928d631625
We will do checkpoint when generating a batch and completing a batch. When the processing time of a batch is greater than the batch interval, checkpointing for completing an old batch may run after checkpointing for generating a new batch. If this happens, checkpoint of an old batch actually has the latest information, so we want to recovery from it. This PR will use the latest checkpoint time as the file name, so that we can always recovery from the latest checkpoint file. Author: Shixiong Zhu <shixiong@databricks.com> Closes #9707 from zsxwing/fix-checkpoint.
1466 lines
56 KiB
Python
1466 lines
56 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import glob
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import os
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import sys
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from itertools import chain
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import time
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import operator
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import tempfile
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import random
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import struct
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import shutil
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from functools import reduce
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try:
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import xmlrunner
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except ImportError:
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xmlrunner = None
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if sys.version_info[:2] <= (2, 6):
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try:
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import unittest2 as unittest
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except ImportError:
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sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
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sys.exit(1)
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else:
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import unittest
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from pyspark.context import SparkConf, SparkContext, RDD
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from pyspark.storagelevel import StorageLevel
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from pyspark.streaming.context import StreamingContext
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from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition
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from pyspark.streaming.flume import FlumeUtils
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from pyspark.streaming.mqtt import MQTTUtils
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from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream
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from pyspark.streaming.listener import StreamingListener
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class PySparkStreamingTestCase(unittest.TestCase):
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timeout = 10 # seconds
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duration = .5
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@classmethod
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def setUpClass(cls):
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class_name = cls.__name__
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conf = SparkConf().set("spark.default.parallelism", 1)
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cls.sc = SparkContext(appName=class_name, conf=conf)
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cls.sc.setCheckpointDir("/tmp")
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@classmethod
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def tearDownClass(cls):
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cls.sc.stop()
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# Clean up in the JVM just in case there has been some issues in Python API
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try:
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jSparkContextOption = SparkContext._jvm.SparkContext.get()
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if jSparkContextOption.nonEmpty():
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jSparkContextOption.get().stop()
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except:
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pass
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def setUp(self):
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self.ssc = StreamingContext(self.sc, self.duration)
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def tearDown(self):
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if self.ssc is not None:
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self.ssc.stop(False)
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# Clean up in the JVM just in case there has been some issues in Python API
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try:
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jStreamingContextOption = StreamingContext._jvm.SparkContext.getActive()
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if jStreamingContextOption.nonEmpty():
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jStreamingContextOption.get().stop(False)
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except:
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pass
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def wait_for(self, result, n):
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start_time = time.time()
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while len(result) < n and time.time() - start_time < self.timeout:
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time.sleep(0.01)
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if len(result) < n:
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print("timeout after", self.timeout)
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def _take(self, dstream, n):
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"""
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Return the first `n` elements in the stream (will start and stop).
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"""
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results = []
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def take(_, rdd):
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if rdd and len(results) < n:
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results.extend(rdd.take(n - len(results)))
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dstream.foreachRDD(take)
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self.ssc.start()
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self.wait_for(results, n)
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return results
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def _collect(self, dstream, n, block=True):
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"""
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Collect each RDDs into the returned list.
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:return: list, which will have the collected items.
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"""
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result = []
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def get_output(_, rdd):
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if rdd and len(result) < n:
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r = rdd.collect()
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if r:
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result.append(r)
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dstream.foreachRDD(get_output)
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if not block:
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return result
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self.ssc.start()
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self.wait_for(result, n)
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return result
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def _test_func(self, input, func, expected, sort=False, input2=None):
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"""
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@param input: dataset for the test. This should be list of lists.
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@param func: wrapped function. This function should return PythonDStream object.
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@param expected: expected output for this testcase.
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"""
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if not isinstance(input[0], RDD):
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input = [self.sc.parallelize(d, 1) for d in input]
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input_stream = self.ssc.queueStream(input)
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if input2 and not isinstance(input2[0], RDD):
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input2 = [self.sc.parallelize(d, 1) for d in input2]
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input_stream2 = self.ssc.queueStream(input2) if input2 is not None else None
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# Apply test function to stream.
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if input2:
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stream = func(input_stream, input_stream2)
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else:
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stream = func(input_stream)
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result = self._collect(stream, len(expected))
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if sort:
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self._sort_result_based_on_key(result)
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self._sort_result_based_on_key(expected)
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self.assertEqual(expected, result)
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def _sort_result_based_on_key(self, outputs):
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"""Sort the list based on first value."""
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for output in outputs:
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output.sort(key=lambda x: x[0])
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class BasicOperationTests(PySparkStreamingTestCase):
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def test_map(self):
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"""Basic operation test for DStream.map."""
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input = [range(1, 5), range(5, 9), range(9, 13)]
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def func(dstream):
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return dstream.map(str)
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expected = [list(map(str, x)) for x in input]
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self._test_func(input, func, expected)
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def test_flatMap(self):
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"""Basic operation test for DStream.faltMap."""
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input = [range(1, 5), range(5, 9), range(9, 13)]
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def func(dstream):
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return dstream.flatMap(lambda x: (x, x * 2))
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expected = [list(chain.from_iterable((map(lambda y: [y, y * 2], x))))
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for x in input]
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self._test_func(input, func, expected)
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def test_filter(self):
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"""Basic operation test for DStream.filter."""
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input = [range(1, 5), range(5, 9), range(9, 13)]
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def func(dstream):
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return dstream.filter(lambda x: x % 2 == 0)
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expected = [[y for y in x if y % 2 == 0] for x in input]
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self._test_func(input, func, expected)
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def test_count(self):
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"""Basic operation test for DStream.count."""
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input = [range(5), range(10), range(20)]
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def func(dstream):
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return dstream.count()
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expected = [[len(x)] for x in input]
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self._test_func(input, func, expected)
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def test_reduce(self):
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"""Basic operation test for DStream.reduce."""
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input = [range(1, 5), range(5, 9), range(9, 13)]
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def func(dstream):
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return dstream.reduce(operator.add)
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expected = [[reduce(operator.add, x)] for x in input]
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self._test_func(input, func, expected)
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def test_reduceByKey(self):
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"""Basic operation test for DStream.reduceByKey."""
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input = [[("a", 1), ("a", 1), ("b", 1), ("b", 1)],
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[("", 1), ("", 1), ("", 1), ("", 1)],
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[(1, 1), (1, 1), (2, 1), (2, 1), (3, 1)]]
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def func(dstream):
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return dstream.reduceByKey(operator.add)
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expected = [[("a", 2), ("b", 2)], [("", 4)], [(1, 2), (2, 2), (3, 1)]]
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self._test_func(input, func, expected, sort=True)
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def test_mapValues(self):
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"""Basic operation test for DStream.mapValues."""
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input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)],
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[(0, 4), (1, 1), (2, 2), (3, 3)],
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[(1, 1), (2, 1), (3, 1), (4, 1)]]
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def func(dstream):
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return dstream.mapValues(lambda x: x + 10)
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expected = [[("a", 12), ("b", 12), ("c", 11), ("d", 11)],
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[(0, 14), (1, 11), (2, 12), (3, 13)],
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[(1, 11), (2, 11), (3, 11), (4, 11)]]
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self._test_func(input, func, expected, sort=True)
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def test_flatMapValues(self):
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"""Basic operation test for DStream.flatMapValues."""
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input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)],
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[(0, 4), (1, 1), (2, 1), (3, 1)],
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[(1, 1), (2, 1), (3, 1), (4, 1)]]
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def func(dstream):
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return dstream.flatMapValues(lambda x: (x, x + 10))
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expected = [[("a", 2), ("a", 12), ("b", 2), ("b", 12),
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("c", 1), ("c", 11), ("d", 1), ("d", 11)],
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[(0, 4), (0, 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)],
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[(1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11), (4, 1), (4, 11)]]
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self._test_func(input, func, expected)
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def test_glom(self):
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"""Basic operation test for DStream.glom."""
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input = [range(1, 5), range(5, 9), range(9, 13)]
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rdds = [self.sc.parallelize(r, 2) for r in input]
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def func(dstream):
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return dstream.glom()
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expected = [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
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self._test_func(rdds, func, expected)
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def test_mapPartitions(self):
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"""Basic operation test for DStream.mapPartitions."""
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input = [range(1, 5), range(5, 9), range(9, 13)]
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rdds = [self.sc.parallelize(r, 2) for r in input]
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def func(dstream):
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def f(iterator):
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yield sum(iterator)
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return dstream.mapPartitions(f)
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expected = [[3, 7], [11, 15], [19, 23]]
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self._test_func(rdds, func, expected)
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def test_countByValue(self):
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"""Basic operation test for DStream.countByValue."""
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input = [list(range(1, 5)) * 2, list(range(5, 7)) + list(range(5, 9)), ["a", "a", "b", ""]]
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def func(dstream):
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return dstream.countByValue()
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expected = [[4], [4], [3]]
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self._test_func(input, func, expected)
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def test_groupByKey(self):
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"""Basic operation test for DStream.groupByKey."""
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input = [[(1, 1), (2, 1), (3, 1), (4, 1)],
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[(1, 1), (1, 1), (1, 1), (2, 1), (2, 1), (3, 1)],
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[("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1), ("", 1)]]
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def func(dstream):
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return dstream.groupByKey().mapValues(list)
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expected = [[(1, [1]), (2, [1]), (3, [1]), (4, [1])],
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[(1, [1, 1, 1]), (2, [1, 1]), (3, [1])],
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[("a", [1, 1]), ("b", [1]), ("", [1, 1, 1])]]
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self._test_func(input, func, expected, sort=True)
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def test_combineByKey(self):
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"""Basic operation test for DStream.combineByKey."""
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input = [[(1, 1), (2, 1), (3, 1), (4, 1)],
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[(1, 1), (1, 1), (1, 1), (2, 1), (2, 1), (3, 1)],
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[("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1), ("", 1)]]
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def func(dstream):
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def add(a, b):
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return a + str(b)
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return dstream.combineByKey(str, add, add)
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expected = [[(1, "1"), (2, "1"), (3, "1"), (4, "1")],
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[(1, "111"), (2, "11"), (3, "1")],
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[("a", "11"), ("b", "1"), ("", "111")]]
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self._test_func(input, func, expected, sort=True)
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def test_repartition(self):
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input = [range(1, 5), range(5, 9)]
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rdds = [self.sc.parallelize(r, 2) for r in input]
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def func(dstream):
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return dstream.repartition(1).glom()
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expected = [[[1, 2, 3, 4]], [[5, 6, 7, 8]]]
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self._test_func(rdds, func, expected)
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def test_union(self):
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input1 = [range(3), range(5), range(6)]
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input2 = [range(3, 6), range(5, 6)]
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def func(d1, d2):
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return d1.union(d2)
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expected = [list(range(6)), list(range(6)), list(range(6))]
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self._test_func(input1, func, expected, input2=input2)
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def test_cogroup(self):
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input = [[(1, 1), (2, 1), (3, 1)],
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[(1, 1), (1, 1), (1, 1), (2, 1)],
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[("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1)]]
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input2 = [[(1, 2)],
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[(4, 1)],
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[("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 2)]]
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def func(d1, d2):
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return d1.cogroup(d2).mapValues(lambda vs: tuple(map(list, vs)))
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expected = [[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))],
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[(1, ([1, 1, 1], [])), (2, ([1], [])), (4, ([], [1]))],
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[("a", ([1, 1], [1, 1])), ("b", ([1], [1])), ("", ([1, 1], [1, 2]))]]
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self._test_func(input, func, expected, sort=True, input2=input2)
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def test_join(self):
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input = [[('a', 1), ('b', 2)]]
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input2 = [[('b', 3), ('c', 4)]]
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def func(a, b):
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return a.join(b)
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expected = [[('b', (2, 3))]]
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self._test_func(input, func, expected, True, input2)
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def test_left_outer_join(self):
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input = [[('a', 1), ('b', 2)]]
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input2 = [[('b', 3), ('c', 4)]]
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def func(a, b):
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return a.leftOuterJoin(b)
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expected = [[('a', (1, None)), ('b', (2, 3))]]
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self._test_func(input, func, expected, True, input2)
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def test_right_outer_join(self):
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input = [[('a', 1), ('b', 2)]]
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input2 = [[('b', 3), ('c', 4)]]
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def func(a, b):
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return a.rightOuterJoin(b)
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expected = [[('b', (2, 3)), ('c', (None, 4))]]
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self._test_func(input, func, expected, True, input2)
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def test_full_outer_join(self):
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input = [[('a', 1), ('b', 2)]]
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input2 = [[('b', 3), ('c', 4)]]
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def func(a, b):
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return a.fullOuterJoin(b)
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expected = [[('a', (1, None)), ('b', (2, 3)), ('c', (None, 4))]]
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self._test_func(input, func, expected, True, input2)
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def test_update_state_by_key(self):
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def updater(vs, s):
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if not s:
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s = []
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s.extend(vs)
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return s
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input = [[('k', i)] for i in range(5)]
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def func(dstream):
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return dstream.updateStateByKey(updater)
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expected = [[0], [0, 1], [0, 1, 2], [0, 1, 2, 3], [0, 1, 2, 3, 4]]
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expected = [[('k', v)] for v in expected]
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self._test_func(input, func, expected)
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|
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class StreamingListenerTests(PySparkStreamingTestCase):
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duration = .5
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|
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class BatchInfoCollector(StreamingListener):
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|
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def __init__(self):
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super(StreamingListener, self).__init__()
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self.batchInfosCompleted = []
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self.batchInfosStarted = []
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self.batchInfosSubmitted = []
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def onBatchSubmitted(self, batchSubmitted):
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self.batchInfosSubmitted.append(batchSubmitted.batchInfo())
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def onBatchStarted(self, batchStarted):
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self.batchInfosStarted.append(batchStarted.batchInfo())
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def onBatchCompleted(self, batchCompleted):
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self.batchInfosCompleted.append(batchCompleted.batchInfo())
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|
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def test_batch_info_reports(self):
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batch_collector = self.BatchInfoCollector()
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self.ssc.addStreamingListener(batch_collector)
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input = [[1], [2], [3], [4]]
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|
|
def func(dstream):
|
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return dstream.map(int)
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expected = [[1], [2], [3], [4]]
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self._test_func(input, func, expected)
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batchInfosSubmitted = batch_collector.batchInfosSubmitted
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batchInfosStarted = batch_collector.batchInfosStarted
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batchInfosCompleted = batch_collector.batchInfosCompleted
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self.wait_for(batchInfosCompleted, 4)
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self.assertGreaterEqual(len(batchInfosSubmitted), 4)
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for info in batchInfosSubmitted:
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self.assertGreaterEqual(info.batchTime().milliseconds(), 0)
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self.assertGreaterEqual(info.submissionTime(), 0)
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for streamId in info.streamIdToInputInfo():
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streamInputInfo = info.streamIdToInputInfo()[streamId]
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self.assertGreaterEqual(streamInputInfo.inputStreamId(), 0)
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self.assertGreaterEqual(streamInputInfo.numRecords, 0)
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for key in streamInputInfo.metadata():
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self.assertIsNotNone(streamInputInfo.metadata()[key])
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self.assertIsNotNone(streamInputInfo.metadataDescription())
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for outputOpId in info.outputOperationInfos():
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outputInfo = info.outputOperationInfos()[outputOpId]
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self.assertGreaterEqual(outputInfo.batchTime().milliseconds(), 0)
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self.assertGreaterEqual(outputInfo.id(), 0)
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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 = [[1], [2], [3], [4], [5], [6], [6], [6], [6], [6]]
|
|
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))
|
|
|
|
|
|
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_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()
|
|
|
|
kafkaTestUtilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\
|
|
.loadClass("org.apache.spark.streaming.kafka.KafkaTestUtils")
|
|
self._kafkaTestUtils = kafkaTestUtilsClz.newInstance()
|
|
self._kafkaTestUtils.setup()
|
|
|
|
def tearDown(self):
|
|
if self._kafkaTestUtils is not None:
|
|
self._kafkaTestUtils.teardown()
|
|
self._kafkaTestUtils = None
|
|
|
|
super(KafkaStreamTests, self).tearDown()
|
|
|
|
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)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "long type not support")
|
|
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)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "long type not support")
|
|
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)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "long type not support")
|
|
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)
|
|
|
|
@unittest.skipIf(sys.version >= "3", "long type not support")
|
|
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())
|
|
|
|
@unittest.skipIf(sys.version >= "3", "long type not support")
|
|
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))])
|
|
|
|
@unittest.skipIf(sys.version >= "3", "long type not support")
|
|
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)
|
|
|
|
|
|
class FlumeStreamTests(PySparkStreamingTestCase):
|
|
timeout = 20 # seconds
|
|
duration = 1
|
|
|
|
def setUp(self):
|
|
super(FlumeStreamTests, self).setUp()
|
|
|
|
utilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
|
|
.loadClass("org.apache.spark.streaming.flume.FlumeTestUtils")
|
|
self._utils = utilsClz.newInstance()
|
|
|
|
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):
|
|
utilsClz = \
|
|
self.sc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
|
|
.loadClass("org.apache.spark.streaming.flume.PollingFlumeTestUtils")
|
|
self._utils = utilsClz.newInstance()
|
|
|
|
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.sendDatAndEnsureAllDataHasBeenReceived()
|
|
|
|
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 MQTTStreamTests(PySparkStreamingTestCase):
|
|
timeout = 20 # seconds
|
|
duration = 1
|
|
|
|
def setUp(self):
|
|
super(MQTTStreamTests, self).setUp()
|
|
|
|
MQTTTestUtilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
|
|
.loadClass("org.apache.spark.streaming.mqtt.MQTTTestUtils")
|
|
self._MQTTTestUtils = MQTTTestUtilsClz.newInstance()
|
|
self._MQTTTestUtils.setup()
|
|
|
|
def tearDown(self):
|
|
if self._MQTTTestUtils is not None:
|
|
self._MQTTTestUtils.teardown()
|
|
self._MQTTTestUtils = None
|
|
|
|
super(MQTTStreamTests, self).tearDown()
|
|
|
|
def _randomTopic(self):
|
|
return "topic-%d" % random.randint(0, 10000)
|
|
|
|
def _startContext(self, topic):
|
|
# Start the StreamingContext and also collect the result
|
|
stream = MQTTUtils.createStream(self.ssc, "tcp://" + self._MQTTTestUtils.brokerUri(), topic)
|
|
result = []
|
|
|
|
def getOutput(_, rdd):
|
|
for data in rdd.collect():
|
|
result.append(data)
|
|
|
|
stream.foreachRDD(getOutput)
|
|
self.ssc.start()
|
|
return result
|
|
|
|
def test_mqtt_stream(self):
|
|
"""Test the Python MQTT stream API."""
|
|
sendData = "MQTT demo for spark streaming"
|
|
topic = self._randomTopic()
|
|
result = self._startContext(topic)
|
|
|
|
def retry():
|
|
self._MQTTTestUtils.publishData(topic, sendData)
|
|
# Because "publishData" sends duplicate messages, here we should use > 0
|
|
self.assertTrue(len(result) > 0)
|
|
self.assertEqual(sendData, result[0])
|
|
|
|
# Retry it because we don't know when the receiver will start.
|
|
self._retry_or_timeout(retry)
|
|
|
|
def _retry_or_timeout(self, test_func):
|
|
start_time = time.time()
|
|
while True:
|
|
try:
|
|
test_func()
|
|
break
|
|
except:
|
|
if time.time() - start_time > self.timeout:
|
|
raise
|
|
time.sleep(0.01)
|
|
|
|
|
|
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)))
|
|
kinesisTestUtilsClz = \
|
|
self.sc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
|
|
.loadClass("org.apache.spark.streaming.kinesis.KinesisTestUtils")
|
|
kinesisTestUtils = kinesisTestUtilsClz.newInstance()
|
|
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-assembly")
|
|
jars = search_jar(kafka_assembly_dir, "spark-streaming-kafka-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/assembly streaming-kafka-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_mqtt_assembly_jar():
|
|
SPARK_HOME = os.environ["SPARK_HOME"]
|
|
mqtt_assembly_dir = os.path.join(SPARK_HOME, "external/mqtt-assembly")
|
|
jars = search_jar(mqtt_assembly_dir, "spark-streaming-mqtt-assembly")
|
|
if not jars:
|
|
raise Exception(
|
|
("Failed to find Spark Streaming MQTT assembly jar in %s. " % mqtt_assembly_dir) +
|
|
"You need to build Spark with "
|
|
"'build/sbt assembly/assembly streaming-mqtt-assembly/assembly' or "
|
|
"'build/mvn package' before running this test")
|
|
elif len(jars) > 1:
|
|
raise Exception(("Found multiple Spark Streaming MQTT assembly JARs: %s; please "
|
|
"remove all but one") % (", ".join(jars)))
|
|
else:
|
|
return jars[0]
|
|
|
|
|
|
def search_mqtt_test_jar():
|
|
SPARK_HOME = os.environ["SPARK_HOME"]
|
|
mqtt_test_dir = os.path.join(SPARK_HOME, "external/mqtt")
|
|
jars = glob.glob(
|
|
os.path.join(mqtt_test_dir, "target/scala-*/spark-streaming-mqtt-test-*.jar"))
|
|
if not jars:
|
|
raise Exception(
|
|
("Failed to find Spark Streaming MQTT test jar in %s. " % mqtt_test_dir) +
|
|
"You need to build Spark with "
|
|
"'build/sbt assembly/assembly streaming-mqtt/test:assembly'")
|
|
elif len(jars) > 1:
|
|
raise Exception(("Found multiple Spark Streaming MQTT test 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, "extras/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__":
|
|
kafka_assembly_jar = search_kafka_assembly_jar()
|
|
flume_assembly_jar = search_flume_assembly_jar()
|
|
mqtt_assembly_jar = search_mqtt_assembly_jar()
|
|
mqtt_test_jar = search_mqtt_test_jar()
|
|
kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar()
|
|
|
|
if kinesis_asl_assembly_jar is None:
|
|
kinesis_jar_present = False
|
|
jars = "%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, mqtt_assembly_jar,
|
|
mqtt_test_jar)
|
|
else:
|
|
kinesis_jar_present = True
|
|
jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, mqtt_assembly_jar,
|
|
mqtt_test_jar, kinesis_asl_assembly_jar)
|
|
|
|
os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
|
|
testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests, CheckpointTests,
|
|
KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests, MQTTStreamTests,
|
|
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/assembly "
|
|
"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/assembly 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)
|