95c95b71ed
## What changes were proposed in this pull request? There is a timeout failure when using `rdd.toLocalIterator()` or `df.toLocalIterator()` for a PySpark RDD and DataFrame: df = spark.createDataFrame([[1],[2],[3]]) it = df.toLocalIterator() row = next(it) df2 = df.repartition(1000) # create many empty partitions which increase materialization time so causing timeout it2 = df2.toLocalIterator() row = next(it2) The cause of this issue is, we open a socket to serve the data from JVM side. We set timeout for connection and reading through the socket in Python side. In Python we use a generator to read the data, so we only begin to connect the socket once we start to ask data from it. If we don't consume it immediately, there is connection timeout. In the other side, the materialization time for RDD partitions is unpredictable. So we can't set a timeout for reading data through the socket. Otherwise, it is very possibly to fail. ## How was this patch tested? Added tests into PySpark. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #16263 from viirya/fix-pyspark-localiterator.
2139 lines
86 KiB
Python
2139 lines
86 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; additional tests are implemented as doctests in
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individual modules.
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"""
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from array import array
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from glob import glob
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import os
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import re
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import shutil
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import subprocess
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import sys
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import tempfile
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import time
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import zipfile
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import random
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import threading
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import hashlib
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from py4j.protocol import Py4JJavaError
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try:
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import xmlrunner
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except ImportError:
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xmlrunner = None
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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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if sys.version_info[0] >= 3:
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xrange = range
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basestring = str
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if sys.version >= "3":
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from io import StringIO
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else:
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from StringIO import StringIO
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from pyspark.conf import SparkConf
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD
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from pyspark.files import SparkFiles
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from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer, \
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CloudPickleSerializer, CompressedSerializer, UTF8Deserializer, NoOpSerializer, \
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PairDeserializer, CartesianDeserializer, AutoBatchedSerializer, AutoSerializer, \
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FlattenedValuesSerializer
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from pyspark.shuffle import Aggregator, ExternalMerger, ExternalSorter
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from pyspark import shuffle
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from pyspark.profiler import BasicProfiler
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_have_scipy = False
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_have_numpy = False
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try:
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import scipy.sparse
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_have_scipy = True
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except:
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# No SciPy, but that's okay, we'll skip those tests
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pass
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try:
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import numpy as np
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_have_numpy = True
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except:
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# No NumPy, but that's okay, we'll skip those tests
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pass
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SPARK_HOME = os.environ["SPARK_HOME"]
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class MergerTests(unittest.TestCase):
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def setUp(self):
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self.N = 1 << 12
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self.l = [i for i in xrange(self.N)]
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self.data = list(zip(self.l, self.l))
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self.agg = Aggregator(lambda x: [x],
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lambda x, y: x.append(y) or x,
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lambda x, y: x.extend(y) or x)
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def test_small_dataset(self):
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m = ExternalMerger(self.agg, 1000)
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m.mergeValues(self.data)
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self.assertEqual(m.spills, 0)
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self.assertEqual(sum(sum(v) for k, v in m.items()),
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sum(xrange(self.N)))
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m = ExternalMerger(self.agg, 1000)
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m.mergeCombiners(map(lambda x_y1: (x_y1[0], [x_y1[1]]), self.data))
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self.assertEqual(m.spills, 0)
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self.assertEqual(sum(sum(v) for k, v in m.items()),
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sum(xrange(self.N)))
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def test_medium_dataset(self):
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m = ExternalMerger(self.agg, 20)
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m.mergeValues(self.data)
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self.assertTrue(m.spills >= 1)
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self.assertEqual(sum(sum(v) for k, v in m.items()),
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sum(xrange(self.N)))
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m = ExternalMerger(self.agg, 10)
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m.mergeCombiners(map(lambda x_y2: (x_y2[0], [x_y2[1]]), self.data * 3))
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self.assertTrue(m.spills >= 1)
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self.assertEqual(sum(sum(v) for k, v in m.items()),
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sum(xrange(self.N)) * 3)
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def test_huge_dataset(self):
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m = ExternalMerger(self.agg, 5, partitions=3)
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m.mergeCombiners(map(lambda k_v: (k_v[0], [str(k_v[1])]), self.data * 10))
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self.assertTrue(m.spills >= 1)
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self.assertEqual(sum(len(v) for k, v in m.items()),
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self.N * 10)
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m._cleanup()
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def test_group_by_key(self):
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def gen_data(N, step):
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for i in range(1, N + 1, step):
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for j in range(i):
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yield (i, [j])
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def gen_gs(N, step=1):
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return shuffle.GroupByKey(gen_data(N, step))
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self.assertEqual(1, len(list(gen_gs(1))))
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self.assertEqual(2, len(list(gen_gs(2))))
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self.assertEqual(100, len(list(gen_gs(100))))
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self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)])
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self.assertTrue(all(list(range(k)) == list(vs) for k, vs in gen_gs(100)))
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for k, vs in gen_gs(50002, 10000):
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self.assertEqual(k, len(vs))
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self.assertEqual(list(range(k)), list(vs))
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ser = PickleSerializer()
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l = ser.loads(ser.dumps(list(gen_gs(50002, 30000))))
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for k, vs in l:
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self.assertEqual(k, len(vs))
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self.assertEqual(list(range(k)), list(vs))
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class SorterTests(unittest.TestCase):
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def test_in_memory_sort(self):
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l = list(range(1024))
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random.shuffle(l)
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sorter = ExternalSorter(1024)
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self.assertEqual(sorted(l), list(sorter.sorted(l)))
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self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
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self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
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self.assertEqual(sorted(l, key=lambda x: -x, reverse=True),
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list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
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def test_external_sort(self):
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class CustomizedSorter(ExternalSorter):
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def _next_limit(self):
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return self.memory_limit
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l = list(range(1024))
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random.shuffle(l)
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sorter = CustomizedSorter(1)
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self.assertEqual(sorted(l), list(sorter.sorted(l)))
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self.assertGreater(shuffle.DiskBytesSpilled, 0)
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last = shuffle.DiskBytesSpilled
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self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
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self.assertGreater(shuffle.DiskBytesSpilled, last)
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last = shuffle.DiskBytesSpilled
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self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
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self.assertGreater(shuffle.DiskBytesSpilled, last)
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last = shuffle.DiskBytesSpilled
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self.assertEqual(sorted(l, key=lambda x: -x, reverse=True),
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list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
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self.assertGreater(shuffle.DiskBytesSpilled, last)
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def test_external_sort_in_rdd(self):
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conf = SparkConf().set("spark.python.worker.memory", "1m")
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sc = SparkContext(conf=conf)
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l = list(range(10240))
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random.shuffle(l)
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rdd = sc.parallelize(l, 4)
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self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect())
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sc.stop()
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class SerializationTestCase(unittest.TestCase):
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def test_namedtuple(self):
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from collections import namedtuple
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from pickle import dumps, loads
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P = namedtuple("P", "x y")
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p1 = P(1, 3)
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p2 = loads(dumps(p1, 2))
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self.assertEqual(p1, p2)
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from pyspark.cloudpickle import dumps
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P2 = loads(dumps(P))
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p3 = P2(1, 3)
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self.assertEqual(p1, p3)
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def test_itemgetter(self):
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from operator import itemgetter
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ser = CloudPickleSerializer()
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d = range(10)
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getter = itemgetter(1)
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getter2 = ser.loads(ser.dumps(getter))
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self.assertEqual(getter(d), getter2(d))
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getter = itemgetter(0, 3)
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getter2 = ser.loads(ser.dumps(getter))
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self.assertEqual(getter(d), getter2(d))
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def test_function_module_name(self):
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ser = CloudPickleSerializer()
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func = lambda x: x
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func2 = ser.loads(ser.dumps(func))
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self.assertEqual(func.__module__, func2.__module__)
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def test_attrgetter(self):
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from operator import attrgetter
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ser = CloudPickleSerializer()
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class C(object):
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def __getattr__(self, item):
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return item
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d = C()
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getter = attrgetter("a")
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getter2 = ser.loads(ser.dumps(getter))
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self.assertEqual(getter(d), getter2(d))
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getter = attrgetter("a", "b")
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getter2 = ser.loads(ser.dumps(getter))
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self.assertEqual(getter(d), getter2(d))
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d.e = C()
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getter = attrgetter("e.a")
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getter2 = ser.loads(ser.dumps(getter))
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self.assertEqual(getter(d), getter2(d))
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getter = attrgetter("e.a", "e.b")
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getter2 = ser.loads(ser.dumps(getter))
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self.assertEqual(getter(d), getter2(d))
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# Regression test for SPARK-3415
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def test_pickling_file_handles(self):
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# to be corrected with SPARK-11160
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if not xmlrunner:
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ser = CloudPickleSerializer()
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out1 = sys.stderr
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out2 = ser.loads(ser.dumps(out1))
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self.assertEqual(out1, out2)
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def test_func_globals(self):
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class Unpicklable(object):
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def __reduce__(self):
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raise Exception("not picklable")
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global exit
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exit = Unpicklable()
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ser = CloudPickleSerializer()
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self.assertRaises(Exception, lambda: ser.dumps(exit))
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def foo():
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sys.exit(0)
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self.assertTrue("exit" in foo.__code__.co_names)
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ser.dumps(foo)
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def test_compressed_serializer(self):
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ser = CompressedSerializer(PickleSerializer())
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try:
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from StringIO import StringIO
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except ImportError:
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from io import BytesIO as StringIO
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io = StringIO()
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ser.dump_stream(["abc", u"123", range(5)], io)
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io.seek(0)
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self.assertEqual(["abc", u"123", range(5)], list(ser.load_stream(io)))
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ser.dump_stream(range(1000), io)
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io.seek(0)
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self.assertEqual(["abc", u"123", range(5)] + list(range(1000)), list(ser.load_stream(io)))
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io.close()
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def test_hash_serializer(self):
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hash(NoOpSerializer())
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hash(UTF8Deserializer())
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hash(PickleSerializer())
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hash(MarshalSerializer())
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hash(AutoSerializer())
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hash(BatchedSerializer(PickleSerializer()))
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hash(AutoBatchedSerializer(MarshalSerializer()))
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hash(PairDeserializer(NoOpSerializer(), UTF8Deserializer()))
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hash(CartesianDeserializer(NoOpSerializer(), UTF8Deserializer()))
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hash(CompressedSerializer(PickleSerializer()))
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hash(FlattenedValuesSerializer(PickleSerializer()))
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class QuietTest(object):
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def __init__(self, sc):
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self.log4j = sc._jvm.org.apache.log4j
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def __enter__(self):
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self.old_level = self.log4j.LogManager.getRootLogger().getLevel()
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self.log4j.LogManager.getRootLogger().setLevel(self.log4j.Level.FATAL)
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.log4j.LogManager.getRootLogger().setLevel(self.old_level)
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class PySparkTestCase(unittest.TestCase):
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def setUp(self):
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self._old_sys_path = list(sys.path)
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class_name = self.__class__.__name__
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self.sc = SparkContext('local[4]', class_name)
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def tearDown(self):
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self.sc.stop()
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sys.path = self._old_sys_path
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class ReusedPySparkTestCase(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.sc = SparkContext('local[4]', cls.__name__)
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@classmethod
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def tearDownClass(cls):
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cls.sc.stop()
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class CheckpointTests(ReusedPySparkTestCase):
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def setUp(self):
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self.checkpointDir = tempfile.NamedTemporaryFile(delete=False)
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os.unlink(self.checkpointDir.name)
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self.sc.setCheckpointDir(self.checkpointDir.name)
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def tearDown(self):
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shutil.rmtree(self.checkpointDir.name)
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def test_basic_checkpointing(self):
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parCollection = self.sc.parallelize([1, 2, 3, 4])
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flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1))
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self.assertFalse(flatMappedRDD.isCheckpointed())
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self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
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flatMappedRDD.checkpoint()
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result = flatMappedRDD.collect()
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time.sleep(1) # 1 second
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self.assertTrue(flatMappedRDD.isCheckpointed())
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self.assertEqual(flatMappedRDD.collect(), result)
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self.assertEqual("file:" + self.checkpointDir.name,
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os.path.dirname(os.path.dirname(flatMappedRDD.getCheckpointFile())))
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def test_checkpoint_and_restore(self):
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parCollection = self.sc.parallelize([1, 2, 3, 4])
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flatMappedRDD = parCollection.flatMap(lambda x: [x])
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self.assertFalse(flatMappedRDD.isCheckpointed())
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self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
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flatMappedRDD.checkpoint()
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flatMappedRDD.count() # forces a checkpoint to be computed
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time.sleep(1) # 1 second
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self.assertTrue(flatMappedRDD.getCheckpointFile() is not None)
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recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(),
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flatMappedRDD._jrdd_deserializer)
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self.assertEqual([1, 2, 3, 4], recovered.collect())
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|
|
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class LocalCheckpointTests(ReusedPySparkTestCase):
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|
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def test_basic_localcheckpointing(self):
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parCollection = self.sc.parallelize([1, 2, 3, 4])
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flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1))
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|
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self.assertFalse(flatMappedRDD.isCheckpointed())
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|
self.assertFalse(flatMappedRDD.isLocallyCheckpointed())
|
|
|
|
flatMappedRDD.localCheckpoint()
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|
result = flatMappedRDD.collect()
|
|
time.sleep(1) # 1 second
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|
self.assertTrue(flatMappedRDD.isCheckpointed())
|
|
self.assertTrue(flatMappedRDD.isLocallyCheckpointed())
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self.assertEqual(flatMappedRDD.collect(), result)
|
|
|
|
|
|
class AddFileTests(PySparkTestCase):
|
|
|
|
def test_add_py_file(self):
|
|
# To ensure that we're actually testing addPyFile's effects, check that
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|
# this job fails due to `userlibrary` not being on the Python path:
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|
# disable logging in log4j temporarily
|
|
def func(x):
|
|
from userlibrary import UserClass
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|
return UserClass().hello()
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|
with QuietTest(self.sc):
|
|
self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first)
|
|
|
|
# Add the file, so the job should now succeed:
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|
path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
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self.sc.addPyFile(path)
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res = self.sc.parallelize(range(2)).map(func).first()
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|
self.assertEqual("Hello World!", res)
|
|
|
|
def test_add_file_locally(self):
|
|
path = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
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|
self.sc.addFile(path)
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|
download_path = SparkFiles.get("hello.txt")
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|
self.assertNotEqual(path, download_path)
|
|
with open(download_path) as test_file:
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|
self.assertEqual("Hello World!\n", test_file.readline())
|
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|
|
def test_add_file_recursively_locally(self):
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|
path = os.path.join(SPARK_HOME, "python/test_support/hello")
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|
self.sc.addFile(path, True)
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|
download_path = SparkFiles.get("hello")
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|
self.assertNotEqual(path, download_path)
|
|
with open(download_path + "/hello.txt") as test_file:
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|
self.assertEqual("Hello World!\n", test_file.readline())
|
|
with open(download_path + "/sub_hello/sub_hello.txt") as test_file:
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|
self.assertEqual("Sub Hello World!\n", test_file.readline())
|
|
|
|
def test_add_py_file_locally(self):
|
|
# To ensure that we're actually testing addPyFile's effects, check that
|
|
# this fails due to `userlibrary` not being on the Python path:
|
|
def func():
|
|
from userlibrary import UserClass
|
|
self.assertRaises(ImportError, func)
|
|
path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
|
|
self.sc.addPyFile(path)
|
|
from userlibrary import UserClass
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self.assertEqual("Hello World!", UserClass().hello())
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|
|
def test_add_egg_file_locally(self):
|
|
# To ensure that we're actually testing addPyFile's effects, check that
|
|
# this fails due to `userlibrary` not being on the Python path:
|
|
def func():
|
|
from userlib import UserClass
|
|
self.assertRaises(ImportError, func)
|
|
path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1.zip")
|
|
self.sc.addPyFile(path)
|
|
from userlib import UserClass
|
|
self.assertEqual("Hello World from inside a package!", UserClass().hello())
|
|
|
|
def test_overwrite_system_module(self):
|
|
self.sc.addPyFile(os.path.join(SPARK_HOME, "python/test_support/SimpleHTTPServer.py"))
|
|
|
|
import SimpleHTTPServer
|
|
self.assertEqual("My Server", SimpleHTTPServer.__name__)
|
|
|
|
def func(x):
|
|
import SimpleHTTPServer
|
|
return SimpleHTTPServer.__name__
|
|
|
|
self.assertEqual(["My Server"], self.sc.parallelize(range(1)).map(func).collect())
|
|
|
|
|
|
class RDDTests(ReusedPySparkTestCase):
|
|
|
|
def test_range(self):
|
|
self.assertEqual(self.sc.range(1, 1).count(), 0)
|
|
self.assertEqual(self.sc.range(1, 0, -1).count(), 1)
|
|
self.assertEqual(self.sc.range(0, 1 << 40, 1 << 39).count(), 2)
|
|
|
|
def test_id(self):
|
|
rdd = self.sc.parallelize(range(10))
|
|
id = rdd.id()
|
|
self.assertEqual(id, rdd.id())
|
|
rdd2 = rdd.map(str).filter(bool)
|
|
id2 = rdd2.id()
|
|
self.assertEqual(id + 1, id2)
|
|
self.assertEqual(id2, rdd2.id())
|
|
|
|
def test_empty_rdd(self):
|
|
rdd = self.sc.emptyRDD()
|
|
self.assertTrue(rdd.isEmpty())
|
|
|
|
def test_sum(self):
|
|
self.assertEqual(0, self.sc.emptyRDD().sum())
|
|
self.assertEqual(6, self.sc.parallelize([1, 2, 3]).sum())
|
|
|
|
def test_to_localiterator(self):
|
|
from time import sleep
|
|
rdd = self.sc.parallelize([1, 2, 3])
|
|
it = rdd.toLocalIterator()
|
|
sleep(5)
|
|
self.assertEqual([1, 2, 3], sorted(it))
|
|
|
|
rdd2 = rdd.repartition(1000)
|
|
it2 = rdd2.toLocalIterator()
|
|
sleep(5)
|
|
self.assertEqual([1, 2, 3], sorted(it2))
|
|
|
|
def test_save_as_textfile_with_unicode(self):
|
|
# Regression test for SPARK-970
|
|
x = u"\u00A1Hola, mundo!"
|
|
data = self.sc.parallelize([x])
|
|
tempFile = tempfile.NamedTemporaryFile(delete=True)
|
|
tempFile.close()
|
|
data.saveAsTextFile(tempFile.name)
|
|
raw_contents = b''.join(open(p, 'rb').read()
|
|
for p in glob(tempFile.name + "/part-0000*"))
|
|
self.assertEqual(x, raw_contents.strip().decode("utf-8"))
|
|
|
|
def test_save_as_textfile_with_utf8(self):
|
|
x = u"\u00A1Hola, mundo!"
|
|
data = self.sc.parallelize([x.encode("utf-8")])
|
|
tempFile = tempfile.NamedTemporaryFile(delete=True)
|
|
tempFile.close()
|
|
data.saveAsTextFile(tempFile.name)
|
|
raw_contents = b''.join(open(p, 'rb').read()
|
|
for p in glob(tempFile.name + "/part-0000*"))
|
|
self.assertEqual(x, raw_contents.strip().decode('utf8'))
|
|
|
|
def test_transforming_cartesian_result(self):
|
|
# Regression test for SPARK-1034
|
|
rdd1 = self.sc.parallelize([1, 2])
|
|
rdd2 = self.sc.parallelize([3, 4])
|
|
cart = rdd1.cartesian(rdd2)
|
|
result = cart.map(lambda x_y3: x_y3[0] + x_y3[1]).collect()
|
|
|
|
def test_transforming_pickle_file(self):
|
|
# Regression test for SPARK-2601
|
|
data = self.sc.parallelize([u"Hello", u"World!"])
|
|
tempFile = tempfile.NamedTemporaryFile(delete=True)
|
|
tempFile.close()
|
|
data.saveAsPickleFile(tempFile.name)
|
|
pickled_file = self.sc.pickleFile(tempFile.name)
|
|
pickled_file.map(lambda x: x).collect()
|
|
|
|
def test_cartesian_on_textfile(self):
|
|
# Regression test for
|
|
path = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
|
|
a = self.sc.textFile(path)
|
|
result = a.cartesian(a).collect()
|
|
(x, y) = result[0]
|
|
self.assertEqual(u"Hello World!", x.strip())
|
|
self.assertEqual(u"Hello World!", y.strip())
|
|
|
|
def test_cartesian_chaining(self):
|
|
# Tests for SPARK-16589
|
|
rdd = self.sc.parallelize(range(10), 2)
|
|
self.assertSetEqual(
|
|
set(rdd.cartesian(rdd).cartesian(rdd).collect()),
|
|
set([((x, y), z) for x in range(10) for y in range(10) for z in range(10)])
|
|
)
|
|
|
|
self.assertSetEqual(
|
|
set(rdd.cartesian(rdd.cartesian(rdd)).collect()),
|
|
set([(x, (y, z)) for x in range(10) for y in range(10) for z in range(10)])
|
|
)
|
|
|
|
self.assertSetEqual(
|
|
set(rdd.cartesian(rdd.zip(rdd)).collect()),
|
|
set([(x, (y, y)) for x in range(10) for y in range(10)])
|
|
)
|
|
|
|
def test_deleting_input_files(self):
|
|
# Regression test for SPARK-1025
|
|
tempFile = tempfile.NamedTemporaryFile(delete=False)
|
|
tempFile.write(b"Hello World!")
|
|
tempFile.close()
|
|
data = self.sc.textFile(tempFile.name)
|
|
filtered_data = data.filter(lambda x: True)
|
|
self.assertEqual(1, filtered_data.count())
|
|
os.unlink(tempFile.name)
|
|
with QuietTest(self.sc):
|
|
self.assertRaises(Exception, lambda: filtered_data.count())
|
|
|
|
def test_sampling_default_seed(self):
|
|
# Test for SPARK-3995 (default seed setting)
|
|
data = self.sc.parallelize(xrange(1000), 1)
|
|
subset = data.takeSample(False, 10)
|
|
self.assertEqual(len(subset), 10)
|
|
|
|
def test_aggregate_mutable_zero_value(self):
|
|
# Test for SPARK-9021; uses aggregate and treeAggregate to build dict
|
|
# representing a counter of ints
|
|
# NOTE: dict is used instead of collections.Counter for Python 2.6
|
|
# compatibility
|
|
from collections import defaultdict
|
|
|
|
# Show that single or multiple partitions work
|
|
data1 = self.sc.range(10, numSlices=1)
|
|
data2 = self.sc.range(10, numSlices=2)
|
|
|
|
def seqOp(x, y):
|
|
x[y] += 1
|
|
return x
|
|
|
|
def comboOp(x, y):
|
|
for key, val in y.items():
|
|
x[key] += val
|
|
return x
|
|
|
|
counts1 = data1.aggregate(defaultdict(int), seqOp, comboOp)
|
|
counts2 = data2.aggregate(defaultdict(int), seqOp, comboOp)
|
|
counts3 = data1.treeAggregate(defaultdict(int), seqOp, comboOp, 2)
|
|
counts4 = data2.treeAggregate(defaultdict(int), seqOp, comboOp, 2)
|
|
|
|
ground_truth = defaultdict(int, dict((i, 1) for i in range(10)))
|
|
self.assertEqual(counts1, ground_truth)
|
|
self.assertEqual(counts2, ground_truth)
|
|
self.assertEqual(counts3, ground_truth)
|
|
self.assertEqual(counts4, ground_truth)
|
|
|
|
def test_aggregate_by_key_mutable_zero_value(self):
|
|
# Test for SPARK-9021; uses aggregateByKey to make a pair RDD that
|
|
# contains lists of all values for each key in the original RDD
|
|
|
|
# list(range(...)) for Python 3.x compatibility (can't use * operator
|
|
# on a range object)
|
|
# list(zip(...)) for Python 3.x compatibility (want to parallelize a
|
|
# collection, not a zip object)
|
|
tuples = list(zip(list(range(10))*2, [1]*20))
|
|
# Show that single or multiple partitions work
|
|
data1 = self.sc.parallelize(tuples, 1)
|
|
data2 = self.sc.parallelize(tuples, 2)
|
|
|
|
def seqOp(x, y):
|
|
x.append(y)
|
|
return x
|
|
|
|
def comboOp(x, y):
|
|
x.extend(y)
|
|
return x
|
|
|
|
values1 = data1.aggregateByKey([], seqOp, comboOp).collect()
|
|
values2 = data2.aggregateByKey([], seqOp, comboOp).collect()
|
|
# Sort lists to ensure clean comparison with ground_truth
|
|
values1.sort()
|
|
values2.sort()
|
|
|
|
ground_truth = [(i, [1]*2) for i in range(10)]
|
|
self.assertEqual(values1, ground_truth)
|
|
self.assertEqual(values2, ground_truth)
|
|
|
|
def test_fold_mutable_zero_value(self):
|
|
# Test for SPARK-9021; uses fold to merge an RDD of dict counters into
|
|
# a single dict
|
|
# NOTE: dict is used instead of collections.Counter for Python 2.6
|
|
# compatibility
|
|
from collections import defaultdict
|
|
|
|
counts1 = defaultdict(int, dict((i, 1) for i in range(10)))
|
|
counts2 = defaultdict(int, dict((i, 1) for i in range(3, 8)))
|
|
counts3 = defaultdict(int, dict((i, 1) for i in range(4, 7)))
|
|
counts4 = defaultdict(int, dict((i, 1) for i in range(5, 6)))
|
|
all_counts = [counts1, counts2, counts3, counts4]
|
|
# Show that single or multiple partitions work
|
|
data1 = self.sc.parallelize(all_counts, 1)
|
|
data2 = self.sc.parallelize(all_counts, 2)
|
|
|
|
def comboOp(x, y):
|
|
for key, val in y.items():
|
|
x[key] += val
|
|
return x
|
|
|
|
fold1 = data1.fold(defaultdict(int), comboOp)
|
|
fold2 = data2.fold(defaultdict(int), comboOp)
|
|
|
|
ground_truth = defaultdict(int)
|
|
for counts in all_counts:
|
|
for key, val in counts.items():
|
|
ground_truth[key] += val
|
|
self.assertEqual(fold1, ground_truth)
|
|
self.assertEqual(fold2, ground_truth)
|
|
|
|
def test_fold_by_key_mutable_zero_value(self):
|
|
# Test for SPARK-9021; uses foldByKey to make a pair RDD that contains
|
|
# lists of all values for each key in the original RDD
|
|
|
|
tuples = [(i, range(i)) for i in range(10)]*2
|
|
# Show that single or multiple partitions work
|
|
data1 = self.sc.parallelize(tuples, 1)
|
|
data2 = self.sc.parallelize(tuples, 2)
|
|
|
|
def comboOp(x, y):
|
|
x.extend(y)
|
|
return x
|
|
|
|
values1 = data1.foldByKey([], comboOp).collect()
|
|
values2 = data2.foldByKey([], comboOp).collect()
|
|
# Sort lists to ensure clean comparison with ground_truth
|
|
values1.sort()
|
|
values2.sort()
|
|
|
|
# list(range(...)) for Python 3.x compatibility
|
|
ground_truth = [(i, list(range(i))*2) for i in range(10)]
|
|
self.assertEqual(values1, ground_truth)
|
|
self.assertEqual(values2, ground_truth)
|
|
|
|
def test_aggregate_by_key(self):
|
|
data = self.sc.parallelize([(1, 1), (1, 1), (3, 2), (5, 1), (5, 3)], 2)
|
|
|
|
def seqOp(x, y):
|
|
x.add(y)
|
|
return x
|
|
|
|
def combOp(x, y):
|
|
x |= y
|
|
return x
|
|
|
|
sets = dict(data.aggregateByKey(set(), seqOp, combOp).collect())
|
|
self.assertEqual(3, len(sets))
|
|
self.assertEqual(set([1]), sets[1])
|
|
self.assertEqual(set([2]), sets[3])
|
|
self.assertEqual(set([1, 3]), sets[5])
|
|
|
|
def test_itemgetter(self):
|
|
rdd = self.sc.parallelize([range(10)])
|
|
from operator import itemgetter
|
|
self.assertEqual([1], rdd.map(itemgetter(1)).collect())
|
|
self.assertEqual([(2, 3)], rdd.map(itemgetter(2, 3)).collect())
|
|
|
|
def test_namedtuple_in_rdd(self):
|
|
from collections import namedtuple
|
|
Person = namedtuple("Person", "id firstName lastName")
|
|
jon = Person(1, "Jon", "Doe")
|
|
jane = Person(2, "Jane", "Doe")
|
|
theDoes = self.sc.parallelize([jon, jane])
|
|
self.assertEqual([jon, jane], theDoes.collect())
|
|
|
|
def test_large_broadcast(self):
|
|
N = 10000
|
|
data = [[float(i) for i in range(300)] for i in range(N)]
|
|
bdata = self.sc.broadcast(data) # 27MB
|
|
m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
|
|
self.assertEqual(N, m)
|
|
|
|
def test_unpersist(self):
|
|
N = 1000
|
|
data = [[float(i) for i in range(300)] for i in range(N)]
|
|
bdata = self.sc.broadcast(data) # 3MB
|
|
bdata.unpersist()
|
|
m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
|
|
self.assertEqual(N, m)
|
|
bdata.destroy()
|
|
try:
|
|
self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
|
|
except Exception as e:
|
|
pass
|
|
else:
|
|
raise Exception("job should fail after destroy the broadcast")
|
|
|
|
def test_multiple_broadcasts(self):
|
|
N = 1 << 21
|
|
b1 = self.sc.broadcast(set(range(N))) # multiple blocks in JVM
|
|
r = list(range(1 << 15))
|
|
random.shuffle(r)
|
|
s = str(r).encode()
|
|
checksum = hashlib.md5(s).hexdigest()
|
|
b2 = self.sc.broadcast(s)
|
|
r = list(set(self.sc.parallelize(range(10), 10).map(
|
|
lambda x: (len(b1.value), hashlib.md5(b2.value).hexdigest())).collect()))
|
|
self.assertEqual(1, len(r))
|
|
size, csum = r[0]
|
|
self.assertEqual(N, size)
|
|
self.assertEqual(checksum, csum)
|
|
|
|
random.shuffle(r)
|
|
s = str(r).encode()
|
|
checksum = hashlib.md5(s).hexdigest()
|
|
b2 = self.sc.broadcast(s)
|
|
r = list(set(self.sc.parallelize(range(10), 10).map(
|
|
lambda x: (len(b1.value), hashlib.md5(b2.value).hexdigest())).collect()))
|
|
self.assertEqual(1, len(r))
|
|
size, csum = r[0]
|
|
self.assertEqual(N, size)
|
|
self.assertEqual(checksum, csum)
|
|
|
|
def test_large_closure(self):
|
|
N = 200000
|
|
data = [float(i) for i in xrange(N)]
|
|
rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data))
|
|
self.assertEqual(N, rdd.first())
|
|
# regression test for SPARK-6886
|
|
self.assertEqual(1, rdd.map(lambda x: (x, 1)).groupByKey().count())
|
|
|
|
def test_zip_with_different_serializers(self):
|
|
a = self.sc.parallelize(range(5))
|
|
b = self.sc.parallelize(range(100, 105))
|
|
self.assertEqual(a.zip(b).collect(), [(0, 100), (1, 101), (2, 102), (3, 103), (4, 104)])
|
|
a = a._reserialize(BatchedSerializer(PickleSerializer(), 2))
|
|
b = b._reserialize(MarshalSerializer())
|
|
self.assertEqual(a.zip(b).collect(), [(0, 100), (1, 101), (2, 102), (3, 103), (4, 104)])
|
|
# regression test for SPARK-4841
|
|
path = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
|
|
t = self.sc.textFile(path)
|
|
cnt = t.count()
|
|
self.assertEqual(cnt, t.zip(t).count())
|
|
rdd = t.map(str)
|
|
self.assertEqual(cnt, t.zip(rdd).count())
|
|
# regression test for bug in _reserializer()
|
|
self.assertEqual(cnt, t.zip(rdd).count())
|
|
|
|
def test_zip_with_different_object_sizes(self):
|
|
# regress test for SPARK-5973
|
|
a = self.sc.parallelize(xrange(10000)).map(lambda i: '*' * i)
|
|
b = self.sc.parallelize(xrange(10000, 20000)).map(lambda i: '*' * i)
|
|
self.assertEqual(10000, a.zip(b).count())
|
|
|
|
def test_zip_with_different_number_of_items(self):
|
|
a = self.sc.parallelize(range(5), 2)
|
|
# different number of partitions
|
|
b = self.sc.parallelize(range(100, 106), 3)
|
|
self.assertRaises(ValueError, lambda: a.zip(b))
|
|
with QuietTest(self.sc):
|
|
# different number of batched items in JVM
|
|
b = self.sc.parallelize(range(100, 104), 2)
|
|
self.assertRaises(Exception, lambda: a.zip(b).count())
|
|
# different number of items in one pair
|
|
b = self.sc.parallelize(range(100, 106), 2)
|
|
self.assertRaises(Exception, lambda: a.zip(b).count())
|
|
# same total number of items, but different distributions
|
|
a = self.sc.parallelize([2, 3], 2).flatMap(range)
|
|
b = self.sc.parallelize([3, 2], 2).flatMap(range)
|
|
self.assertEqual(a.count(), b.count())
|
|
self.assertRaises(Exception, lambda: a.zip(b).count())
|
|
|
|
def test_count_approx_distinct(self):
|
|
rdd = self.sc.parallelize(xrange(1000))
|
|
self.assertTrue(950 < rdd.countApproxDistinct(0.03) < 1050)
|
|
self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.03) < 1050)
|
|
self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.03) < 1050)
|
|
self.assertTrue(950 < rdd.map(lambda x: (x, -x)).countApproxDistinct(0.03) < 1050)
|
|
|
|
rdd = self.sc.parallelize([i % 20 for i in range(1000)], 7)
|
|
self.assertTrue(18 < rdd.countApproxDistinct() < 22)
|
|
self.assertTrue(18 < rdd.map(float).countApproxDistinct() < 22)
|
|
self.assertTrue(18 < rdd.map(str).countApproxDistinct() < 22)
|
|
self.assertTrue(18 < rdd.map(lambda x: (x, -x)).countApproxDistinct() < 22)
|
|
|
|
self.assertRaises(ValueError, lambda: rdd.countApproxDistinct(0.00000001))
|
|
|
|
def test_histogram(self):
|
|
# empty
|
|
rdd = self.sc.parallelize([])
|
|
self.assertEqual([0], rdd.histogram([0, 10])[1])
|
|
self.assertEqual([0, 0], rdd.histogram([0, 4, 10])[1])
|
|
self.assertRaises(ValueError, lambda: rdd.histogram(1))
|
|
|
|
# out of range
|
|
rdd = self.sc.parallelize([10.01, -0.01])
|
|
self.assertEqual([0], rdd.histogram([0, 10])[1])
|
|
self.assertEqual([0, 0], rdd.histogram((0, 4, 10))[1])
|
|
|
|
# in range with one bucket
|
|
rdd = self.sc.parallelize(range(1, 5))
|
|
self.assertEqual([4], rdd.histogram([0, 10])[1])
|
|
self.assertEqual([3, 1], rdd.histogram([0, 4, 10])[1])
|
|
|
|
# in range with one bucket exact match
|
|
self.assertEqual([4], rdd.histogram([1, 4])[1])
|
|
|
|
# out of range with two buckets
|
|
rdd = self.sc.parallelize([10.01, -0.01])
|
|
self.assertEqual([0, 0], rdd.histogram([0, 5, 10])[1])
|
|
|
|
# out of range with two uneven buckets
|
|
rdd = self.sc.parallelize([10.01, -0.01])
|
|
self.assertEqual([0, 0], rdd.histogram([0, 4, 10])[1])
|
|
|
|
# in range with two buckets
|
|
rdd = self.sc.parallelize([1, 2, 3, 5, 6])
|
|
self.assertEqual([3, 2], rdd.histogram([0, 5, 10])[1])
|
|
|
|
# in range with two bucket and None
|
|
rdd = self.sc.parallelize([1, 2, 3, 5, 6, None, float('nan')])
|
|
self.assertEqual([3, 2], rdd.histogram([0, 5, 10])[1])
|
|
|
|
# in range with two uneven buckets
|
|
rdd = self.sc.parallelize([1, 2, 3, 5, 6])
|
|
self.assertEqual([3, 2], rdd.histogram([0, 5, 11])[1])
|
|
|
|
# mixed range with two uneven buckets
|
|
rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.0, 11.01])
|
|
self.assertEqual([4, 3], rdd.histogram([0, 5, 11])[1])
|
|
|
|
# mixed range with four uneven buckets
|
|
rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1])
|
|
self.assertEqual([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1])
|
|
|
|
# mixed range with uneven buckets and NaN
|
|
rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0,
|
|
199.0, 200.0, 200.1, None, float('nan')])
|
|
self.assertEqual([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1])
|
|
|
|
# out of range with infinite buckets
|
|
rdd = self.sc.parallelize([10.01, -0.01, float('nan'), float("inf")])
|
|
self.assertEqual([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1])
|
|
|
|
# invalid buckets
|
|
self.assertRaises(ValueError, lambda: rdd.histogram([]))
|
|
self.assertRaises(ValueError, lambda: rdd.histogram([1]))
|
|
self.assertRaises(ValueError, lambda: rdd.histogram(0))
|
|
self.assertRaises(TypeError, lambda: rdd.histogram({}))
|
|
|
|
# without buckets
|
|
rdd = self.sc.parallelize(range(1, 5))
|
|
self.assertEqual(([1, 4], [4]), rdd.histogram(1))
|
|
|
|
# without buckets single element
|
|
rdd = self.sc.parallelize([1])
|
|
self.assertEqual(([1, 1], [1]), rdd.histogram(1))
|
|
|
|
# without bucket no range
|
|
rdd = self.sc.parallelize([1] * 4)
|
|
self.assertEqual(([1, 1], [4]), rdd.histogram(1))
|
|
|
|
# without buckets basic two
|
|
rdd = self.sc.parallelize(range(1, 5))
|
|
self.assertEqual(([1, 2.5, 4], [2, 2]), rdd.histogram(2))
|
|
|
|
# without buckets with more requested than elements
|
|
rdd = self.sc.parallelize([1, 2])
|
|
buckets = [1 + 0.2 * i for i in range(6)]
|
|
hist = [1, 0, 0, 0, 1]
|
|
self.assertEqual((buckets, hist), rdd.histogram(5))
|
|
|
|
# invalid RDDs
|
|
rdd = self.sc.parallelize([1, float('inf')])
|
|
self.assertRaises(ValueError, lambda: rdd.histogram(2))
|
|
rdd = self.sc.parallelize([float('nan')])
|
|
self.assertRaises(ValueError, lambda: rdd.histogram(2))
|
|
|
|
# string
|
|
rdd = self.sc.parallelize(["ab", "ac", "b", "bd", "ef"], 2)
|
|
self.assertEqual([2, 2], rdd.histogram(["a", "b", "c"])[1])
|
|
self.assertEqual((["ab", "ef"], [5]), rdd.histogram(1))
|
|
self.assertRaises(TypeError, lambda: rdd.histogram(2))
|
|
|
|
def test_repartitionAndSortWithinPartitions(self):
|
|
rdd = self.sc.parallelize([(0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)], 2)
|
|
|
|
repartitioned = rdd.repartitionAndSortWithinPartitions(2, lambda key: key % 2)
|
|
partitions = repartitioned.glom().collect()
|
|
self.assertEqual(partitions[0], [(0, 5), (0, 8), (2, 6)])
|
|
self.assertEqual(partitions[1], [(1, 3), (3, 8), (3, 8)])
|
|
|
|
def test_repartition_no_skewed(self):
|
|
num_partitions = 20
|
|
a = self.sc.parallelize(range(int(1000)), 2)
|
|
l = a.repartition(num_partitions).glom().map(len).collect()
|
|
zeros = len([x for x in l if x == 0])
|
|
self.assertTrue(zeros == 0)
|
|
l = a.coalesce(num_partitions, True).glom().map(len).collect()
|
|
zeros = len([x for x in l if x == 0])
|
|
self.assertTrue(zeros == 0)
|
|
|
|
def test_distinct(self):
|
|
rdd = self.sc.parallelize((1, 2, 3)*10, 10)
|
|
self.assertEqual(rdd.getNumPartitions(), 10)
|
|
self.assertEqual(rdd.distinct().count(), 3)
|
|
result = rdd.distinct(5)
|
|
self.assertEqual(result.getNumPartitions(), 5)
|
|
self.assertEqual(result.count(), 3)
|
|
|
|
def test_external_group_by_key(self):
|
|
self.sc._conf.set("spark.python.worker.memory", "1m")
|
|
N = 200001
|
|
kv = self.sc.parallelize(xrange(N)).map(lambda x: (x % 3, x))
|
|
gkv = kv.groupByKey().cache()
|
|
self.assertEqual(3, gkv.count())
|
|
filtered = gkv.filter(lambda kv: kv[0] == 1)
|
|
self.assertEqual(1, filtered.count())
|
|
self.assertEqual([(1, N // 3)], filtered.mapValues(len).collect())
|
|
self.assertEqual([(N // 3, N // 3)],
|
|
filtered.values().map(lambda x: (len(x), len(list(x)))).collect())
|
|
result = filtered.collect()[0][1]
|
|
self.assertEqual(N // 3, len(result))
|
|
self.assertTrue(isinstance(result.data, shuffle.ExternalListOfList))
|
|
|
|
def test_sort_on_empty_rdd(self):
|
|
self.assertEqual([], self.sc.parallelize(zip([], [])).sortByKey().collect())
|
|
|
|
def test_sample(self):
|
|
rdd = self.sc.parallelize(range(0, 100), 4)
|
|
wo = rdd.sample(False, 0.1, 2).collect()
|
|
wo_dup = rdd.sample(False, 0.1, 2).collect()
|
|
self.assertSetEqual(set(wo), set(wo_dup))
|
|
wr = rdd.sample(True, 0.2, 5).collect()
|
|
wr_dup = rdd.sample(True, 0.2, 5).collect()
|
|
self.assertSetEqual(set(wr), set(wr_dup))
|
|
wo_s10 = rdd.sample(False, 0.3, 10).collect()
|
|
wo_s20 = rdd.sample(False, 0.3, 20).collect()
|
|
self.assertNotEqual(set(wo_s10), set(wo_s20))
|
|
wr_s11 = rdd.sample(True, 0.4, 11).collect()
|
|
wr_s21 = rdd.sample(True, 0.4, 21).collect()
|
|
self.assertNotEqual(set(wr_s11), set(wr_s21))
|
|
|
|
def test_null_in_rdd(self):
|
|
jrdd = self.sc._jvm.PythonUtils.generateRDDWithNull(self.sc._jsc)
|
|
rdd = RDD(jrdd, self.sc, UTF8Deserializer())
|
|
self.assertEqual([u"a", None, u"b"], rdd.collect())
|
|
rdd = RDD(jrdd, self.sc, NoOpSerializer())
|
|
self.assertEqual([b"a", None, b"b"], rdd.collect())
|
|
|
|
def test_multiple_python_java_RDD_conversions(self):
|
|
# Regression test for SPARK-5361
|
|
data = [
|
|
(u'1', {u'director': u'David Lean'}),
|
|
(u'2', {u'director': u'Andrew Dominik'})
|
|
]
|
|
data_rdd = self.sc.parallelize(data)
|
|
data_java_rdd = data_rdd._to_java_object_rdd()
|
|
data_python_rdd = self.sc._jvm.SerDeUtil.javaToPython(data_java_rdd)
|
|
converted_rdd = RDD(data_python_rdd, self.sc)
|
|
self.assertEqual(2, converted_rdd.count())
|
|
|
|
# conversion between python and java RDD threw exceptions
|
|
data_java_rdd = converted_rdd._to_java_object_rdd()
|
|
data_python_rdd = self.sc._jvm.SerDeUtil.javaToPython(data_java_rdd)
|
|
converted_rdd = RDD(data_python_rdd, self.sc)
|
|
self.assertEqual(2, converted_rdd.count())
|
|
|
|
def test_narrow_dependency_in_join(self):
|
|
rdd = self.sc.parallelize(range(10)).map(lambda x: (x, x))
|
|
parted = rdd.partitionBy(2)
|
|
self.assertEqual(2, parted.union(parted).getNumPartitions())
|
|
self.assertEqual(rdd.getNumPartitions() + 2, parted.union(rdd).getNumPartitions())
|
|
self.assertEqual(rdd.getNumPartitions() + 2, rdd.union(parted).getNumPartitions())
|
|
|
|
tracker = self.sc.statusTracker()
|
|
|
|
self.sc.setJobGroup("test1", "test", True)
|
|
d = sorted(parted.join(parted).collect())
|
|
self.assertEqual(10, len(d))
|
|
self.assertEqual((0, (0, 0)), d[0])
|
|
jobId = tracker.getJobIdsForGroup("test1")[0]
|
|
self.assertEqual(2, len(tracker.getJobInfo(jobId).stageIds))
|
|
|
|
self.sc.setJobGroup("test2", "test", True)
|
|
d = sorted(parted.join(rdd).collect())
|
|
self.assertEqual(10, len(d))
|
|
self.assertEqual((0, (0, 0)), d[0])
|
|
jobId = tracker.getJobIdsForGroup("test2")[0]
|
|
self.assertEqual(3, len(tracker.getJobInfo(jobId).stageIds))
|
|
|
|
self.sc.setJobGroup("test3", "test", True)
|
|
d = sorted(parted.cogroup(parted).collect())
|
|
self.assertEqual(10, len(d))
|
|
self.assertEqual([[0], [0]], list(map(list, d[0][1])))
|
|
jobId = tracker.getJobIdsForGroup("test3")[0]
|
|
self.assertEqual(2, len(tracker.getJobInfo(jobId).stageIds))
|
|
|
|
self.sc.setJobGroup("test4", "test", True)
|
|
d = sorted(parted.cogroup(rdd).collect())
|
|
self.assertEqual(10, len(d))
|
|
self.assertEqual([[0], [0]], list(map(list, d[0][1])))
|
|
jobId = tracker.getJobIdsForGroup("test4")[0]
|
|
self.assertEqual(3, len(tracker.getJobInfo(jobId).stageIds))
|
|
|
|
# Regression test for SPARK-6294
|
|
def test_take_on_jrdd(self):
|
|
rdd = self.sc.parallelize(xrange(1 << 20)).map(lambda x: str(x))
|
|
rdd._jrdd.first()
|
|
|
|
def test_sortByKey_uses_all_partitions_not_only_first_and_last(self):
|
|
# Regression test for SPARK-5969
|
|
seq = [(i * 59 % 101, i) for i in range(101)] # unsorted sequence
|
|
rdd = self.sc.parallelize(seq)
|
|
for ascending in [True, False]:
|
|
sort = rdd.sortByKey(ascending=ascending, numPartitions=5)
|
|
self.assertEqual(sort.collect(), sorted(seq, reverse=not ascending))
|
|
sizes = sort.glom().map(len).collect()
|
|
for size in sizes:
|
|
self.assertGreater(size, 0)
|
|
|
|
def test_pipe_functions(self):
|
|
data = ['1', '2', '3']
|
|
rdd = self.sc.parallelize(data)
|
|
with QuietTest(self.sc):
|
|
self.assertEqual([], rdd.pipe('cc').collect())
|
|
self.assertRaises(Py4JJavaError, rdd.pipe('cc', checkCode=True).collect)
|
|
result = rdd.pipe('cat').collect()
|
|
result.sort()
|
|
for x, y in zip(data, result):
|
|
self.assertEqual(x, y)
|
|
self.assertRaises(Py4JJavaError, rdd.pipe('grep 4', checkCode=True).collect)
|
|
self.assertEqual([], rdd.pipe('grep 4').collect())
|
|
|
|
|
|
class ProfilerTests(PySparkTestCase):
|
|
|
|
def setUp(self):
|
|
self._old_sys_path = list(sys.path)
|
|
class_name = self.__class__.__name__
|
|
conf = SparkConf().set("spark.python.profile", "true")
|
|
self.sc = SparkContext('local[4]', class_name, conf=conf)
|
|
|
|
def test_profiler(self):
|
|
self.do_computation()
|
|
|
|
profilers = self.sc.profiler_collector.profilers
|
|
self.assertEqual(1, len(profilers))
|
|
id, profiler, _ = profilers[0]
|
|
stats = profiler.stats()
|
|
self.assertTrue(stats is not None)
|
|
width, stat_list = stats.get_print_list([])
|
|
func_names = [func_name for fname, n, func_name in stat_list]
|
|
self.assertTrue("heavy_foo" in func_names)
|
|
|
|
old_stdout = sys.stdout
|
|
sys.stdout = io = StringIO()
|
|
self.sc.show_profiles()
|
|
self.assertTrue("heavy_foo" in io.getvalue())
|
|
sys.stdout = old_stdout
|
|
|
|
d = tempfile.gettempdir()
|
|
self.sc.dump_profiles(d)
|
|
self.assertTrue("rdd_%d.pstats" % id in os.listdir(d))
|
|
|
|
def test_custom_profiler(self):
|
|
class TestCustomProfiler(BasicProfiler):
|
|
def show(self, id):
|
|
self.result = "Custom formatting"
|
|
|
|
self.sc.profiler_collector.profiler_cls = TestCustomProfiler
|
|
|
|
self.do_computation()
|
|
|
|
profilers = self.sc.profiler_collector.profilers
|
|
self.assertEqual(1, len(profilers))
|
|
_, profiler, _ = profilers[0]
|
|
self.assertTrue(isinstance(profiler, TestCustomProfiler))
|
|
|
|
self.sc.show_profiles()
|
|
self.assertEqual("Custom formatting", profiler.result)
|
|
|
|
def do_computation(self):
|
|
def heavy_foo(x):
|
|
for i in range(1 << 18):
|
|
x = 1
|
|
|
|
rdd = self.sc.parallelize(range(100))
|
|
rdd.foreach(heavy_foo)
|
|
|
|
|
|
class InputFormatTests(ReusedPySparkTestCase):
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
ReusedPySparkTestCase.setUpClass()
|
|
cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
|
|
os.unlink(cls.tempdir.name)
|
|
cls.sc._jvm.WriteInputFormatTestDataGenerator.generateData(cls.tempdir.name, cls.sc._jsc)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
ReusedPySparkTestCase.tearDownClass()
|
|
shutil.rmtree(cls.tempdir.name)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "serialize array of byte")
|
|
def test_sequencefiles(self):
|
|
basepath = self.tempdir.name
|
|
ints = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text").collect())
|
|
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
|
|
self.assertEqual(ints, ei)
|
|
|
|
doubles = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfdouble/",
|
|
"org.apache.hadoop.io.DoubleWritable",
|
|
"org.apache.hadoop.io.Text").collect())
|
|
ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')]
|
|
self.assertEqual(doubles, ed)
|
|
|
|
bytes = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfbytes/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.BytesWritable").collect())
|
|
ebs = [(1, bytearray('aa', 'utf-8')),
|
|
(1, bytearray('aa', 'utf-8')),
|
|
(2, bytearray('aa', 'utf-8')),
|
|
(2, bytearray('bb', 'utf-8')),
|
|
(2, bytearray('bb', 'utf-8')),
|
|
(3, bytearray('cc', 'utf-8'))]
|
|
self.assertEqual(bytes, ebs)
|
|
|
|
text = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sftext/",
|
|
"org.apache.hadoop.io.Text",
|
|
"org.apache.hadoop.io.Text").collect())
|
|
et = [(u'1', u'aa'),
|
|
(u'1', u'aa'),
|
|
(u'2', u'aa'),
|
|
(u'2', u'bb'),
|
|
(u'2', u'bb'),
|
|
(u'3', u'cc')]
|
|
self.assertEqual(text, et)
|
|
|
|
bools = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfbool/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.BooleanWritable").collect())
|
|
eb = [(1, False), (1, True), (2, False), (2, False), (2, True), (3, True)]
|
|
self.assertEqual(bools, eb)
|
|
|
|
nulls = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfnull/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.BooleanWritable").collect())
|
|
en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)]
|
|
self.assertEqual(nulls, en)
|
|
|
|
maps = self.sc.sequenceFile(basepath + "/sftestdata/sfmap/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.MapWritable").collect()
|
|
em = [(1, {}),
|
|
(1, {3.0: u'bb'}),
|
|
(2, {1.0: u'aa'}),
|
|
(2, {1.0: u'cc'}),
|
|
(3, {2.0: u'dd'})]
|
|
for v in maps:
|
|
self.assertTrue(v in em)
|
|
|
|
# arrays get pickled to tuples by default
|
|
tuples = sorted(self.sc.sequenceFile(
|
|
basepath + "/sftestdata/sfarray/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.spark.api.python.DoubleArrayWritable").collect())
|
|
et = [(1, ()),
|
|
(2, (3.0, 4.0, 5.0)),
|
|
(3, (4.0, 5.0, 6.0))]
|
|
self.assertEqual(tuples, et)
|
|
|
|
# with custom converters, primitive arrays can stay as arrays
|
|
arrays = sorted(self.sc.sequenceFile(
|
|
basepath + "/sftestdata/sfarray/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.spark.api.python.DoubleArrayWritable",
|
|
valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect())
|
|
ea = [(1, array('d')),
|
|
(2, array('d', [3.0, 4.0, 5.0])),
|
|
(3, array('d', [4.0, 5.0, 6.0]))]
|
|
self.assertEqual(arrays, ea)
|
|
|
|
clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/",
|
|
"org.apache.hadoop.io.Text",
|
|
"org.apache.spark.api.python.TestWritable").collect())
|
|
cname = u'org.apache.spark.api.python.TestWritable'
|
|
ec = [(u'1', {u'__class__': cname, u'double': 1.0, u'int': 1, u'str': u'test1'}),
|
|
(u'2', {u'__class__': cname, u'double': 2.3, u'int': 2, u'str': u'test2'}),
|
|
(u'3', {u'__class__': cname, u'double': 3.1, u'int': 3, u'str': u'test3'}),
|
|
(u'4', {u'__class__': cname, u'double': 4.2, u'int': 4, u'str': u'test4'}),
|
|
(u'5', {u'__class__': cname, u'double': 5.5, u'int': 5, u'str': u'test56'})]
|
|
self.assertEqual(clazz, ec)
|
|
|
|
unbatched_clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/",
|
|
"org.apache.hadoop.io.Text",
|
|
"org.apache.spark.api.python.TestWritable",
|
|
).collect())
|
|
self.assertEqual(unbatched_clazz, ec)
|
|
|
|
def test_oldhadoop(self):
|
|
basepath = self.tempdir.name
|
|
ints = sorted(self.sc.hadoopFile(basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.mapred.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text").collect())
|
|
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
|
|
self.assertEqual(ints, ei)
|
|
|
|
hellopath = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
|
|
oldconf = {"mapred.input.dir": hellopath}
|
|
hello = self.sc.hadoopRDD("org.apache.hadoop.mapred.TextInputFormat",
|
|
"org.apache.hadoop.io.LongWritable",
|
|
"org.apache.hadoop.io.Text",
|
|
conf=oldconf).collect()
|
|
result = [(0, u'Hello World!')]
|
|
self.assertEqual(hello, result)
|
|
|
|
def test_newhadoop(self):
|
|
basepath = self.tempdir.name
|
|
ints = sorted(self.sc.newAPIHadoopFile(
|
|
basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text").collect())
|
|
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
|
|
self.assertEqual(ints, ei)
|
|
|
|
hellopath = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
|
|
newconf = {"mapred.input.dir": hellopath}
|
|
hello = self.sc.newAPIHadoopRDD("org.apache.hadoop.mapreduce.lib.input.TextInputFormat",
|
|
"org.apache.hadoop.io.LongWritable",
|
|
"org.apache.hadoop.io.Text",
|
|
conf=newconf).collect()
|
|
result = [(0, u'Hello World!')]
|
|
self.assertEqual(hello, result)
|
|
|
|
def test_newolderror(self):
|
|
basepath = self.tempdir.name
|
|
self.assertRaises(Exception, lambda: self.sc.hadoopFile(
|
|
basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text"))
|
|
|
|
self.assertRaises(Exception, lambda: self.sc.newAPIHadoopFile(
|
|
basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.mapred.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text"))
|
|
|
|
def test_bad_inputs(self):
|
|
basepath = self.tempdir.name
|
|
self.assertRaises(Exception, lambda: self.sc.sequenceFile(
|
|
basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.io.NotValidWritable",
|
|
"org.apache.hadoop.io.Text"))
|
|
self.assertRaises(Exception, lambda: self.sc.hadoopFile(
|
|
basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.mapred.NotValidInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text"))
|
|
self.assertRaises(Exception, lambda: self.sc.newAPIHadoopFile(
|
|
basepath + "/sftestdata/sfint/",
|
|
"org.apache.hadoop.mapreduce.lib.input.NotValidInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text"))
|
|
|
|
def test_converters(self):
|
|
# use of custom converters
|
|
basepath = self.tempdir.name
|
|
maps = sorted(self.sc.sequenceFile(
|
|
basepath + "/sftestdata/sfmap/",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.MapWritable",
|
|
keyConverter="org.apache.spark.api.python.TestInputKeyConverter",
|
|
valueConverter="org.apache.spark.api.python.TestInputValueConverter").collect())
|
|
em = [(u'\x01', []),
|
|
(u'\x01', [3.0]),
|
|
(u'\x02', [1.0]),
|
|
(u'\x02', [1.0]),
|
|
(u'\x03', [2.0])]
|
|
self.assertEqual(maps, em)
|
|
|
|
def test_binary_files(self):
|
|
path = os.path.join(self.tempdir.name, "binaryfiles")
|
|
os.mkdir(path)
|
|
data = b"short binary data"
|
|
with open(os.path.join(path, "part-0000"), 'wb') as f:
|
|
f.write(data)
|
|
[(p, d)] = self.sc.binaryFiles(path).collect()
|
|
self.assertTrue(p.endswith("part-0000"))
|
|
self.assertEqual(d, data)
|
|
|
|
def test_binary_records(self):
|
|
path = os.path.join(self.tempdir.name, "binaryrecords")
|
|
os.mkdir(path)
|
|
with open(os.path.join(path, "part-0000"), 'w') as f:
|
|
for i in range(100):
|
|
f.write('%04d' % i)
|
|
result = self.sc.binaryRecords(path, 4).map(int).collect()
|
|
self.assertEqual(list(range(100)), result)
|
|
|
|
|
|
class OutputFormatTests(ReusedPySparkTestCase):
|
|
|
|
def setUp(self):
|
|
self.tempdir = tempfile.NamedTemporaryFile(delete=False)
|
|
os.unlink(self.tempdir.name)
|
|
|
|
def tearDown(self):
|
|
shutil.rmtree(self.tempdir.name, ignore_errors=True)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "serialize array of byte")
|
|
def test_sequencefiles(self):
|
|
basepath = self.tempdir.name
|
|
ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')]
|
|
self.sc.parallelize(ei).saveAsSequenceFile(basepath + "/sfint/")
|
|
ints = sorted(self.sc.sequenceFile(basepath + "/sfint/").collect())
|
|
self.assertEqual(ints, ei)
|
|
|
|
ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')]
|
|
self.sc.parallelize(ed).saveAsSequenceFile(basepath + "/sfdouble/")
|
|
doubles = sorted(self.sc.sequenceFile(basepath + "/sfdouble/").collect())
|
|
self.assertEqual(doubles, ed)
|
|
|
|
ebs = [(1, bytearray(b'\x00\x07spam\x08')), (2, bytearray(b'\x00\x07spam\x08'))]
|
|
self.sc.parallelize(ebs).saveAsSequenceFile(basepath + "/sfbytes/")
|
|
bytes = sorted(self.sc.sequenceFile(basepath + "/sfbytes/").collect())
|
|
self.assertEqual(bytes, ebs)
|
|
|
|
et = [(u'1', u'aa'),
|
|
(u'2', u'bb'),
|
|
(u'3', u'cc')]
|
|
self.sc.parallelize(et).saveAsSequenceFile(basepath + "/sftext/")
|
|
text = sorted(self.sc.sequenceFile(basepath + "/sftext/").collect())
|
|
self.assertEqual(text, et)
|
|
|
|
eb = [(1, False), (1, True), (2, False), (2, False), (2, True), (3, True)]
|
|
self.sc.parallelize(eb).saveAsSequenceFile(basepath + "/sfbool/")
|
|
bools = sorted(self.sc.sequenceFile(basepath + "/sfbool/").collect())
|
|
self.assertEqual(bools, eb)
|
|
|
|
en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)]
|
|
self.sc.parallelize(en).saveAsSequenceFile(basepath + "/sfnull/")
|
|
nulls = sorted(self.sc.sequenceFile(basepath + "/sfnull/").collect())
|
|
self.assertEqual(nulls, en)
|
|
|
|
em = [(1, {}),
|
|
(1, {3.0: u'bb'}),
|
|
(2, {1.0: u'aa'}),
|
|
(2, {1.0: u'cc'}),
|
|
(3, {2.0: u'dd'})]
|
|
self.sc.parallelize(em).saveAsSequenceFile(basepath + "/sfmap/")
|
|
maps = self.sc.sequenceFile(basepath + "/sfmap/").collect()
|
|
for v in maps:
|
|
self.assertTrue(v, em)
|
|
|
|
def test_oldhadoop(self):
|
|
basepath = self.tempdir.name
|
|
dict_data = [(1, {}),
|
|
(1, {"row1": 1.0}),
|
|
(2, {"row2": 2.0})]
|
|
self.sc.parallelize(dict_data).saveAsHadoopFile(
|
|
basepath + "/oldhadoop/",
|
|
"org.apache.hadoop.mapred.SequenceFileOutputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.MapWritable")
|
|
result = self.sc.hadoopFile(
|
|
basepath + "/oldhadoop/",
|
|
"org.apache.hadoop.mapred.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.MapWritable").collect()
|
|
for v in result:
|
|
self.assertTrue(v, dict_data)
|
|
|
|
conf = {
|
|
"mapred.output.format.class": "org.apache.hadoop.mapred.SequenceFileOutputFormat",
|
|
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.value.class": "org.apache.hadoop.io.MapWritable",
|
|
"mapred.output.dir": basepath + "/olddataset/"
|
|
}
|
|
self.sc.parallelize(dict_data).saveAsHadoopDataset(conf)
|
|
input_conf = {"mapred.input.dir": basepath + "/olddataset/"}
|
|
result = self.sc.hadoopRDD(
|
|
"org.apache.hadoop.mapred.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.MapWritable",
|
|
conf=input_conf).collect()
|
|
for v in result:
|
|
self.assertTrue(v, dict_data)
|
|
|
|
def test_newhadoop(self):
|
|
basepath = self.tempdir.name
|
|
data = [(1, ""),
|
|
(1, "a"),
|
|
(2, "bcdf")]
|
|
self.sc.parallelize(data).saveAsNewAPIHadoopFile(
|
|
basepath + "/newhadoop/",
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text")
|
|
result = sorted(self.sc.newAPIHadoopFile(
|
|
basepath + "/newhadoop/",
|
|
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text").collect())
|
|
self.assertEqual(result, data)
|
|
|
|
conf = {
|
|
"mapreduce.outputformat.class":
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
|
|
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.value.class": "org.apache.hadoop.io.Text",
|
|
"mapred.output.dir": basepath + "/newdataset/"
|
|
}
|
|
self.sc.parallelize(data).saveAsNewAPIHadoopDataset(conf)
|
|
input_conf = {"mapred.input.dir": basepath + "/newdataset/"}
|
|
new_dataset = sorted(self.sc.newAPIHadoopRDD(
|
|
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.hadoop.io.Text",
|
|
conf=input_conf).collect())
|
|
self.assertEqual(new_dataset, data)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "serialize of array")
|
|
def test_newhadoop_with_array(self):
|
|
basepath = self.tempdir.name
|
|
# use custom ArrayWritable types and converters to handle arrays
|
|
array_data = [(1, array('d')),
|
|
(1, array('d', [1.0, 2.0, 3.0])),
|
|
(2, array('d', [3.0, 4.0, 5.0]))]
|
|
self.sc.parallelize(array_data).saveAsNewAPIHadoopFile(
|
|
basepath + "/newhadoop/",
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.spark.api.python.DoubleArrayWritable",
|
|
valueConverter="org.apache.spark.api.python.DoubleArrayToWritableConverter")
|
|
result = sorted(self.sc.newAPIHadoopFile(
|
|
basepath + "/newhadoop/",
|
|
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.spark.api.python.DoubleArrayWritable",
|
|
valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect())
|
|
self.assertEqual(result, array_data)
|
|
|
|
conf = {
|
|
"mapreduce.outputformat.class":
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
|
|
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.value.class": "org.apache.spark.api.python.DoubleArrayWritable",
|
|
"mapred.output.dir": basepath + "/newdataset/"
|
|
}
|
|
self.sc.parallelize(array_data).saveAsNewAPIHadoopDataset(
|
|
conf,
|
|
valueConverter="org.apache.spark.api.python.DoubleArrayToWritableConverter")
|
|
input_conf = {"mapred.input.dir": basepath + "/newdataset/"}
|
|
new_dataset = sorted(self.sc.newAPIHadoopRDD(
|
|
"org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
|
|
"org.apache.hadoop.io.IntWritable",
|
|
"org.apache.spark.api.python.DoubleArrayWritable",
|
|
valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter",
|
|
conf=input_conf).collect())
|
|
self.assertEqual(new_dataset, array_data)
|
|
|
|
def test_newolderror(self):
|
|
basepath = self.tempdir.name
|
|
rdd = self.sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x))
|
|
self.assertRaises(Exception, lambda: rdd.saveAsHadoopFile(
|
|
basepath + "/newolderror/saveAsHadoopFile/",
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat"))
|
|
self.assertRaises(Exception, lambda: rdd.saveAsNewAPIHadoopFile(
|
|
basepath + "/newolderror/saveAsNewAPIHadoopFile/",
|
|
"org.apache.hadoop.mapred.SequenceFileOutputFormat"))
|
|
|
|
def test_bad_inputs(self):
|
|
basepath = self.tempdir.name
|
|
rdd = self.sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x))
|
|
self.assertRaises(Exception, lambda: rdd.saveAsHadoopFile(
|
|
basepath + "/badinputs/saveAsHadoopFile/",
|
|
"org.apache.hadoop.mapred.NotValidOutputFormat"))
|
|
self.assertRaises(Exception, lambda: rdd.saveAsNewAPIHadoopFile(
|
|
basepath + "/badinputs/saveAsNewAPIHadoopFile/",
|
|
"org.apache.hadoop.mapreduce.lib.output.NotValidOutputFormat"))
|
|
|
|
def test_converters(self):
|
|
# use of custom converters
|
|
basepath = self.tempdir.name
|
|
data = [(1, {3.0: u'bb'}),
|
|
(2, {1.0: u'aa'}),
|
|
(3, {2.0: u'dd'})]
|
|
self.sc.parallelize(data).saveAsNewAPIHadoopFile(
|
|
basepath + "/converters/",
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
|
|
keyConverter="org.apache.spark.api.python.TestOutputKeyConverter",
|
|
valueConverter="org.apache.spark.api.python.TestOutputValueConverter")
|
|
converted = sorted(self.sc.sequenceFile(basepath + "/converters/").collect())
|
|
expected = [(u'1', 3.0),
|
|
(u'2', 1.0),
|
|
(u'3', 2.0)]
|
|
self.assertEqual(converted, expected)
|
|
|
|
def test_reserialization(self):
|
|
basepath = self.tempdir.name
|
|
x = range(1, 5)
|
|
y = range(1001, 1005)
|
|
data = list(zip(x, y))
|
|
rdd = self.sc.parallelize(x).zip(self.sc.parallelize(y))
|
|
rdd.saveAsSequenceFile(basepath + "/reserialize/sequence")
|
|
result1 = sorted(self.sc.sequenceFile(basepath + "/reserialize/sequence").collect())
|
|
self.assertEqual(result1, data)
|
|
|
|
rdd.saveAsHadoopFile(
|
|
basepath + "/reserialize/hadoop",
|
|
"org.apache.hadoop.mapred.SequenceFileOutputFormat")
|
|
result2 = sorted(self.sc.sequenceFile(basepath + "/reserialize/hadoop").collect())
|
|
self.assertEqual(result2, data)
|
|
|
|
rdd.saveAsNewAPIHadoopFile(
|
|
basepath + "/reserialize/newhadoop",
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat")
|
|
result3 = sorted(self.sc.sequenceFile(basepath + "/reserialize/newhadoop").collect())
|
|
self.assertEqual(result3, data)
|
|
|
|
conf4 = {
|
|
"mapred.output.format.class": "org.apache.hadoop.mapred.SequenceFileOutputFormat",
|
|
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.value.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.dir": basepath + "/reserialize/dataset"}
|
|
rdd.saveAsHadoopDataset(conf4)
|
|
result4 = sorted(self.sc.sequenceFile(basepath + "/reserialize/dataset").collect())
|
|
self.assertEqual(result4, data)
|
|
|
|
conf5 = {"mapreduce.outputformat.class":
|
|
"org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
|
|
"mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.value.class": "org.apache.hadoop.io.IntWritable",
|
|
"mapred.output.dir": basepath + "/reserialize/newdataset"}
|
|
rdd.saveAsNewAPIHadoopDataset(conf5)
|
|
result5 = sorted(self.sc.sequenceFile(basepath + "/reserialize/newdataset").collect())
|
|
self.assertEqual(result5, data)
|
|
|
|
def test_malformed_RDD(self):
|
|
basepath = self.tempdir.name
|
|
# non-batch-serialized RDD[[(K, V)]] should be rejected
|
|
data = [[(1, "a")], [(2, "aa")], [(3, "aaa")]]
|
|
rdd = self.sc.parallelize(data, len(data))
|
|
self.assertRaises(Exception, lambda: rdd.saveAsSequenceFile(
|
|
basepath + "/malformed/sequence"))
|
|
|
|
|
|
class DaemonTests(unittest.TestCase):
|
|
def connect(self, port):
|
|
from socket import socket, AF_INET, SOCK_STREAM
|
|
sock = socket(AF_INET, SOCK_STREAM)
|
|
sock.connect(('127.0.0.1', port))
|
|
# send a split index of -1 to shutdown the worker
|
|
sock.send(b"\xFF\xFF\xFF\xFF")
|
|
sock.close()
|
|
return True
|
|
|
|
def do_termination_test(self, terminator):
|
|
from subprocess import Popen, PIPE
|
|
from errno import ECONNREFUSED
|
|
|
|
# start daemon
|
|
daemon_path = os.path.join(os.path.dirname(__file__), "daemon.py")
|
|
python_exec = sys.executable or os.environ.get("PYSPARK_PYTHON")
|
|
daemon = Popen([python_exec, daemon_path], stdin=PIPE, stdout=PIPE)
|
|
|
|
# read the port number
|
|
port = read_int(daemon.stdout)
|
|
|
|
# daemon should accept connections
|
|
self.assertTrue(self.connect(port))
|
|
|
|
# request shutdown
|
|
terminator(daemon)
|
|
time.sleep(1)
|
|
|
|
# daemon should no longer accept connections
|
|
try:
|
|
self.connect(port)
|
|
except EnvironmentError as exception:
|
|
self.assertEqual(exception.errno, ECONNREFUSED)
|
|
else:
|
|
self.fail("Expected EnvironmentError to be raised")
|
|
|
|
def test_termination_stdin(self):
|
|
"""Ensure that daemon and workers terminate when stdin is closed."""
|
|
self.do_termination_test(lambda daemon: daemon.stdin.close())
|
|
|
|
def test_termination_sigterm(self):
|
|
"""Ensure that daemon and workers terminate on SIGTERM."""
|
|
from signal import SIGTERM
|
|
self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM))
|
|
|
|
|
|
class WorkerTests(ReusedPySparkTestCase):
|
|
def test_cancel_task(self):
|
|
temp = tempfile.NamedTemporaryFile(delete=True)
|
|
temp.close()
|
|
path = temp.name
|
|
|
|
def sleep(x):
|
|
import os
|
|
import time
|
|
with open(path, 'w') as f:
|
|
f.write("%d %d" % (os.getppid(), os.getpid()))
|
|
time.sleep(100)
|
|
|
|
# start job in background thread
|
|
def run():
|
|
try:
|
|
self.sc.parallelize(range(1), 1).foreach(sleep)
|
|
except Exception:
|
|
pass
|
|
import threading
|
|
t = threading.Thread(target=run)
|
|
t.daemon = True
|
|
t.start()
|
|
|
|
daemon_pid, worker_pid = 0, 0
|
|
while True:
|
|
if os.path.exists(path):
|
|
with open(path) as f:
|
|
data = f.read().split(' ')
|
|
daemon_pid, worker_pid = map(int, data)
|
|
break
|
|
time.sleep(0.1)
|
|
|
|
# cancel jobs
|
|
self.sc.cancelAllJobs()
|
|
t.join()
|
|
|
|
for i in range(50):
|
|
try:
|
|
os.kill(worker_pid, 0)
|
|
time.sleep(0.1)
|
|
except OSError:
|
|
break # worker was killed
|
|
else:
|
|
self.fail("worker has not been killed after 5 seconds")
|
|
|
|
try:
|
|
os.kill(daemon_pid, 0)
|
|
except OSError:
|
|
self.fail("daemon had been killed")
|
|
|
|
# run a normal job
|
|
rdd = self.sc.parallelize(xrange(100), 1)
|
|
self.assertEqual(100, rdd.map(str).count())
|
|
|
|
def test_after_exception(self):
|
|
def raise_exception(_):
|
|
raise Exception()
|
|
rdd = self.sc.parallelize(xrange(100), 1)
|
|
with QuietTest(self.sc):
|
|
self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
|
|
self.assertEqual(100, rdd.map(str).count())
|
|
|
|
def test_after_jvm_exception(self):
|
|
tempFile = tempfile.NamedTemporaryFile(delete=False)
|
|
tempFile.write(b"Hello World!")
|
|
tempFile.close()
|
|
data = self.sc.textFile(tempFile.name, 1)
|
|
filtered_data = data.filter(lambda x: True)
|
|
self.assertEqual(1, filtered_data.count())
|
|
os.unlink(tempFile.name)
|
|
with QuietTest(self.sc):
|
|
self.assertRaises(Exception, lambda: filtered_data.count())
|
|
|
|
rdd = self.sc.parallelize(xrange(100), 1)
|
|
self.assertEqual(100, rdd.map(str).count())
|
|
|
|
def test_accumulator_when_reuse_worker(self):
|
|
from pyspark.accumulators import INT_ACCUMULATOR_PARAM
|
|
acc1 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
|
|
self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc1.add(x))
|
|
self.assertEqual(sum(range(100)), acc1.value)
|
|
|
|
acc2 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
|
|
self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc2.add(x))
|
|
self.assertEqual(sum(range(100)), acc2.value)
|
|
self.assertEqual(sum(range(100)), acc1.value)
|
|
|
|
def test_reuse_worker_after_take(self):
|
|
rdd = self.sc.parallelize(xrange(100000), 1)
|
|
self.assertEqual(0, rdd.first())
|
|
|
|
def count():
|
|
try:
|
|
rdd.count()
|
|
except Exception:
|
|
pass
|
|
|
|
t = threading.Thread(target=count)
|
|
t.daemon = True
|
|
t.start()
|
|
t.join(5)
|
|
self.assertTrue(not t.isAlive())
|
|
self.assertEqual(100000, rdd.count())
|
|
|
|
def test_with_different_versions_of_python(self):
|
|
rdd = self.sc.parallelize(range(10))
|
|
rdd.count()
|
|
version = self.sc.pythonVer
|
|
self.sc.pythonVer = "2.0"
|
|
try:
|
|
with QuietTest(self.sc):
|
|
self.assertRaises(Py4JJavaError, lambda: rdd.count())
|
|
finally:
|
|
self.sc.pythonVer = version
|
|
|
|
|
|
class SparkSubmitTests(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
self.programDir = tempfile.mkdtemp()
|
|
self.sparkSubmit = os.path.join(os.environ.get("SPARK_HOME"), "bin", "spark-submit")
|
|
|
|
def tearDown(self):
|
|
shutil.rmtree(self.programDir)
|
|
|
|
def createTempFile(self, name, content, dir=None):
|
|
"""
|
|
Create a temp file with the given name and content and return its path.
|
|
Strips leading spaces from content up to the first '|' in each line.
|
|
"""
|
|
pattern = re.compile(r'^ *\|', re.MULTILINE)
|
|
content = re.sub(pattern, '', content.strip())
|
|
if dir is None:
|
|
path = os.path.join(self.programDir, name)
|
|
else:
|
|
os.makedirs(os.path.join(self.programDir, dir))
|
|
path = os.path.join(self.programDir, dir, name)
|
|
with open(path, "w") as f:
|
|
f.write(content)
|
|
return path
|
|
|
|
def createFileInZip(self, name, content, ext=".zip", dir=None, zip_name=None):
|
|
"""
|
|
Create a zip archive containing a file with the given content and return its path.
|
|
Strips leading spaces from content up to the first '|' in each line.
|
|
"""
|
|
pattern = re.compile(r'^ *\|', re.MULTILINE)
|
|
content = re.sub(pattern, '', content.strip())
|
|
if dir is None:
|
|
path = os.path.join(self.programDir, name + ext)
|
|
else:
|
|
path = os.path.join(self.programDir, dir, zip_name + ext)
|
|
zip = zipfile.ZipFile(path, 'w')
|
|
zip.writestr(name, content)
|
|
zip.close()
|
|
return path
|
|
|
|
def create_spark_package(self, artifact_name):
|
|
group_id, artifact_id, version = artifact_name.split(":")
|
|
self.createTempFile("%s-%s.pom" % (artifact_id, version), ("""
|
|
|<?xml version="1.0" encoding="UTF-8"?>
|
|
|<project xmlns="http://maven.apache.org/POM/4.0.0"
|
|
| xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
|
|
| xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
|
|
| http://maven.apache.org/xsd/maven-4.0.0.xsd">
|
|
| <modelVersion>4.0.0</modelVersion>
|
|
| <groupId>%s</groupId>
|
|
| <artifactId>%s</artifactId>
|
|
| <version>%s</version>
|
|
|</project>
|
|
""" % (group_id, artifact_id, version)).lstrip(),
|
|
os.path.join(group_id, artifact_id, version))
|
|
self.createFileInZip("%s.py" % artifact_id, """
|
|
|def myfunc(x):
|
|
| return x + 1
|
|
""", ".jar", os.path.join(group_id, artifact_id, version),
|
|
"%s-%s" % (artifact_id, version))
|
|
|
|
def test_single_script(self):
|
|
"""Submit and test a single script file"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect())
|
|
""")
|
|
proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[2, 4, 6]", out.decode('utf-8'))
|
|
|
|
def test_script_with_local_functions(self):
|
|
"""Submit and test a single script file calling a global function"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|
|
|
|def foo(x):
|
|
| return x * 3
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(foo).collect())
|
|
""")
|
|
proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[3, 6, 9]", out.decode('utf-8'))
|
|
|
|
def test_module_dependency(self):
|
|
"""Submit and test a script with a dependency on another module"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|from mylib import myfunc
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(myfunc).collect())
|
|
""")
|
|
zip = self.createFileInZip("mylib.py", """
|
|
|def myfunc(x):
|
|
| return x + 1
|
|
""")
|
|
proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, script],
|
|
stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[2, 3, 4]", out.decode('utf-8'))
|
|
|
|
def test_module_dependency_on_cluster(self):
|
|
"""Submit and test a script with a dependency on another module on a cluster"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|from mylib import myfunc
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(myfunc).collect())
|
|
""")
|
|
zip = self.createFileInZip("mylib.py", """
|
|
|def myfunc(x):
|
|
| return x + 1
|
|
""")
|
|
proc = subprocess.Popen([self.sparkSubmit, "--py-files", zip, "--master",
|
|
"local-cluster[1,1,1024]", script],
|
|
stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[2, 3, 4]", out.decode('utf-8'))
|
|
|
|
def test_package_dependency(self):
|
|
"""Submit and test a script with a dependency on a Spark Package"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|from mylib import myfunc
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(myfunc).collect())
|
|
""")
|
|
self.create_spark_package("a:mylib:0.1")
|
|
proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories",
|
|
"file:" + self.programDir, script], stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[2, 3, 4]", out.decode('utf-8'))
|
|
|
|
def test_package_dependency_on_cluster(self):
|
|
"""Submit and test a script with a dependency on a Spark Package on a cluster"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|from mylib import myfunc
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(myfunc).collect())
|
|
""")
|
|
self.create_spark_package("a:mylib:0.1")
|
|
proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories",
|
|
"file:" + self.programDir, "--master",
|
|
"local-cluster[1,1,1024]", script], stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[2, 3, 4]", out.decode('utf-8'))
|
|
|
|
def test_single_script_on_cluster(self):
|
|
"""Submit and test a single script on a cluster"""
|
|
script = self.createTempFile("test.py", """
|
|
|from pyspark import SparkContext
|
|
|
|
|
|def foo(x):
|
|
| return x * 2
|
|
|
|
|
|sc = SparkContext()
|
|
|print(sc.parallelize([1, 2, 3]).map(foo).collect())
|
|
""")
|
|
# this will fail if you have different spark.executor.memory
|
|
# in conf/spark-defaults.conf
|
|
proc = subprocess.Popen(
|
|
[self.sparkSubmit, "--master", "local-cluster[1,1,1024]", script],
|
|
stdout=subprocess.PIPE)
|
|
out, err = proc.communicate()
|
|
self.assertEqual(0, proc.returncode)
|
|
self.assertIn("[2, 4, 6]", out.decode('utf-8'))
|
|
|
|
|
|
class ContextTests(unittest.TestCase):
|
|
|
|
def test_failed_sparkcontext_creation(self):
|
|
# Regression test for SPARK-1550
|
|
self.assertRaises(Exception, lambda: SparkContext("an-invalid-master-name"))
|
|
|
|
def test_get_or_create(self):
|
|
with SparkContext.getOrCreate() as sc:
|
|
self.assertTrue(SparkContext.getOrCreate() is sc)
|
|
|
|
def test_parallelize_eager_cleanup(self):
|
|
with SparkContext() as sc:
|
|
temp_files = os.listdir(sc._temp_dir)
|
|
rdd = sc.parallelize([0, 1, 2])
|
|
post_parallalize_temp_files = os.listdir(sc._temp_dir)
|
|
self.assertEqual(temp_files, post_parallalize_temp_files)
|
|
|
|
def test_set_conf(self):
|
|
# This is for an internal use case. When there is an existing SparkContext,
|
|
# SparkSession's builder needs to set configs into SparkContext's conf.
|
|
sc = SparkContext()
|
|
sc._conf.set("spark.test.SPARK16224", "SPARK16224")
|
|
self.assertEqual(sc._jsc.sc().conf().get("spark.test.SPARK16224"), "SPARK16224")
|
|
sc.stop()
|
|
|
|
def test_stop(self):
|
|
sc = SparkContext()
|
|
self.assertNotEqual(SparkContext._active_spark_context, None)
|
|
sc.stop()
|
|
self.assertEqual(SparkContext._active_spark_context, None)
|
|
|
|
def test_with(self):
|
|
with SparkContext() as sc:
|
|
self.assertNotEqual(SparkContext._active_spark_context, None)
|
|
self.assertEqual(SparkContext._active_spark_context, None)
|
|
|
|
def test_with_exception(self):
|
|
try:
|
|
with SparkContext() as sc:
|
|
self.assertNotEqual(SparkContext._active_spark_context, None)
|
|
raise Exception()
|
|
except:
|
|
pass
|
|
self.assertEqual(SparkContext._active_spark_context, None)
|
|
|
|
def test_with_stop(self):
|
|
with SparkContext() as sc:
|
|
self.assertNotEqual(SparkContext._active_spark_context, None)
|
|
sc.stop()
|
|
self.assertEqual(SparkContext._active_spark_context, None)
|
|
|
|
def test_progress_api(self):
|
|
with SparkContext() as sc:
|
|
sc.setJobGroup('test_progress_api', '', True)
|
|
rdd = sc.parallelize(range(10)).map(lambda x: time.sleep(100))
|
|
|
|
def run():
|
|
try:
|
|
rdd.count()
|
|
except Exception:
|
|
pass
|
|
t = threading.Thread(target=run)
|
|
t.daemon = True
|
|
t.start()
|
|
# wait for scheduler to start
|
|
time.sleep(1)
|
|
|
|
tracker = sc.statusTracker()
|
|
jobIds = tracker.getJobIdsForGroup('test_progress_api')
|
|
self.assertEqual(1, len(jobIds))
|
|
job = tracker.getJobInfo(jobIds[0])
|
|
self.assertEqual(1, len(job.stageIds))
|
|
stage = tracker.getStageInfo(job.stageIds[0])
|
|
self.assertEqual(rdd.getNumPartitions(), stage.numTasks)
|
|
|
|
sc.cancelAllJobs()
|
|
t.join()
|
|
# wait for event listener to update the status
|
|
time.sleep(1)
|
|
|
|
job = tracker.getJobInfo(jobIds[0])
|
|
self.assertEqual('FAILED', job.status)
|
|
self.assertEqual([], tracker.getActiveJobsIds())
|
|
self.assertEqual([], tracker.getActiveStageIds())
|
|
|
|
sc.stop()
|
|
|
|
def test_startTime(self):
|
|
with SparkContext() as sc:
|
|
self.assertGreater(sc.startTime, 0)
|
|
|
|
|
|
class ConfTests(unittest.TestCase):
|
|
def test_memory_conf(self):
|
|
memoryList = ["1T", "1G", "1M", "1024K"]
|
|
for memory in memoryList:
|
|
sc = SparkContext(conf=SparkConf().set("spark.python.worker.memory", memory))
|
|
l = list(range(1024))
|
|
random.shuffle(l)
|
|
rdd = sc.parallelize(l, 4)
|
|
self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect())
|
|
sc.stop()
|
|
|
|
|
|
@unittest.skipIf(not _have_scipy, "SciPy not installed")
|
|
class SciPyTests(PySparkTestCase):
|
|
|
|
"""General PySpark tests that depend on scipy """
|
|
|
|
def test_serialize(self):
|
|
from scipy.special import gammaln
|
|
x = range(1, 5)
|
|
expected = list(map(gammaln, x))
|
|
observed = self.sc.parallelize(x).map(gammaln).collect()
|
|
self.assertEqual(expected, observed)
|
|
|
|
|
|
@unittest.skipIf(not _have_numpy, "NumPy not installed")
|
|
class NumPyTests(PySparkTestCase):
|
|
|
|
"""General PySpark tests that depend on numpy """
|
|
|
|
def test_statcounter_array(self):
|
|
x = self.sc.parallelize([np.array([1.0, 1.0]), np.array([2.0, 2.0]), np.array([3.0, 3.0])])
|
|
s = x.stats()
|
|
self.assertSequenceEqual([2.0, 2.0], s.mean().tolist())
|
|
self.assertSequenceEqual([1.0, 1.0], s.min().tolist())
|
|
self.assertSequenceEqual([3.0, 3.0], s.max().tolist())
|
|
self.assertSequenceEqual([1.0, 1.0], s.sampleStdev().tolist())
|
|
|
|
stats_dict = s.asDict()
|
|
self.assertEqual(3, stats_dict['count'])
|
|
self.assertSequenceEqual([2.0, 2.0], stats_dict['mean'].tolist())
|
|
self.assertSequenceEqual([1.0, 1.0], stats_dict['min'].tolist())
|
|
self.assertSequenceEqual([3.0, 3.0], stats_dict['max'].tolist())
|
|
self.assertSequenceEqual([6.0, 6.0], stats_dict['sum'].tolist())
|
|
self.assertSequenceEqual([1.0, 1.0], stats_dict['stdev'].tolist())
|
|
self.assertSequenceEqual([1.0, 1.0], stats_dict['variance'].tolist())
|
|
|
|
stats_sample_dict = s.asDict(sample=True)
|
|
self.assertEqual(3, stats_dict['count'])
|
|
self.assertSequenceEqual([2.0, 2.0], stats_sample_dict['mean'].tolist())
|
|
self.assertSequenceEqual([1.0, 1.0], stats_sample_dict['min'].tolist())
|
|
self.assertSequenceEqual([3.0, 3.0], stats_sample_dict['max'].tolist())
|
|
self.assertSequenceEqual([6.0, 6.0], stats_sample_dict['sum'].tolist())
|
|
self.assertSequenceEqual(
|
|
[0.816496580927726, 0.816496580927726], stats_sample_dict['stdev'].tolist())
|
|
self.assertSequenceEqual(
|
|
[0.6666666666666666, 0.6666666666666666], stats_sample_dict['variance'].tolist())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.tests import *
|
|
if not _have_scipy:
|
|
print("NOTE: Skipping SciPy tests as it does not seem to be installed")
|
|
if not _have_numpy:
|
|
print("NOTE: Skipping NumPy tests as it does not seem to be installed")
|
|
if xmlrunner:
|
|
unittest.main(testRunner=xmlrunner.XMLTestRunner(output='target/test-reports'))
|
|
else:
|
|
unittest.main()
|
|
if not _have_scipy:
|
|
print("NOTE: SciPy tests were skipped as it does not seem to be installed")
|
|
if not _have_numpy:
|
|
print("NOTE: NumPy tests were skipped as it does not seem to be installed")
|