2d05f325dc
It's the first step: generalize UnresolvedGetField to support all map, struct, and array TODO: add `apply` in Scala and `__getitem__` in Python, and unify the `getItem` and `getField` methods to one single API(or should we keep them for compatibility?). Author: Wenchen Fan <cloud0fan@outlook.com> Closes #5744 from cloud-fan/generalize and squashes the following commits: 715c589 [Wenchen Fan] address comments 7ea5b31 [Wenchen Fan] fix python test 4f0833a [Wenchen Fan] add python test f515d69 [Wenchen Fan] add apply method and test cases 8df6199 [Wenchen Fan] fix python test 239730c [Wenchen Fan] fix test compile 2a70526 [Wenchen Fan] use _bin_op in dataframe.py 6bf72bc [Wenchen Fan] address comments 3f880c3 [Wenchen Fan] add java doc ab35ab5 [Wenchen Fan] fix python test b5961a9 [Wenchen Fan] fix style c9d85f5 [Wenchen Fan] generalize UnresolvedGetField to support all map, struct, and array
750 lines
32 KiB
Python
750 lines
32 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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"""
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Unit tests for pyspark.sql; additional tests are implemented as doctests in
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individual modules.
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"""
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import os
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import sys
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import pydoc
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import shutil
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import tempfile
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import pickle
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import functools
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import datetime
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import py4j
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if sys.version_info[:2] <= (2, 6):
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try:
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import unittest2 as unittest
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except ImportError:
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sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
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sys.exit(1)
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else:
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import unittest
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from pyspark.sql import SQLContext, HiveContext, Column, Row
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from pyspark.sql.types import *
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from pyspark.sql.types import UserDefinedType, _infer_type
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from pyspark.tests import ReusedPySparkTestCase
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from pyspark.sql.functions import UserDefinedFunction
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class ExamplePointUDT(UserDefinedType):
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"""
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User-defined type (UDT) for ExamplePoint.
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"""
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@classmethod
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def sqlType(self):
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return ArrayType(DoubleType(), False)
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@classmethod
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def module(cls):
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return 'pyspark.tests'
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@classmethod
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def scalaUDT(cls):
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return 'org.apache.spark.sql.test.ExamplePointUDT'
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def serialize(self, obj):
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return [obj.x, obj.y]
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def deserialize(self, datum):
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return ExamplePoint(datum[0], datum[1])
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class ExamplePoint:
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"""
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An example class to demonstrate UDT in Scala, Java, and Python.
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"""
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__UDT__ = ExamplePointUDT()
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def __init__(self, x, y):
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self.x = x
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self.y = y
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def __repr__(self):
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return "ExamplePoint(%s,%s)" % (self.x, self.y)
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def __str__(self):
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return "(%s,%s)" % (self.x, self.y)
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def __eq__(self, other):
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return isinstance(other, ExamplePoint) and \
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other.x == self.x and other.y == self.y
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class DataTypeTests(unittest.TestCase):
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# regression test for SPARK-6055
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def test_data_type_eq(self):
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lt = LongType()
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lt2 = pickle.loads(pickle.dumps(LongType()))
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self.assertEquals(lt, lt2)
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class SQLTests(ReusedPySparkTestCase):
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@classmethod
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def setUpClass(cls):
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ReusedPySparkTestCase.setUpClass()
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cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
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os.unlink(cls.tempdir.name)
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cls.sqlCtx = SQLContext(cls.sc)
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cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
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rdd = cls.sc.parallelize(cls.testData, 2)
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cls.df = rdd.toDF()
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@classmethod
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def tearDownClass(cls):
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ReusedPySparkTestCase.tearDownClass()
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shutil.rmtree(cls.tempdir.name, ignore_errors=True)
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def test_udf_with_callable(self):
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d = [Row(number=i, squared=i**2) for i in range(10)]
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rdd = self.sc.parallelize(d)
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data = self.sqlCtx.createDataFrame(rdd)
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class PlusFour:
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def __call__(self, col):
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if col is not None:
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return col + 4
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call = PlusFour()
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pudf = UserDefinedFunction(call, LongType())
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res = data.select(pudf(data['number']).alias('plus_four'))
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self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85)
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def test_udf_with_partial_function(self):
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d = [Row(number=i, squared=i**2) for i in range(10)]
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rdd = self.sc.parallelize(d)
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data = self.sqlCtx.createDataFrame(rdd)
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def some_func(col, param):
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if col is not None:
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return col + param
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pfunc = functools.partial(some_func, param=4)
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pudf = UserDefinedFunction(pfunc, LongType())
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res = data.select(pudf(data['number']).alias('plus_four'))
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self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85)
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def test_udf(self):
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self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
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[row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect()
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self.assertEqual(row[0], 5)
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def test_udf2(self):
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self.sqlCtx.registerFunction("strlen", lambda string: len(string), IntegerType())
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self.sqlCtx.createDataFrame(self.sc.parallelize([Row(a="test")])).registerTempTable("test")
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[res] = self.sqlCtx.sql("SELECT strlen(a) FROM test WHERE strlen(a) > 1").collect()
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self.assertEqual(4, res[0])
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def test_udf_with_array_type(self):
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d = [Row(l=list(range(3)), d={"key": list(range(5))})]
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rdd = self.sc.parallelize(d)
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self.sqlCtx.createDataFrame(rdd).registerTempTable("test")
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self.sqlCtx.registerFunction("copylist", lambda l: list(l), ArrayType(IntegerType()))
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self.sqlCtx.registerFunction("maplen", lambda d: len(d), IntegerType())
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[(l1, l2)] = self.sqlCtx.sql("select copylist(l), maplen(d) from test").collect()
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self.assertEqual(list(range(3)), l1)
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self.assertEqual(1, l2)
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def test_broadcast_in_udf(self):
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bar = {"a": "aa", "b": "bb", "c": "abc"}
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foo = self.sc.broadcast(bar)
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self.sqlCtx.registerFunction("MYUDF", lambda x: foo.value[x] if x else '')
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[res] = self.sqlCtx.sql("SELECT MYUDF('c')").collect()
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self.assertEqual("abc", res[0])
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[res] = self.sqlCtx.sql("SELECT MYUDF('')").collect()
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self.assertEqual("", res[0])
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def test_basic_functions(self):
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rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
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df = self.sqlCtx.jsonRDD(rdd)
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df.count()
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df.collect()
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df.schema
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# cache and checkpoint
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self.assertFalse(df.is_cached)
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df.persist()
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df.unpersist()
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df.cache()
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self.assertTrue(df.is_cached)
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self.assertEqual(2, df.count())
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df.registerTempTable("temp")
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df = self.sqlCtx.sql("select foo from temp")
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df.count()
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df.collect()
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def test_apply_schema_to_row(self):
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df = self.sqlCtx.jsonRDD(self.sc.parallelize(["""{"a":2}"""]))
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df2 = self.sqlCtx.createDataFrame(df.map(lambda x: x), df.schema)
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self.assertEqual(df.collect(), df2.collect())
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rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x))
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df3 = self.sqlCtx.createDataFrame(rdd, df.schema)
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self.assertEqual(10, df3.count())
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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.sqlCtx.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.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.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.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_infer_schema(self):
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d = [Row(l=[], d={}, s=None),
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Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")}, s="")]
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rdd = self.sc.parallelize(d)
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df = self.sqlCtx.createDataFrame(rdd)
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self.assertEqual([], df.map(lambda r: r.l).first())
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self.assertEqual([None, ""], df.map(lambda r: r.s).collect())
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df.registerTempTable("test")
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result = self.sqlCtx.sql("SELECT l[0].a from test where d['key'].d = '2'")
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self.assertEqual(1, result.head()[0])
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df2 = self.sqlCtx.createDataFrame(rdd, samplingRatio=1.0)
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self.assertEqual(df.schema, df2.schema)
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self.assertEqual({}, df2.map(lambda r: r.d).first())
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self.assertEqual([None, ""], df2.map(lambda r: r.s).collect())
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df2.registerTempTable("test2")
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result = self.sqlCtx.sql("SELECT l[0].a from test2 where d['key'].d = '2'")
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self.assertEqual(1, result.head()[0])
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def test_infer_nested_schema(self):
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NestedRow = Row("f1", "f2")
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nestedRdd1 = self.sc.parallelize([NestedRow([1, 2], {"row1": 1.0}),
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NestedRow([2, 3], {"row2": 2.0})])
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df = self.sqlCtx.inferSchema(nestedRdd1)
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self.assertEqual(Row(f1=[1, 2], f2={u'row1': 1.0}), df.collect()[0])
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nestedRdd2 = self.sc.parallelize([NestedRow([[1, 2], [2, 3]], [1, 2]),
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NestedRow([[2, 3], [3, 4]], [2, 3])])
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df = self.sqlCtx.inferSchema(nestedRdd2)
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self.assertEqual(Row(f1=[[1, 2], [2, 3]], f2=[1, 2]), df.collect()[0])
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from collections import namedtuple
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CustomRow = namedtuple('CustomRow', 'field1 field2')
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rdd = self.sc.parallelize([CustomRow(field1=1, field2="row1"),
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CustomRow(field1=2, field2="row2"),
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CustomRow(field1=3, field2="row3")])
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df = self.sqlCtx.inferSchema(rdd)
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self.assertEquals(Row(field1=1, field2=u'row1'), df.first())
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def test_apply_schema(self):
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from datetime import date, datetime
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rdd = self.sc.parallelize([(127, -128, -32768, 32767, 2147483647, 1.0,
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date(2010, 1, 1), datetime(2010, 1, 1, 1, 1, 1),
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{"a": 1}, (2,), [1, 2, 3], None)])
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schema = StructType([
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StructField("byte1", ByteType(), False),
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StructField("byte2", ByteType(), False),
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StructField("short1", ShortType(), False),
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StructField("short2", ShortType(), False),
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StructField("int1", IntegerType(), False),
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StructField("float1", FloatType(), False),
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StructField("date1", DateType(), False),
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StructField("time1", TimestampType(), False),
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StructField("map1", MapType(StringType(), IntegerType(), False), False),
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StructField("struct1", StructType([StructField("b", ShortType(), False)]), False),
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StructField("list1", ArrayType(ByteType(), False), False),
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StructField("null1", DoubleType(), True)])
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df = self.sqlCtx.createDataFrame(rdd, schema)
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results = df.map(lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int1, x.float1, x.date1,
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x.time1, x.map1["a"], x.struct1.b, x.list1, x.null1))
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r = (127, -128, -32768, 32767, 2147483647, 1.0, date(2010, 1, 1),
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datetime(2010, 1, 1, 1, 1, 1), 1, 2, [1, 2, 3], None)
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self.assertEqual(r, results.first())
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df.registerTempTable("table2")
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r = self.sqlCtx.sql("SELECT byte1 - 1 AS byte1, byte2 + 1 AS byte2, " +
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"short1 + 1 AS short1, short2 - 1 AS short2, int1 - 1 AS int1, " +
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"float1 + 1.5 as float1 FROM table2").first()
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self.assertEqual((126, -127, -32767, 32766, 2147483646, 2.5), tuple(r))
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from pyspark.sql.types import _parse_schema_abstract, _infer_schema_type
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rdd = self.sc.parallelize([(127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1),
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{"a": 1}, (2,), [1, 2, 3])])
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abstract = "byte1 short1 float1 time1 map1{} struct1(b) list1[]"
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schema = _parse_schema_abstract(abstract)
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typedSchema = _infer_schema_type(rdd.first(), schema)
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df = self.sqlCtx.createDataFrame(rdd, typedSchema)
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r = (127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1), {"a": 1}, Row(b=2), [1, 2, 3])
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self.assertEqual(r, tuple(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_convert_row_to_dict(self):
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row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})
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self.assertEqual(1, row.asDict()['l'][0].a)
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df = self.sc.parallelize([row]).toDF()
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df.registerTempTable("test")
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row = self.sqlCtx.sql("select l, d from test").head()
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self.assertEqual(1, row.asDict()["l"][0].a)
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self.assertEqual(1.0, row.asDict()['d']['key'].c)
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def test_infer_schema_with_udt(self):
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from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
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row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
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df = self.sc.parallelize([row]).toDF()
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schema = df.schema
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field = [f for f in schema.fields if f.name == "point"][0]
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self.assertEqual(type(field.dataType), ExamplePointUDT)
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df.registerTempTable("labeled_point")
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point = self.sqlCtx.sql("SELECT point FROM labeled_point").head().point
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self.assertEqual(point, ExamplePoint(1.0, 2.0))
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def test_apply_schema_with_udt(self):
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from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
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row = (1.0, ExamplePoint(1.0, 2.0))
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rdd = self.sc.parallelize([row])
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schema = StructType([StructField("label", DoubleType(), False),
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StructField("point", ExamplePointUDT(), False)])
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df = rdd.toDF(schema)
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point = df.head().point
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self.assertEquals(point, ExamplePoint(1.0, 2.0))
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def test_parquet_with_udt(self):
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from pyspark.sql.tests import ExamplePoint
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row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))
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df0 = self.sc.parallelize([row]).toDF()
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output_dir = os.path.join(self.tempdir.name, "labeled_point")
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df0.saveAsParquetFile(output_dir)
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df1 = self.sqlCtx.parquetFile(output_dir)
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point = df1.head().point
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self.assertEquals(point, ExamplePoint(1.0, 2.0))
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def test_column_operators(self):
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ci = self.df.key
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cs = self.df.value
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c = ci == cs
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self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
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rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
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self.assertTrue(all(isinstance(c, Column) for c in rcc))
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cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs]
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self.assertTrue(all(isinstance(c, Column) for c in cb))
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cbool = (ci & ci), (ci | ci), (~ci)
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self.assertTrue(all(isinstance(c, Column) for c in cbool))
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css = cs.like('a'), cs.rlike('a'), cs.asc(), cs.desc(), cs.startswith('a'), cs.endswith('a')
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self.assertTrue(all(isinstance(c, Column) for c in css))
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self.assertTrue(isinstance(ci.cast(LongType()), Column))
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def test_column_select(self):
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df = self.df
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self.assertEqual(self.testData, df.select("*").collect())
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self.assertEqual(self.testData, df.select(df.key, df.value).collect())
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self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect())
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def test_freqItems(self):
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vals = [Row(a=1, b=-2.0) if i % 2 == 0 else Row(a=i, b=i * 1.0) for i in range(100)]
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df = self.sc.parallelize(vals).toDF()
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items = df.stat.freqItems(("a", "b"), 0.4).collect()[0]
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self.assertTrue(1 in items[0])
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self.assertTrue(-2.0 in items[1])
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def test_aggregator(self):
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df = self.df
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g = df.groupBy()
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self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0]))
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self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
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from pyspark.sql import functions
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self.assertEqual((0, u'99'),
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tuple(g.agg(functions.first(df.key), functions.last(df.value)).first()))
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self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0])
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self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0])
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def test_corr(self):
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import math
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df = self.sc.parallelize([Row(a=i, b=math.sqrt(i)) for i in range(10)]).toDF()
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|
corr = df.stat.corr("a", "b")
|
|
self.assertTrue(abs(corr - 0.95734012) < 1e-6)
|
|
|
|
def test_cov(self):
|
|
df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF()
|
|
cov = df.stat.cov("a", "b")
|
|
self.assertTrue(abs(cov - 55.0 / 3) < 1e-6)
|
|
|
|
def test_crosstab(self):
|
|
df = self.sc.parallelize([Row(a=i % 3, b=i % 2) for i in range(1, 7)]).toDF()
|
|
ct = df.stat.crosstab("a", "b").collect()
|
|
ct = sorted(ct, key=lambda x: x[0])
|
|
for i, row in enumerate(ct):
|
|
self.assertEqual(row[0], str(i))
|
|
self.assertTrue(row[1], 1)
|
|
self.assertTrue(row[2], 1)
|
|
|
|
def test_math_functions(self):
|
|
df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF()
|
|
from pyspark.sql import functions
|
|
import math
|
|
|
|
def get_values(l):
|
|
return [j[0] for j in l]
|
|
|
|
def assert_close(a, b):
|
|
c = get_values(b)
|
|
diff = [abs(v - c[k]) < 1e-6 for k, v in enumerate(a)]
|
|
return sum(diff) == len(a)
|
|
assert_close([math.cos(i) for i in range(10)],
|
|
df.select(functions.cos(df.a)).collect())
|
|
assert_close([math.cos(i) for i in range(10)],
|
|
df.select(functions.cos("a")).collect())
|
|
assert_close([math.sin(i) for i in range(10)],
|
|
df.select(functions.sin(df.a)).collect())
|
|
assert_close([math.sin(i) for i in range(10)],
|
|
df.select(functions.sin(df['a'])).collect())
|
|
assert_close([math.pow(i, 2 * i) for i in range(10)],
|
|
df.select(functions.pow(df.a, df.b)).collect())
|
|
assert_close([math.pow(i, 2) for i in range(10)],
|
|
df.select(functions.pow(df.a, 2)).collect())
|
|
assert_close([math.pow(i, 2) for i in range(10)],
|
|
df.select(functions.pow(df.a, 2.0)).collect())
|
|
assert_close([math.hypot(i, 2 * i) for i in range(10)],
|
|
df.select(functions.hypot(df.a, df.b)).collect())
|
|
|
|
def test_rand_functions(self):
|
|
df = self.df
|
|
from pyspark.sql import functions
|
|
rnd = df.select('key', functions.rand()).collect()
|
|
for row in rnd:
|
|
assert row[1] >= 0.0 and row[1] <= 1.0, "got: %s" % row[1]
|
|
rndn = df.select('key', functions.randn(5)).collect()
|
|
for row in rndn:
|
|
assert row[1] >= -4.0 and row[1] <= 4.0, "got: %s" % row[1]
|
|
|
|
def test_between_function(self):
|
|
df = self.sc.parallelize([
|
|
Row(a=1, b=2, c=3),
|
|
Row(a=2, b=1, c=3),
|
|
Row(a=4, b=1, c=4)]).toDF()
|
|
self.assertEqual([Row(a=2, b=1, c=3), Row(a=4, b=1, c=4)],
|
|
df.filter(df.a.between(df.b, df.c)).collect())
|
|
|
|
def test_save_and_load(self):
|
|
df = self.df
|
|
tmpPath = tempfile.mkdtemp()
|
|
shutil.rmtree(tmpPath)
|
|
df.save(tmpPath, "org.apache.spark.sql.json", "error")
|
|
actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json")
|
|
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
|
|
|
|
schema = StructType([StructField("value", StringType(), True)])
|
|
actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json", schema)
|
|
self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect()))
|
|
|
|
df.save(tmpPath, "org.apache.spark.sql.json", "overwrite")
|
|
actual = self.sqlCtx.load(tmpPath, "org.apache.spark.sql.json")
|
|
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
|
|
|
|
df.save(source="org.apache.spark.sql.json", mode="overwrite", path=tmpPath,
|
|
noUse="this options will not be used in save.")
|
|
actual = self.sqlCtx.load(source="org.apache.spark.sql.json", path=tmpPath,
|
|
noUse="this options will not be used in load.")
|
|
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
|
|
|
|
defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default",
|
|
"org.apache.spark.sql.parquet")
|
|
self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
|
|
actual = self.sqlCtx.load(path=tmpPath)
|
|
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
|
|
self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
|
|
|
|
shutil.rmtree(tmpPath)
|
|
|
|
def test_help_command(self):
|
|
# Regression test for SPARK-5464
|
|
rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
|
|
df = self.sqlCtx.jsonRDD(rdd)
|
|
# render_doc() reproduces the help() exception without printing output
|
|
pydoc.render_doc(df)
|
|
pydoc.render_doc(df.foo)
|
|
pydoc.render_doc(df.take(1))
|
|
|
|
def test_access_column(self):
|
|
df = self.df
|
|
self.assertTrue(isinstance(df.key, Column))
|
|
self.assertTrue(isinstance(df['key'], Column))
|
|
self.assertTrue(isinstance(df[0], Column))
|
|
self.assertRaises(IndexError, lambda: df[2])
|
|
self.assertRaises(IndexError, lambda: df["bad_key"])
|
|
self.assertRaises(TypeError, lambda: df[{}])
|
|
|
|
def test_access_nested_types(self):
|
|
df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
|
|
self.assertEqual(1, df.select(df.l[0]).first()[0])
|
|
self.assertEqual(1, df.select(df.l.getItem(0)).first()[0])
|
|
self.assertEqual(1, df.select(df.r.a).first()[0])
|
|
self.assertEqual("b", df.select(df.r.getField("b")).first()[0])
|
|
self.assertEqual("v", df.select(df.d["k"]).first()[0])
|
|
self.assertEqual("v", df.select(df.d.getItem("k")).first()[0])
|
|
|
|
def test_field_accessor(self):
|
|
df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
|
|
self.assertEqual(1, df.select(df.l[0]).first()[0])
|
|
self.assertEqual(1, df.select(df.r["a"]).first()[0])
|
|
self.assertEqual("b", df.select(df.r["b"]).first()[0])
|
|
self.assertEqual("v", df.select(df.d["k"]).first()[0])
|
|
|
|
def test_infer_long_type(self):
|
|
longrow = [Row(f1='a', f2=100000000000000)]
|
|
df = self.sc.parallelize(longrow).toDF()
|
|
self.assertEqual(df.schema.fields[1].dataType, LongType())
|
|
|
|
# this saving as Parquet caused issues as well.
|
|
output_dir = os.path.join(self.tempdir.name, "infer_long_type")
|
|
df.saveAsParquetFile(output_dir)
|
|
df1 = self.sqlCtx.parquetFile(output_dir)
|
|
self.assertEquals('a', df1.first().f1)
|
|
self.assertEquals(100000000000000, df1.first().f2)
|
|
|
|
self.assertEqual(_infer_type(1), LongType())
|
|
self.assertEqual(_infer_type(2**10), LongType())
|
|
self.assertEqual(_infer_type(2**20), LongType())
|
|
self.assertEqual(_infer_type(2**31 - 1), LongType())
|
|
self.assertEqual(_infer_type(2**31), LongType())
|
|
self.assertEqual(_infer_type(2**61), LongType())
|
|
self.assertEqual(_infer_type(2**71), LongType())
|
|
|
|
def test_filter_with_datetime(self):
|
|
time = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000)
|
|
date = time.date()
|
|
row = Row(date=date, time=time)
|
|
df = self.sqlCtx.createDataFrame([row])
|
|
self.assertEqual(1, df.filter(df.date == date).count())
|
|
self.assertEqual(1, df.filter(df.time == time).count())
|
|
self.assertEqual(0, df.filter(df.date > date).count())
|
|
self.assertEqual(0, df.filter(df.time > time).count())
|
|
|
|
def test_dropna(self):
|
|
schema = StructType([
|
|
StructField("name", StringType(), True),
|
|
StructField("age", IntegerType(), True),
|
|
StructField("height", DoubleType(), True)])
|
|
|
|
# shouldn't drop a non-null row
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', 50, 80.1)], schema).dropna().count(),
|
|
1)
|
|
|
|
# dropping rows with a single null value
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, 80.1)], schema).dropna().count(),
|
|
0)
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, 80.1)], schema).dropna(how='any').count(),
|
|
0)
|
|
|
|
# if how = 'all', only drop rows if all values are null
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, 80.1)], schema).dropna(how='all').count(),
|
|
1)
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(None, None, None)], schema).dropna(how='all').count(),
|
|
0)
|
|
|
|
# how and subset
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', 50, None)], schema).dropna(how='any', subset=['name', 'age']).count(),
|
|
1)
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, None)], schema).dropna(how='any', subset=['name', 'age']).count(),
|
|
0)
|
|
|
|
# threshold
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(),
|
|
1)
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, None)], schema).dropna(thresh=2).count(),
|
|
0)
|
|
|
|
# threshold and subset
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', 50, None)], schema).dropna(thresh=2, subset=['name', 'age']).count(),
|
|
1)
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', None, 180.9)], schema).dropna(thresh=2, subset=['name', 'age']).count(),
|
|
0)
|
|
|
|
# thresh should take precedence over how
|
|
self.assertEqual(self.sqlCtx.createDataFrame(
|
|
[(u'Alice', 50, None)], schema).dropna(
|
|
how='any', thresh=2, subset=['name', 'age']).count(),
|
|
1)
|
|
|
|
def test_fillna(self):
|
|
schema = StructType([
|
|
StructField("name", StringType(), True),
|
|
StructField("age", IntegerType(), True),
|
|
StructField("height", DoubleType(), True)])
|
|
|
|
# fillna shouldn't change non-null values
|
|
row = self.sqlCtx.createDataFrame([(u'Alice', 10, 80.1)], schema).fillna(50).first()
|
|
self.assertEqual(row.age, 10)
|
|
|
|
# fillna with int
|
|
row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50).first()
|
|
self.assertEqual(row.age, 50)
|
|
self.assertEqual(row.height, 50.0)
|
|
|
|
# fillna with double
|
|
row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50.1).first()
|
|
self.assertEqual(row.age, 50)
|
|
self.assertEqual(row.height, 50.1)
|
|
|
|
# fillna with string
|
|
row = self.sqlCtx.createDataFrame([(None, None, None)], schema).fillna("hello").first()
|
|
self.assertEqual(row.name, u"hello")
|
|
self.assertEqual(row.age, None)
|
|
|
|
# fillna with subset specified for numeric cols
|
|
row = self.sqlCtx.createDataFrame(
|
|
[(None, None, None)], schema).fillna(50, subset=['name', 'age']).first()
|
|
self.assertEqual(row.name, None)
|
|
self.assertEqual(row.age, 50)
|
|
self.assertEqual(row.height, None)
|
|
|
|
# fillna with subset specified for numeric cols
|
|
row = self.sqlCtx.createDataFrame(
|
|
[(None, None, None)], schema).fillna("haha", subset=['name', 'age']).first()
|
|
self.assertEqual(row.name, "haha")
|
|
self.assertEqual(row.age, None)
|
|
self.assertEqual(row.height, None)
|
|
|
|
def test_bitwise_operations(self):
|
|
from pyspark.sql import functions
|
|
row = Row(a=170, b=75)
|
|
df = self.sqlCtx.createDataFrame([row])
|
|
result = df.select(df.a.bitwiseAND(df.b)).collect()[0].asDict()
|
|
self.assertEqual(170 & 75, result['(a & b)'])
|
|
result = df.select(df.a.bitwiseOR(df.b)).collect()[0].asDict()
|
|
self.assertEqual(170 | 75, result['(a | b)'])
|
|
result = df.select(df.a.bitwiseXOR(df.b)).collect()[0].asDict()
|
|
self.assertEqual(170 ^ 75, result['(a ^ b)'])
|
|
result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict()
|
|
self.assertEqual(~75, result['~b'])
|
|
|
|
|
|
class HiveContextSQLTests(ReusedPySparkTestCase):
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
ReusedPySparkTestCase.setUpClass()
|
|
cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
|
|
try:
|
|
cls.sc._jvm.org.apache.hadoop.hive.conf.HiveConf()
|
|
except py4j.protocol.Py4JError:
|
|
cls.sqlCtx = None
|
|
return
|
|
except TypeError:
|
|
cls.sqlCtx = None
|
|
return
|
|
os.unlink(cls.tempdir.name)
|
|
_scala_HiveContext =\
|
|
cls.sc._jvm.org.apache.spark.sql.hive.test.TestHiveContext(cls.sc._jsc.sc())
|
|
cls.sqlCtx = HiveContext(cls.sc, _scala_HiveContext)
|
|
cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
|
|
cls.df = cls.sc.parallelize(cls.testData).toDF()
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
ReusedPySparkTestCase.tearDownClass()
|
|
shutil.rmtree(cls.tempdir.name, ignore_errors=True)
|
|
|
|
def test_save_and_load_table(self):
|
|
if self.sqlCtx is None:
|
|
return # no hive available, skipped
|
|
|
|
df = self.df
|
|
tmpPath = tempfile.mkdtemp()
|
|
shutil.rmtree(tmpPath)
|
|
df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "append", path=tmpPath)
|
|
actual = self.sqlCtx.createExternalTable("externalJsonTable", tmpPath,
|
|
"org.apache.spark.sql.json")
|
|
self.assertTrue(
|
|
sorted(df.collect()) ==
|
|
sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect()))
|
|
self.assertTrue(
|
|
sorted(df.collect()) ==
|
|
sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect()))
|
|
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
|
|
self.sqlCtx.sql("DROP TABLE externalJsonTable")
|
|
|
|
df.saveAsTable("savedJsonTable", "org.apache.spark.sql.json", "overwrite", path=tmpPath)
|
|
schema = StructType([StructField("value", StringType(), True)])
|
|
actual = self.sqlCtx.createExternalTable("externalJsonTable",
|
|
source="org.apache.spark.sql.json",
|
|
schema=schema, path=tmpPath,
|
|
noUse="this options will not be used")
|
|
self.assertTrue(
|
|
sorted(df.collect()) ==
|
|
sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect()))
|
|
self.assertTrue(
|
|
sorted(df.select("value").collect()) ==
|
|
sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect()))
|
|
self.assertTrue(sorted(df.select("value").collect()) == sorted(actual.collect()))
|
|
self.sqlCtx.sql("DROP TABLE savedJsonTable")
|
|
self.sqlCtx.sql("DROP TABLE externalJsonTable")
|
|
|
|
defaultDataSourceName = self.sqlCtx.getConf("spark.sql.sources.default",
|
|
"org.apache.spark.sql.parquet")
|
|
self.sqlCtx.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
|
|
df.saveAsTable("savedJsonTable", path=tmpPath, mode="overwrite")
|
|
actual = self.sqlCtx.createExternalTable("externalJsonTable", path=tmpPath)
|
|
self.assertTrue(
|
|
sorted(df.collect()) ==
|
|
sorted(self.sqlCtx.sql("SELECT * FROM savedJsonTable").collect()))
|
|
self.assertTrue(
|
|
sorted(df.collect()) ==
|
|
sorted(self.sqlCtx.sql("SELECT * FROM externalJsonTable").collect()))
|
|
self.assertTrue(sorted(df.collect()) == sorted(actual.collect()))
|
|
self.sqlCtx.sql("DROP TABLE savedJsonTable")
|
|
self.sqlCtx.sql("DROP TABLE externalJsonTable")
|
|
self.sqlCtx.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
|
|
|
|
shutil.rmtree(tmpPath)
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|