spark-instrumented-optimizer/python/pyspark/sql/tests/test_pandas_udf_scalar.py

#
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# 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
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import datetime
import os
import random
import shutil
import sys
import tempfile
import time
import unittest

if sys.version >= '3':
    unicode = str

from datetime import date, datetime
from decimal import Decimal
from distutils.version import LooseVersion

from pyspark.rdd import PythonEvalType
from pyspark.sql import Column
from pyspark.sql.functions import array, col, expr, lit, sum, struct, udf, pandas_udf
from pyspark.sql.types import Row
from pyspark.sql.types import *
from pyspark.sql.utils import AnalysisException
from pyspark.testing.sqlutils import ReusedSQLTestCase, test_compiled,\
    test_not_compiled_message, have_pandas, have_pyarrow, pandas_requirement_message, \
    pyarrow_requirement_message
from pyspark.testing.utils import QuietTest


@unittest.skipIf(
    not have_pandas or not have_pyarrow,
    pandas_requirement_message or pyarrow_requirement_message)
class ScalarPandasUDFTests(ReusedSQLTestCase):

    @classmethod
    def setUpClass(cls):
        ReusedSQLTestCase.setUpClass()

        # Synchronize default timezone between Python and Java
        cls.tz_prev = os.environ.get("TZ", None)  # save current tz if set
        tz = "America/Los_Angeles"
        os.environ["TZ"] = tz
        time.tzset()

        cls.sc.environment["TZ"] = tz
        cls.spark.conf.set("spark.sql.session.timeZone", tz)

    @classmethod
    def tearDownClass(cls):
        del os.environ["TZ"]
        if cls.tz_prev is not None:
            os.environ["TZ"] = cls.tz_prev
        time.tzset()
        ReusedSQLTestCase.tearDownClass()

    @property
    def nondeterministic_vectorized_udf(self):
        import pandas as pd
        import numpy as np

        @pandas_udf('double')
        def random_udf(v):
            return pd.Series(np.random.random(len(v)))
        random_udf = random_udf.asNondeterministic()
        return random_udf

    def test_pandas_udf_tokenize(self):
        tokenize = pandas_udf(lambda s: s.apply(lambda str: str.split(' ')),
                              ArrayType(StringType()))
        self.assertEqual(tokenize.returnType, ArrayType(StringType()))
        df = self.spark.createDataFrame([("hi boo",), ("bye boo",)], ["vals"])
        result = df.select(tokenize("vals").alias("hi"))
        self.assertEqual([Row(hi=[u'hi', u'boo']), Row(hi=[u'bye', u'boo'])], result.collect())

    def test_pandas_udf_nested_arrays(self):
        tokenize = pandas_udf(lambda s: s.apply(lambda str: [str.split(' ')]),
                              ArrayType(ArrayType(StringType())))
        self.assertEqual(tokenize.returnType, ArrayType(ArrayType(StringType())))
        df = self.spark.createDataFrame([("hi boo",), ("bye boo",)], ["vals"])
        result = df.select(tokenize("vals").alias("hi"))
        self.assertEqual([Row(hi=[[u'hi', u'boo']]), Row(hi=[[u'bye', u'boo']])], result.collect())

    def test_vectorized_udf_basic(self):
        df = self.spark.range(10).select(
            col('id').cast('string').alias('str'),
            col('id').cast('int').alias('int'),
            col('id').alias('long'),
            col('id').cast('float').alias('float'),
            col('id').cast('double').alias('double'),
            col('id').cast('decimal').alias('decimal'),
            col('id').cast('boolean').alias('bool'),
            array(col('id')).alias('array_long'))
        f = lambda x: x
        str_f = pandas_udf(f, StringType())
        int_f = pandas_udf(f, IntegerType())
        long_f = pandas_udf(f, LongType())
        float_f = pandas_udf(f, FloatType())
        double_f = pandas_udf(f, DoubleType())
        decimal_f = pandas_udf(f, DecimalType())
        bool_f = pandas_udf(f, BooleanType())
        array_long_f = pandas_udf(f, ArrayType(LongType()))
        res = df.select(str_f(col('str')), int_f(col('int')),
                        long_f(col('long')), float_f(col('float')),
                        double_f(col('double')), decimal_f('decimal'),
                        bool_f(col('bool')), array_long_f('array_long'))
        self.assertEquals(df.collect(), res.collect())

    def test_register_nondeterministic_vectorized_udf_basic(self):
        random_pandas_udf = pandas_udf(
            lambda x: random.randint(6, 6) + x, IntegerType()).asNondeterministic()
        self.assertEqual(random_pandas_udf.deterministic, False)
        self.assertEqual(random_pandas_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
        nondeterministic_pandas_udf = self.spark.catalog.registerFunction(
            "randomPandasUDF", random_pandas_udf)
        self.assertEqual(nondeterministic_pandas_udf.deterministic, False)
        self.assertEqual(nondeterministic_pandas_udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
        [row] = self.spark.sql("SELECT randomPandasUDF(1)").collect()
        self.assertEqual(row[0], 7)

    def test_vectorized_udf_null_boolean(self):
        data = [(True,), (True,), (None,), (False,)]
        schema = StructType().add("bool", BooleanType())
        df = self.spark.createDataFrame(data, schema)
        bool_f = pandas_udf(lambda x: x, BooleanType())
        res = df.select(bool_f(col('bool')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_byte(self):
        data = [(None,), (2,), (3,), (4,)]
        schema = StructType().add("byte", ByteType())
        df = self.spark.createDataFrame(data, schema)
        byte_f = pandas_udf(lambda x: x, ByteType())
        res = df.select(byte_f(col('byte')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_short(self):
        data = [(None,), (2,), (3,), (4,)]
        schema = StructType().add("short", ShortType())
        df = self.spark.createDataFrame(data, schema)
        short_f = pandas_udf(lambda x: x, ShortType())
        res = df.select(short_f(col('short')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_int(self):
        data = [(None,), (2,), (3,), (4,)]
        schema = StructType().add("int", IntegerType())
        df = self.spark.createDataFrame(data, schema)
        int_f = pandas_udf(lambda x: x, IntegerType())
        res = df.select(int_f(col('int')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_long(self):
        data = [(None,), (2,), (3,), (4,)]
        schema = StructType().add("long", LongType())
        df = self.spark.createDataFrame(data, schema)
        long_f = pandas_udf(lambda x: x, LongType())
        res = df.select(long_f(col('long')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_float(self):
        data = [(3.0,), (5.0,), (-1.0,), (None,)]
        schema = StructType().add("float", FloatType())
        df = self.spark.createDataFrame(data, schema)
        float_f = pandas_udf(lambda x: x, FloatType())
        res = df.select(float_f(col('float')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_double(self):
        data = [(3.0,), (5.0,), (-1.0,), (None,)]
        schema = StructType().add("double", DoubleType())
        df = self.spark.createDataFrame(data, schema)
        double_f = pandas_udf(lambda x: x, DoubleType())
        res = df.select(double_f(col('double')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_decimal(self):
        data = [(Decimal(3.0),), (Decimal(5.0),), (Decimal(-1.0),), (None,)]
        schema = StructType().add("decimal", DecimalType(38, 18))
        df = self.spark.createDataFrame(data, schema)
        decimal_f = pandas_udf(lambda x: x, DecimalType(38, 18))
        res = df.select(decimal_f(col('decimal')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_string(self):
        data = [("foo",), (None,), ("bar",), ("bar",)]
        schema = StructType().add("str", StringType())
        df = self.spark.createDataFrame(data, schema)
        str_f = pandas_udf(lambda x: x, StringType())
        res = df.select(str_f(col('str')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_string_in_udf(self):
        import pandas as pd
        df = self.spark.range(10)
        str_f = pandas_udf(lambda x: pd.Series(map(str, x)), StringType())
        actual = df.select(str_f(col('id')))
        expected = df.select(col('id').cast('string'))
        self.assertEquals(expected.collect(), actual.collect())

    def test_vectorized_udf_datatype_string(self):
        df = self.spark.range(10).select(
            col('id').cast('string').alias('str'),
            col('id').cast('int').alias('int'),
            col('id').alias('long'),
            col('id').cast('float').alias('float'),
            col('id').cast('double').alias('double'),
            col('id').cast('decimal').alias('decimal'),
            col('id').cast('boolean').alias('bool'))
        f = lambda x: x
        str_f = pandas_udf(f, 'string')
        int_f = pandas_udf(f, 'integer')
        long_f = pandas_udf(f, 'long')
        float_f = pandas_udf(f, 'float')
        double_f = pandas_udf(f, 'double')
        decimal_f = pandas_udf(f, 'decimal(38, 18)')
        bool_f = pandas_udf(f, 'boolean')
        res = df.select(str_f(col('str')), int_f(col('int')),
                        long_f(col('long')), float_f(col('float')),
                        double_f(col('double')), decimal_f('decimal'),
                        bool_f(col('bool')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_null_binary(self):
        import pyarrow as pa

        if LooseVersion(pa.__version__) < LooseVersion("0.10.0"):
            with QuietTest(self.sc):
                with self.assertRaisesRegexp(
                        NotImplementedError,
                        'Invalid returnType.*scalar Pandas UDF.*BinaryType'):
                    pandas_udf(lambda x: x, BinaryType())
        else:
            data = [(bytearray(b"a"),), (None,), (bytearray(b"bb"),), (bytearray(b"ccc"),)]
            schema = StructType().add("binary", BinaryType())
            df = self.spark.createDataFrame(data, schema)
            str_f = pandas_udf(lambda x: x, BinaryType())
            res = df.select(str_f(col('binary')))
            self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_array_type(self):
        data = [([1, 2],), ([3, 4],)]
        array_schema = StructType([StructField("array", ArrayType(IntegerType()))])
        df = self.spark.createDataFrame(data, schema=array_schema)
        array_f = pandas_udf(lambda x: x, ArrayType(IntegerType()))
        result = df.select(array_f(col('array')))
        self.assertEquals(df.collect(), result.collect())

    def test_vectorized_udf_null_array(self):
        data = [([1, 2],), (None,), (None,), ([3, 4],), (None,)]
        array_schema = StructType([StructField("array", ArrayType(IntegerType()))])
        df = self.spark.createDataFrame(data, schema=array_schema)
        array_f = pandas_udf(lambda x: x, ArrayType(IntegerType()))
        result = df.select(array_f(col('array')))
        self.assertEquals(df.collect(), result.collect())

    def test_vectorized_udf_struct_type(self):
        import pandas as pd

        df = self.spark.range(10)
        return_type = StructType([
            StructField('id', LongType()),
            StructField('str', StringType())])

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

        f = pandas_udf(func, returnType=return_type)

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

        actual = df.select(f(col('id')).alias('struct')).collect()
        self.assertEqual(expected, actual)

        g = pandas_udf(func, 'id: long, str: string')
        actual = df.select(g(col('id')).alias('struct')).collect()
        self.assertEqual(expected, actual)

    def test_vectorized_udf_struct_complex(self):
        import pandas as pd

        df = self.spark.range(10)
        return_type = StructType([
            StructField('ts', TimestampType()),
            StructField('arr', ArrayType(LongType()))])

        @pandas_udf(returnType=return_type)
        def f(id):
            return pd.DataFrame({'ts': id.apply(lambda i: pd.Timestamp(i)),
                                 'arr': id.apply(lambda i: [i, i + 1])})

        actual = df.withColumn('f', f(col('id'))).collect()
        for i, row in enumerate(actual):
            id, f = row
            self.assertEqual(i, id)
            self.assertEqual(pd.Timestamp(i).to_pydatetime(), f[0])
            self.assertListEqual([i, i + 1], f[1])

    def test_vectorized_udf_nested_struct(self):
        nested_type = StructType([
            StructField('id', IntegerType()),
            StructField('nested', StructType([
                StructField('foo', StringType()),
                StructField('bar', FloatType())
            ]))
        ])

        with QuietTest(self.sc):
            with self.assertRaisesRegexp(
                    Exception,
                    'Invalid returnType with scalar Pandas UDFs'):
                pandas_udf(lambda x: x, returnType=nested_type)

    def test_vectorized_udf_complex(self):
        df = self.spark.range(10).select(
            col('id').cast('int').alias('a'),
            col('id').cast('int').alias('b'),
            col('id').cast('double').alias('c'))
        add = pandas_udf(lambda x, y: x + y, IntegerType())
        power2 = pandas_udf(lambda x: 2 ** x, IntegerType())
        mul = pandas_udf(lambda x, y: x * y, DoubleType())
        res = df.select(add(col('a'), col('b')), power2(col('a')), mul(col('b'), col('c')))
        expected = df.select(expr('a + b'), expr('power(2, a)'), expr('b * c'))
        self.assertEquals(expected.collect(), res.collect())

    def test_vectorized_udf_exception(self):
        df = self.spark.range(10)
        raise_exception = pandas_udf(lambda x: x * (1 / 0), LongType())
        with QuietTest(self.sc):
            with self.assertRaisesRegexp(Exception, 'division( or modulo)? by zero'):
                df.select(raise_exception(col('id'))).collect()

    def test_vectorized_udf_invalid_length(self):
        import pandas as pd

        df = self.spark.range(10)
        raise_exception = pandas_udf(lambda _: pd.Series(1), LongType())
        with QuietTest(self.sc):
            with self.assertRaisesRegexp(
                    Exception,
                    'Result vector from pandas_udf was not the required length'):
                df.select(raise_exception(col('id'))).collect()

    def test_vectorized_udf_chained(self):
        df = self.spark.range(10)
        f = pandas_udf(lambda x: x + 1, LongType())
        g = pandas_udf(lambda x: x - 1, LongType())
        res = df.select(g(f(col('id'))))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_wrong_return_type(self):
        with QuietTest(self.sc):
            with self.assertRaisesRegexp(
                    NotImplementedError,
                    'Invalid returnType.*scalar Pandas UDF.*MapType'):
                pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType()))

    def test_vectorized_udf_return_scalar(self):
        df = self.spark.range(10)
        f = pandas_udf(lambda x: 1.0, DoubleType())
        with QuietTest(self.sc):
            with self.assertRaisesRegexp(Exception, 'Return.*type.*Series'):
                df.select(f(col('id'))).collect()

    def test_vectorized_udf_decorator(self):
        df = self.spark.range(10)

        @pandas_udf(returnType=LongType())
        def identity(x):
            return x
        res = df.select(identity(col('id')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_empty_partition(self):
        df = self.spark.createDataFrame(self.sc.parallelize([Row(id=1)], 2))
        f = pandas_udf(lambda x: x, LongType())
        res = df.select(f(col('id')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_struct_with_empty_partition(self):
        df = self.spark.createDataFrame(self.sc.parallelize([Row(id=1)], 2))\
            .withColumn('name', lit('John Doe'))

        @pandas_udf("first string, last string")
        def split_expand(n):
            return n.str.split(expand=True)

        result = df.select(split_expand('name')).collect()
        self.assertEqual(1, len(result))
        row = result[0]
        self.assertEqual('John', row[0]['first'])
        self.assertEqual('Doe', row[0]['last'])

    def test_vectorized_udf_varargs(self):
        df = self.spark.createDataFrame(self.sc.parallelize([Row(id=1)], 2))
        f = pandas_udf(lambda *v: v[0], LongType())
        res = df.select(f(col('id')))
        self.assertEquals(df.collect(), res.collect())

    def test_vectorized_udf_unsupported_types(self):
        with QuietTest(self.sc):
            with self.assertRaisesRegexp(
                    NotImplementedError,
                    'Invalid returnType.*scalar Pandas UDF.*MapType'):
                pandas_udf(lambda x: x, MapType(StringType(), IntegerType()))
            with self.assertRaisesRegexp(
                    NotImplementedError,
                    'Invalid returnType.*scalar Pandas UDF.*ArrayType.StructType'):
                pandas_udf(lambda x: x, ArrayType(StructType([StructField('a', IntegerType())])))

    def test_vectorized_udf_dates(self):
        schema = StructType().add("idx", LongType()).add("date", DateType())
        data = [(0, date(1969, 1, 1),),
                (1, date(2012, 2, 2),),
                (2, None,),
                (3, date(2100, 4, 4),),
                (4, date(2262, 4, 12),)]
        df = self.spark.createDataFrame(data, schema=schema)

        date_copy = pandas_udf(lambda t: t, returnType=DateType())
        df = df.withColumn("date_copy", date_copy(col("date")))

        @pandas_udf(returnType=StringType())
        def check_data(idx, date, date_copy):
            import pandas as pd
            msgs = []
            is_equal = date.isnull()
            for i in range(len(idx)):
                if (is_equal[i] and data[idx[i]][1] is None) or \
                        date[i] == data[idx[i]][1]:
                    msgs.append(None)
                else:
                    msgs.append(
                        "date values are not equal (date='%s': data[%d][1]='%s')"
                        % (date[i], idx[i], data[idx[i]][1]))
            return pd.Series(msgs)

        result = df.withColumn("check_data",
                               check_data(col("idx"), col("date"), col("date_copy"))).collect()

        self.assertEquals(len(data), len(result))
        for i in range(len(result)):
            self.assertEquals(data[i][1], result[i][1])  # "date" col
            self.assertEquals(data[i][1], result[i][2])  # "date_copy" col
            self.assertIsNone(result[i][3])  # "check_data" col

    def test_vectorized_udf_timestamps(self):
        schema = StructType([
            StructField("idx", LongType(), True),
            StructField("timestamp", TimestampType(), True)])
        data = [(0, datetime(1969, 1, 1, 1, 1, 1)),
                (1, datetime(2012, 2, 2, 2, 2, 2)),
                (2, None),
                (3, datetime(2100, 3, 3, 3, 3, 3))]

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

        # Check that a timestamp passed through a pandas_udf will not be altered by timezone calc
        f_timestamp_copy = pandas_udf(lambda t: t, returnType=TimestampType())
        df = df.withColumn("timestamp_copy", f_timestamp_copy(col("timestamp")))

        @pandas_udf(returnType=StringType())
        def check_data(idx, timestamp, timestamp_copy):
            import pandas as pd
            msgs = []
            is_equal = timestamp.isnull()  # use this array to check values are equal
            for i in range(len(idx)):
                # Check that timestamps are as expected in the UDF
                if (is_equal[i] and data[idx[i]][1] is None) or \
                        timestamp[i].to_pydatetime() == data[idx[i]][1]:
                    msgs.append(None)
                else:
                    msgs.append(
                        "timestamp values are not equal (timestamp='%s': data[%d][1]='%s')"
                        % (timestamp[i], idx[i], data[idx[i]][1]))
            return pd.Series(msgs)

        result = df.withColumn("check_data", check_data(col("idx"), col("timestamp"),
                                                        col("timestamp_copy"))).collect()
        # Check that collection values are correct
        self.assertEquals(len(data), len(result))
        for i in range(len(result)):
            self.assertEquals(data[i][1], result[i][1])  # "timestamp" col
            self.assertEquals(data[i][1], result[i][2])  # "timestamp_copy" col
            self.assertIsNone(result[i][3])  # "check_data" col

    def test_vectorized_udf_return_timestamp_tz(self):
        import pandas as pd

        df = self.spark.range(10)

        @pandas_udf(returnType=TimestampType())
        def gen_timestamps(id):
            ts = [pd.Timestamp(i, unit='D', tz='America/Los_Angeles') for i in id]
            return pd.Series(ts)

        result = df.withColumn("ts", gen_timestamps(col("id"))).collect()
        spark_ts_t = TimestampType()
        for r in result:
            i, ts = r
            ts_tz = pd.Timestamp(i, unit='D', tz='America/Los_Angeles').to_pydatetime()
            expected = spark_ts_t.fromInternal(spark_ts_t.toInternal(ts_tz))
            self.assertEquals(expected, ts)

    def test_vectorized_udf_check_config(self):
        import pandas as pd

        with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 3}):
            df = self.spark.range(10, numPartitions=1)

            @pandas_udf(returnType=LongType())
            def check_records_per_batch(x):
                return pd.Series(x.size).repeat(x.size)

            result = df.select(check_records_per_batch(col("id"))).collect()
            for (r,) in result:
                self.assertTrue(r <= 3)

    def test_vectorized_udf_timestamps_respect_session_timezone(self):
        import pandas as pd

        schema = StructType([
            StructField("idx", LongType(), True),
            StructField("timestamp", TimestampType(), True)])
        data = [(1, datetime(1969, 1, 1, 1, 1, 1)),
                (2, datetime(2012, 2, 2, 2, 2, 2)),
                (3, None),
                (4, datetime(2100, 3, 3, 3, 3, 3))]
        df = self.spark.createDataFrame(data, schema=schema)

        f_timestamp_copy = pandas_udf(lambda ts: ts, TimestampType())
        internal_value = pandas_udf(
            lambda ts: ts.apply(lambda ts: ts.value if ts is not pd.NaT else None), LongType())

        timezone = "America/New_York"
        with self.sql_conf({
                "spark.sql.execution.pandas.respectSessionTimeZone": False,
                "spark.sql.session.timeZone": timezone}):
            df_la = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \
                .withColumn("internal_value", internal_value(col("timestamp")))
            result_la = df_la.select(col("idx"), col("internal_value")).collect()
            # Correct result_la by adjusting 3 hours difference between Los Angeles and New York
            diff = 3 * 60 * 60 * 1000 * 1000 * 1000
            result_la_corrected = \
                df_la.select(col("idx"), col("tscopy"), col("internal_value") + diff).collect()

        with self.sql_conf({
                "spark.sql.execution.pandas.respectSessionTimeZone": True,
                "spark.sql.session.timeZone": timezone}):
            df_ny = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \
                .withColumn("internal_value", internal_value(col("timestamp")))
            result_ny = df_ny.select(col("idx"), col("tscopy"), col("internal_value")).collect()

            self.assertNotEqual(result_ny, result_la)
            self.assertEqual(result_ny, result_la_corrected)

    def test_nondeterministic_vectorized_udf(self):
        # Test that nondeterministic UDFs are evaluated only once in chained UDF evaluations
        @pandas_udf('double')
        def plus_ten(v):
            return v + 10
        random_udf = self.nondeterministic_vectorized_udf

        df = self.spark.range(10).withColumn('rand', random_udf(col('id')))
        result1 = df.withColumn('plus_ten(rand)', plus_ten(df['rand'])).toPandas()

        self.assertEqual(random_udf.deterministic, False)
        self.assertTrue(result1['plus_ten(rand)'].equals(result1['rand'] + 10))

    def test_nondeterministic_vectorized_udf_in_aggregate(self):
        df = self.spark.range(10)
        random_udf = self.nondeterministic_vectorized_udf

        with QuietTest(self.sc):
            with self.assertRaisesRegexp(AnalysisException, 'nondeterministic'):
                df.groupby(df.id).agg(sum(random_udf(df.id))).collect()
            with self.assertRaisesRegexp(AnalysisException, 'nondeterministic'):
                df.agg(sum(random_udf(df.id))).collect()

    def test_register_vectorized_udf_basic(self):
        df = self.spark.range(10).select(
            col('id').cast('int').alias('a'),
            col('id').cast('int').alias('b'))
        original_add = pandas_udf(lambda x, y: x + y, IntegerType())
        self.assertEqual(original_add.deterministic, True)
        self.assertEqual(original_add.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
        new_add = self.spark.catalog.registerFunction("add1", original_add)
        res1 = df.select(new_add(col('a'), col('b')))
        res2 = self.spark.sql(
            "SELECT add1(t.a, t.b) FROM (SELECT id as a, id as b FROM range(10)) t")
        expected = df.select(expr('a + b'))
        self.assertEquals(expected.collect(), res1.collect())
        self.assertEquals(expected.collect(), res2.collect())

    # Regression test for SPARK-23314
    def test_timestamp_dst(self):
        # Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am
        dt = [datetime(2015, 11, 1, 0, 30),
              datetime(2015, 11, 1, 1, 30),
              datetime(2015, 11, 1, 2, 30)]
        df = self.spark.createDataFrame(dt, 'timestamp').toDF('time')
        foo_udf = pandas_udf(lambda x: x, 'timestamp')
        result = df.withColumn('time', foo_udf(df.time))
        self.assertEquals(df.collect(), result.collect())

    @unittest.skipIf(sys.version_info[:2] < (3, 5), "Type hints are supported from Python 3.5.")
    def test_type_annotation(self):
        from pyspark.sql.functions import pandas_udf
        # Regression test to check if type hints can be used. See SPARK-23569.
        # Note that it throws an error during compilation in lower Python versions if 'exec'
        # is not used. Also, note that we explicitly use another dictionary to avoid modifications
        # in the current 'locals()'.
        #
        # Hyukjin: I think it's an ugly way to test issues about syntax specific in
        # higher versions of Python, which we shouldn't encourage. This was the last resort
        # I could come up with at that time.
        _locals = {}
        exec(
            "import pandas as pd\ndef noop(col: pd.Series) -> pd.Series: return col",
            _locals)
        df = self.spark.range(1).select(pandas_udf(f=_locals['noop'], returnType='bigint')('id'))
        self.assertEqual(df.first()[0], 0)

    def test_mixed_udf(self):
        import pandas as pd

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

        # Test mixture of multiple UDFs and Pandas UDFs.

        @udf('int')
        def f1(x):
            assert type(x) == int
            return x + 1

        @pandas_udf('int')
        def f2(x):
            assert type(x) == pd.Series
            return x + 10

        @udf('int')
        def f3(x):
            assert type(x) == int
            return x + 100

        @pandas_udf('int')
        def f4(x):
            assert type(x) == pd.Series
            return x + 1000

        # Test single expression with chained UDFs
        df_chained_1 = df.withColumn('f2_f1', f2(f1(df['v'])))
        df_chained_2 = df.withColumn('f3_f2_f1', f3(f2(f1(df['v']))))
        df_chained_3 = df.withColumn('f4_f3_f2_f1', f4(f3(f2(f1(df['v'])))))
        df_chained_4 = df.withColumn('f4_f2_f1', f4(f2(f1(df['v']))))
        df_chained_5 = df.withColumn('f4_f3_f1', f4(f3(f1(df['v']))))

        expected_chained_1 = df.withColumn('f2_f1', df['v'] + 11)
        expected_chained_2 = df.withColumn('f3_f2_f1', df['v'] + 111)
        expected_chained_3 = df.withColumn('f4_f3_f2_f1', df['v'] + 1111)
        expected_chained_4 = df.withColumn('f4_f2_f1', df['v'] + 1011)
        expected_chained_5 = df.withColumn('f4_f3_f1', df['v'] + 1101)

        self.assertEquals(expected_chained_1.collect(), df_chained_1.collect())
        self.assertEquals(expected_chained_2.collect(), df_chained_2.collect())
        self.assertEquals(expected_chained_3.collect(), df_chained_3.collect())
        self.assertEquals(expected_chained_4.collect(), df_chained_4.collect())
        self.assertEquals(expected_chained_5.collect(), df_chained_5.collect())

        # Test multiple mixed UDF expressions in a single projection
        df_multi_1 = df \
            .withColumn('f1', f1(col('v'))) \
            .withColumn('f2', f2(col('v'))) \
            .withColumn('f3', f3(col('v'))) \
            .withColumn('f4', f4(col('v'))) \
            .withColumn('f2_f1', f2(col('f1'))) \
            .withColumn('f3_f1', f3(col('f1'))) \
            .withColumn('f4_f1', f4(col('f1'))) \
            .withColumn('f3_f2', f3(col('f2'))) \
            .withColumn('f4_f2', f4(col('f2'))) \
            .withColumn('f4_f3', f4(col('f3'))) \
            .withColumn('f3_f2_f1', f3(col('f2_f1'))) \
            .withColumn('f4_f2_f1', f4(col('f2_f1'))) \
            .withColumn('f4_f3_f1', f4(col('f3_f1'))) \
            .withColumn('f4_f3_f2', f4(col('f3_f2'))) \
            .withColumn('f4_f3_f2_f1', f4(col('f3_f2_f1')))

        # Test mixed udfs in a single expression
        df_multi_2 = df \
            .withColumn('f1', f1(col('v'))) \
            .withColumn('f2', f2(col('v'))) \
            .withColumn('f3', f3(col('v'))) \
            .withColumn('f4', f4(col('v'))) \
            .withColumn('f2_f1', f2(f1(col('v')))) \
            .withColumn('f3_f1', f3(f1(col('v')))) \
            .withColumn('f4_f1', f4(f1(col('v')))) \
            .withColumn('f3_f2', f3(f2(col('v')))) \
            .withColumn('f4_f2', f4(f2(col('v')))) \
            .withColumn('f4_f3', f4(f3(col('v')))) \
            .withColumn('f3_f2_f1', f3(f2(f1(col('v'))))) \
            .withColumn('f4_f2_f1', f4(f2(f1(col('v'))))) \
            .withColumn('f4_f3_f1', f4(f3(f1(col('v'))))) \
            .withColumn('f4_f3_f2', f4(f3(f2(col('v'))))) \
            .withColumn('f4_f3_f2_f1', f4(f3(f2(f1(col('v'))))))

        expected = df \
            .withColumn('f1', df['v'] + 1) \
            .withColumn('f2', df['v'] + 10) \
            .withColumn('f3', df['v'] + 100) \
            .withColumn('f4', df['v'] + 1000) \
            .withColumn('f2_f1', df['v'] + 11) \
            .withColumn('f3_f1', df['v'] + 101) \
            .withColumn('f4_f1', df['v'] + 1001) \
            .withColumn('f3_f2', df['v'] + 110) \
            .withColumn('f4_f2', df['v'] + 1010) \
            .withColumn('f4_f3', df['v'] + 1100) \
            .withColumn('f3_f2_f1', df['v'] + 111) \
            .withColumn('f4_f2_f1', df['v'] + 1011) \
            .withColumn('f4_f3_f1', df['v'] + 1101) \
            .withColumn('f4_f3_f2', df['v'] + 1110) \
            .withColumn('f4_f3_f2_f1', df['v'] + 1111)

        self.assertEquals(expected.collect(), df_multi_1.collect())
        self.assertEquals(expected.collect(), df_multi_2.collect())

    def test_mixed_udf_and_sql(self):
        import pandas as pd

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

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

        @udf('int')
        def f1(x):
            assert type(x) == int
            return x + 1

        def f2(x):
            assert type(x) == Column
            return x + 10

        @pandas_udf('int')
        def f3(x):
            assert type(x) == pd.Series
            return x + 100

        df1 = df.withColumn('f1', f1(df['v'])) \
            .withColumn('f2', f2(df['v'])) \
            .withColumn('f3', f3(df['v'])) \
            .withColumn('f1_f2', f1(f2(df['v']))) \
            .withColumn('f1_f3', f1(f3(df['v']))) \
            .withColumn('f2_f1', f2(f1(df['v']))) \
            .withColumn('f2_f3', f2(f3(df['v']))) \
            .withColumn('f3_f1', f3(f1(df['v']))) \
            .withColumn('f3_f2', f3(f2(df['v']))) \
            .withColumn('f1_f2_f3', f1(f2(f3(df['v'])))) \
            .withColumn('f1_f3_f2', f1(f3(f2(df['v'])))) \
            .withColumn('f2_f1_f3', f2(f1(f3(df['v'])))) \
            .withColumn('f2_f3_f1', f2(f3(f1(df['v'])))) \
            .withColumn('f3_f1_f2', f3(f1(f2(df['v'])))) \
            .withColumn('f3_f2_f1', f3(f2(f1(df['v']))))

        expected = df.withColumn('f1', df['v'] + 1) \
            .withColumn('f2', df['v'] + 10) \
            .withColumn('f3', df['v'] + 100) \
            .withColumn('f1_f2', df['v'] + 11) \
            .withColumn('f1_f3', df['v'] + 101) \
            .withColumn('f2_f1', df['v'] + 11) \
            .withColumn('f2_f3', df['v'] + 110) \
            .withColumn('f3_f1', df['v'] + 101) \
            .withColumn('f3_f2', df['v'] + 110) \
            .withColumn('f1_f2_f3', df['v'] + 111) \
            .withColumn('f1_f3_f2', df['v'] + 111) \
            .withColumn('f2_f1_f3', df['v'] + 111) \
            .withColumn('f2_f3_f1', df['v'] + 111) \
            .withColumn('f3_f1_f2', df['v'] + 111) \
            .withColumn('f3_f2_f1', df['v'] + 111)

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

    # SPARK-24721
    @unittest.skipIf(not test_compiled, test_not_compiled_message)
    def test_datasource_with_udf(self):
        # Same as SQLTests.test_datasource_with_udf, but with Pandas UDF
        # This needs to a separate test because Arrow dependency is optional
        import pandas as pd
        import numpy as np

        path = tempfile.mkdtemp()
        shutil.rmtree(path)

        try:
            self.spark.range(1).write.mode("overwrite").format('csv').save(path)
            filesource_df = self.spark.read.option('inferSchema', True).csv(path).toDF('i')
            datasource_df = self.spark.read \
                .format("org.apache.spark.sql.sources.SimpleScanSource") \
                .option('from', 0).option('to', 1).load().toDF('i')
            datasource_v2_df = self.spark.read \
                .format("org.apache.spark.sql.sources.v2.SimpleDataSourceV2") \
                .load().toDF('i', 'j')

            c1 = pandas_udf(lambda x: x + 1, 'int')(lit(1))
            c2 = pandas_udf(lambda x: x + 1, 'int')(col('i'))

            f1 = pandas_udf(lambda x: pd.Series(np.repeat(False, len(x))), 'boolean')(lit(1))
            f2 = pandas_udf(lambda x: pd.Series(np.repeat(False, len(x))), 'boolean')(col('i'))

            for df in [filesource_df, datasource_df, datasource_v2_df]:
                result = df.withColumn('c', c1)
                expected = df.withColumn('c', lit(2))
                self.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)


if __name__ == "__main__":
    from pyspark.sql.tests.test_pandas_udf_scalar import *

    try:
        import xmlrunner
        testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
    except ImportError:
        testRunner = None
    unittest.main(testRunner=testRunner, verbosity=2)