# # 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. # import glob import os import sys from itertools import chain import time import operator import tempfile import random import struct import shutil from functools import reduce 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 >= "3": long = int from pyspark.context import SparkConf, SparkContext, RDD from pyspark.storagelevel import StorageLevel from pyspark.streaming.context import StreamingContext from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition from pyspark.streaming.flume import FlumeUtils from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream from pyspark.streaming.listener import StreamingListener class PySparkStreamingTestCase(unittest.TestCase): timeout = 10 # seconds duration = .5 @classmethod def setUpClass(cls): class_name = cls.__name__ conf = SparkConf().set("spark.default.parallelism", 1) cls.sc = SparkContext(appName=class_name, conf=conf) cls.sc.setCheckpointDir("/tmp") @classmethod def tearDownClass(cls): cls.sc.stop() # Clean up in the JVM just in case there has been some issues in Python API try: jSparkContextOption = SparkContext._jvm.SparkContext.get() if jSparkContextOption.nonEmpty(): jSparkContextOption.get().stop() except: pass def setUp(self): self.ssc = StreamingContext(self.sc, self.duration) def tearDown(self): if self.ssc is not None: self.ssc.stop(False) # Clean up in the JVM just in case there has been some issues in Python API try: jStreamingContextOption = StreamingContext._jvm.SparkContext.getActive() if jStreamingContextOption.nonEmpty(): jStreamingContextOption.get().stop(False) except: pass def wait_for(self, result, n): start_time = time.time() while len(result) < n and time.time() - start_time < self.timeout: time.sleep(0.01) if len(result) < n: print("timeout after", self.timeout) def _take(self, dstream, n): """ Return the first `n` elements in the stream (will start and stop). """ results = [] def take(_, rdd): if rdd and len(results) < n: results.extend(rdd.take(n - len(results))) dstream.foreachRDD(take) self.ssc.start() self.wait_for(results, n) return results def _collect(self, dstream, n, block=True): """ Collect each RDDs into the returned list. :return: list, which will have the collected items. """ result = [] def get_output(_, rdd): if rdd and len(result) < n: r = rdd.collect() if r: result.append(r) dstream.foreachRDD(get_output) if not block: return result self.ssc.start() self.wait_for(result, n) return result def _test_func(self, input, func, expected, sort=False, input2=None): """ @param input: dataset for the test. This should be list of lists. @param func: wrapped function. This function should return PythonDStream object. @param expected: expected output for this testcase. """ if not isinstance(input[0], RDD): input = [self.sc.parallelize(d, 1) for d in input] input_stream = self.ssc.queueStream(input) if input2 and not isinstance(input2[0], RDD): input2 = [self.sc.parallelize(d, 1) for d in input2] input_stream2 = self.ssc.queueStream(input2) if input2 is not None else None # Apply test function to stream. if input2: stream = func(input_stream, input_stream2) else: stream = func(input_stream) result = self._collect(stream, len(expected)) if sort: self._sort_result_based_on_key(result) self._sort_result_based_on_key(expected) self.assertEqual(expected, result) def _sort_result_based_on_key(self, outputs): """Sort the list based on first value.""" for output in outputs: output.sort(key=lambda x: x[0]) class BasicOperationTests(PySparkStreamingTestCase): def test_map(self): """Basic operation test for DStream.map.""" input = [range(1, 5), range(5, 9), range(9, 13)] def func(dstream): return dstream.map(str) expected = [list(map(str, x)) for x in input] self._test_func(input, func, expected) def test_flatMap(self): """Basic operation test for DStream.faltMap.""" input = [range(1, 5), range(5, 9), range(9, 13)] def func(dstream): return dstream.flatMap(lambda x: (x, x * 2)) expected = [list(chain.from_iterable((map(lambda y: [y, y * 2], x)))) for x in input] self._test_func(input, func, expected) def test_filter(self): """Basic operation test for DStream.filter.""" input = [range(1, 5), range(5, 9), range(9, 13)] def func(dstream): return dstream.filter(lambda x: x % 2 == 0) expected = [[y for y in x if y % 2 == 0] for x in input] self._test_func(input, func, expected) def test_count(self): """Basic operation test for DStream.count.""" input = [range(5), range(10), range(20)] def func(dstream): return dstream.count() expected = [[len(x)] for x in input] self._test_func(input, func, expected) def test_reduce(self): """Basic operation test for DStream.reduce.""" input = [range(1, 5), range(5, 9), range(9, 13)] def func(dstream): return dstream.reduce(operator.add) expected = [[reduce(operator.add, x)] for x in input] self._test_func(input, func, expected) def test_reduceByKey(self): """Basic operation test for DStream.reduceByKey.""" input = [[("a", 1), ("a", 1), ("b", 1), ("b", 1)], [("", 1), ("", 1), ("", 1), ("", 1)], [(1, 1), (1, 1), (2, 1), (2, 1), (3, 1)]] def func(dstream): return dstream.reduceByKey(operator.add) expected = [[("a", 2), ("b", 2)], [("", 4)], [(1, 2), (2, 2), (3, 1)]] self._test_func(input, func, expected, sort=True) def test_mapValues(self): """Basic operation test for DStream.mapValues.""" input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)], [(0, 4), (1, 1), (2, 2), (3, 3)], [(1, 1), (2, 1), (3, 1), (4, 1)]] def func(dstream): return dstream.mapValues(lambda x: x + 10) expected = [[("a", 12), ("b", 12), ("c", 11), ("d", 11)], [(0, 14), (1, 11), (2, 12), (3, 13)], [(1, 11), (2, 11), (3, 11), (4, 11)]] self._test_func(input, func, expected, sort=True) def test_flatMapValues(self): """Basic operation test for DStream.flatMapValues.""" input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)], [(0, 4), (1, 1), (2, 1), (3, 1)], [(1, 1), (2, 1), (3, 1), (4, 1)]] def func(dstream): return dstream.flatMapValues(lambda x: (x, x + 10)) expected = [[("a", 2), ("a", 12), ("b", 2), ("b", 12), ("c", 1), ("c", 11), ("d", 1), ("d", 11)], [(0, 4), (0, 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)], [(1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11), (4, 1), (4, 11)]] self._test_func(input, func, expected) def test_glom(self): """Basic operation test for DStream.glom.""" input = [range(1, 5), range(5, 9), range(9, 13)] rdds = [self.sc.parallelize(r, 2) for r in input] def func(dstream): return dstream.glom() expected = [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]] self._test_func(rdds, func, expected) def test_mapPartitions(self): """Basic operation test for DStream.mapPartitions.""" input = [range(1, 5), range(5, 9), range(9, 13)] rdds = [self.sc.parallelize(r, 2) for r in input] def func(dstream): def f(iterator): yield sum(iterator) return dstream.mapPartitions(f) expected = [[3, 7], [11, 15], [19, 23]] self._test_func(rdds, func, expected) def test_countByValue(self): """Basic operation test for DStream.countByValue.""" input = [list(range(1, 5)) * 2, list(range(5, 7)) + list(range(5, 9)), ["a", "a", "b", ""]] def func(dstream): return dstream.countByValue() expected = [[(1, 2), (2, 2), (3, 2), (4, 2)], [(5, 2), (6, 2), (7, 1), (8, 1)], [("a", 2), ("b", 1), ("", 1)]] self._test_func(input, func, expected, sort=True) def test_groupByKey(self): """Basic operation test for DStream.groupByKey.""" input = [[(1, 1), (2, 1), (3, 1), (4, 1)], [(1, 1), (1, 1), (1, 1), (2, 1), (2, 1), (3, 1)], [("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1), ("", 1)]] def func(dstream): return dstream.groupByKey().mapValues(list) expected = [[(1, [1]), (2, [1]), (3, [1]), (4, [1])], [(1, [1, 1, 1]), (2, [1, 1]), (3, [1])], [("a", [1, 1]), ("b", [1]), ("", [1, 1, 1])]] self._test_func(input, func, expected, sort=True) def test_combineByKey(self): """Basic operation test for DStream.combineByKey.""" input = [[(1, 1), (2, 1), (3, 1), (4, 1)], [(1, 1), (1, 1), (1, 1), (2, 1), (2, 1), (3, 1)], [("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1), ("", 1)]] def func(dstream): def add(a, b): return a + str(b) return dstream.combineByKey(str, add, add) expected = [[(1, "1"), (2, "1"), (3, "1"), (4, "1")], [(1, "111"), (2, "11"), (3, "1")], [("a", "11"), ("b", "1"), ("", "111")]] self._test_func(input, func, expected, sort=True) def test_repartition(self): input = [range(1, 5), range(5, 9)] rdds = [self.sc.parallelize(r, 2) for r in input] def func(dstream): return dstream.repartition(1).glom() expected = [[[1, 2, 3, 4]], [[5, 6, 7, 8]]] self._test_func(rdds, func, expected) def test_union(self): input1 = [range(3), range(5), range(6)] input2 = [range(3, 6), range(5, 6)] def func(d1, d2): return d1.union(d2) expected = [list(range(6)), list(range(6)), list(range(6))] self._test_func(input1, func, expected, input2=input2) def test_cogroup(self): input = [[(1, 1), (2, 1), (3, 1)], [(1, 1), (1, 1), (1, 1), (2, 1)], [("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1)]] input2 = [[(1, 2)], [(4, 1)], [("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 2)]] def func(d1, d2): return d1.cogroup(d2).mapValues(lambda vs: tuple(map(list, vs))) expected = [[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))], [(1, ([1, 1, 1], [])), (2, ([1], [])), (4, ([], [1]))], [("a", ([1, 1], [1, 1])), ("b", ([1], [1])), ("", ([1, 1], [1, 2]))]] self._test_func(input, func, expected, sort=True, input2=input2) def test_join(self): input = [[('a', 1), ('b', 2)]] input2 = [[('b', 3), ('c', 4)]] def func(a, b): return a.join(b) expected = [[('b', (2, 3))]] self._test_func(input, func, expected, True, input2) def test_left_outer_join(self): input = [[('a', 1), ('b', 2)]] input2 = [[('b', 3), ('c', 4)]] def func(a, b): return a.leftOuterJoin(b) expected = [[('a', (1, None)), ('b', (2, 3))]] self._test_func(input, func, expected, True, input2) def test_right_outer_join(self): input = [[('a', 1), ('b', 2)]] input2 = [[('b', 3), ('c', 4)]] def func(a, b): return a.rightOuterJoin(b) expected = [[('b', (2, 3)), ('c', (None, 4))]] self._test_func(input, func, expected, True, input2) def test_full_outer_join(self): input = [[('a', 1), ('b', 2)]] input2 = [[('b', 3), ('c', 4)]] def func(a, b): return a.fullOuterJoin(b) expected = [[('a', (1, None)), ('b', (2, 3)), ('c', (None, 4))]] self._test_func(input, func, expected, True, input2) def test_update_state_by_key(self): def updater(vs, s): if not s: s = [] s.extend(vs) return s input = [[('k', i)] for i in range(5)] def func(dstream): return dstream.updateStateByKey(updater) expected = [[0], [0, 1], [0, 1, 2], [0, 1, 2, 3], [0, 1, 2, 3, 4]] expected = [[('k', v)] for v in expected] self._test_func(input, func, expected) def test_update_state_by_key_initial_rdd(self): def updater(vs, s): if not s: s = [] s.extend(vs) return s initial = [('k', [0, 1])] initial = self.sc.parallelize(initial, 1) input = [[('k', i)] for i in range(2, 5)] def func(dstream): return dstream.updateStateByKey(updater, initialRDD=initial) expected = [[0, 1, 2], [0, 1, 2, 3], [0, 1, 2, 3, 4]] expected = [[('k', v)] for v in expected] self._test_func(input, func, expected) def test_failed_func(self): # Test failure in # TransformFunction.apply(rdd: Option[RDD[_]], time: Time) input = [self.sc.parallelize([d], 1) for d in range(4)] input_stream = self.ssc.queueStream(input) def failed_func(i): raise ValueError("This is a special error") input_stream.map(failed_func).pprint() self.ssc.start() try: self.ssc.awaitTerminationOrTimeout(10) except: import traceback failure = traceback.format_exc() self.assertTrue("This is a special error" in failure) return self.fail("a failed func should throw an error") def test_failed_func2(self): # Test failure in # TransformFunction.apply(rdd: Option[RDD[_]], rdd2: Option[RDD[_]], time: Time) input = [self.sc.parallelize([d], 1) for d in range(4)] input_stream1 = self.ssc.queueStream(input) input_stream2 = self.ssc.queueStream(input) def failed_func(rdd1, rdd2): raise ValueError("This is a special error") input_stream1.transformWith(failed_func, input_stream2, True).pprint() self.ssc.start() try: self.ssc.awaitTerminationOrTimeout(10) except: import traceback failure = traceback.format_exc() self.assertTrue("This is a special error" in failure) return self.fail("a failed func should throw an error") def test_failed_func_with_reseting_failure(self): input = [self.sc.parallelize([d], 1) for d in range(4)] input_stream = self.ssc.queueStream(input) def failed_func(i): if i == 1: # Make it fail in the second batch raise ValueError("This is a special error") else: return i # We should be able to see the results of the 3rd and 4th batches even if the second batch # fails expected = [[0], [2], [3]] self.assertEqual(expected, self._collect(input_stream.map(failed_func), 3)) try: self.ssc.awaitTerminationOrTimeout(10) except: import traceback failure = traceback.format_exc() self.assertTrue("This is a special error" in failure) return self.fail("a failed func should throw an error") class StreamingListenerTests(PySparkStreamingTestCase): duration = .5 class BatchInfoCollector(StreamingListener): def __init__(self): super(StreamingListener, self).__init__() self.batchInfosCompleted = [] self.batchInfosStarted = [] self.batchInfosSubmitted = [] def onBatchSubmitted(self, batchSubmitted): self.batchInfosSubmitted.append(batchSubmitted.batchInfo()) def onBatchStarted(self, batchStarted): self.batchInfosStarted.append(batchStarted.batchInfo()) def onBatchCompleted(self, batchCompleted): self.batchInfosCompleted.append(batchCompleted.batchInfo()) def test_batch_info_reports(self): batch_collector = self.BatchInfoCollector() self.ssc.addStreamingListener(batch_collector) input = [[1], [2], [3], [4]] def func(dstream): return dstream.map(int) expected = [[1], [2], [3], [4]] self._test_func(input, func, expected) batchInfosSubmitted = batch_collector.batchInfosSubmitted batchInfosStarted = batch_collector.batchInfosStarted batchInfosCompleted = batch_collector.batchInfosCompleted self.wait_for(batchInfosCompleted, 4) self.assertGreaterEqual(len(batchInfosSubmitted), 4) for info in batchInfosSubmitted: self.assertGreaterEqual(info.batchTime().milliseconds(), 0) self.assertGreaterEqual(info.submissionTime(), 0) for streamId in info.streamIdToInputInfo(): streamInputInfo = info.streamIdToInputInfo()[streamId] self.assertGreaterEqual(streamInputInfo.inputStreamId(), 0) self.assertGreaterEqual(streamInputInfo.numRecords, 0) for key in streamInputInfo.metadata(): self.assertIsNotNone(streamInputInfo.metadata()[key]) self.assertIsNotNone(streamInputInfo.metadataDescription()) for outputOpId in info.outputOperationInfos(): outputInfo = info.outputOperationInfos()[outputOpId] self.assertGreaterEqual(outputInfo.batchTime().milliseconds(), 0) self.assertGreaterEqual(outputInfo.id(), 0) self.assertIsNotNone(outputInfo.name()) self.assertIsNotNone(outputInfo.description()) self.assertGreaterEqual(outputInfo.startTime(), -1) self.assertGreaterEqual(outputInfo.endTime(), -1) self.assertIsNone(outputInfo.failureReason()) self.assertEqual(info.schedulingDelay(), -1) self.assertEqual(info.processingDelay(), -1) self.assertEqual(info.totalDelay(), -1) self.assertEqual(info.numRecords(), 0) self.assertGreaterEqual(len(batchInfosStarted), 4) for info in batchInfosStarted: self.assertGreaterEqual(info.batchTime().milliseconds(), 0) self.assertGreaterEqual(info.submissionTime(), 0) for streamId in info.streamIdToInputInfo(): streamInputInfo = info.streamIdToInputInfo()[streamId] self.assertGreaterEqual(streamInputInfo.inputStreamId(), 0) self.assertGreaterEqual(streamInputInfo.numRecords, 0) for key in streamInputInfo.metadata(): self.assertIsNotNone(streamInputInfo.metadata()[key]) self.assertIsNotNone(streamInputInfo.metadataDescription()) for outputOpId in info.outputOperationInfos(): outputInfo = info.outputOperationInfos()[outputOpId] self.assertGreaterEqual(outputInfo.batchTime().milliseconds(), 0) self.assertGreaterEqual(outputInfo.id(), 0) self.assertIsNotNone(outputInfo.name()) self.assertIsNotNone(outputInfo.description()) self.assertGreaterEqual(outputInfo.startTime(), -1) self.assertGreaterEqual(outputInfo.endTime(), -1) self.assertIsNone(outputInfo.failureReason()) self.assertGreaterEqual(info.schedulingDelay(), 0) self.assertEqual(info.processingDelay(), -1) self.assertEqual(info.totalDelay(), -1) self.assertEqual(info.numRecords(), 0) self.assertGreaterEqual(len(batchInfosCompleted), 4) for info in batchInfosCompleted: self.assertGreaterEqual(info.batchTime().milliseconds(), 0) self.assertGreaterEqual(info.submissionTime(), 0) for streamId in info.streamIdToInputInfo(): streamInputInfo = info.streamIdToInputInfo()[streamId] self.assertGreaterEqual(streamInputInfo.inputStreamId(), 0) self.assertGreaterEqual(streamInputInfo.numRecords, 0) for key in streamInputInfo.metadata(): self.assertIsNotNone(streamInputInfo.metadata()[key]) self.assertIsNotNone(streamInputInfo.metadataDescription()) for outputOpId in info.outputOperationInfos(): outputInfo = info.outputOperationInfos()[outputOpId] self.assertGreaterEqual(outputInfo.batchTime().milliseconds(), 0) self.assertGreaterEqual(outputInfo.id(), 0) self.assertIsNotNone(outputInfo.name()) self.assertIsNotNone(outputInfo.description()) self.assertGreaterEqual(outputInfo.startTime(), 0) self.assertGreaterEqual(outputInfo.endTime(), 0) self.assertIsNone(outputInfo.failureReason()) self.assertGreaterEqual(info.schedulingDelay(), 0) self.assertGreaterEqual(info.processingDelay(), 0) self.assertGreaterEqual(info.totalDelay(), 0) self.assertEqual(info.numRecords(), 0) class WindowFunctionTests(PySparkStreamingTestCase): timeout = 15 def test_window(self): input = [range(1), range(2), range(3), range(4), range(5)] def func(dstream): return dstream.window(1.5, .5).count() expected = [[1], [3], [6], [9], [12], [9], [5]] self._test_func(input, func, expected) def test_count_by_window(self): input = [range(1), range(2), range(3), range(4), range(5)] def func(dstream): return dstream.countByWindow(1.5, .5) expected = [[1], [3], [6], [9], [12], [9], [5]] self._test_func(input, func, expected) def test_count_by_window_large(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): return dstream.countByWindow(2.5, .5) expected = [[1], [3], [6], [10], [15], [20], [18], [15], [11], [6]] self._test_func(input, func, expected) def test_count_by_value_and_window(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): return dstream.countByValueAndWindow(2.5, .5) expected = [[(0, 1)], [(0, 2), (1, 1)], [(0, 3), (1, 2), (2, 1)], [(0, 4), (1, 3), (2, 2), (3, 1)], [(0, 5), (1, 4), (2, 3), (3, 2), (4, 1)], [(0, 5), (1, 5), (2, 4), (3, 3), (4, 2), (5, 1)], [(0, 4), (1, 4), (2, 4), (3, 3), (4, 2), (5, 1)], [(0, 3), (1, 3), (2, 3), (3, 3), (4, 2), (5, 1)], [(0, 2), (1, 2), (2, 2), (3, 2), (4, 2), (5, 1)], [(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1)]] self._test_func(input, func, expected) def test_group_by_key_and_window(self): input = [[('a', i)] for i in range(5)] def func(dstream): return dstream.groupByKeyAndWindow(1.5, .5).mapValues(list) expected = [[('a', [0])], [('a', [0, 1])], [('a', [0, 1, 2])], [('a', [1, 2, 3])], [('a', [2, 3, 4])], [('a', [3, 4])], [('a', [4])]] self._test_func(input, func, expected) def test_reduce_by_invalid_window(self): input1 = [range(3), range(5), range(1), range(6)] d1 = self.ssc.queueStream(input1) self.assertRaises(ValueError, lambda: d1.reduceByKeyAndWindow(None, None, 0.1, 0.1)) self.assertRaises(ValueError, lambda: d1.reduceByKeyAndWindow(None, None, 1, 0.1)) def test_reduce_by_key_and_window_with_none_invFunc(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): return dstream.map(lambda x: (x, 1))\ .reduceByKeyAndWindow(operator.add, None, 5, 1)\ .filter(lambda kv: kv[1] > 0).count() expected = [[2], [4], [6], [6], [6], [6]] self._test_func(input, func, expected) class StreamingContextTests(PySparkStreamingTestCase): duration = 0.1 setupCalled = False def _add_input_stream(self): inputs = [range(1, x) for x in range(101)] stream = self.ssc.queueStream(inputs) self._collect(stream, 1, block=False) def test_stop_only_streaming_context(self): self._add_input_stream() self.ssc.start() self.ssc.stop(False) self.assertEqual(len(self.sc.parallelize(range(5), 5).glom().collect()), 5) def test_stop_multiple_times(self): self._add_input_stream() self.ssc.start() self.ssc.stop(False) self.ssc.stop(False) def test_queue_stream(self): input = [list(range(i + 1)) for i in range(3)] dstream = self.ssc.queueStream(input) result = self._collect(dstream, 3) self.assertEqual(input, result) def test_text_file_stream(self): d = tempfile.mkdtemp() self.ssc = StreamingContext(self.sc, self.duration) dstream2 = self.ssc.textFileStream(d).map(int) result = self._collect(dstream2, 2, block=False) self.ssc.start() for name in ('a', 'b'): time.sleep(1) with open(os.path.join(d, name), "w") as f: f.writelines(["%d\n" % i for i in range(10)]) self.wait_for(result, 2) self.assertEqual([list(range(10)), list(range(10))], result) def test_binary_records_stream(self): d = tempfile.mkdtemp() self.ssc = StreamingContext(self.sc, self.duration) dstream = self.ssc.binaryRecordsStream(d, 10).map( lambda v: struct.unpack("10b", bytes(v))) result = self._collect(dstream, 2, block=False) self.ssc.start() for name in ('a', 'b'): time.sleep(1) with open(os.path.join(d, name), "wb") as f: f.write(bytearray(range(10))) self.wait_for(result, 2) self.assertEqual([list(range(10)), list(range(10))], [list(v[0]) for v in result]) def test_union(self): input = [list(range(i + 1)) for i in range(3)] dstream = self.ssc.queueStream(input) dstream2 = self.ssc.queueStream(input) dstream3 = self.ssc.union(dstream, dstream2) result = self._collect(dstream3, 3) expected = [i * 2 for i in input] self.assertEqual(expected, result) def test_transform(self): dstream1 = self.ssc.queueStream([[1]]) dstream2 = self.ssc.queueStream([[2]]) dstream3 = self.ssc.queueStream([[3]]) def func(rdds): rdd1, rdd2, rdd3 = rdds return rdd2.union(rdd3).union(rdd1) dstream = self.ssc.transform([dstream1, dstream2, dstream3], func) self.assertEqual([2, 3, 1], self._take(dstream, 3)) def test_get_active(self): self.assertEqual(StreamingContext.getActive(), None) # Verify that getActive() returns the active context self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count()) self.ssc.start() self.assertEqual(StreamingContext.getActive(), self.ssc) # Verify that getActive() returns None self.ssc.stop(False) self.assertEqual(StreamingContext.getActive(), None) # Verify that if the Java context is stopped, then getActive() returns None self.ssc = StreamingContext(self.sc, self.duration) self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count()) self.ssc.start() self.assertEqual(StreamingContext.getActive(), self.ssc) self.ssc._jssc.stop(False) self.assertEqual(StreamingContext.getActive(), None) def test_get_active_or_create(self): # Test StreamingContext.getActiveOrCreate() without checkpoint data # See CheckpointTests for tests with checkpoint data self.ssc = None self.assertEqual(StreamingContext.getActive(), None) def setupFunc(): ssc = StreamingContext(self.sc, self.duration) ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count()) self.setupCalled = True return ssc # Verify that getActiveOrCreate() (w/o checkpoint) calls setupFunc when no context is active self.setupCalled = False self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc) self.assertTrue(self.setupCalled) # Verify that getActiveOrCreate() retuns active context and does not call the setupFunc self.ssc.start() self.setupCalled = False self.assertEqual(StreamingContext.getActiveOrCreate(None, setupFunc), self.ssc) self.assertFalse(self.setupCalled) # Verify that getActiveOrCreate() calls setupFunc after active context is stopped self.ssc.stop(False) self.setupCalled = False self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc) self.assertTrue(self.setupCalled) # Verify that if the Java context is stopped, then getActive() returns None self.ssc = StreamingContext(self.sc, self.duration) self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count()) self.ssc.start() self.assertEqual(StreamingContext.getActive(), self.ssc) self.ssc._jssc.stop(False) self.setupCalled = False self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc) self.assertTrue(self.setupCalled) def test_await_termination_or_timeout(self): self._add_input_stream() self.ssc.start() self.assertFalse(self.ssc.awaitTerminationOrTimeout(0.001)) self.ssc.stop(False) self.assertTrue(self.ssc.awaitTerminationOrTimeout(0.001)) class CheckpointTests(unittest.TestCase): setupCalled = False @staticmethod def tearDownClass(): # Clean up in the JVM just in case there has been some issues in Python API if SparkContext._jvm is not None: jStreamingContextOption = \ SparkContext._jvm.org.apache.spark.streaming.StreamingContext.getActive() if jStreamingContextOption.nonEmpty(): jStreamingContextOption.get().stop() def setUp(self): self.ssc = None self.sc = None self.cpd = None def tearDown(self): if self.ssc is not None: self.ssc.stop(True) if self.sc is not None: self.sc.stop() if self.cpd is not None: shutil.rmtree(self.cpd) def test_transform_function_serializer_failure(self): inputd = tempfile.mkdtemp() self.cpd = tempfile.mkdtemp("test_transform_function_serializer_failure") def setup(): conf = SparkConf().set("spark.default.parallelism", 1) sc = SparkContext(conf=conf) ssc = StreamingContext(sc, 0.5) # A function that cannot be serialized def process(time, rdd): sc.parallelize(range(1, 10)) ssc.textFileStream(inputd).foreachRDD(process) return ssc self.ssc = StreamingContext.getOrCreate(self.cpd, setup) try: self.ssc.start() except: import traceback failure = traceback.format_exc() self.assertTrue( "It appears that you are attempting to reference SparkContext" in failure) return self.fail("using SparkContext in process should fail because it's not Serializable") def test_get_or_create_and_get_active_or_create(self): inputd = tempfile.mkdtemp() outputd = tempfile.mkdtemp() + "/" def updater(vs, s): return sum(vs, s or 0) def setup(): conf = SparkConf().set("spark.default.parallelism", 1) sc = SparkContext(conf=conf) ssc = StreamingContext(sc, 0.5) dstream = ssc.textFileStream(inputd).map(lambda x: (x, 1)) wc = dstream.updateStateByKey(updater) wc.map(lambda x: "%s,%d" % x).saveAsTextFiles(outputd + "test") wc.checkpoint(.5) self.setupCalled = True return ssc # Verify that getOrCreate() calls setup() in absence of checkpoint files self.cpd = tempfile.mkdtemp("test_streaming_cps") self.setupCalled = False self.ssc = StreamingContext.getOrCreate(self.cpd, setup) self.assertTrue(self.setupCalled) self.ssc.start() def check_output(n): while not os.listdir(outputd): time.sleep(0.01) time.sleep(1) # make sure mtime is larger than the previous one with open(os.path.join(inputd, str(n)), 'w') as f: f.writelines(["%d\n" % i for i in range(10)]) while True: p = os.path.join(outputd, max(os.listdir(outputd))) if '_SUCCESS' not in os.listdir(p): # not finished time.sleep(0.01) continue ordd = self.ssc.sparkContext.textFile(p).map(lambda line: line.split(",")) d = ordd.values().map(int).collect() if not d: time.sleep(0.01) continue self.assertEqual(10, len(d)) s = set(d) self.assertEqual(1, len(s)) m = s.pop() if n > m: continue self.assertEqual(n, m) break check_output(1) check_output(2) # Verify the getOrCreate() recovers from checkpoint files self.ssc.stop(True, True) time.sleep(1) self.setupCalled = False self.ssc = StreamingContext.getOrCreate(self.cpd, setup) self.assertFalse(self.setupCalled) self.ssc.start() check_output(3) # Verify that getOrCreate() uses existing SparkContext self.ssc.stop(True, True) time.sleep(1) self.sc = SparkContext(conf=SparkConf()) self.setupCalled = False self.ssc = StreamingContext.getOrCreate(self.cpd, setup) self.assertFalse(self.setupCalled) self.assertTrue(self.ssc.sparkContext == self.sc) # Verify the getActiveOrCreate() recovers from checkpoint files self.ssc.stop(True, True) time.sleep(1) self.setupCalled = False self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup) self.assertFalse(self.setupCalled) self.ssc.start() check_output(4) # Verify that getActiveOrCreate() returns active context self.setupCalled = False self.assertEqual(StreamingContext.getActiveOrCreate(self.cpd, setup), self.ssc) self.assertFalse(self.setupCalled) # Verify that getActiveOrCreate() uses existing SparkContext self.ssc.stop(True, True) time.sleep(1) self.sc = SparkContext(conf=SparkConf()) self.setupCalled = False self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup) self.assertFalse(self.setupCalled) self.assertTrue(self.ssc.sparkContext == self.sc) # Verify that getActiveOrCreate() calls setup() in absence of checkpoint files self.ssc.stop(True, True) shutil.rmtree(self.cpd) # delete checkpoint directory time.sleep(1) self.setupCalled = False self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup) self.assertTrue(self.setupCalled) # Stop everything self.ssc.stop(True, True) class KafkaStreamTests(PySparkStreamingTestCase): timeout = 20 # seconds duration = 1 def setUp(self): super(KafkaStreamTests, self).setUp() self._kafkaTestUtils = self.ssc._jvm.org.apache.spark.streaming.kafka.KafkaTestUtils() self._kafkaTestUtils.setup() def tearDown(self): super(KafkaStreamTests, self).tearDown() if self._kafkaTestUtils is not None: self._kafkaTestUtils.teardown() self._kafkaTestUtils = None def _randomTopic(self): return "topic-%d" % random.randint(0, 10000) def _validateStreamResult(self, sendData, stream): result = {} for i in chain.from_iterable(self._collect(stream.map(lambda x: x[1]), sum(sendData.values()))): result[i] = result.get(i, 0) + 1 self.assertEqual(sendData, result) def _validateRddResult(self, sendData, rdd): result = {} for i in rdd.map(lambda x: x[1]).collect(): result[i] = result.get(i, 0) + 1 self.assertEqual(sendData, result) def test_kafka_stream(self): """Test the Python Kafka stream API.""" topic = self._randomTopic() sendData = {"a": 3, "b": 5, "c": 10} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), "test-streaming-consumer", {topic: 1}, {"auto.offset.reset": "smallest"}) self._validateStreamResult(sendData, stream) def test_kafka_direct_stream(self): """Test the Python direct Kafka stream API.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(), "auto.offset.reset": "smallest"} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) self._validateStreamResult(sendData, stream) def test_kafka_direct_stream_from_offset(self): """Test the Python direct Kafka stream API with start offset specified.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} fromOffsets = {TopicAndPartition(topic, 0): long(0)} kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams, fromOffsets) self._validateStreamResult(sendData, stream) def test_kafka_rdd(self): """Test the Python direct Kafka RDD API.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2} offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))] kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges) self._validateRddResult(sendData, rdd) def test_kafka_rdd_with_leaders(self): """Test the Python direct Kafka RDD API with leaders.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))] kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} address = self._kafkaTestUtils.brokerAddress().split(":") leaders = {TopicAndPartition(topic, 0): Broker(address[0], int(address[1]))} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders) self._validateRddResult(sendData, rdd) def test_kafka_rdd_get_offsetRanges(self): """Test Python direct Kafka RDD get OffsetRanges.""" topic = self._randomTopic() sendData = {"a": 3, "b": 4, "c": 5} offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))] kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges) self.assertEqual(offsetRanges, rdd.offsetRanges()) def test_kafka_direct_stream_foreach_get_offsetRanges(self): """Test the Python direct Kafka stream foreachRDD get offsetRanges.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(), "auto.offset.reset": "smallest"} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) offsetRanges = [] def getOffsetRanges(_, rdd): for o in rdd.offsetRanges(): offsetRanges.append(o) stream.foreachRDD(getOffsetRanges) self.ssc.start() self.wait_for(offsetRanges, 1) self.assertEqual(offsetRanges, [OffsetRange(topic, 0, long(0), long(6))]) def test_kafka_direct_stream_transform_get_offsetRanges(self): """Test the Python direct Kafka stream transform get offsetRanges.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(), "auto.offset.reset": "smallest"} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) offsetRanges = [] def transformWithOffsetRanges(rdd): for o in rdd.offsetRanges(): offsetRanges.append(o) return rdd # Test whether it is ok mixing KafkaTransformedDStream and TransformedDStream together, # only the TransformedDstreams can be folded together. stream.transform(transformWithOffsetRanges).map(lambda kv: kv[1]).count().pprint() self.ssc.start() self.wait_for(offsetRanges, 1) self.assertEqual(offsetRanges, [OffsetRange(topic, 0, long(0), long(6))]) def test_topic_and_partition_equality(self): topic_and_partition_a = TopicAndPartition("foo", 0) topic_and_partition_b = TopicAndPartition("foo", 0) topic_and_partition_c = TopicAndPartition("bar", 0) topic_and_partition_d = TopicAndPartition("foo", 1) self.assertEqual(topic_and_partition_a, topic_and_partition_b) self.assertNotEqual(topic_and_partition_a, topic_and_partition_c) self.assertNotEqual(topic_and_partition_a, topic_and_partition_d) def test_kafka_direct_stream_transform_with_checkpoint(self): """Test the Python direct Kafka stream transform with checkpoint correctly recovered.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(), "auto.offset.reset": "smallest"} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) offsetRanges = [] def transformWithOffsetRanges(rdd): for o in rdd.offsetRanges(): offsetRanges.append(o) return rdd self.ssc.stop(False) self.ssc = None tmpdir = "checkpoint-test-%d" % random.randint(0, 10000) def setup(): ssc = StreamingContext(self.sc, 0.5) ssc.checkpoint(tmpdir) stream = KafkaUtils.createDirectStream(ssc, [topic], kafkaParams) stream.transform(transformWithOffsetRanges).count().pprint() return ssc try: ssc1 = StreamingContext.getOrCreate(tmpdir, setup) ssc1.start() self.wait_for(offsetRanges, 1) self.assertEqual(offsetRanges, [OffsetRange(topic, 0, long(0), long(6))]) # To make sure some checkpoint is written time.sleep(3) ssc1.stop(False) ssc1 = None # Restart again to make sure the checkpoint is recovered correctly ssc2 = StreamingContext.getOrCreate(tmpdir, setup) ssc2.start() ssc2.awaitTermination(3) ssc2.stop(stopSparkContext=False, stopGraceFully=True) ssc2 = None finally: shutil.rmtree(tmpdir) def test_kafka_rdd_message_handler(self): """Test Python direct Kafka RDD MessageHandler.""" topic = self._randomTopic() sendData = {"a": 1, "b": 1, "c": 2} offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))] kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} def getKeyAndDoubleMessage(m): return m and (m.key, m.message * 2) self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, messageHandler=getKeyAndDoubleMessage) self._validateRddResult({"aa": 1, "bb": 1, "cc": 2}, rdd) def test_kafka_direct_stream_message_handler(self): """Test the Python direct Kafka stream MessageHandler.""" topic = self._randomTopic() sendData = {"a": 1, "b": 2, "c": 3} kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(), "auto.offset.reset": "smallest"} self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) def getKeyAndDoubleMessage(m): return m and (m.key, m.message * 2) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams, messageHandler=getKeyAndDoubleMessage) self._validateStreamResult({"aa": 1, "bb": 2, "cc": 3}, stream) class FlumeStreamTests(PySparkStreamingTestCase): timeout = 20 # seconds duration = 1 def setUp(self): super(FlumeStreamTests, self).setUp() self._utils = self.ssc._jvm.org.apache.spark.streaming.flume.FlumeTestUtils() def tearDown(self): if self._utils is not None: self._utils.close() self._utils = None super(FlumeStreamTests, self).tearDown() def _startContext(self, n, compressed): # Start the StreamingContext and also collect the result dstream = FlumeUtils.createStream(self.ssc, "localhost", self._utils.getTestPort(), enableDecompression=compressed) result = [] def get_output(_, rdd): for event in rdd.collect(): if len(result) < n: result.append(event) dstream.foreachRDD(get_output) self.ssc.start() return result def _validateResult(self, input, result): # Validate both the header and the body header = {"test": "header"} self.assertEqual(len(input), len(result)) for i in range(0, len(input)): self.assertEqual(header, result[i][0]) self.assertEqual(input[i], result[i][1]) def _writeInput(self, input, compressed): # Try to write input to the receiver until success or timeout start_time = time.time() while True: try: self._utils.writeInput(input, compressed) break except: if time.time() - start_time < self.timeout: time.sleep(0.01) else: raise def test_flume_stream(self): input = [str(i) for i in range(1, 101)] result = self._startContext(len(input), False) self._writeInput(input, False) self.wait_for(result, len(input)) self._validateResult(input, result) def test_compressed_flume_stream(self): input = [str(i) for i in range(1, 101)] result = self._startContext(len(input), True) self._writeInput(input, True) self.wait_for(result, len(input)) self._validateResult(input, result) class FlumePollingStreamTests(PySparkStreamingTestCase): timeout = 20 # seconds duration = 1 maxAttempts = 5 def setUp(self): self._utils = self.sc._jvm.org.apache.spark.streaming.flume.PollingFlumeTestUtils() def tearDown(self): if self._utils is not None: self._utils.close() self._utils = None def _writeAndVerify(self, ports): # Set up the streaming context and input streams ssc = StreamingContext(self.sc, self.duration) try: addresses = [("localhost", port) for port in ports] dstream = FlumeUtils.createPollingStream( ssc, addresses, maxBatchSize=self._utils.eventsPerBatch(), parallelism=5) outputBuffer = [] def get_output(_, rdd): for e in rdd.collect(): outputBuffer.append(e) dstream.foreachRDD(get_output) ssc.start() self._utils.sendDataAndEnsureAllDataHasBeenReceived() self.wait_for(outputBuffer, self._utils.getTotalEvents()) outputHeaders = [event[0] for event in outputBuffer] outputBodies = [event[1] for event in outputBuffer] self._utils.assertOutput(outputHeaders, outputBodies) finally: ssc.stop(False) def _testMultipleTimes(self, f): attempt = 0 while True: try: f() break except: attempt += 1 if attempt >= self.maxAttempts: raise else: import traceback traceback.print_exc() def _testFlumePolling(self): try: port = self._utils.startSingleSink() self._writeAndVerify([port]) self._utils.assertChannelsAreEmpty() finally: self._utils.close() def _testFlumePollingMultipleHosts(self): try: port = self._utils.startSingleSink() self._writeAndVerify([port]) self._utils.assertChannelsAreEmpty() finally: self._utils.close() def test_flume_polling(self): self._testMultipleTimes(self._testFlumePolling) def test_flume_polling_multiple_hosts(self): self._testMultipleTimes(self._testFlumePollingMultipleHosts) class KinesisStreamTests(PySparkStreamingTestCase): def test_kinesis_stream_api(self): # Don't start the StreamingContext because we cannot test it in Jenkins kinesisStream1 = KinesisUtils.createStream( self.ssc, "myAppNam", "mySparkStream", "https://kinesis.us-west-2.amazonaws.com", "us-west-2", InitialPositionInStream.LATEST, 2, StorageLevel.MEMORY_AND_DISK_2) kinesisStream2 = KinesisUtils.createStream( self.ssc, "myAppNam", "mySparkStream", "https://kinesis.us-west-2.amazonaws.com", "us-west-2", InitialPositionInStream.LATEST, 2, StorageLevel.MEMORY_AND_DISK_2, "awsAccessKey", "awsSecretKey") def test_kinesis_stream(self): if not are_kinesis_tests_enabled: sys.stderr.write( "Skipped test_kinesis_stream (enable by setting environment variable %s=1" % kinesis_test_environ_var) return import random kinesisAppName = ("KinesisStreamTests-%d" % abs(random.randint(0, 10000000))) kinesisTestUtils = self.ssc._jvm.org.apache.spark.streaming.kinesis.KinesisTestUtils() try: kinesisTestUtils.createStream() aWSCredentials = kinesisTestUtils.getAWSCredentials() stream = KinesisUtils.createStream( self.ssc, kinesisAppName, kinesisTestUtils.streamName(), kinesisTestUtils.endpointUrl(), kinesisTestUtils.regionName(), InitialPositionInStream.LATEST, 10, StorageLevel.MEMORY_ONLY, aWSCredentials.getAWSAccessKeyId(), aWSCredentials.getAWSSecretKey()) outputBuffer = [] def get_output(_, rdd): for e in rdd.collect(): outputBuffer.append(e) stream.foreachRDD(get_output) self.ssc.start() testData = [i for i in range(1, 11)] expectedOutput = set([str(i) for i in testData]) start_time = time.time() while time.time() - start_time < 120: kinesisTestUtils.pushData(testData) if expectedOutput == set(outputBuffer): break time.sleep(10) self.assertEqual(expectedOutput, set(outputBuffer)) except: import traceback traceback.print_exc() raise finally: self.ssc.stop(False) kinesisTestUtils.deleteStream() kinesisTestUtils.deleteDynamoDBTable(kinesisAppName) # Search jar in the project dir using the jar name_prefix for both sbt build and maven build because # the artifact jars are in different directories. def search_jar(dir, name_prefix): # We should ignore the following jars ignored_jar_suffixes = ("javadoc.jar", "sources.jar", "test-sources.jar", "tests.jar") jars = (glob.glob(os.path.join(dir, "target/scala-*/" + name_prefix + "-*.jar")) + # sbt build glob.glob(os.path.join(dir, "target/" + name_prefix + "_*.jar"))) # maven build return [jar for jar in jars if not jar.endswith(ignored_jar_suffixes)] def search_kafka_assembly_jar(): SPARK_HOME = os.environ["SPARK_HOME"] kafka_assembly_dir = os.path.join(SPARK_HOME, "external/kafka-0-8-assembly") jars = search_jar(kafka_assembly_dir, "spark-streaming-kafka-0-8-assembly") if not jars: raise Exception( ("Failed to find Spark Streaming kafka assembly jar in %s. " % kafka_assembly_dir) + "You need to build Spark with " "'build/sbt assembly/package streaming-kafka-0-8-assembly/assembly' or " "'build/mvn package' before running this test.") elif len(jars) > 1: raise Exception(("Found multiple Spark Streaming Kafka assembly JARs: %s; please " "remove all but one") % (", ".join(jars))) else: return jars[0] def search_flume_assembly_jar(): SPARK_HOME = os.environ["SPARK_HOME"] flume_assembly_dir = os.path.join(SPARK_HOME, "external/flume-assembly") jars = search_jar(flume_assembly_dir, "spark-streaming-flume-assembly") if not jars: raise Exception( ("Failed to find Spark Streaming Flume assembly jar in %s. " % flume_assembly_dir) + "You need to build Spark with " "'build/sbt assembly/assembly streaming-flume-assembly/assembly' or " "'build/mvn package' before running this test.") elif len(jars) > 1: raise Exception(("Found multiple Spark Streaming Flume assembly JARs: %s; please " "remove all but one") % (", ".join(jars))) else: return jars[0] def search_kinesis_asl_assembly_jar(): SPARK_HOME = os.environ["SPARK_HOME"] kinesis_asl_assembly_dir = os.path.join(SPARK_HOME, "external/kinesis-asl-assembly") jars = search_jar(kinesis_asl_assembly_dir, "spark-streaming-kinesis-asl-assembly") if not jars: return None elif len(jars) > 1: raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs: %s; please " "remove all but one") % (", ".join(jars))) else: return jars[0] # Must be same as the variable and condition defined in KinesisTestUtils.scala kinesis_test_environ_var = "ENABLE_KINESIS_TESTS" are_kinesis_tests_enabled = os.environ.get(kinesis_test_environ_var) == '1' if __name__ == "__main__": from pyspark.streaming.tests import * kafka_assembly_jar = search_kafka_assembly_jar() flume_assembly_jar = search_flume_assembly_jar() kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar() if kinesis_asl_assembly_jar is None: kinesis_jar_present = False jars = "%s,%s" % (kafka_assembly_jar, flume_assembly_jar) else: kinesis_jar_present = True jars = "%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar) os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests, CheckpointTests, KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests, StreamingListenerTests] if kinesis_jar_present is True: testcases.append(KinesisStreamTests) elif are_kinesis_tests_enabled is False: sys.stderr.write("Skipping all Kinesis Python tests as the optional Kinesis project was " "not compiled into a JAR. To run these tests, " "you need to build Spark with 'build/sbt -Pkinesis-asl assembly/package " "streaming-kinesis-asl-assembly/assembly' or " "'build/mvn -Pkinesis-asl package' before running this test.") else: raise Exception( ("Failed to find Spark Streaming Kinesis assembly jar in %s. " % kinesis_asl_assembly_dir) + "You need to build Spark with 'build/sbt -Pkinesis-asl " "assembly/package streaming-kinesis-asl-assembly/assembly'" "or 'build/mvn -Pkinesis-asl package' before running this test.") sys.stderr.write("Running tests: %s \n" % (str(testcases))) failed = False for testcase in testcases: sys.stderr.write("[Running %s]\n" % (testcase)) tests = unittest.TestLoader().loadTestsFromTestCase(testcase) if xmlrunner: result = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=3).run(tests) if not result.wasSuccessful(): failed = True else: result = unittest.TextTestRunner(verbosity=3).run(tests) if not result.wasSuccessful(): failed = True sys.exit(failed)