DataFrame(data=data_dict)
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def test_toPandas_fallback_enabled(self):
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ts = datetime.datetime(2015, 11, 1, 0, 30)
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with self.sql_conf({"spark.sql.execution.arrow.pyspark.fallback.enabled": True}):
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schema = StructType([StructField("a", ArrayType(TimestampType()), True)])
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df = self.spark.createDataFrame([([ts],)], schema=schema)
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with QuietTest(self.sc):
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with self.warnings_lock:
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with warnings.catch_warnings(record=True) as warns:
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# we want the warnings to appear even if this test is run from a subclass
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warnings.simplefilter("always")
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pdf = df.toPandas()
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# Catch and check the last UserWarning.
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user_warns = [
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warn.message for warn in warns if isinstance(warn.message, UserWarning)]
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self.assertTrue(len(user_warns) > 0)
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self.assertTrue(
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"Attempting non-optimization" in str(user_warns[-1]))
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assert_frame_equal(pdf, pd.DataFrame({"a": [[ts]]}))
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def test_toPandas_fallback_disabled(self):
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schema = StructType([StructField("a", ArrayType(TimestampType()), True)])
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df = self.spark.createDataFrame([(None,)], schema=schema)
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with QuietTest(self.sc):
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with self.warnings_lock:
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with self.assertRaisesRegex(Exception, 'Unsupported type'):
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df.toPandas()
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def test_null_conversion(self):
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df_null = self.spark.createDataFrame([tuple([None for _ in range(len(self.data[0]))])] +
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self.data)
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pdf = df_null.toPandas()
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null_counts = pdf.isnull().sum().tolist()
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self.assertTrue(all([c == 1 for c in null_counts]))
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def _toPandas_arrow_toggle(self, df):
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with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
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pdf = df.toPandas()
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pdf_arrow = df.toPandas()
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return pdf, pdf_arrow
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def test_toPandas_arrow_toggle(self):
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df = self.spark.createDataFrame(self.data, schema=self.schema)
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pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
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expected = self.create_pandas_data_frame()
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assert_frame_equal(expected, pdf)
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assert_frame_equal(expected, pdf_arrow)
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def test_toPandas_respect_session_timezone(self):
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df = self.spark.createDataFrame(self.data, schema=self.schema)
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timezone = "America/Los_Angeles"
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with self.sql_conf({"spark.sql.session.timeZone": timezone}):
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pdf_la, pdf_arrow_la = self._toPandas_arrow_toggle(df)
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assert_frame_equal(pdf_arrow_la, pdf_la)
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timezone = "America/New_York"
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with self.sql_conf({"spark.sql.session.timeZone": timezone}):
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pdf_ny, pdf_arrow_ny = self._toPandas_arrow_toggle(df)
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assert_frame_equal(pdf_arrow_ny, pdf_ny)
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self.assertFalse(pdf_ny.equals(pdf_la))
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from pyspark.sql.pandas.types import _check_series_convert_timestamps_local_tz
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pdf_la_corrected = pdf_la.copy()
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for field in self.schema:
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if isinstance(field.dataType, TimestampType):
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pdf_la_corrected[field.name] = _check_series_convert_timestamps_local_tz(
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pdf_la_corrected[field.name], timezone)
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assert_frame_equal(pdf_ny, pdf_la_corrected)
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def test_pandas_round_trip(self):
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pdf = self.create_pandas_data_frame()
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df = self.spark.createDataFrame(self.data, schema=self.schema)
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pdf_arrow = df.toPandas()
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assert_frame_equal(pdf_arrow, pdf)
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def test_filtered_frame(self):
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df = self.spark.range(3).toDF("i")
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pdf = df.filter("i < 0").toPandas()
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self.assertEqual(len(pdf.columns), 1)
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self.assertEqual(pdf.columns[0], "i")
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self.assertTrue(pdf.empty)
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def test_no_partition_frame(self):
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schema = StructType([StructField("field1", StringType(), True)])
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df = self.spark.createDataFrame(self.sc.emptyRDD(), schema)
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pdf = df.toPandas()
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self.assertEqual(len(pdf.columns), 1)
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self.assertEqual(pdf.columns[0], "field1")
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self.assertTrue(pdf.empty)
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def test_propagates_spark_exception(self):
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df = self.spark.range(3).toDF("i")
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def raise_exception():
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raise Exception("My error")
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exception_udf = udf(raise_exception, IntegerType())
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df = df.withColumn("error", exception_udf())
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with QuietTest(self.sc):
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with self.assertRaisesRegex(Exception, 'My error'):
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df.toPandas()
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def _createDataFrame_toggle(self, pdf, schema=None):
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with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
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df_no_arrow = self.spark.createDataFrame(pdf, schema=schema)
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df_arrow = self.spark.createDataFrame(pdf, schema=schema)
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return df_no_arrow, df_arrow
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def test_createDataFrame_toggle(self):
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pdf = self.create_pandas_data_frame()
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df_no_arrow, df_arrow = self._createDataFrame_toggle(pdf, schema=self.schema)
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self.assertEqual(df_no_arrow.collect(), df_arrow.collect())
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def test_createDataFrame_respect_session_timezone(self):
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from datetime import timedelta
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pdf = self.create_pandas_data_frame()
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timezone = "America/Los_Angeles"
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with self.sql_conf({"spark.sql.session.timeZone": timezone}):
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df_no_arrow_la, df_arrow_la = self._createDataFrame_toggle(pdf, schema=self.schema)
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result_la = df_no_arrow_la.collect()
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result_arrow_la = df_arrow_la.collect()
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self.assertEqual(result_la, result_arrow_la)
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timezone = "America/New_York"
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with self.sql_conf({"spark.sql.session.timeZone": timezone}):
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df_no_arrow_ny, df_arrow_ny = self._createDataFrame_toggle(pdf, schema=self.schema)
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result_ny = df_no_arrow_ny.collect()
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result_arrow_ny = df_arrow_ny.collect()
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self.assertEqual(result_ny, result_arrow_ny)
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self.assertNotEqual(result_ny, result_la)
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# Correct result_la by adjusting 3 hours difference between Los Angeles and New York
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result_la_corrected = [Row(**{k: v - timedelta(hours=3) if k == '8_timestamp_t' else v
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for k, v in row.asDict().items()})
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for row in result_la]
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self.assertEqual(result_ny, result_la_corrected)
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def test_createDataFrame_with_schema(self):
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pdf = self.create_pandas_data_frame()
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df = self.spark.createDataFrame(pdf, schema=self.schema)
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self.assertEqual(self.schema, df.schema)
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pdf_arrow = df.toPandas()
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assert_frame_equal(pdf_arrow, pdf)
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def test_createDataFrame_with_incorrect_schema(self):
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pdf = self.create_pandas_data_frame()
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fields = list(self.schema)
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fields[5], fields[6] = fields[6], fields[5] # swap decimal with date
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wrong_schema = StructType(fields)
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with self.sql_conf({"spark.sql.execution.pandas.convertToArrowArraySafely": False}):
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with QuietTest(self.sc):
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with self.assertRaisesRegex(Exception, "[D|d]ecimal.*got.*date"):
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self.spark.createDataFrame(pdf, schema=wrong_schema)
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def test_createDataFrame_with_names(self):
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pdf = self.create_pandas_data_frame()
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new_names = list(map(str, range(len(self.schema.fieldNames()))))
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# Test that schema as a list of column names gets applied
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df = self.spark.createDataFrame(pdf, schema=list(new_names))
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self.assertEqual(df.schema.fieldNames(), new_names)
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# Test that schema as tuple of column names gets applied
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df = self.spark.createDataFrame(pdf, schema=tuple(new_names))
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self.assertEqual(df.schema.fieldNames(), new_names)
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def test_createDataFrame_column_name_encoding(self):
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pdf = pd.DataFrame({u'a': [1]})
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columns = self.spark.createDataFrame(pdf).columns
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self.assertTrue(isinstance(columns[0], str))
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self.assertEqual(columns[0], 'a')
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columns = self.spark.createDataFrame(pdf, [u'b']).columns
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self.assertTrue(isinstance(columns[0], str))
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self.assertEqual(columns[0], 'b')
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def test_createDataFrame_with_single_data_type(self):
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with QuietTest(self.sc):
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with self.assertRaisesRegex(ValueError, ".*IntegerType.*not supported.*"):
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self.spark.createDataFrame(pd.DataFrame({"a": [1]}), schema="int")
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def test_createDataFrame_does_not_modify_input(self):
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# Some series get converted for Spark to consume, this makes sure input is unchanged
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pdf = self.create_pandas_data_frame()
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# Use a nanosecond value to make sure it is not truncated
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pdf.iloc[0, 7] = pd.Timestamp(1)
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# Integers with nulls will get NaNs filled with 0 and will be casted
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pdf.iloc[1, 1] = None
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pdf_copy = pdf.copy(deep=True)
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self.spark.createDataFrame(pdf, schema=self.schema)
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self.assertTrue(pdf.equals(pdf_copy))
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def test_schema_conversion_roundtrip(self):
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from pyspark.sql.pandas.types import from_arrow_schema, to_arrow_schema
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arrow_schema = to_arrow_schema(self.schema)
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schema_rt = from_arrow_schema(arrow_schema)
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self.assertEqual(self.schema, schema_rt)
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def test_createDataFrame_with_array_type(self):
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pdf = pd.DataFrame({"a": [[1, 2], [3, 4]], "b": [[u"x", u"y"], [u"y", u"z"]]})
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df, df_arrow = self._createDataFrame_toggle(pdf)
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result = df.collect()
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result_arrow = df_arrow.collect()
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expected = [tuple(list(e) for e in rec) for rec in pdf.to_records(index=False)]
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for r in range(len(expected)):
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for e in range(len(expected[r])):
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self.assertTrue(expected[r][e] == result_arrow[r][e] and
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result[r][e] == result_arrow[r][e])
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def test_toPandas_with_array_type(self):
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expected = [([1, 2], [u"x", u"y"]), ([3, 4], [u"y", u"z"])]
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array_schema = StructType([StructField("a", ArrayType(IntegerType())),
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StructField("b", ArrayType(StringType()))])
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df = self.spark.createDataFrame(expected, schema=array_schema)
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pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
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result = [tuple(list(e) for e in rec) for rec in pdf.to_records(index=False)]
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result_arrow = [tuple(list(e) for e in rec) for rec in pdf_arrow.to_records(index=False)]
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for r in range(len(expected)):
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for e in range(len(expected[r])):
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self.assertTrue(expected[r][e] == result_arrow[r][e] and
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result[r][e] == result_arrow[r][e])
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def test_createDataFrame_with_map_type(self):
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map_data = [{"a": 1}, {"b": 2, "c": 3}, {}, None, {"d": None}]
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pdf = pd.DataFrame({"id": [0, 1, 2, 3, 4], "m": map_data})
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schema = "id long, m map<string, long>"
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with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
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df = self.spark.createDataFrame(pdf, schema=schema)
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if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
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with QuietTest(self.sc):
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with self.assertRaisesRegex(Exception, "MapType.*only.*pyarrow 2.0.0"):
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self.spark.createDataFrame(pdf, schema=schema)
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else:
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df_arrow = self.spark.createDataFrame(pdf, schema=schema)
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result = df.collect()
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result_arrow = df_arrow.collect()
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self.assertEqual(len(result), len(result_arrow))
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for row, row_arrow in zip(result, result_arrow):
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i, m = row
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_, m_arrow = row_arrow
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self.assertEqual(m, map_data[i])
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self.assertEqual(m_arrow, map_data[i])
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def test_toPandas_with_map_type(self):
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pdf = pd.DataFrame({"id": [0, 1, 2, 3],
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"m": [{}, {"a": 1}, {"a": 1, "b": 2}, {"a": 1, "b": 2, "c": 3}]})
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with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
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df = self.spark.createDataFrame(pdf, schema="id long, m map<string, long>")
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if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
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with QuietTest(self.sc):
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with self.assertRaisesRegex(Exception, "MapType.*only.*pyarrow 2.0.0"):
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df.toPandas()
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else:
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pdf_non, pdf_arrow = self._toPandas_arrow_toggle(df)
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assert_frame_equal(pdf_arrow, pdf_non)
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def test_toPandas_with_map_type_nulls(self):
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pdf = pd.DataFrame({"id": [0, 1, 2, 3, 4],
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"m": [{"a": 1}, {"b": 2, "c": 3}, {}, None, {"d": None}]})
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with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
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df = self.spark.createDataFrame(pdf, schema="id long, m map<string, long>")
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if LooseVersion(pa.__version__) < LooseVersion("2.0.0"):
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with QuietTest(self.sc):
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with self.assertRaisesRegex(Exception, "MapType.*only.*pyarrow 2.0.0"):
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df.toPandas()
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else:
|
|
pdf_non, pdf_arrow = self._toPandas_arrow_toggle(df)
|
|
assert_frame_equal(pdf_arrow, pdf_non)
|
|
|
|
def test_createDataFrame_with_int_col_names(self):
|
|
import numpy as np
|
|
pdf = pd.DataFrame(np.random.rand(4, 2))
|
|
df, df_arrow = self._createDataFrame_toggle(pdf)
|
|
pdf_col_names = [str(c) for c in pdf.columns]
|
|
self.assertEqual(pdf_col_names, df.columns)
|
|
self.assertEqual(pdf_col_names, df_arrow.columns)
|
|
|
|
def test_createDataFrame_fallback_enabled(self):
|
|
ts = datetime.datetime(2015, 11, 1, 0, 30)
|
|
with QuietTest(self.sc):
|
|
with self.sql_conf({"spark.sql.execution.arrow.pyspark.fallback.enabled": True}):
|
|
with warnings.catch_warnings(record=True) as warns:
|
|
# we want the warnings to appear even if this test is run from a subclass
|
|
warnings.simplefilter("always")
|
|
df = self.spark.createDataFrame(
|
|
pd.DataFrame({"a": [[ts]]}), "a: array<timestamp>")
|
|
# Catch and check the last UserWarning.
|
|
user_warns = [
|
|
warn.message for warn in warns if isinstance(warn.message, UserWarning)]
|
|
self.assertTrue(len(user_warns) > 0)
|
|
self.assertTrue(
|
|
"Attempting non-optimization" in str(user_warns[-1]))
|
|
self.assertEqual(df.collect(), [Row(a=[ts])])
|
|
|
|
def test_createDataFrame_fallback_disabled(self):
|
|
with QuietTest(self.sc):
|
|
with self.assertRaisesRegex(TypeError, 'Unsupported type'):
|
|
self.spark.createDataFrame(
|
|
pd.DataFrame({"a": [[datetime.datetime(2015, 11, 1, 0, 30)]]}),
|
|
"a: array<timestamp>")
|
|
|
|
# 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.datetime(2015, 11, 1, 0, 30),
|
|
datetime.datetime(2015, 11, 1, 1, 30),
|
|
datetime.datetime(2015, 11, 1, 2, 30)]
|
|
pdf = pd.DataFrame({'time': dt})
|
|
|
|
df_from_python = self.spark.createDataFrame(dt, 'timestamp').toDF('time')
|
|
df_from_pandas = self.spark.createDataFrame(pdf)
|
|
|
|
assert_frame_equal(pdf, df_from_python.toPandas())
|
|
assert_frame_equal(pdf, df_from_pandas.toPandas())
|
|
|
|
# Regression test for SPARK-28003
|
|
def test_timestamp_nat(self):
|
|
dt = [pd.NaT, pd.Timestamp('2019-06-11'), None] * 100
|
|
pdf = pd.DataFrame({'time': dt})
|
|
df_no_arrow, df_arrow = self._createDataFrame_toggle(pdf)
|
|
|
|
assert_frame_equal(pdf, df_no_arrow.toPandas())
|
|
assert_frame_equal(pdf, df_arrow.toPandas())
|
|
|
|
def test_toPandas_batch_order(self):
|
|
|
|
def delay_first_part(partition_index, iterator):
|
|
if partition_index == 0:
|
|
time.sleep(0.1)
|
|
return iterator
|
|
|
|
# Collects Arrow RecordBatches out of order in driver JVM then re-orders in Python
|
|
def run_test(num_records, num_parts, max_records, use_delay=False):
|
|
df = self.spark.range(num_records, numPartitions=num_parts).toDF("a")
|
|
if use_delay:
|
|
df = df.rdd.mapPartitionsWithIndex(delay_first_part).toDF()
|
|
with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": max_records}):
|
|
pdf, pdf_arrow = self._toPandas_arrow_toggle(df)
|
|
assert_frame_equal(pdf, pdf_arrow)
|
|
|
|
cases = [
|
|
(1024, 512, 2), # Use large num partitions for more likely collecting out of order
|
|
(64, 8, 2, True), # Use delay in first partition to force collecting out of order
|
|
(64, 64, 1), # Test single batch per partition
|
|
(64, 1, 64), # Test single partition, single batch
|
|
(64, 1, 8), # Test single partition, multiple batches
|
|
(30, 7, 2), # Test different sized partitions
|
|
]
|
|
|
|
for case in cases:
|
|
run_test(*case)
|
|
|
|
def test_createDateFrame_with_category_type(self):
|
|
pdf = pd.DataFrame({"A": [u"a", u"b", u"c", u"a"]})
|
|
pdf["B"] = pdf["A"].astype('category')
|
|
category_first_element = dict(enumerate(pdf['B'].cat.categories))[0]
|
|
|
|
with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": True}):
|
|
arrow_df = self.spark.createDataFrame(pdf)
|
|
arrow_type = arrow_df.dtypes[1][1]
|
|
result_arrow = arrow_df.toPandas()
|
|
arrow_first_category_element = result_arrow["B"][0]
|
|
|
|
with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": False}):
|
|
df = self.spark.createDataFrame(pdf)
|
|
spark_type = df.dtypes[1][1]
|
|
result_spark = df.toPandas()
|
|
spark_first_category_element = result_spark["B"][0]
|
|
|
|
assert_frame_equal(result_spark, result_arrow)
|
|
|
|
# ensure original category elements are string
|
|
self.assertIsInstance(category_first_element, str)
|
|
# spark data frame and arrow execution mode enabled data frame type must match pandas
|
|
self.assertEqual(spark_type, 'string')
|
|
self.assertEqual(arrow_type, 'string')
|
|
self.assertIsInstance(arrow_first_category_element, str)
|
|
self.assertIsInstance(spark_first_category_element, str)
|
|
|
|
def test_createDataFrame_with_float_index(self):
|
|
# SPARK-32098: float index should not produce duplicated or truncated Spark DataFrame
|
|
self.assertEqual(
|
|
self.spark.createDataFrame(
|
|
pd.DataFrame({'a': [1, 2, 3]}, index=[2., 3., 4.])).distinct().count(), 3)
|
|
|
|
def test_no_partition_toPandas(self):
|
|
# SPARK-32301: toPandas should work from a Spark DataFrame with no partitions
|
|
# Forward-ported from SPARK-32300.
|
|
pdf = self.spark.sparkContext.emptyRDD().toDF("col1 int").toPandas()
|
|
self.assertEqual(len(pdf), 0)
|
|
self.assertEqual(list(pdf.columns), ["col1"])
|
|
|
|
def test_createDataFrame_empty_partition(self):
|
|
pdf = pd.DataFrame({"c1": [1], "c2": ["string"]})
|
|
df = self.spark.createDataFrame(pdf)
|
|
self.assertEqual([Row(c1=1, c2='string')], df.collect())
|
|
self.assertGreater(self.spark.sparkContext.defaultParallelism, len(pdf))
|
|
|
|
|
|
@unittest.skipIf(
|
|
not have_pandas or not have_pyarrow,
|
|
pandas_requirement_message or pyarrow_requirement_message) # type: ignore
|
|
class MaxResultArrowTests(unittest.TestCase):
|
|
# These tests are separate as 'spark.driver.maxResultSize' configuration
|
|
# is a static configuration to Spark context.
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.spark = SparkSession(SparkContext(
|
|
'local[4]', cls.__name__, conf=SparkConf().set("spark.driver.maxResultSize", "10k")))
|
|
|
|
# Explicitly enable Arrow and disable fallback.
|
|
cls.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
|
|
cls.spark.conf.set("spark.sql.execution.arrow.pyspark.fallback.enabled", "false")
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
if hasattr(cls, "spark"):
|
|
cls.spark.stop()
|
|
|
|
def test_exception_by_max_results(self):
|
|
with self.assertRaisesRegex(Exception, "is bigger than"):
|
|
self.spark.range(0, 10000, 1, 100).toPandas()
|
|
|
|
|
|
class EncryptionArrowTests(ArrowTests):
|
|
|
|
@classmethod
|
|
def conf(cls):
|
|
return super(EncryptionArrowTests, cls).conf().set("spark.io.encryption.enabled", "true")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.sql.tests.test_arrow import * # noqa: F401
|
|
|
|
try:
|
|
import xmlrunner # type: ignore
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|