def test_run(self):
"""
Test drain's run function (in a same thread)
"""
source = range(5)
destination = Queue()
drain = Drain(source, destination)
drain.run()
assert destination.qsize() == 5
def test_run(self):
"""
Test drain's run function (in a same thread)
"""
source = range(5)
destination = Queue()
drain = Drain(source, destination)
drain.run()
assert destination.qsize() == 5
def test_interrupt_and_wait(self):
"""
Test we can interrupt the drain
"""
source = range(1000000)
destination = Queue()
drain = Drain(source, destination)
drain.start()
drain.join()
assert destination.qsize() == 1000000
def test_partially_reversed_data(self, data):
results_queue = Queue()
chunks = list(random_split(data))
chunks[5], chunks[6] = chunks[6], chunks[5]
pipeline = Aggregator(
TimeChopper(DataPoller(source=chunks, poll_period=0.1),
cache_size=3), AGGR_CONFIG, False)
drain = Drain(pipeline, results_queue)
drain.run()
assert results_queue.qsize() == MAX_TS
def test_partially_reversed_data(self, data):
results_queue = Queue()
chunks = list(random_split(data))
chunks[5], chunks[6] = chunks[6], chunks[5]
pipeline = Aggregator(
TimeChopper(
DataPoller(
source=chunks, poll_period=0.1), cache_size=3),
AGGR_CONFIG,
False)
drain = Drain(pipeline, results_queue)
drain.run()
assert results_queue.qsize() == MAX_TS
def test_slow_producer(self, data):
results_queue = Queue()
chunks = list(random_split(data))
chunks[5], chunks[6] = chunks[6], chunks[5]
def producer():
for chunk in chunks:
if np.random.random() > 0.5:
yield None
yield chunk
pipeline = Aggregator(
TimeChopper(DataPoller(source=producer(), poll_period=0.1),
cache_size=3), AGGR_CONFIG, False)
drain = Drain(pipeline, results_queue)
drain.run()
assert results_queue.qsize() == MAX_TS
def test_slow_producer(self, data):
results_queue = Queue()
chunks = list(random_split(data))
chunks[5], chunks[6] = chunks[6], chunks[5]
def producer():
for chunk in chunks:
if np.random.random() > 0.5:
yield None
yield chunk
pipeline = Aggregator(
TimeChopper(
DataPoller(
source=producer(), poll_period=0.1), cache_size=3),
AGGR_CONFIG,
False)
drain = Drain(pipeline, results_queue)
drain.run()
assert results_queue.qsize() == MAX_TS
def start_test(self):
self.reader = self.generator.get_reader()
self.stats_reader = self.generator.get_stats_reader()
aggregator_config = self.load_config()
verbose_histogram = True
if verbose_histogram:
logger.info("using verbose histogram")
if self.reader and self.stats_reader:
pipeline = Aggregator(
TimeChopper(DataPoller(source=self.reader, poll_period=1),
cache_size=3), aggregator_config,
verbose_histogram)
self.drain = Drain(pipeline, self.results)
self.drain.start()
self.stats_drain = Drain(
Chopper(DataPoller(source=self.stats_reader, poll_period=1)),
self.stats)
self.stats_drain.start()
else:
logger.warning(
"Generator not found. Generator must provide a reader and a stats_reader interface"
)
def start_test(self):
self.reader = self.generator.get_reader()
self.stats_reader = self.generator.get_stats_reader()
aggregator_config = json.loads(
resource_string(__name__, 'config/phout.json').decode('utf8'))
verbose_histogram = True
if verbose_histogram:
logger.info("using verbose histogram")
if self.reader and self.stats_reader:
pipeline = Aggregator(
TimeChopper(DataPoller(source=self.reader, poll_period=1),
cache_size=3), aggregator_config,
verbose_histogram)
self.drain = Drain(pipeline, self.results)
self.drain.start()
self.stats_drain = Drain(
Chopper(DataPoller(source=self.stats_reader, poll_period=1)),
self.stats)
self.stats_drain.start()
else:
raise PluginImplementationError(
"Generator must pass a Reader and a StatsReader"
" to Aggregator before starting test")
def test_interrupt_and_wait(self):
"""
Test we can interrupt the drain
"""
source = range(1000000)
destination = Queue()
drain = Drain(source, destination)
drain.start()
drain.join()
assert destination.qsize() == 1000000
class TankAggregator(object):
"""
Plugin that manages aggregation and stats collection
"""
SECTION = 'aggregator'
@staticmethod
def get_key():
return __file__
def __init__(self, generator):
# AbstractPlugin.__init__(self, core, cfg)
"""
:type generator: GeneratorPlugin
"""
self.generator = generator
self.listeners = [] # [LoggingListener()]
self.results = q.Queue()
self.stats = q.Queue()
self.data_cache = {}
self.stat_cache = {}
def start_test(self):
self.reader = self.generator.get_reader()
self.stats_reader = self.generator.get_stats_reader()
aggregator_config = json.loads(
resource_string(__name__, 'config/phout.json').decode('utf8'))
verbose_histogram = True
if verbose_histogram:
logger.info("using verbose histogram")
if self.reader and self.stats_reader:
pipeline = Aggregator(
TimeChopper(DataPoller(source=self.reader, poll_period=1),
cache_size=3), aggregator_config,
verbose_histogram)
self.drain = Drain(pipeline, self.results)
self.drain.start()
self.stats_drain = Drain(
Chopper(DataPoller(source=self.stats_reader, poll_period=1)),
self.stats)
self.stats_drain.start()
else:
raise PluginImplementationError(
"Generator must pass a Reader and a StatsReader"
" to Aggregator before starting test")
def _collect_data(self):
"""
Collect data, cache it and send to listeners
"""
data = get_from_queue(self.results)
stats = get_from_queue(self.stats)
logger.debug("Data timestamps:\n%s" % [d.get('ts') for d in data])
logger.debug("Stats timestamps:\n%s" % [d.get('ts') for d in stats])
for item in data:
ts = item['ts']
if ts in self.stat_cache:
# send items
data_item = item
stat_item = self.stat_cache.pop(ts)
self.__notify_listeners(data_item, stat_item)
else:
self.data_cache[ts] = item
for item in stats:
ts = item['ts']
if ts in self.data_cache:
# send items
data_item = self.data_cache.pop(ts)
stat_item = item
self.__notify_listeners(data_item, stat_item)
else:
self.stat_cache[ts] = item
def is_test_finished(self):
self._collect_data()
return -1
def end_test(self, retcode):
retcode = self.generator.end_test(retcode)
if self.reader:
self.reader.close()
if self.stats_reader:
self.stats_reader.close()
if self.drain:
self.drain.join()
if self.stats_drain:
self.stats_drain.join()
self._collect_data()
return retcode
def add_result_listener(self, listener):
self.listeners.append(listener)
def __notify_listeners(self, data, stats):
""" notify all listeners about aggregate data and stats """
for listener in self.listeners:
listener.on_aggregated_data(data, stats)