2018-11-14 01:51:11 -05:00
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#
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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 shutil
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import tempfile
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import time
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from pyspark.sql import Row
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from pyspark.sql.functions import lit
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from pyspark.sql.types import *
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from pyspark.testing.sqlutils import ReusedSQLTestCase, UTCOffsetTimezone
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class SerdeTests(ReusedSQLTestCase):
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def test_serialize_nested_array_and_map(self):
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d = [Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})]
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rdd = self.sc.parallelize(d)
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df = self.spark.createDataFrame(rdd)
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row = df.head()
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self.assertEqual(1, len(row.l))
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self.assertEqual(1, row.l[0].a)
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self.assertEqual("2", row.d["key"].d)
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l = df.rdd.map(lambda x: x.l).first()
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self.assertEqual(1, len(l))
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self.assertEqual('s', l[0].b)
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d = df.rdd.map(lambda x: x.d).first()
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self.assertEqual(1, len(d))
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self.assertEqual(1.0, d["key"].c)
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row = df.rdd.map(lambda x: x.d["key"]).first()
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self.assertEqual(1.0, row.c)
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self.assertEqual("2", row.d)
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def test_select_null_literal(self):
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df = self.spark.sql("select null as col")
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self.assertEqual(Row(col=None), df.first())
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def test_struct_in_map(self):
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d = [Row(m={Row(i=1): Row(s="")})]
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df = self.sc.parallelize(d).toDF()
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k, v = list(df.head().m.items())[0]
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self.assertEqual(1, k.i)
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self.assertEqual("", v.s)
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def test_filter_with_datetime(self):
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time = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000)
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date = time.date()
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row = Row(date=date, time=time)
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df = self.spark.createDataFrame([row])
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self.assertEqual(1, df.filter(df.date == date).count())
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self.assertEqual(1, df.filter(df.time == time).count())
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self.assertEqual(0, df.filter(df.date > date).count())
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self.assertEqual(0, df.filter(df.time > time).count())
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def test_filter_with_datetime_timezone(self):
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dt1 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(0))
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dt2 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(1))
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row = Row(date=dt1)
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df = self.spark.createDataFrame([row])
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self.assertEqual(0, df.filter(df.date == dt2).count())
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self.assertEqual(1, df.filter(df.date > dt2).count())
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self.assertEqual(0, df.filter(df.date < dt2).count())
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def test_time_with_timezone(self):
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day = datetime.date.today()
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now = datetime.datetime.now()
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ts = time.mktime(now.timetuple())
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# class in __main__ is not serializable
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from pyspark.testing.sqlutils import UTCOffsetTimezone
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utc = UTCOffsetTimezone()
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utcnow = datetime.datetime.utcfromtimestamp(ts) # without microseconds
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# add microseconds to utcnow (keeping year,month,day,hour,minute,second)
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utcnow = datetime.datetime(*(utcnow.timetuple()[:6] + (now.microsecond, utc)))
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df = self.spark.createDataFrame([(day, now, utcnow)])
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day1, now1, utcnow1 = df.first()
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self.assertEqual(day1, day)
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self.assertEqual(now, now1)
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self.assertEqual(now, utcnow1)
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# regression test for SPARK-19561
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def test_datetime_at_epoch(self):
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epoch = datetime.datetime.fromtimestamp(0)
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df = self.spark.createDataFrame([Row(date=epoch)])
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first = df.select('date', lit(epoch).alias('lit_date')).first()
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self.assertEqual(first['date'], epoch)
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self.assertEqual(first['lit_date'], epoch)
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def test_decimal(self):
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from decimal import Decimal
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schema = StructType([StructField("decimal", DecimalType(10, 5))])
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df = self.spark.createDataFrame([(Decimal("3.14159"),)], schema)
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row = df.select(df.decimal + 1).first()
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self.assertEqual(row[0], Decimal("4.14159"))
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tmpPath = tempfile.mkdtemp()
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shutil.rmtree(tmpPath)
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df.write.parquet(tmpPath)
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df2 = self.spark.read.parquet(tmpPath)
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row = df2.first()
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self.assertEqual(row[0], Decimal("3.14159"))
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def test_BinaryType_serialization(self):
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# Pyrolite version <= 4.9 could not serialize BinaryType with Python3 SPARK-17808
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# The empty bytearray is test for SPARK-21534.
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schema = StructType([StructField('mybytes', BinaryType())])
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data = [[bytearray(b'here is my data')],
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[bytearray(b'and here is some more')],
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[bytearray(b'')]]
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df = self.spark.createDataFrame(data, schema=schema)
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df.collect()
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2019-05-03 01:40:13 -04:00
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def test_int_array_serialization(self):
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# Note that this test seems dependent on parallelism.
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2019-05-04 00:21:08 -04:00
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# This issue is because internal object map in Pyrolite is not cleared after op code
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# STOP. If we use protocol 4 to pickle Python objects, op code MEMOIZE will store
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# objects in the map. We need to clear up it to make sure next unpickling works on
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# clear map.
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2019-05-03 01:40:13 -04:00
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data = self.spark.sparkContext.parallelize([[1, 2, 3, 4]] * 100, numSlices=12)
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df = self.spark.createDataFrame(data, "array<integer>")
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self.assertEqual(len(list(filter(lambda r: None in r.value, df.collect()))), 0)
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2018-11-14 01:51:11 -05:00
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if __name__ == "__main__":
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import unittest
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from pyspark.sql.tests.test_serde import *
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try:
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import xmlrunner
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2019-06-23 20:58:17 -04:00
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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2018-11-14 01:51:11 -05:00
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except ImportError:
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2018-11-14 23:30:52 -05:00
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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