# # 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 glob import glob import os import re import shutil import subprocess import sys import tempfile import time import zipfile import random import threading import hashlib from py4j.protocol import Py4JJavaError try: import xmlrunner except ImportError: xmlrunner = None if sys.version_info[:2] <= (2, 6): try: import unittest2 as unittest except ImportError: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier') sys.exit(1) else: import unittest if sys.version_info[0] >= 3: xrange = range basestring = str if sys.version >= "3": from io import StringIO else: from StringIO import StringIO from pyspark.conf import SparkConf from pyspark.context import SparkContext from pyspark.rdd import RDD from pyspark.files import SparkFiles from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer, \ CloudPickleSerializer, CompressedSerializer, UTF8Deserializer, NoOpSerializer, \ PairDeserializer, CartesianDeserializer, AutoBatchedSerializer, AutoSerializer, \ FlattenedValuesSerializer from pyspark.shuffle import Aggregator, ExternalMerger, ExternalSorter from pyspark import shuffle from pyspark.profiler import BasicProfiler _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 MergerTests(unittest.TestCase): def setUp(self): self.N = 1 << 12 self.l = [i for i in xrange(self.N)] self.data = list(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_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.items()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 1000) m.mergeCombiners(map(lambda x_y1: (x_y1[0], [x_y1[1]]), self.data)) self.assertEqual(m.spills, 0) self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) def test_medium_dataset(self): m = ExternalMerger(self.agg, 20) m.mergeValues(self.data) self.assertTrue(m.spills >= 1) self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 10) m.mergeCombiners(map(lambda x_y2: (x_y2[0], [x_y2[1]]), self.data * 3)) self.assertTrue(m.spills >= 1) self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N)) * 3) def test_huge_dataset(self): m = ExternalMerger(self.agg, 5, partitions=3) m.mergeCombiners(map(lambda k_v: (k_v[0], [str(k_v[1])]), self.data * 10)) self.assertTrue(m.spills >= 1) self.assertEqual(sum(len(v) for k, v in m.items()), self.N * 10) m._cleanup() def test_group_by_key(self): def gen_data(N, step): for i in range(1, N + 1, step): for j in range(i): yield (i, [j]) def gen_gs(N, step=1): return shuffle.GroupByKey(gen_data(N, step)) self.assertEqual(1, len(list(gen_gs(1)))) self.assertEqual(2, len(list(gen_gs(2)))) self.assertEqual(100, len(list(gen_gs(100)))) self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)]) self.assertTrue(all(list(range(k)) == list(vs) for k, vs in gen_gs(100))) for k, vs in gen_gs(50002, 10000): self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs)) ser = PickleSerializer() l = ser.loads(ser.dumps(list(gen_gs(50002, 30000)))) for k, vs in l: self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs)) class SorterTests(unittest.TestCase): def test_in_memory_sort(self): l = list(range(1024)) random.shuffle(l) sorter = ExternalSorter(1024) self.assertEqual(sorted(l), list(sorter.sorted(l))) self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) self.assertEqual(sorted(l, key=lambda x: -x, reverse=True), list(sorter.sorted(l, key=lambda x: -x, reverse=True))) def test_external_sort(self): class CustomizedSorter(ExternalSorter): def _next_limit(self): return self.memory_limit l = list(range(1024)) random.shuffle(l) sorter = CustomizedSorter(1) self.assertEqual(sorted(l), list(sorter.sorted(l))) self.assertGreater(shuffle.DiskBytesSpilled, 0) last = shuffle.DiskBytesSpilled self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) self.assertGreater(shuffle.DiskBytesSpilled, last) last = shuffle.DiskBytesSpilled self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) self.assertGreater(shuffle.DiskBytesSpilled, last) last = shuffle.DiskBytesSpilled self.assertEqual(sorted(l, key=lambda x: -x, reverse=True), list(sorter.sorted(l, key=lambda x: -x, reverse=True))) self.assertGreater(shuffle.DiskBytesSpilled, last) def test_external_sort_in_rdd(self): conf = SparkConf().set("spark.python.worker.memory", "1m") sc = SparkContext(conf=conf) l = list(range(10240)) random.shuffle(l) rdd = sc.parallelize(l, 4) self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect()) sc.stop() class SerializationTestCase(unittest.TestCase): def test_namedtuple(self): from collections import namedtuple from pickle import dumps, loads P = namedtuple("P", "x y") p1 = P(1, 3) p2 = loads(dumps(p1, 2)) self.assertEqual(p1, p2) from pyspark.cloudpickle import dumps P2 = loads(dumps(P)) p3 = P2(1, 3) self.assertEqual(p1, p3) def test_itemgetter(self): from operator import itemgetter ser = CloudPickleSerializer() d = range(10) getter = itemgetter(1) getter2 = ser.loads(ser.dumps(getter)) self.assertEqual(getter(d), getter2(d)) getter = itemgetter(0, 3) getter2 = ser.loads(ser.dumps(getter)) self.assertEqual(getter(d), getter2(d)) def test_function_module_name(self): ser = CloudPickleSerializer() func = lambda x: x func2 = ser.loads(ser.dumps(func)) self.assertEqual(func.__module__, func2.__module__) def test_attrgetter(self): from operator import attrgetter ser = CloudPickleSerializer() class C(object): def __getattr__(self, item): return item d = C() getter = attrgetter("a") getter2 = ser.loads(ser.dumps(getter)) self.assertEqual(getter(d), getter2(d)) getter = attrgetter("a", "b") getter2 = ser.loads(ser.dumps(getter)) self.assertEqual(getter(d), getter2(d)) d.e = C() getter = attrgetter("e.a") getter2 = ser.loads(ser.dumps(getter)) self.assertEqual(getter(d), getter2(d)) getter = attrgetter("e.a", "e.b") getter2 = ser.loads(ser.dumps(getter)) self.assertEqual(getter(d), getter2(d)) # Regression test for SPARK-3415 def test_pickling_file_handles(self): # to be corrected with SPARK-11160 if not xmlrunner: ser = CloudPickleSerializer() out1 = sys.stderr out2 = ser.loads(ser.dumps(out1)) self.assertEqual(out1, out2) def test_func_globals(self): class Unpicklable(object): def __reduce__(self): raise Exception("not picklable") global exit exit = Unpicklable() ser = CloudPickleSerializer() self.assertRaises(Exception, lambda: ser.dumps(exit)) def foo(): sys.exit(0) self.assertTrue("exit" in foo.__code__.co_names) ser.dumps(foo) def test_compressed_serializer(self): ser = CompressedSerializer(PickleSerializer()) try: from StringIO import StringIO except ImportError: from io import BytesIO as StringIO io = StringIO() ser.dump_stream(["abc", u"123", range(5)], io) io.seek(0) self.assertEqual(["abc", u"123", range(5)], list(ser.load_stream(io))) ser.dump_stream(range(1000), io) io.seek(0) self.assertEqual(["abc", u"123", range(5)] + list(range(1000)), list(ser.load_stream(io))) io.close() def test_hash_serializer(self): hash(NoOpSerializer()) hash(UTF8Deserializer()) hash(PickleSerializer()) hash(MarshalSerializer()) hash(AutoSerializer()) hash(BatchedSerializer(PickleSerializer())) hash(AutoBatchedSerializer(MarshalSerializer())) hash(PairDeserializer(NoOpSerializer(), UTF8Deserializer())) hash(CartesianDeserializer(NoOpSerializer(), UTF8Deserializer())) hash(CompressedSerializer(PickleSerializer())) hash(FlattenedValuesSerializer(PickleSerializer())) class QuietTest(object): def __init__(self, sc): self.log4j = sc._jvm.org.apache.log4j def __enter__(self): self.old_level = self.log4j.LogManager.getRootLogger().getLevel() self.log4j.LogManager.getRootLogger().setLevel(self.log4j.Level.FATAL) def __exit__(self, exc_type, exc_val, exc_tb): self.log4j.LogManager.getRootLogger().setLevel(self.old_level) 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) def tearDown(self): self.sc.stop() sys.path = self._old_sys_path class ReusedPySparkTestCase(unittest.TestCase): @classmethod def setUpClass(cls): cls.sc = SparkContext('local[4]', cls.__name__) @classmethod def tearDownClass(cls): cls.sc.stop() class CheckpointTests(ReusedPySparkTestCase): def setUp(self): self.checkpointDir = tempfile.NamedTemporaryFile(delete=False) os.unlink(self.checkpointDir.name) self.sc.setCheckpointDir(self.checkpointDir.name) def 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.assertEqual([1, 2, 3, 4], recovered.collect()) class AddFileTests(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 def func(x): from userlibrary import UserClass return UserClass().hello() with QuietTest(self.sc): self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first) # 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.assertEqual("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 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.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_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.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_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.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_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.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) 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), (""" | | | 4.0.0 | %s | %s | %s | """ % (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")