class AsyncWorker(object):
"""
Fetches from Kafka topics and processes them.
:param consumer_topic: Name of the Kafka topic for consume.
:type consumer_topic: str
:param service: Service function which is executed every time when job is processed.
Service must get as argument str or dict type object.
:type service: callable
:param consumer_conf: config for Kafka consumer.
:type consumer_conf: dict
:param failed_topic: Kafka topic for produce unprocessed messages from consumer_topic.
:type failed_topic: str
:param producer_conf: config for Kafka producer for producing unprocessed messages.
:type producer_conf: dict
"""
def __init__(self, consumer_topic: str, service: Callable,
consumer_conf: dict, failed_topic: str, producer_conf: dict):
self._consumer_topic = consumer_topic
self._consumer = Consumer(consumer_conf)
self._service = service
self._failed_topic = failed_topic # use naming like <project name>_<version>_<consumer_topic><retry/failed>
self._producer = AsyncProducer(producer_conf)
def __repr__(self):
"""Return the string representation of the worker.
:return: String representation of the worker.
:rtype: str
"""
return 'Worker(Consumer={}, consume_topic={})'.format(
self._consumer, self._consumer_topic)
def __del__(self): # pragma: no cover
# noinspection PyBroadException
try:
self._consumer.close()
except Exception:
pass
async def _exec_service(self, message_value):
if iscoroutinefunction(self._service):
res = await self._service(message_value)
else:
res = self._service(message_value)
return res
async def _process_message(self, msg: Message):
"""
De-serialize message and execute service.
:param msg: Kafka message.
:type msg: confluent_kafka.Message`
"""
LOGGER.info(
'Processing Message(topic={}, partition={}, offset={}) ...'.format(
msg.topic, msg.partition, msg.offset))
service_repr = get_call_repr(self._service)
LOGGER.info('Executing job {}'.format(service_repr))
try:
message_value = _decode_msg_value(msg.value())
res = await self._exec_service(message_value)
except KeyboardInterrupt:
LOGGER.error('Job was interrupted: {}'.format(msg.offset()))
except Exception as err:
LOGGER.exception('Job {} raised an exception: {}'.format(
msg.offset(), err))
await self._producer.produce(topic=self._failed_topic,
value=msg.value(),
error=str(err))
else:
LOGGER.info('Job {} returned: {}'.format(msg.offset(), res))
@property
def consumer_topic(self):
"""Return the name of the Kafka topic.
:return: Name of the Kafka topic.
:rtype: str
"""
return self._consumer_topic
@property
def consumer(self):
"""Return the Kafka consumer instance.
:return: Kafka consumer instance.
:rtype: kafka.KafkaConsumer
"""
return self._consumer
@property
def service(self):
"""Return the service function.
:return: Callback function, or None if not set.
:rtype: callable | None
"""
return self._service
async def start(self,
max_messages: int = math.inf,
commit_offsets: bool = True) -> int:
"""Start processing Kafka messages and executing jobs.
:param max_messages: Maximum number of Kafka messages to process before stopping. If not set, worker runs until
interrupted.
:type max_messages: int
:param commit_offsets: If set to True, consumer offsets are committed every time a message is processed
(default: True).
:type commit_offsets: bool
:return: Total number of messages processed.
:rtype: int
"""
LOGGER.info('Starting {} ...'.format(self))
self._consumer.unsubscribe()
self._consumer.subscribe([self.consumer_topic])
LOGGER.info(" Try get messages from position: {}".format(
self._consumer.position(self._consumer.assignment())))
messages_processed = 0
while messages_processed < max_messages:
loop = asyncio.get_event_loop()
# awaiting place for processing messages in other coroutines
messages = await loop.run_in_executor(
None, partial(self._consumer.consume, 10, 2.0))
LOGGER.debug(" Try get messages from position: {}".format(
self._consumer.position(self._consumer.assignment())))
if not messages:
LOGGER.debug("Messages not found")
continue
for msg in messages:
if msg.error():
LOGGER.error("Consumer error: {}".format(msg.error()))
LOGGER.info("Get message with offset {}".format(msg.offset()))
asyncio.create_task(self._process_message(msg))
if commit_offsets:
self._consumer.commit()
messages_processed += 1
self._consumer.close()
return messages_processed
def compute_achieved_throughput(broker, partitions_with_offsets, result_dict):
partitions_with_offsets = {}
input_consumer = Consumer({
'bootstrap.servers': broker,
'group.id': str(uuid.uuid4()),
# 'group.id': 'achieved_throughput_measurer',
'auto.offset.reset': 'earliest',
'enable.auto.commit': True,
'auto.commit.interval.ms': 1000,
'api.version.request': True,
'max.poll.interval.ms': 60000
})
output_consumer = Consumer({
'bootstrap.servers': broker,
'group.id': str(uuid.uuid4()),
# 'group.id': 'achieved_throughput_measurer',
'auto.offset.reset': 'earliest',
'enable.auto.commit': True,
'auto.commit.interval.ms': 1000,
'api.version.request': True,
'max.poll.interval.ms': 60000
})
if 'input' in partitions_with_offsets and len(
partitions_with_offsets['input']) > 0:
input_consumer.assign(partitions_with_offsets['input'])
else:
input_consumer.subscribe(['read', 'update', 'transfer'])
if 'output' in partitions_with_offsets and len(
partitions_with_offsets['output']) > 0:
output_consumer.assign(partitions_with_offsets['output'])
else:
output_consumer.subscribe(['responses'])
while True:
msgs = input_consumer.consume(timeout=5, num_messages=500)
if len(msgs) == 0:
break
for msg in msgs:
try:
wrapped = Wrapper()
wrapped.ParseFromString(msg.value())
result = {}
result['operation'] = msg.topic()
result['input_time'] = msg.timestamp()[1]
result_dict[wrapped.request_id] = result
except DecodeError as e:
print("Could not decode?")
pass
partitions_with_offsets['input'] = input_consumer.position(
input_consumer.assignment())
input_consumer.close()
total_messages = 0
start_time = 0
end_time = 0
first = True
while True:
msgs = output_consumer.consume(timeout=5, num_messages=500)
if len(msgs) == 0:
break
for msg in msgs:
response = Response()
response.ParseFromString(msg.value())
key = response.request_id
status_code = response.status_code
if key in result_dict:
if first:
start_time = msg.timestamp()[1] / 1000
first = False
total_messages += 1
end_time = msg.timestamp()[1] / 1000
result_dict[key]['output_time'] = msg.timestamp()[1]
result_dict[key]['status_code'] = status_code
partitions_with_offsets['output'] = output_consumer.position(
output_consumer.assignment())
output_consumer.close()
print("Total messages considered: " + str(total_messages))
if total_messages == 0 or end_time - start_time == 0:
return 0
return total_messages / (end_time - start_time)