# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """ Unit tests for PySpark; additional tests are implemented as doctests in individual modules. """ from array import array from fileinput import input from glob import glob import os import re import shutil import subprocess import sys import tempfile import time import zipfile if sys.version_info[:2] <= (2, 6): import unittest2 as unittest else: import unittest from pyspark.context import SparkContext from pyspark.files import SparkFiles from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger _have_scipy = False _have_numpy = False try: import scipy.sparse _have_scipy = True except: # No SciPy, but that's okay, we'll skip those tests pass try: import numpy as np _have_numpy = True except: # No NumPy, but that's okay, we'll skip those tests pass SPARK_HOME = os.environ["SPARK_HOME"] class TestMerger(unittest.TestCase): def setUp(self): self.N = 1 << 16 self.l = [i for i in xrange(self.N)] self.data = zip(self.l, self.l) self.agg = Aggregator(lambda x: [x], lambda x, y: x.append(y) or x, lambda x, y: x.extend(y) or x) def test_in_memory(self): m = InMemoryMerger(self.agg) m.mergeValues(self.data) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), sum(xrange(self.N))) m = InMemoryMerger(self.agg) m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data)) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), sum(xrange(self.N))) def test_small_dataset(self): m = ExternalMerger(self.agg, 1000) m.mergeValues(self.data) self.assertEqual(m.spills, 0) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 1000) m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data)) self.assertEqual(m.spills, 0) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), sum(xrange(self.N))) def test_medium_dataset(self): m = ExternalMerger(self.agg, 10) m.mergeValues(self.data) self.assertTrue(m.spills >= 1) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 10) m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data * 3)) self.assertTrue(m.spills >= 1) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), sum(xrange(self.N)) * 3) def test_huge_dataset(self): m = ExternalMerger(self.agg, 10) m.mergeCombiners(map(lambda (k, v): (k, [str(v)]), self.data * 10)) self.assertTrue(m.spills >= 1) self.assertEqual(sum(len(v) for k, v in m._recursive_merged_items(0)), self.N * 10) m._cleanup() class SerializationTestCase(unittest.TestCase): def test_namedtuple(self): from collections import namedtuple from cPickle import dumps, loads P = namedtuple("P", "x y") p1 = P(1, 3) p2 = loads(dumps(p1, 2)) self.assertEquals(p1, p2) class PySparkTestCase(unittest.TestCase): def setUp(self): self._old_sys_path = list(sys.path) class_name = self.__class__.__name__ self.sc = SparkContext('local[4]', class_name, batchSize=2) def tearDown(self): self.sc.stop() sys.path = self._old_sys_path class TestCheckpoint(PySparkTestCase): def setUp(self): PySparkTestCase.setUp(self) self.checkpointDir = tempfile.NamedTemporaryFile(delete=False) os.unlink(self.checkpointDir.name) self.sc.setCheckpointDir(self.checkpointDir.name) def tearDown(self): PySparkTestCase.tearDown(self) shutil.rmtree(self.checkpointDir.name) def test_basic_checkpointing(self): parCollection = self.sc.parallelize([1, 2, 3, 4]) flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1)) self.assertFalse(flatMappedRDD.isCheckpointed()) self.assertTrue(flatMappedRDD.getCheckpointFile() is None) flatMappedRDD.checkpoint() result = flatMappedRDD.collect() time.sleep(1) # 1 second self.assertTrue(flatMappedRDD.isCheckpointed()) self.assertEqual(flatMappedRDD.collect(), result) self.assertEqual("file:" + self.checkpointDir.name, os.path.dirname(os.path.dirname(flatMappedRDD.getCheckpointFile()))) def test_checkpoint_and_restore(self): parCollection = self.sc.parallelize([1, 2, 3, 4]) flatMappedRDD = parCollection.flatMap(lambda x: [x]) self.assertFalse(flatMappedRDD.isCheckpointed()) self.assertTrue(flatMappedRDD.getCheckpointFile() is None) flatMappedRDD.checkpoint() flatMappedRDD.count() # forces a checkpoint to be computed time.sleep(1) # 1 second self.assertTrue(flatMappedRDD.getCheckpointFile() is not None) recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(), flatMappedRDD._jrdd_deserializer) self.assertEquals([1, 2, 3, 4], recovered.collect()) class TestAddFile(PySparkTestCase): def test_add_py_file(self): # To ensure that we're actually testing addPyFile's effects, check that # this job fails due to `userlibrary` not being on the Python path: # disable logging in log4j temporarily log4j = self.sc._jvm.org.apache.log4j old_level = log4j.LogManager.getRootLogger().getLevel() log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL) def func(x): from userlibrary import UserClass return UserClass().hello() self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first) log4j.LogManager.getRootLogger().setLevel(old_level) # Add the file, so the job should now succeed: path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py") self.sc.addPyFile(path) res = self.sc.parallelize(range(2)).map(func).first() self.assertEqual("Hello World!", res) def test_add_file_locally(self): path = os.path.join(SPARK_HOME, "python/test_support/hello.txt") self.sc.addFile(path) download_path = SparkFiles.get("hello.txt") self.assertNotEqual(path, download_path) with open(download_path) as test_file: self.assertEquals("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.addFile(path) from userlibrary import UserClass self.assertEqual("Hello World!", UserClass().hello()) 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-py2.7.egg") self.sc.addPyFile(path) from userlib import UserClass self.assertEqual("Hello World from inside a package!", UserClass().hello()) class TestRDDFunctions(PySparkTestCase): def test_failed_sparkcontext_creation(self): # Regression test for SPARK-1550 self.sc.stop() self.assertRaises(Exception, lambda: SparkContext("an-invalid-master-name")) self.sc = SparkContext("local") 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 = ''.join(input(glob(tempFile.name + "/part-0000*"))) self.assertEqual(x, unicode(raw_contents.strip(), "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 = ''.join(input(glob(tempFile.name + "/part-0000*"))) self.assertEqual(x, unicode(raw_contents.strip(), "utf-8")) 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, y): x + y).collect() def test_transforming_pickle_file(self): # Regression test for SPARK-2601 data = self.sc.parallelize(["Hello", "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.txt") a = self.sc.textFile(path) result = a.cartesian(a).collect() (x, y) = result[0] self.assertEqual("Hello World!", x.strip()) self.assertEqual("Hello World!", y.strip()) def test_deleting_input_files(self): # Regression test for SPARK-1025 tempFile = tempfile.NamedTemporaryFile(delete=False) tempFile.write("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) self.assertRaises(Exception, lambda: filtered_data.count()) def testAggregateByKey(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.assertEquals([jon, jane], theDoes.collect()) def test_large_broadcast(self): N = 100000 data = [[float(i) for i in range(300)] for i in range(N)] bdata = self.sc.broadcast(data) # 270MB m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum() self.assertEquals(N, m) 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)]) 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)) # 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.assertEquals(a.count(), b.count()) self.assertRaises(Exception, lambda: a.zip(b).count()) def test_histogram(self): # empty rdd = self.sc.parallelize([]) self.assertEquals([0], rdd.histogram([0, 10])[1]) self.assertEquals([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.assertEquals([0], rdd.histogram([0, 10])[1]) self.assertEquals([0, 0], rdd.histogram((0, 4, 10))[1]) # in range with one bucket rdd = self.sc.parallelize(range(1, 5)) self.assertEquals([4], rdd.histogram([0, 10])[1]) self.assertEquals([3, 1], rdd.histogram([0, 4, 10])[1]) # in range with one bucket exact match self.assertEquals([4], rdd.histogram([1, 4])[1]) # out of range with two buckets rdd = self.sc.parallelize([10.01, -0.01]) self.assertEquals([0, 0], rdd.histogram([0, 5, 10])[1]) # out of range with two uneven buckets rdd = self.sc.parallelize([10.01, -0.01]) self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1]) # in range with two buckets rdd = self.sc.parallelize([1, 2, 3, 5, 6]) self.assertEquals([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.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1]) # in range with two uneven buckets rdd = self.sc.parallelize([1, 2, 3, 5, 6]) self.assertEquals([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.assertEquals([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.assertEquals([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.assertEquals([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.assertEquals([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.assertEquals(([1, 4], [4]), rdd.histogram(1)) # without buckets single element rdd = self.sc.parallelize([1]) self.assertEquals(([1, 1], [1]), rdd.histogram(1)) # without bucket no range rdd = self.sc.parallelize([1] * 4) self.assertEquals(([1, 1], [4]), rdd.histogram(1)) # without buckets basic two rdd = self.sc.parallelize(range(1, 5)) self.assertEquals(([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.assertEquals((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.assertEquals([2, 2], rdd.histogram(["a", "b", "c"])[1]) self.assertEquals((["ab", "ef"], [5]), rdd.histogram(1)) self.assertRaises(TypeError, lambda: rdd.histogram(2)) # mixed RDD rdd = self.sc.parallelize([1, 4, "ab", "ac", "b"], 2) self.assertEquals([1, 1], rdd.histogram([0, 4, 10])[1]) self.assertEquals([2, 1], rdd.histogram(["a", "b", "c"])[1]) self.assertEquals(([1, "b"], [5]), rdd.histogram(1)) self.assertRaises(TypeError, lambda: rdd.histogram(2)) class TestIO(PySparkTestCase): def test_stdout_redirection(self): import subprocess def func(x): subprocess.check_call('ls', shell=True) self.sc.parallelize([1]).foreach(func) class TestInputFormat(PySparkTestCase): def setUp(self): PySparkTestCase.setUp(self) self.tempdir = tempfile.NamedTemporaryFile(delete=False) os.unlink(self.tempdir.name) self.sc._jvm.WriteInputFormatTestDataGenerator.generateData(self.tempdir.name, self.sc._jsc) def tearDown(self): PySparkTestCase.tearDown(self) shutil.rmtree(self.tempdir.name) 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 = sorted(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'})] self.assertEqual(maps, 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()) ec = (u'1', {u'__class__': u'org.apache.spark.api.python.TestWritable', u'double': 54.0, u'int': 123, u'str': u'test1'}) self.assertEqual(clazz[0], ec) unbatched_clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/", "org.apache.hadoop.io.Text", "org.apache.spark.api.python.TestWritable", batchSize=1).collect()) self.assertEqual(unbatched_clazz[0], 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.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.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) class TestOutputFormat(PySparkTestCase): def setUp(self): PySparkTestCase.setUp(self) self.tempdir = tempfile.NamedTemporaryFile(delete=False) os.unlink(self.tempdir.name) def tearDown(self): PySparkTestCase.tearDown(self) shutil.rmtree(self.tempdir.name, ignore_errors=True) 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 = sorted(self.sc.sequenceFile(basepath + "/sfmap/").collect()) self.assertEqual(maps, 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 = sorted(self.sc.hadoopFile( basepath + "/oldhadoop/", "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable").collect()) self.assertEqual(result, 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/"} old_dataset = sorted(self.sc.hadoopRDD( "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable", conf=input_conf).collect()) self.assertEqual(old_dataset, dict_data) @unittest.skipIf(sys.version_info[:2] <= (2, 6), "Skipped on 2.6 until SPARK-2951 is fixed") def test_newhadoop(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 = 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_unbatched_save_and_read(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, numSlices=len(ei)).saveAsSequenceFile( basepath + "/unbatched/") unbatched_sequence = sorted(self.sc.sequenceFile( basepath + "/unbatched/", batchSize=1).collect()) self.assertEqual(unbatched_sequence, ei) unbatched_hadoopFile = sorted(self.sc.hadoopFile( basepath + "/unbatched/", "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.Text", batchSize=1).collect()) self.assertEqual(unbatched_hadoopFile, ei) unbatched_newAPIHadoopFile = sorted(self.sc.newAPIHadoopFile( basepath + "/unbatched/", "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.Text", batchSize=1).collect()) self.assertEqual(unbatched_newAPIHadoopFile, ei) oldconf = {"mapred.input.dir": basepath + "/unbatched/"} unbatched_hadoopRDD = sorted(self.sc.hadoopRDD( "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.Text", conf=oldconf, batchSize=1).collect()) self.assertEqual(unbatched_hadoopRDD, ei) newconf = {"mapred.input.dir": basepath + "/unbatched/"} unbatched_newAPIHadoopRDD = sorted(self.sc.newAPIHadoopRDD( "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.Text", conf=newconf, batchSize=1).collect()) self.assertEqual(unbatched_newAPIHadoopRDD, ei) 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, numSlices=len(data)) self.assertRaises(Exception, lambda: rdd.saveAsSequenceFile( basepath + "/malformed/sequence")) class TestDaemon(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("\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") daemon = Popen([sys.executable, 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 TestWorker(PySparkTestCase): 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(): self.sc.parallelize(range(1)).foreach(sleep) 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): data = open(path).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") def test_fd_leak(self): N = 1100 # fd limit is 1024 by default rdd = self.sc.parallelize(range(N), N) self.assertEquals(N, rdd.count()) class TestSparkSubmit(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): """ 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()) path = os.path.join(self.programDir, name) with open(path, "w") as f: f.write(content) return path def createFileInZip(self, name, content): """ 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()) path = os.path.join(self.programDir, name + ".zip") zip = zipfile.ZipFile(path, 'w') zip.writestr(name, content) zip.close() return path 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) 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) 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) 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[1,1,512]", script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) self.assertIn("[2, 3, 4]", out) 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() """) proc = subprocess.Popen( [self.sparkSubmit, "--master", "local-cluster[1,1,512]", script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) self.assertIn("[2, 4, 6]", out) @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 = 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()) if __name__ == "__main__": 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" 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"