class BaseExporter(object):
def __init__(self, **options):
options = Options(**options)
self.export_interval = options.export_interval
self.max_batch_size = options.max_batch_size
# TODO: queue should be moved to tracer
# too much refactor work, leave to the next PR
self._queue = Queue(capacity=options.queue_capacity)
# TODO: worker should not be created in the base exporter
self._worker = Worker(self._queue, self)
self._worker.start()
atexit.register(self._worker.stop, options.grace_period)
# Ideally we don't want to have `emit`
# Exporter will have one public method - `export`, which is a blocking
# method, running inside worker threads.
def emit(self, batch, event=None):
raise NotImplementedError # pragma: NO COVER
# TODO: we shouldn't have this at the beginning
# Tracer should own the queue, exporter shouldn't even know if the
# source is a queue or not.
# Tracer puts span_data into the queue.
# Worker gets span_data from the src (here is the queue) and feed into
# the dst (exporter).
# Exporter defines the MTU (max_batch_size) and export_interval.
# There can be one worker for each queue, or multiple workers for each
# queue, or shared workers among queues (e.g. queue for traces, queue
# for logs).
def export(self, items):
self._queue.puts(items, block=False) # pragma: NO COVER
class BaseLogHandler(logging.Handler):
def __init__(self):
super(BaseLogHandler, self).__init__()
self._queue = Queue(capacity=8192) # TODO: make this configurable
self._worker = Worker(self._queue, self)
self._worker.start()
def close(self):
self._worker.stop()
def createLock(self):
self.lock = None
def emit(self, record):
self._queue.put(record, block=False)
def _export(self, batch, event=None):
try:
return self.export(batch)
finally:
if event:
event.set()
def export(self, batch):
raise NotImplementedError # pragma: NO COVER
def flush(self, timeout=None):
self._queue.flush(timeout=timeout)
def __init__(self, **options):
super(BaseLogHandler, self).__init__()
self.options = Options(**options)
utils.validate_instrumentation_key(self.options.instrumentation_key)
if not 0 <= self.options.logging_sampling_rate <= 1:
raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = None
if self.options.enable_local_storage:
self.storage = LocalFileStorage(
path=self.options.storage_path,
max_size=self.options.storage_max_size,
maintenance_period=self.options.storage_maintenance_period,
retention_period=self.options.storage_retention_period,
source=self.__class__.__name__,
)
self._telemetry_processors = []
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))
self._queue = Queue(capacity=self.options.queue_capacity)
self._worker = Worker(self._queue, self)
self._worker.start()
atexit.register(self.close, self.options.grace_period)
# start statsbeat on exporter instantiation
if not os.environ.get("APPLICATIONINSIGHTS_STATSBEAT_DISABLED_ALL"):
statsbeat_metrics.collect_statsbeat_metrics(self.options)
# For redirects
self._consecutive_redirects = 0 # To prevent circular redirects
class BaseLogHandler(logging.Handler):
def __init__(self, **options):
super(BaseLogHandler, self).__init__()
self.options = Options(**options)
utils.validate_instrumentation_key(self.options.instrumentation_key)
if not 0 <= self.options.logging_sampling_rate <= 1:
raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = None
if self.options.enable_local_storage:
self.storage = LocalFileStorage(
path=self.options.storage_path,
max_size=self.options.storage_max_size,
maintenance_period=self.options.storage_maintenance_period,
retention_period=self.options.storage_retention_period,
source=self.__class__.__name__,
)
self._telemetry_processors = []
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))
self._queue = Queue(capacity=self.options.queue_capacity)
self._worker = Worker(self._queue, self)
self._worker.start()
def _export(self, batch, event=None): # pragma: NO COVER
try:
if batch:
envelopes = [self.log_record_to_envelope(x) for x in batch]
envelopes = self.apply_telemetry_processors(envelopes)
result = self._transmit(envelopes)
# Only store files if local storage enabled
if self.storage and result > 0:
self.storage.put(envelopes, result)
if event:
if isinstance(event, QueueExitEvent):
self._transmit_from_storage() # send files before exit
return
if len(batch) < self.options.max_batch_size:
self._transmit_from_storage()
finally:
if event:
event.set()
def close(self):
if self.storage:
self.storage.close()
if self._worker:
self._worker.stop()
def createLock(self):
self.lock = None
def emit(self, record):
self._queue.put(record, block=False)
def log_record_to_envelope(self, record):
raise NotImplementedError # pragma: NO COVER
def flush(self, timeout=None):
self._queue.flush(timeout=timeout)
def __init__(self, **options):
options = Options(**options)
self.export_interval = options.export_interval
self.max_batch_size = options.max_batch_size
# TODO: queue should be moved to tracer
# too much refactor work, leave to the next PR
self._queue = Queue(capacity=options.queue_capacity)
# TODO: worker should not be created in the base exporter
self._worker = Worker(self._queue, self)
self._worker.start()
atexit.register(self._worker.stop, options.grace_period)
def test_gets(self):
queue = Queue(capacity=10)
queue.puts((1, 2, 3))
result = queue.gets(count=5, timeout=TIMEOUT)
self.assertEqual(result, (1, 2, 3))
queue.puts((1, 2, 3, 4, 5))
result = queue.gets(count=3, timeout=TIMEOUT)
self.assertEqual(result, (1, 2, 3))
result = queue.gets(count=3, timeout=TIMEOUT)
self.assertEqual(result, (4, 5))
def __init__(self, **options):
super(BaseLogHandler, self).__init__()
self.options = Options(**options)
utils.validate_instrumentation_key(self.options.instrumentation_key)
if not 0 <= self.options.logging_sampling_rate <= 1:
raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = LocalFileStorage(
path=self.options.storage_path,
max_size=self.options.storage_max_size,
maintenance_period=self.options.storage_maintenance_period,
retention_period=self.options.storage_retention_period,
)
self._telemetry_processors = []
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))
self._queue = Queue(capacity=8192) # TODO: make this configurable
self._worker = Worker(self._queue, self)
self._worker.start()
def test_flush_timeout(self):
queue = Queue(capacity=10)
self.assertEqual(queue.flush(timeout=TIMEOUT), 0)
queue.put('test', timeout=TIMEOUT)
self.assertIsNone(queue.flush(timeout=TIMEOUT))
queue.puts(range(100), timeout=TIMEOUT)
self.assertIsNone(queue.flush(timeout=TIMEOUT))
def proc():
for item in queue.gets(count=1, timeout=TIMEOUT):
if isinstance(item, QueueEvent):
item.set()
task = PeriodicTask(TIMEOUT / 10, proc)
task.start()
try:
self.assertIsNotNone(queue.flush())
finally:
task.cancel()
task.join()
def __init__(self, **options):
super(BaseLogHandler, self).__init__()
self.options = Options(**options)
utils.validate_instrumentation_key(self.options.instrumentation_key)
if not 0 <= self.options.logging_sampling_rate <= 1:
raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = LocalFileStorage(
path=self.options.storage_path,
max_size=self.options.storage_max_size,
maintenance_period=self.options.storage_maintenance_period,
retention_period=self.options.storage_retention_period,
source=self.__class__.__name__,
)
self._telemetry_processors = []
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))
self._queue = Queue(capacity=self.options.queue_capacity)
self._worker = Worker(self._queue, self)
self._worker.start()
heartbeat_metrics.enable_heartbeat_metrics(
self.options.connection_string, self.options.instrumentation_key)
def test_gets_event(self):
queue = Queue(capacity=10)
event = QueueEvent('test')
queue.puts((event, 1, 2, 3, event))
result = queue.gets(count=5, timeout=TIMEOUT)
self.assertEqual(result, (event, ))
result = queue.gets(count=5, timeout=TIMEOUT)
self.assertEqual(result, (1, 2, 3, event))
task = PeriodicTask(TIMEOUT / 10, lambda: queue.put(1))
task.start()
try:
result = queue.gets(count=5, timeout=TIMEOUT)
self.assertEqual(result, (1, 1, 1, 1, 1))
finally:
task.cancel()
task.join()
def __init__(self):
super(BaseLogHandler, self).__init__()
self._queue = Queue(capacity=8192) # TODO: make this configurable
self._worker = Worker(self._queue, self)
self._worker.start()
def test_puts_timeout(self):
queue = Queue(capacity=10)
queue.puts(range(100), timeout=TIMEOUT)
class BaseLogHandler(logging.Handler):
def __init__(self, **options):
super(BaseLogHandler, self).__init__()
self.options = Options(**options)
utils.validate_instrumentation_key(self.options.instrumentation_key)
if not 0 <= self.options.logging_sampling_rate <= 1:
raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = None
if self.options.enable_local_storage:
self.storage = LocalFileStorage(
path=self.options.storage_path,
max_size=self.options.storage_max_size,
maintenance_period=self.options.storage_maintenance_period,
retention_period=self.options.storage_retention_period,
source=self.__class__.__name__,
)
self._telemetry_processors = []
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))
self._queue = Queue(capacity=self.options.queue_capacity)
self._worker = Worker(self._queue, self)
self._worker.start()
atexit.register(self.close, self.options.grace_period)
# start statsbeat on exporter instantiation
if not os.environ.get("APPLICATIONINSIGHTS_STATSBEAT_DISABLED_ALL"):
statsbeat_metrics.collect_statsbeat_metrics(self.options)
# For redirects
self._consecutive_redirects = 0 # To prevent circular redirects
def _export(self, batch, event=None): # pragma: NO COVER
try:
if batch:
envelopes = [self.log_record_to_envelope(x) for x in batch]
envelopes = self.apply_telemetry_processors(envelopes)
result = self._transmit(envelopes)
# Only store files if local storage enabled
if self.storage and result > 0:
self.storage.put(envelopes, result)
if event:
if isinstance(event, QueueExitEvent):
self._transmit_from_storage() # send files before exit
return
if len(batch) < self.options.max_batch_size:
self._transmit_from_storage()
finally:
if event:
event.set()
def close(self, timeout=None):
if self.storage:
self.storage.close()
if self._worker:
self._worker.stop(timeout)
def createLock(self):
self.lock = None
def emit(self, record):
self._queue.put(record, block=False)
def log_record_to_envelope(self, record):
raise NotImplementedError # pragma: NO COVER
def flush(self, timeout=None):
if self._queue.is_empty():
return
# We must check the worker thread is alive, because otherwise flush
# is useless. Also, it would deadlock if no timeout is given, and the
# queue isn't empty.
# This is a very possible scenario during process termination, when
# atexit first calls handler.close() and then logging.shutdown(),
# that in turn calls handler.flush() without arguments.
if not self._worker.is_alive():
logger.warning(
"Can't flush %s, worker thread is dead. "
"Any pending messages will be lost.", self)
return
self._queue.flush(timeout=timeout)
class BaseLogHandler(logging.Handler):
def __init__(self, **options):
super(BaseLogHandler, self).__init__()
self.options = Options(**options)
logger.debug("Setting up AzureLogHandler")
if not 0 <= self.options.logging_sampling_rate <= 1:
raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = LocalFileStorage(
path=self.options.storage_path,
max_size=self.options.storage_max_size,
maintenance_period=self.options.storage_maintenance_period,
retention_period=self.options.storage_retention_period,
source=self.__class__.__name__,
)
self._telemetry_processors = []
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))
self._queue = Queue(capacity=8192) # TODO: make this configurable
self._worker = Worker(self._queue, self)
self._worker.start()
# TODO: Make enable/disable heartbeat configurable. Disabling it for now.
# heartbeat_metrics.enable_heartbeat_metrics(
# self.options.connection_string, self.options.instrumentation_key)
def _export(self, batch, event=None): # pragma: NO COVER
try:
if batch:
envelopes = [self.log_record_to_envelope(x) for x in batch]
envelopes = self.apply_telemetry_processors(envelopes)
result = self._transmit(envelopes)
if result > 0:
self.storage.put(envelopes, result)
if event:
if isinstance(event, QueueExitEvent):
self._transmit_from_storage() # send files before exit
return
if len(batch) < self.options.max_batch_size:
self._transmit_from_storage()
finally:
if event:
event.set()
def close(self):
self.storage.close()
self._worker.stop()
def createLock(self):
self.lock = None
def emit(self, record):
self._queue.put(record, block=False)
def log_record_to_envelope(self, record):
raise NotImplementedError # pragma: NO COVER
def flush(self, timeout=None):
self._queue.flush(timeout=timeout)
