def test_create_topic_exception(self, mocker):
mock_kafka_admin = mocker.Mock()
mocker.patch('confluent_kafka.admin.AdminClient',
return_value=mock_kafka_admin)
from confluent_kafka.cimpl import KafkaException
mock_kafka_admin.create_topics = mocker.MagicMock(
side_effect=KafkaException('fo'))
kafka_manager = KafkaTopicManager('kafka.servicex.org:1272')
result = kafka_manager.create_topic('my-topic',
max_message_size=1024,
num_partitions=100)
assert not result
def basic_consume_loop(consumer, topics):
try:
consumer.assign(topics)
durs = []
i=0
message = {}
while running:
msg = consumer.poll(timeout=1.0)
if msg is None: continue
message = {}
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write('%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
message = loads(msg.value().decode("utf-8"))
#print(message)
if not message['dur_evt_inf'] is None:
i = i + 1
durs.append(message['dur_evt_inf'])
if(i==1000000):
break
#durs.append(m['dur_evt_inf'])
if (i % 1000 == 0):
print(message)
#now2 = datetime.now()
print(i)
finally:
# Close down consumer to commit final offsets.
consumer.close()
#print(durs)
mean = statistics.mean(durs)
median = statistics.median(durs)
max1 = max(durs)
min2 = min(durs)
print('max=' + str(max1))
print('min=' + str(min2))
print('avg=' + str(mean))
print('med=' + str(median))
print('total obs =' + str(len(durs)))
def poll(self):
msg = self._consumer.poll(self._config["poll_timeout"])
if msg is not None:
err = msg.error()
if err:
if err.code() == KafkaError._PARTITION_EOF:
return None
else:
self._logger.info(
"KafkaConsumer Error {} at pid {}: topic={} partition=[{}] reached end at offset {}\n"
.format(err.code(), os.getpid(), msg.topic(),
msg.partition(), msg.offset()))
raise KafkaException(err)
if msg.value():
return msg
def run(self):
try:
self.consumer.assign(self.tls)
i = 0
while (True):
msg = self.consumer.poll(timeout=1.0)
if msg is None: continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
message = loads(msg.value().decode("utf-8"))
ingest_ts = message['ingestTs']
message_id = message['message_id']
truth = message['Class']
y_hat = truth ## Replace with model.predict & model.learn_one(
inference_ts = time.time()
out = {}
out['ingest_ts'] = ingest_ts
out['message_id'] = message_id
out['truth'] = truth ## model.learn_one(Y,Y_HAT)
out['y_hat'] = y_hat
out['inference_ts'] = inference_ts
out['dur_evt_inf'] = inference_ts - ingest_ts
i = i + 1
# sprint(self.result_t)
k = str(i)
v = json.dumps(out).encode('utf-8')
#self.producer.produce(self.result_t, value=v, key=k)
# self.producer.flush()
if (i % 2 == 0):
print('sending to ' + self.result_t + ' ' +
str(self.result_t_p) + ' ' + str(out))
self.producer.flush()
#self.producer.close()
finally:
# Close down consumer to commit final offsets.
self.consumer.close()
def process_msgs(self, msgs) -> bool:
points = []
for msg in msgs:
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
return False
if DEBUG == "true":
print('Received message: %s' % msg.value().decode('utf-8'),
flush=True)
data_input = json.loads(msg.value().decode('utf-8'))
if self.filter_msg(data_input):
body = {
"measurement": self.data_measurement,
"fields": get_field_values(self.field_config, data_input)
}
if self.try_time:
try:
body["time"] = Tree(data_input).execute(
'$.' + self.data_time_mapping)
except SyntaxError as err:
print(
'Disabling reading time from message, error occurred:',
err.msg,
flush=True)
print(
'Influx will set time to time of arrival by default',
flush=True)
self.try_time = False
if len(self.tag_config) > 0:
body["tags"] = get_field_values(self.tag_config,
data_input)
if DEBUG == "true":
print('Write message: %s' % body, flush=True)
points.append(body)
try:
self.influx_client.write_points(points,
time_precision=self.time_precision)
except exceptions.InfluxDBClientError as e:
print('Received Influx error: %s', e.content, flush=True)
return True
def run_consumer(container_manager):
schema_registry_conf = {'url': config['kafka']['schema_registry']}
schema_registry_client = SchemaRegistryClient(schema_registry_conf)
avro_deserializer = AvroDeserializer(schemas.run_record_schema,
schema_registry_client)
string_deserializer = StringDeserializer('utf_8')
conf = {
'bootstrap.servers': config['kafka']['servers'],
'key.deserializer': string_deserializer,
'value.deserializer': avro_deserializer,
'group.id': "runs-consumers",
'auto.offset.reset': 'earliest',
'enable.auto.commit': 'false'
}
consumer = DeserializingConsumer(conf)
print('[+] Listening for incoming runs')
try:
consumer_topics = [config['kafka']['runs-topic']]
consumer.subscribe(consumer_topics)
while True:
try:
msg = consumer.poll(timeout=1.0)
if msg is None:
continue
if msg.error():
raise KafkaException(msg.error())
else:
print('[-] Run initialization')
print(msg.value())
consumer.commit(asynchronous=False)
# handlers.handle_run_execution(container_manager, msg.value())
threading.Thread(target=handlers.handle_run_execution,
args=(container_manager,
msg.value())).start()
except ConsumeError as e:
print(
f'[Exception] error_code: {e.code()} message: {e.message()} exception: {e}'
)
finally:
consumer.close()
def mensagem_dados_empresa_consumer(self):
try:
self.__consumer.subscribe(self.__topics)
mensagem = self.__consumer.poll(timeout=1.0)
if mensagem is None:
return None
if mensagem.error():
if mensagem.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write('%% %s [%d] reached end at offset %d\n' %
(mensagem.topic(), mensagem.partition(),
mensagem.offset()))
elif mensagem.error():
raise KafkaException(mensagem.error())
else:
return mensagem
except Exception:
print("Erro!")
def get_watched_messages(self, interval=0.0):
logging.debug(
"Checking messages that appeared on kafka topics: %r",
self.watching_topics)
res = []
while True:
message = self.consumer.poll(interval)
if message is None:
break # done reading
if message.error():
raise KafkaException("kafka consumer error: {}".format(
message.error()))
res.append(message)
# TODO: consumer.close()
return res
def basic_consume_loop(consumer, topics):
try:
consumer.subscribe(topics)
while running:
msg = consumer.poll(timeout=1.0)
if msg is None: continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
msg_process(msg)
finally:
# Close down consumer to commit final offsets.
consumer.close()
def get_watched_messages(self, interval=0.0, predicate=lambda x: True):
logging.debug(
"Checking messages that appeared on kafka topics: %r",
self.watching_topics)
res = []
start = time.time()
while True:
msg = self.consumer.poll(interval)
if msg is None or time.time() - start > interval:
break # done reading
if msg.error():
raise KafkaException("kafka consumer error: {}".format(
msg.error()))
logging.debug(
"Potential message: %r",
(msg.partition(), msg.key(), msg.headers(), msg.value()))
if predicate(msg):
res.append(msg)
# TODO: consumer.close()
return res
def run(self):
### Set up model, metric, and starting timestamp for this model instance ###
model = self.model
metric = self.metric
print('MODEL and METRIC before any messages consumed:', model, metric)
start_consume_ts = time.time()
print('Start Consume Time ' + str(start_consume_ts))
######
try:
self.consumer.assign(self.tls)
i = 0
while (True):
msg = self.consumer.poll(timeout=1.0)
if msg is None: continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
message = loads(msg.value().decode("utf-8"))
ingest_ts = message[
'ingestTs'] # Ingestion time from Kafka
message.pop('ingestTs')
message_id = message['message_id'] # Message ID from Kafka
message.pop('message_id')
truth = float(
message['Class']) # True label supplied by Kafka
message.pop(
'Class'
) # Remove label remove passing the data for prediction (could combine with above)
message.pop('ignore')
x = message
for k, v in x.items():
x[k] = float(v)
y_hat = model.score_one(
x) #model.predict_one(x) # make a prediction
if (i % 1000 == 0):
print('x: ', x)
for k, v in x.items():
print(type(x[k]))
print('model: ', model)
print('y_hat: ', y_hat)
inference_ts = time.time()
metric = metric.update(truth, y_hat) # update the metric
model = model.learn_one(
x) # model.learn_one(x,y) # make the model learn
learn_ts = time.time()
out = {}
out['ingest_ts'] = ingest_ts
out['learn_ts'] = learn_ts
out['message_id'] = message_id
out['truth'] = truth
out['y_hat'] = y_hat
out['inference_ts'] = inference_ts
out['dur_evt_inf'] = inference_ts - ingest_ts
out['dur_start_inf'] = inference_ts - start_consume_ts
out['dur_inf_learn'] = learn_ts - inference_ts
out['model_metric'] = metric.get()
out['ignore'] = False
i = i + 1
# sprint(self.result_t)
k = str(i)
v = json.dumps(out).encode('utf-8')
self.producer.produce(self.result_t, value=v, key=k)
# self.producer.flush()
if (i % 1000 == 0):
print('sending to ' + self.result_t + ' ' +
str(self.result_t_p) + ' ' + str(out))
self.producer.flush()
if (i % 100000 == 0):
try:
pickle.dump(model, open("model.pkl", "wb"))
except:
os.remove("model.pkl")
pickle.dump(model, open("model.pkl", "wb"))
#self.producer.close()
finally:
# Close down consumer to commit final offsets.
self.consumer.close()
def consumer(networkName, topic):
es = Elasticsearch('192.168.5.71:9200')
conf = {
'bootstrap.servers': "192.168.5.41:9092",
'group.id': "demo",
'auto.offset.reset': 'smallest'
}
consumer = Consumer(conf)
topics = [topic]
consumer.subscribe(topics)
running = True
try:
consumer.subscribe(topics)
msg_count = 0
nd_count = 0
json_object = ""
while running:
msg = consumer.poll(timeout=1.0)
if msg is None: continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
# msg_process(msg)
# print(msg.value().decode("utf-8"))
# messageStr = (msg.value())
if "_index" in msg.value().decode("utf-8"):
json_object = "{"
nd_count += 1
if nd_count > 0:
json_object += (msg.value().decode("utf-8"))
if msg.value().decode("utf-8").strip() == ",":
# wrpcap('filtered.pcap', msg.value(), append=True)
with open('json_file_pretty', 'a',
encoding='utf-8') as outfile:
# json.dump(messageStr, fp)
if json_object != "":
x = json.loads(
json.dumps(json_object,
sort_keys=True,
indent=4))
print(x)
print(networkName)
json.dump(x, outfile)
json_object = ""
nd_count = 0
# doc = {
# 'text': messageStr,
# 'timestamp': datetime.now(),
# }
# es.index(index="test-index", body=doc, refresh='wait_for')
msg_count += 1
# if msg_count % MIN_COMMIT_COUNT == 0:
# consumer.commit(async=False)
finally:
# Close down consumer to commit final offsets.
consumer.close()