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# Copyright 2019 Optimizely | ||
# Licensed 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. | ||
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import abc | ||
import threading | ||
import time | ||
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from datetime import timedelta | ||
from six.moves import queue | ||
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from optimizely import logger as _logging | ||
from optimizely.event_dispatcher import EventDispatcher as default_event_dispatcher | ||
from optimizely.helpers import validator | ||
from .user_event import UserEvent | ||
from .event_factory import EventFactory | ||
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ABC = abc.ABCMeta('ABC', (object,), {'__slots__': ()}) | ||
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class BaseEventProcessor(ABC): | ||
""" Class encapsulating event processing. Override with your own implementation. """ | ||
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@abc.abstractmethod | ||
def process(user_event): | ||
pass | ||
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class BatchEventProcessor(BaseEventProcessor): | ||
""" | ||
BatchEventProcessor is a batched implementation of the BaseEventProcessor. | ||
The BatchEventProcessor maintains a single consumer thread that pulls events off of | ||
the blocking queue and buffers them for either a configured batch size or for a | ||
maximum duration before the resulting LogEvent is sent to the EventDispatcher. | ||
""" | ||
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_DEFAULT_QUEUE_CAPACITY = 1000 | ||
_DEFAULT_BATCH_SIZE = 10 | ||
_DEFAULT_FLUSH_INTERVAL = timedelta(seconds=30) | ||
_DEFAULT_TIMEOUT_INTERVAL = timedelta(seconds=5) | ||
_SHUTDOWN_SIGNAL = object() | ||
_FLUSH_SIGNAL = object() | ||
LOCK = threading.Lock() | ||
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def __init__(self, | ||
event_dispatcher, | ||
logger, | ||
start_on_init=False, | ||
event_queue=None, | ||
batch_size=None, | ||
flush_interval=None, | ||
timeout_interval=None): | ||
""" BatchEventProcessor init method to configure event batching. | ||
Args: | ||
event_dispatcher: Provides a dispatch_event method which if given a URL and params sends a request to it. | ||
logger: Provides a log method to log messages. By default nothing would be logged. | ||
start_on_init: Optional boolean param which starts the consumer thread if set to True. | ||
Default value is False. | ||
event_queue: Optional component which accumulates the events until dispacthed. | ||
batch_size: Optional param which defines the upper limit on the number of events in event_queue after which | ||
the event_queue will be flushed. | ||
flush_interval: Optional floating point number representing time interval in seconds after which event_queue will | ||
be flushed. | ||
timeout_interval: Optional floating point number representing time interval in seconds before joining the consumer | ||
thread. | ||
""" | ||
self.event_dispatcher = event_dispatcher or default_event_dispatcher | ||
self.logger = _logging.adapt_logger(logger or _logging.NoOpLogger()) | ||
self.event_queue = event_queue or queue.Queue(maxsize=self._DEFAULT_QUEUE_CAPACITY) | ||
self.batch_size = batch_size if self._validate_intantiation_props(batch_size, 'batch_size') \ | ||
else self._DEFAULT_BATCH_SIZE | ||
self.flush_interval = timedelta(seconds=flush_interval) \ | ||
if self._validate_intantiation_props(flush_interval, 'flush_interval') \ | ||
else self._DEFAULT_FLUSH_INTERVAL | ||
self.timeout_interval = timedelta(seconds=timeout_interval) \ | ||
if self._validate_intantiation_props(timeout_interval, 'timeout_interval') \ | ||
else self._DEFAULT_TIMEOUT_INTERVAL | ||
self._is_started = False | ||
self._current_batch = list() | ||
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if start_on_init is True: | ||
self.start() | ||
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@property | ||
def is_started(self): | ||
""" Property to check if consumer thread is alive or not. """ | ||
return self._is_started | ||
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def _validate_intantiation_props(self, prop, prop_name): | ||
""" Method to determine if instantiation properties like batch_size, flush_interval | ||
and timeout_interval are valid. | ||
Args: | ||
prop: Property value that needs to be validated. | ||
prop_name: Property name. | ||
Returns: | ||
False if property value is None or less than 1 or not a finite number. | ||
False if property name is batch_size and value is a floating point number. | ||
True otherwise. | ||
""" | ||
if (prop_name == 'batch_size' and not isinstance(prop, int)) or prop is None or prop < 1 or \ | ||
not validator.is_finite_number(prop): | ||
self.logger.info('Using default value for {}.'.format(prop_name)) | ||
return False | ||
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return True | ||
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def _get_time(self, _time=None): | ||
""" Method to return rounded off time as integer in seconds. If _time is None, uses current time. | ||
Args: | ||
_time: time in seconds that needs to be rounded off. | ||
Returns: | ||
Integer time in seconds. | ||
""" | ||
if _time is None: | ||
return int(round(time.time())) | ||
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return int(round(_time)) | ||
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def start(self): | ||
""" Starts the batch processing thread to batch events. """ | ||
if self.is_started: | ||
self.logger.warning('Service already started') | ||
return | ||
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self.flushing_interval_deadline = self._get_time() + self._get_time(self.flush_interval.total_seconds()) | ||
self.executor = threading.Thread(target=self._run) | ||
self.executor.setDaemon(True) | ||
self.executor.start() | ||
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self._is_started = True | ||
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def _run(self): | ||
""" Triggered as part of the thread which batches events or flushes event_queue and sleeps | ||
periodically if queue is empty. | ||
""" | ||
try: | ||
while True: | ||
if self._get_time() > self.flushing_interval_deadline: | ||
self._flush_queue() | ||
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try: | ||
item = self.event_queue.get(True, 0.05) | ||
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except queue.Empty: | ||
time.sleep(0.05) | ||
continue | ||
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if item == self._SHUTDOWN_SIGNAL: | ||
self.logger.debug('Received shutdown signal.') | ||
break | ||
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if item == self._FLUSH_SIGNAL: | ||
self.logger.debug('Received flush signal.') | ||
self._flush_queue() | ||
continue | ||
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if isinstance(item, UserEvent): | ||
self._add_to_batch(item) | ||
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except Exception as exception: | ||
self.logger.error('Uncaught exception processing buffer. Error: ' + str(exception)) | ||
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finally: | ||
self.logger.info('Exiting processing loop. Attempting to flush pending events.') | ||
self._flush_queue() | ||
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def flush(self): | ||
""" Adds flush signal to event_queue. """ | ||
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self.event_queue.put(self._FLUSH_SIGNAL) | ||
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def _flush_queue(self): | ||
""" Flushes event_queue by dispatching events. """ | ||
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if len(self._current_batch) == 0: | ||
return | ||
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with self.LOCK: | ||
to_process_batch = list(self._current_batch) | ||
self._current_batch = list() | ||
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log_event = EventFactory.create_log_event(to_process_batch, self.logger) | ||
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try: | ||
self.event_dispatcher.dispatch_event(log_event) | ||
except Exception as e: | ||
self.logger.error('Error dispatching event: ' + str(log_event) + ' ' + str(e)) | ||
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def process(self, user_event): | ||
if not isinstance(user_event, UserEvent): | ||
self.logger.error('Provided event is in an invalid format.') | ||
return | ||
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self.logger.debug('Received user_event: ' + str(user_event)) | ||
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try: | ||
self.event_queue.put_nowait(user_event) | ||
except queue.Full: | ||
self.logger.debug('Payload not accepted by the queue. Current size: {}'.format(str(self.event_queue.qsize()))) | ||
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def _add_to_batch(self, user_event): | ||
if self._should_split(user_event): | ||
self._flush_queue() | ||
self._current_batch = list() | ||
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# Reset the deadline if starting a new batch. | ||
if len(self._current_batch) == 0: | ||
self.flushing_interval_deadline = self._get_time() + \ | ||
self._get_time(self.flush_interval.total_seconds()) | ||
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with self.LOCK: | ||
self._current_batch.append(user_event) | ||
if len(self._current_batch) >= self.batch_size: | ||
self._flush_queue() | ||
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def _should_split(self, user_event): | ||
if len(self._current_batch) == 0: | ||
return False | ||
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current_context = self._current_batch[-1].event_context | ||
new_context = user_event.event_context | ||
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if current_context.revision != new_context.revision: | ||
return True | ||
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if current_context.project_id != new_context.project_id: | ||
return True | ||
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return False | ||
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def stop(self): | ||
""" Stops and disposes batch event processor. """ | ||
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self.event_queue.put(self._SHUTDOWN_SIGNAL) | ||
self.executor.join(self.timeout_interval.total_seconds()) | ||
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if self.executor.isAlive(): | ||
self.logger.error('Timeout exceeded while attempting to close for ' + str(self.timeout_interval) + ' ms.') | ||
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self.logger.warning('Stopping Scheduler.') | ||
self._is_started = False |
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