def produce():
p = Producer({'bootstrap.servers': KAFKA_SERVER})
i = 0
while True:
p.produce(KAFKA_TOPIC, "message" + str(i))
i += 1
def test_produce_headers_should_fail():
""" Test produce() with timestamp arg """
p = Producer({'socket.timeout.ms': 10,
'error_cb': error_cb,
'message.timeout.ms': 10})
with pytest.raises(NotImplementedError) as e:
p.produce('mytopic', value='somedata', key='a key', headers=[('headerkey', 'headervalue')])
assert 'Producer message headers requires confluent-kafka-python built for librdkafka version >=v0.11.4' in str(e)
class KafkaPublisher(object):
def __init__(self, connection, asynchronous=True):
from confluent_kafka import Producer
self.producer = Producer(connection or {})
self.asynchronous = asynchronous
def publish(self, channel, value, key=None):
self.producer.produce(topic=channel, value=value, key=key)
if not self.asynchronous:
self.producer.flush()
def publish(datasource="USB"):
p = Producer({'bootstrap.servers': 'localhost:9092'})
if datasource=="USB":
inputf=Streaming_AbstractGenerator.StreamAbsGen("USBWWAN_stream","USBWWAN")
else:
inputf=Streaming_AbstractGenerator.StreamAbsGen("KingCobra","KingCobra")
for data in inputf:
print "publishing data:",data," in Kafka Topic"
try:
p.produce('neuronraindata', data.encode('utf-8'))
except:
pass
def test_fatal():
""" Test fatal exceptions """
# Configure an invalid broker and make sure the ALL_BROKERS_DOWN
# error is seen in the error callback.
p = Producer({'error_cb': error_cb})
with pytest.raises(KafkaException) as exc:
KafkaError._test_raise_fatal()
err = exc.value.args[0]
assert isinstance(err, KafkaError)
assert err.fatal() is True
p.poll(0) # Need some p use to avoid flake8 unused warning
def test_produce_timestamp():
""" Test produce() with timestamp arg """
p = Producer({'socket.timeout.ms': 10,
'error_cb': error_cb,
'message.timeout.ms': 10})
# Requires librdkafka >=v0.9.4
try:
p.produce('mytopic', timestamp=1234567)
except NotImplementedError:
# Should only fail on non-supporting librdkafka
if libversion()[1] >= 0x00090400:
raise
p.flush()
def test_error_cb():
""" Test the error callback. """
global seen_all_brokers_down
# Configure an invalid broker and make sure the ALL_BROKERS_DOWN
# error is seen in the error callback.
p = Producer({'bootstrap.servers': '127.0.0.1:1', 'socket.timeout.ms': 10,
'error_cb': error_cb})
t_end = time.time() + 5
while not seen_all_brokers_down and time.time() < t_end:
p.poll(1)
assert seen_all_brokers_down
class KafkaWorkflowCommunicationSender(object):
_requires = ['confluent-kafka']
def __init__(self, message_converter=ProtobufWorkflowCommunicationConverter):
kafka_config = walkoff.config.Config.WORKFLOW_COMMUNICATION_KAFKA_CONFIG
self.producer = Producer(kafka_config)
self.topic = walkoff.config.Config.WORKFLOW_COMMUNICATION_KAFKA_TOPIC
self.message_converter = message_converter
def shutdown(self):
self.producer.flush()
@staticmethod
def _delivery_callback(err, msg):
if err is not None:
logger.error('Kafka message delivery failed: {}'.format(err))
def pause_workflow(self, workflow_execution_id):
"""Pauses a workflow currently executing.
Args:
workflow_execution_id (UUID): The execution ID of the workflow.
"""
logger.info('Pausing workflow {0}'.format(workflow_execution_id))
message = self.message_converter.create_workflow_pause_message(workflow_execution_id)
self._send_workflow_communication_message(message, workflow_execution_id)
def abort_workflow(self, workflow_execution_id):
"""Aborts a workflow currently executing.
Args:
workflow_execution_id (UUID): The execution ID of the workflow.
"""
logger.info('Aborting running workflow {0}'.format(workflow_execution_id))
message = self.message_converter.create_workflow_abort_message(workflow_execution_id)
self._send_workflow_communication_message(message, workflow_execution_id)
def send_exit_to_workers(self):
"""Sends the exit message over the communication sockets, otherwise worker receiver threads will hang"""
message = self.message_converter.create_worker_exit_message()
self._send_workflow_communication_message(message, None)
def _send_workflow_communication_message(self, message, workflow_id):
self._send_message(message, self.topic, workflow_id)
def _send_message(self, message, topic, key):
self.producer.produce(topic, message, key=key, callback=self._delivery_callback)
def open(self):
""" Open a connection to the Kafka service.
Should return False if initialization fails.
"""
LOG.info("Opening connection to the remote Kafka services at %s"
% self.hosts)
self._kafka_producer = Producer(**self._conf)
return True
def __init__(self, execution_db, message_converter=ProtobufWorkflowResultsConverter, socket_id=None):
self._ready = False
self.id_ = socket_id
kafka_config = walkoff.config.Config.WORKFLOW_RESULTS_KAFKA_CONFIG
self.producer = Producer(kafka_config)
self.execution_db = execution_db
self.topic = walkoff.config.Config.WORKFLOW_RESULTS_KAFKA_TOPIC
self.message_converter = message_converter
if self.check_status():
self._ready = True
def connect(self):
self.consumer = Consumer({
'bootstrap.servers': self.bootstrap_servers,
'group.id': self.group_id,
'default.topic.config': {
'auto.offset.reset': 'smallest'
}
})
print("subscribing to %s" % self.consumer_topic)
self.consumer.subscribe([
self.consumer_topic
])
print("Subscribed to topic %s " % self.consumer_topic)
self.producer = Producer({
'bootstrap.servers': self.bootstrap_servers,
'group.id': self.group_id
})
def producer(args, sniff_timeout_ms=500, sniff_promisc=True):
""" Captures packets from a network interface and sends them to a Kafka topic. """
# setup the signal handler
signal.signal(signal.SIGINT, signal_handler)
global producer_args
producer_args = args
# connect to kafka
logging.info("Connecting to Kafka; %s", args.kafka_configs)
kafka_producer = Producer(args.kafka_configs)
# initialize packet capture
logging.info("Starting packet capture")
capture = pcapy.open_live(args.interface, args.snaplen, sniff_promisc, sniff_timeout_ms)
pkts_in = 0
try:
while not finished.is_set() and (args.max_packets <= 0 or pkts_in < args.max_packets):
# capture a packet
(pkt_hdr, pkt_raw) = capture.next()
if pkt_hdr is not None:
logging.debug("Packet received: pkts_in=%d, pkt_len=%s", pkts_in, pkt_hdr.getlen())
pkts_in += 1
pkt_ts = timestamp(pkt_hdr)
kafka_producer.produce(args.kafka_topic, key=pack_ts(pkt_ts), value=pkt_raw, callback=delivery_callback)
# pretty print, if needed
if args.pretty_print > 0 and pkts_in % args.pretty_print == 0:
print 'Packet received[%s]' % (pkts_in)
# serve the callback queue
kafka_producer.poll(0)
finally:
# flush all messages
logging.info("Waiting for '%d' message(s) to flush", len(kafka_producer))
kafka_producer.flush()
# pkts_out may not be initialized if the callback was never executed
pkts_out = 0
if hasattr(delivery_callback, "pkts_out"):
pkts_out = delivery_callback.pkts_out
logging.info("'%d' packet(s) in, '%d' packet(s) out", pkts_in, pkts_out)
def test_dr_msg_errstr():
"""
Test that the error string for failed messages works (issue #129).
The underlying problem is that librdkafka reuses the message payload
for error value on Consumer messages, but on Producer messages the
payload is the original payload and no rich error string exists.
"""
p = Producer({"message.timeout.ms": 10})
def handle_dr(err, msg):
# Neither message payloads must not affect the error string.
assert err is not None
assert err.code() == KafkaError._MSG_TIMED_OUT
assert "Message timed out" in err.str()
# Unicode safe string
p.produce('mytopic', "This is the message payload", on_delivery=handle_dr)
# Invalid unicode sequence
p.produce('mytopic', "\xc2\xc2", on_delivery=handle_dr)
p.flush()
class Publisher():
def __init__(self, config={'bootstrap.servers': 'pulsing.jhk.org:9092', 'retries': 3, 'api.version.request': True}):
super().__init__()
self.__producer = Producer(config)
self.logger = logging.getLogger(__name__)
def publish(self, topic, data):
self.logger.debug('publish %s - %s', topic, data)
self.__producer.produce(topic, data.encode('utf-8'))
self.__producer.flush()
@property
def producer(self):
return self.__producer
def __eq__(self, other):
return self.__producer == other.__producer
def __str__(self):
return self.__producer.__str__()
def __hash__(self):
return self.__producer.__hash__()
def __init__(self, message_converter=ProtobufWorkflowCommunicationConverter):
kafka_config = walkoff.config.Config.WORKFLOW_COMMUNICATION_KAFKA_CONFIG
self.producer = Producer(kafka_config)
self.topic = walkoff.config.Config.WORKFLOW_COMMUNICATION_KAFKA_TOPIC
self.message_converter = message_converter
def __init__(
self,
storage_name: str,
raw_topic: Optional[str],
replacements_topic: Optional[str],
max_batch_size: int,
max_batch_time_ms: int,
bootstrap_servers: Sequence[str],
group_id: str,
commit_log_topic: Optional[str],
auto_offset_reset: str,
queued_max_messages_kbytes: int,
queued_min_messages: int,
rapidjson_deserialize: bool,
rapidjson_serialize: bool,
commit_retry_policy: Optional[RetryPolicy] = None,
) -> None:
self.storage = get_writable_storage(storage_name)
self.bootstrap_servers = bootstrap_servers
stream_loader = self.storage.get_table_writer().get_stream_loader()
self.raw_topic: Topic
if raw_topic is not None:
self.raw_topic = Topic(raw_topic)
else:
self.raw_topic = Topic(stream_loader.get_default_topic_spec().topic_name)
self.replacements_topic: Optional[Topic]
if replacements_topic is not None:
self.replacements_topic = Topic(replacements_topic)
else:
replacement_topic_spec = stream_loader.get_replacement_topic_spec()
if replacement_topic_spec is not None:
self.replacements_topic = Topic(replacement_topic_spec.topic_name)
else:
self.replacements_topic = None
self.commit_log_topic: Optional[Topic]
if commit_log_topic is not None:
self.commit_log_topic = Topic(commit_log_topic)
else:
commit_log_topic_spec = stream_loader.get_commit_log_topic_spec()
if commit_log_topic_spec is not None:
self.commit_log_topic = Topic(commit_log_topic_spec.topic_name)
else:
self.commit_log_topic = None
# XXX: This can result in a producer being built in cases where it's
# not actually required.
self.producer = Producer(
{
"bootstrap.servers": ",".join(self.bootstrap_servers),
"partitioner": "consistent",
"message.max.bytes": 50000000, # 50MB, default is 1MB
}
)
self.metrics = MetricsWrapper(
environment.metrics,
"consumer",
tags={"group": group_id, "storage": storage_name},
)
self.max_batch_size = max_batch_size
self.max_batch_time_ms = max_batch_time_ms
self.group_id = group_id
self.auto_offset_reset = auto_offset_reset
self.queued_max_messages_kbytes = queued_max_messages_kbytes
self.queued_min_messages = queued_min_messages
if commit_retry_policy is None:
commit_retry_policy = BasicRetryPolicy(
3,
constant_delay(1),
lambda e: isinstance(e, KafkaException)
and e.args[0].code()
in (
KafkaError.REQUEST_TIMED_OUT,
KafkaError.NOT_COORDINATOR_FOR_GROUP,
KafkaError._WAIT_COORD,
),
)
self.__commit_retry_policy = commit_retry_policy
self.__rapidjson_deserialize = rapidjson_deserialize
self.__rapidjson_serialize = rapidjson_serialize
def __init__(self, config={'bootstrap.servers': 'pulsing.jhk.org:9092', 'retries': 3, 'api.version.request': True}):
super().__init__()
self.__producer = Producer(config)
self.logger = logging.getLogger(__name__)
# -*- coding: utf-8 -*-
from confluent_kafka import Producer
p = Producer({
'bootstrap.servers': '127.0.0.1:9092',
})
def delivery_report(err, msg):
""" Called once for each message produced to indicate delivery result.
Triggered by poll() or flush(). """
if err is not None:
print('Message delivery failed: {}'.format(err))
else:
print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))
some_data_source = ['124', '245']
for data in some_data_source:
# Trigger any available delivery report callbacks from previous produce() calls
p.poll(0)
# Asynchronously produce a message, the delivery report callback
# will be triggered from poll() above, or flush() below, when the message has
# been successfully delivered or failed permanently.
p.produce('mytopic', data.encode('utf-8'), callback=delivery_report)
# Wait for any outstanding messages to be delivered and delivery report
# callbacks to be triggered.
p.flush()
class KafkaConnector(object):
"""Simple wrapper class to configure a simple kafka consumer
and producer pair, so that they can be used to perform simple
filter() and map() operations over the received tweets"""
def __init__(
self,
group_id=None,
consumer_topic='consumer_limbo',
producer_topic='consumer_limbo',
logging_topic='minteressa_stats',
bootstrap_servers='kafka:9092'
):
self.group_id = group_id
self.bootstrap_servers = bootstrap_servers
self.consumer_topic = consumer_topic
self.producer_topic = producer_topic
self.logging_topic = logging_topic
self.consumer = None
self.producer = None
def listen(self):
while True:
msg = self.consumer.poll()
if msg is None:
continue
if msg.error():
# Error or event
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():
# Error
raise KafkaException(msg.error())
else:
# Proper message
sys.stdout.write(
'%s [partition-%d] at offset %d with key %s:\n' %
(
msg.topic(),
msg.partition(),
msg.offset(),
str(msg.key())
)
)
yield msg
def connect(self):
self.consumer = Consumer({
'bootstrap.servers': self.bootstrap_servers,
'group.id': self.group_id,
'default.topic.config': {
'auto.offset.reset': 'smallest'
}
})
print("subscribing to %s" % self.consumer_topic)
self.consumer.subscribe([
self.consumer_topic
])
print("Subscribed to topic %s " % self.consumer_topic)
self.producer = Producer({
'bootstrap.servers': self.bootstrap_servers,
'group.id': self.group_id
})
def send(self, message, producer_topic=None):
producer_topic = producer_topic \
if producer_topic is not None \
else self.producer_topic
self.producer.produce(
producer_topic,
message
)
# self.producer.flush()
def log(self, message, logging_topic=None):
logging_topic = logging_topic \
if logging_topic is not None \
else self.logging_topic
self.producer.produce(logging_topic, message)
self.producer.flush()
def close(self):
self.consumer.close()
self.producer.close()
def _connect(self) -> Producer:
return Producer(self._config)
def test_basic_api():
""" Basic API tests, these wont really do anything since there is no
broker configured. """
try:
p = Producer()
except TypeError as e:
assert str(e) == "expected configuration dict"
p = Producer({'socket.timeout.ms': 10,
'error_cb': error_cb,
'message.timeout.ms': 10})
p.produce('mytopic')
p.produce('mytopic', value='somedata', key='a key')
def on_delivery(err, msg):
print('delivery', str)
# Since there is no broker, produced messages should time out.
assert err.code() == KafkaError._MSG_TIMED_OUT
p.produce(topic='another_topic', value='testing', partition=9,
callback=on_delivery)
p.poll(0.001)
p.flush(0.002)
p.flush()
try:
p.list_topics(timeout=0.2)
except KafkaException as e:
assert e.args[0].code() in (KafkaError._TIMED_OUT, KafkaError._TRANSPORT)
class KafkaProducer(object):
def __init__(self, test_number, producer_id, broker_manager, acks_mode,
in_flight_limit, print_mod):
self._stopping = False
self.broker_manager = broker_manager
self.producer_id = f"PRODUCER(Test:{test_number} Id:{producer_id})"
self.producer = None
self.acks_mode = acks_mode
self.message_type = None
self.terminate = False
self.key_count = 1
self.in_flight_limit = in_flight_limit
self.print_mod = print_mod
# message tracking
self.curr_pos = 0
self.pos_acks = 0
self.neg_acks = 0
self.key_index = 0
self.keys = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
self.val = 1
self.msg_set = set()
self.pending_ack = set()
def create_producer(self):
self.producer = Producer({
'bootstrap.servers':
self.broker_manager.get_bootstrap_servers(),
'message.send.max.retries':
0,
#'batch.num.messages': 1000,
#'stats_cb': my_stats_callback,
#'statistics.interval.ms': 100,
'default.topic.config': {
'request.required.acks': self.acks_mode
}
})
def get_actor(self):
return self.producer_id
def delivery_report(self, err, msg):
if self.terminate:
return
val = msg.value().decode("utf-8")
self.pending_ack.remove(val)
if err:
self.neg_acks += 1
#console_out(err, self.get_actor())
else:
self.pos_acks += 1
self.msg_set.add(val)
if (self.neg_acks + self.pos_acks) % self.print_mod == 0:
console_out(
f"Pos acks: {self.pos_acks} Neg acks: {self.neg_acks}",
self.get_actor())
def configure_as_sequence(self, sequence_count):
self.key_count = sequence_count
self.message_type = "sequence"
def configure_as_partitioned_sequence(self, sequence_count):
self.key_count = sequence_count
self.message_type = "partitioned-sequence"
def start_producing(self, topic, msg_count):
for msg_index in range(0, msg_count):
if self.terminate:
break
self.producer.poll(0)
body = None
if self.message_type == "partitioned-sequence":
topic = f"{topic}-{self.keys[self.key_index]}"
body = f"{self.keys[self.key_index]}={self.val}"
elif self.message_type == "sequence":
body = f"{self.keys[self.key_index]}={self.val}"
else:
body = uuid.uuid4()
while len(self.pending_ack) > self.in_flight_limit:
time.sleep(0.2)
self.producer.poll(0)
self.producer.produce(topic,
value=body.encode('utf-8'),
key=self.keys[self.key_index],
callback=self.delivery_report)
self.pending_ack.add(body)
self.curr_pos += 1
self.key_index += 1
if self.key_index == self.key_count:
self.key_index = 0
self.val += 1
def print_final_count(self):
console_out(
f"Final Count => Sent: {self.curr_pos} Pos acks: {self.pos_acks} Neg acks: {self.neg_acks} Undeliverable: {self.undeliverable} No Acks: {self.no_acks}",
self.get_actor())
def get_msg_set(self):
return self.msg_set
def stop_producing(self):
self.terminate = True
self.print_final_count()
from confluent_kafka import Producer
p = Producer({'streams.producer.default.stream': '/demo-stream'})
some_data_source = ["msg1", "msg2", "msg3"]
for data in some_data_source:
p.produce('topic1', data.encode('utf-8'))
p.flush()
print("function= ", func)
result = eval(func)
print(result)
print()
return result
#main (creating connection to clients and sending recieving required results)
if __name__ == "__main__":
c = Consumer({
'bootstrap.servers': sys.argv[1],
'group.id': '1',
'auto.offset.reset': 'earliest'
})
c.subscribe(['server'])
p = Producer({'bootstrap.servers': sys.argv[1]})
while True:
msg = c.poll(1.0)
if msg is None:
continue
if msg.error():
print("Consumer error: {}".format(msg.error()))
continue
# decode the message consumed
notification = msg.value().decode('utf-8')
params = notification.split('$')
data = params[0]
topic = params[1]
def __init__(self, server_addr: str, topic: str):
self.producer = Producer({'bootstrap.servers': server_addr})
self.topic = topic
async def produce(topic_name):
"""Produces data into the Kafka Topic"""
p = Producer({"bootstrap.servers": BROKER_URL})
while True:
p.produce(topic_name, ClickEvent().serialize())
await asyncio.sleep(1.0)
class Zeus:
''' Class to hold methods and variables for Inference. '''
def __init__(self):
''' Method called when object of class is created. '''
# Initialize Result Topic Consumer
self.frames_consumer = Consumer({
'bootstrap.servers': 'localhost:9092',
'group.id': 'cameras',
'auto.offset.reset': 'earliest'
})
self.frames_consumer.subscribe([frames_topic])
# Initialize Frames Topic Producer
self.inference_producer = Producer({
'bootstrap.servers': 'localhost:9092',
'message.max.bytes': '10000000'
})
# Get options
self.opt = opts().init()
self.opt.debug = max(self.opt.debug, 1)
# Instantiate the Model
self.detector = CtdetDetector(self.opt)
def delivery_report(self, err, msg):
''' Called once for each message produced to indicate delivery result.
Triggered by poll() or flush(). '''
if err is not None:
print('Message delivery failed: {}'.format(err))
else:
pass
def infer(self):
''' Method to share inferred knowledge '''
print('Ready for Inference!')
start = 0
while True:
start = time.time()
self.inference_producer.poll(0)
data = self.frames_consumer.poll()
if data is None:
time.sleep(0.01)
continue
if data.error():
print("Consumer error: {}".format(data.error()))
continue
data = pickle.loads(data.value())
# Parse the Data
batch = data['Batch'] # Batch of Images
batch_len = data['BatchLength'] # Length of Batch
height = data['ImageHeight'] # Height of Image
width = data['ImageWidth'] # Width of Image
channels = data['Channels'] # Color Channels
# Get batch in NumPy ndarray
# batch = np.fromstring(batch, np.uint8).reshape((batch_len, height, width, channels))
# Perform Inference
results = self.detector.run(batch)
results = results['results']
# Cleanse the result
results_scrubbed = list()
for result in results.keys():
classes = list()
bbox = list()
confidence = list()
for cat in range(1, num_classes + 1):
for val in results[result][cat]:
conf = val[4]
if not conf > thresh_conf:
continue
x1 = val[0]
y1 = val[1]
x2 = val[2]
y2 = val[3]
classes.append(cat)
confidence.append(conf)
bbox.append([x1, y1, x2, y2])
results_scrubbed.append([classes, confidence, bbox])
data = dict()
data.update({'Batch': batch})
data.update({'BatchLength': batch_len})
data.update({'ImageHeight': height})
data.update({'ImageWidth': width})
data.update({'Channels': channels})
data.update({'Results': results_scrubbed})
self.inference_producer.produce(inference_topic,
pickle.dumps(data),
callback=self.delivery_report)
self.inference_producer.flush()
print(time.time() - start, end='\r')
self.frames_consumer.close()
sys.stderr.write(
f'%% Message delivered to topic:[{msg.topic()}]-partition:[{msg.partition():d}] @ offset[{msg.offset()}]\n'
)
# 主程式進入點
if __name__ == '__main__':
# 步驟1. 設定要連線到Kafka集群的相關設定
props = {
# Kafka集群在那裡?
'bootstrap.servers': 'localhost:9092', # <-- 置換成要連接的Kafka集群
'error_cb': error_cb # 設定接收error訊息的callback函數
}
# 步驟2. 產生一個Kafka的Producer的實例
producer = Producer(**props)
# 步驟3. 指定想要發佈訊息的topic名稱
topicName = 'ak03.asyncsending'
msgCount = 100000 # 10萬筆
try:
print('Start sending messages ...')
time_start = int(round(time.time() * 1000))
# produce(topic, [value], [key], [partition], [on_delivery], [timestamp], [headers])
'''
// ** 示範: Asynchronous Send **
// 透過一個Callback函式我們可以非同步地取得由Broker回覆訊息發佈的ack結果
// 這種方法可以取得Broker回覆訊息發佈的ack結果, 同時又可以取得好的throughput (建議的作法)
'''
def __init__(self, connection, asynchronous=True):
from confluent_kafka import Producer
self.producer = Producer(connection or {})
self.asynchronous = asynchronous
import sys
if __name__ == '__main__':
if len(sys.argv) != 3:
sys.stderr.write('Usage: %s <bootstrap-brokers> <topic>\n' % sys.argv[0])
sys.exit(1)
broker = sys.argv[1]
topic = sys.argv[2]
# Producer configuration
# See https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
conf = {'bootstrap.servers': broker}
# Create Producer instance
p = Producer(**conf)
# Optional per-message delivery callback (triggered by poll() or flush())
# when a message has been successfully delivered or permanently
# failed delivery (after retries).
def delivery_callback(err, msg):
if err:
sys.stderr.write('%% Message failed delivery: %s\n' % err)
else:
sys.stderr.write('%% Message delivered to %s [%d] @ %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
# Read lines from stdin, produce each line to Kafka
for line in sys.stdin:
try:
# Produce line (without newline)
def __init__(
self,
storage_key: StorageKey,
raw_topic: Optional[str],
replacements_topic: Optional[str],
max_batch_size: int,
max_batch_time_ms: int,
bootstrap_servers: Sequence[str],
group_id: str,
commit_log_topic: Optional[str],
auto_offset_reset: str,
queued_max_messages_kbytes: int,
queued_min_messages: int,
strategy_factory_type: StrategyFactoryType,
processes: Optional[int],
input_block_size: Optional[int],
output_block_size: Optional[int],
commit_retry_policy: Optional[RetryPolicy] = None,
profile_path: Optional[str] = None,
) -> None:
self.storage = get_writable_storage(storage_key)
self.bootstrap_servers = bootstrap_servers
stream_loader = self.storage.get_table_writer().get_stream_loader()
self.raw_topic: Topic
if raw_topic is not None:
self.raw_topic = Topic(raw_topic)
else:
self.raw_topic = Topic(stream_loader.get_default_topic_spec().topic_name)
self.replacements_topic: Optional[Topic]
if replacements_topic is not None:
self.replacements_topic = Topic(replacements_topic)
else:
replacement_topic_spec = stream_loader.get_replacement_topic_spec()
if replacement_topic_spec is not None:
self.replacements_topic = Topic(replacement_topic_spec.topic_name)
else:
self.replacements_topic = None
self.commit_log_topic: Optional[Topic]
if commit_log_topic is not None:
self.commit_log_topic = Topic(commit_log_topic)
else:
commit_log_topic_spec = stream_loader.get_commit_log_topic_spec()
if commit_log_topic_spec is not None:
self.commit_log_topic = Topic(commit_log_topic_spec.topic_name)
else:
self.commit_log_topic = None
# XXX: This can result in a producer being built in cases where it's
# not actually required.
self.producer = Producer(
{
"bootstrap.servers": ",".join(self.bootstrap_servers),
"partitioner": "consistent",
"message.max.bytes": 50000000, # 50MB, default is 1MB
}
)
self.metrics = MetricsWrapper(
environment.metrics,
"consumer",
tags={"group": group_id, "storage": storage_key.value},
)
self.max_batch_size = max_batch_size
self.max_batch_time_ms = max_batch_time_ms
self.group_id = group_id
self.auto_offset_reset = auto_offset_reset
self.queued_max_messages_kbytes = queued_max_messages_kbytes
self.queued_min_messages = queued_min_messages
self.strategy_factory_type = strategy_factory_type
self.processes = processes
self.input_block_size = input_block_size
self.output_block_size = output_block_size
self.__profile_path = profile_path
if (
self.processes is not None
and self.strategy_factory_type is not StrategyFactoryType.STREAMING
):
raise ValueError(
"process count can only be specified when using streaming strategy"
)
if commit_retry_policy is None:
commit_retry_policy = BasicRetryPolicy(
3,
constant_delay(1),
lambda e: isinstance(e, KafkaException)
and e.args[0].code()
in (
KafkaError.REQUEST_TIMED_OUT,
KafkaError.NOT_COORDINATOR_FOR_GROUP,
KafkaError._WAIT_COORD,
),
)
self.__commit_retry_policy = commit_retry_policy
from confluent_kafka import Producer
p = Producer({'bootstrap.servers': 'localhost'})
def delivery_report(err, msg):
""" Called once for each message produced to indicate delivery result.
Triggered by poll() or flush(). """
if err is not None:
print('Message delivery failed: {}'.format(err))
else:
print('Message delivered to {} [{}]'.format(msg.topic(),
msg.partition()))
for x in range(10):
p.produce('test.foo',
'das ist nur ein Test: %d' % x,
callback=delivery_report)
p.flush()
from confluent_kafka import Producer
import avro.schema
import avro.io
import io
import random
if __name__ == "__main__":
conf = {'bootstrap.servers': 'localhost:9092'}
producer = Producer(**conf)
# Kafka topic
topic = "topic-example-200"
# Path to profile.avsc avro schema
schema_path = "profile.avsc"
schema = avro.schema.Parse(open(schema_path).read())
for i in range(10):
writer = avro.io.DatumWriter(schema)
bytes_writer = io.BytesIO()
encoder = avro.io.BinaryEncoder(bytes_writer)
writer.write(
{
"name": "tom",
"number1": random.randint(0, 10),
"number2": 2.0
}, encoder)
raw_bytes = bytes_writer.getvalue()
producer.produce(topic, raw_bytes)
producer.flush()
import pyodbc
from confluent_kafka import Producer
print ('---login--- ')
conn = pyodbc.connect("DRIVER={ODBC Driver 17 for SQL Server};SERVER=DevSQL01;DATABASE=HackStream;UID=greatscott;PWD=H4ppyFunB4ll;")
cursor = conn.cursor()
cursor.execute('SELECT MAX(ActionId) FROM HackStream.dbo.Funnel')
for row in cursor:
print (row)
print ('--end--')
p = Producer({'bootstrap.servers': '172.16.43.33:9092'})
p.produce('Funnel', key='hello', value='world')
p.flush(10)
from confluent_kafka import Producer
some_data_source = ['dddd','ffff','gggg']
p = Producer({'bootstrap.servers':'172.17.0.4'})
for data in some_data_source:
p.produce('ragnatopic', data.encode('utf-8'))
p.flush()
img_path = "data/1mb.jpg"
# img_path = "../data/10mb.jpg"
image = open(img_path, 'rb') # open binary file in read mode
image_read = image.read()
data = base64.b64encode(image_read)
print("size 1: ", len(image_read))
print("size 2: ", len(data))
# broker = "freepsw-template-centos-4cpu-1:9092"
broker = "kafka-test:9092"
topic = "latency-test"
# See https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md
p = Producer({
'bootstrap.servers': broker,
'acks': 1,
# 'socket.nagle.disable': True,
'message.max.bytes': 15000000
})
conf = {
'bootstrap.servers': broker,
'group.id': 'my-group',
'auto.offset.reset': 'latest',
# 'socket.nagle.disable': True,
# 'fetch.wait.max.ms': 0,
# 'heartbeat.interval.ms': '1000',
'message.max.bytes': 15000000
}
logger = logging.getLogger('consumer')
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
import json
admin = AdminClient({'bootstrap.servers': 'kafka-1:9092'})
metadata = admin.list_topics(timeout=10)
topics = metadata.topics
if 'citibike.station.update.1' not in topics:
admin.create_topics([
NewTopic('citibike.station.update.1',
num_partitions=3,
replication_factor=1)
])
p = Producer({
'bootstrap.servers': 'kafka-1:9092',
'api.version.request': True
})
@retry(wait_exponential_multiplier=1000, wait_exponential_max=10000)
def scrape_station_status():
r = requests.get(
'https://gbfs.citibikenyc.com/gbfs/en/station_status.json')
if r.status_code != 200:
raise IOError('Failed request.')
else:
print('scraping data')
return ujson.loads(r.content.decode('utf-8'))
scrape_station_status()
# Reads S3 image keys from a text file and produces them to a Kafka topic.
from confluent_kafka import Producer
from time import sleep
if __name__ == "__main__":
S3_KEYS_PATH = 's3_keys_test.txt'
KAFKA_SERVER = 'localhost:9092'
OUTPUT_TOPIC = 'image-s3-keys'
producer = Producer({"bootstrap.servers": KAFKA_SERVER})
for i, s3_key in enumerate(map(str.strip, open(S3_KEYS_PATH))):
producer.produce(OUTPUT_TOPIC, key=s3_key, value=s3_key)
print('%d, key %s' % (i, s3_key))
sleep(0.0001)
producer.flush()
class KafkaDestination(object):
""" syslog-ng Apache Kafka destination.
"""
_kafka_producer = None
_conf = dict()
def __init__(self):
self.hosts = None
self.topic = None
self.msg_key = None
self.partition = None
self.programs = None
self.group_id = None
self.broker_version = None
self.verbose = False
self.display_stats = False
self.producer_config = None
def init(self, args):
""" This method is called at initialization time.
Should return False if initialization fails.
"""
if 'producer_config' in args:
try:
self.producer_config = ast.literal_eval(args['producer_config'])
self._conf.update(self.producer_config)
except ValueError:
LOG.error("Given config %s is not in a Python dict format."
% args['producer_config'])
try:
self.hosts = args['hosts']
self.topic = args['topic']
self._conf['bootstrap.servers'] = self.hosts
except KeyError:
LOG.error("Missing `hosts` or `topic` option...")
return False
if 'msg_key' in args:
self.msg_key = args['msg_key']
LOG.info("Message key used will be %s" % self.msg_key)
if 'partition' in args:
self.partition = args['partition']
LOG.info("Partition to produce to %s" % self.partition)
# optional `programs` parameter to filter out messages
if 'programs' in args:
self.programs = parse_str_list(args['programs'])
LOG.info("Programs to filter against %s" % self.programs)
if 'group_id' in args:
self.group_id = args['group_id']
self._conf['group.id'] = self.group_id
LOG.info("Broker group_id=%s" % self.group_id)
if 'broker_version' in args:
self.broker_version = args['broker_version']
if '.'.join(self.broker_version.split('.')[:2]) in ('0.10', '0.11'):
self._conf['api.version.request'] = True
else:
self._conf['broker.version.fallback'] = self.broker_version
self._conf['api.version.request'] = False
LOG.info("Broker version=%s" % self.broker_version)
else:
self.broker_version = DEFAULT_BROKER_VERSION_FALLBACK
self._conf[
'broker.version.fallback'] = DEFAULT_BROKER_VERSION_FALLBACK
self._conf['api.version.request'] = False
LOG.warn("Default broker version fallback %s "
"will be applied here." % DEFAULT_BROKER_VERSION_FALLBACK)
self._conf['on_delivery'] = delivery_callback
if 'verbose' in args:
# provide a global `on_delivery` callback in the `Producer()` config
# dict better for memory consumptions vs per message callback.
self.verbose = ast.literal_eval(args['verbose'])
if not self.verbose:
# only interested in delivery failures here. We do provide a
# global on_delivery callback in the Producer() config dict and
# also set delivery.report.only.error.
self._conf['delivery.report.only.error'] = True
LOG.info("Verbose mode is OFF: you will not be able to see "
"messages in here. Failures only. Use 'verbose=('True')' "
"in your destination options to see successfully "
"processed messages in your logs.")
# display broker stats?
if 'display_stats' in args:
self.display_stats = ast.literal_eval(args['display_stats'])
if self.display_stats:
self._conf['stats_cb'] = stats_callback
LOG.info("Broker statistics will be displayed.")
LOG.info(
"Initialization of Kafka Python driver w/ args=%s" % self._conf)
return True
def open(self):
""" Open a connection to the Kafka service.
Should return False if initialization fails.
"""
LOG.info("Opening connection to the remote Kafka services at %s"
% self.hosts)
self._kafka_producer = Producer(**self._conf)
return True
def is_opened(self):
""" Check if the connection to Kafka is able to receive messages.
Should return False if target is not open.
"""
return self._kafka_producer is not None
def close(self):
""" Close the connection to the Kafka service.
"""
LOG.debug("KafkaDestination.close()....")
if self._kafka_producer is not None:
LOG.debug("Flushing producer w/ a timeout of 30 seconds...")
self._kafka_producer.flush(30)
return True
# noinspection PyMethodMayBeStatic
def deinit(self):
""" This method is called at deinitialization time.
"""
LOG.debug("KafkaDestination.deinit()....")
if self._kafka_producer:
self._kafka_producer = None
return True
def send(self, ro_msg):
""" Send a message to the target service
It should return True to indicate success, False will suspend the
destination for a period specified by the time-reopen() option.
:return: True or False
"""
# do nothing if msg is empty
if not ro_msg:
return True
# no syslog-ng `values-pair` here we dealing with `LogMessage`
if type(ro_msg) != dict:
# syslog-ng `LogMessage` is read-only
# goal is rfc5424 we cannot use values-pair because of memory leaks
try:
msg = {'FACILITY': ro_msg.FACILITY, 'PRIORITY': ro_msg.PRIORITY,
'HOST': ro_msg.HOST, 'PROGRAM': ro_msg.PROGRAM,
'DATE': ro_msg.DATE, 'MESSAGE': ro_msg.MESSAGE}
except AttributeError:
LOG.error("Your version of syslog-ng is not supported. "
"Please use syslog-ng 3.7.x")
return False
else:
LOG.warn("You are using `values-pair` if you are using "
"syslog-ng <= 3.11 it is known to be leaking...")
msg = ro_msg
try:
# check if we do have a program filter defined.
msg_program = msg['PROGRAM']
if self.programs is not None:
if msg_program not in self.programs:
# notify of success
return True
if msg_program == 'firewall':
firewall_msg = msg['MESSAGE']
msg['MESSAGE'] = parse_firewall_msg(firewall_msg)
elif msg_program == 'nat':
nat_msg = msg['MESSAGE']
msg['MESSAGE'] = parse_nat_msg(nat_msg)
# convert date string to UNIX timestamp
msg_date = msg['DATE']
if msg_date is not None:
msg['DATE'] = date_str_to_timestamp(msg_date)
msg_string = str(msg)
kwargs = {}
if self.msg_key and self.msg_key in msg.keys():
kwargs['key'] = msg[self.msg_key]
if self.partition:
try:
kwargs['partition'] = int(self.partition)
except ValueError:
LOG.warning(
"Ignore partition=%s because it is not an int."
% self.partition)
self._kafka_producer.produce(self.topic, msg_string, **kwargs)
# `poll()` doesn't do any sleeping at all if you give it 0, all
# it does is grab a mutex, check a queue, and release the mutex.
# It is okay to call poll(0) after each produce call, the
# performance impact is negligible, if any.
self._kafka_producer.poll(0)
except BufferError:
LOG.error("Producer queue is full. This message will be discarded. "
"%d messages waiting to be delivered.",
len(self._kafka_producer))
# do not return False here as the destination would be closed
# and we would have to restart syslog-ng
sleep(5)
return True
except (KafkaException, UnicodeEncodeError) as e:
LOG.error("An error occurred while trying to send messages... "
"See details: %s" % e, exc_info=True)
sleep(5)
# do not return False here as the destination would be closed
# and we would have to restart syslog-ng
return True
return True
class KafkaWorkflowResultsSender(object):
def __init__(self, execution_db, message_converter=ProtobufWorkflowResultsConverter, socket_id=None):
self._ready = False
self.id_ = socket_id
kafka_config = walkoff.config.Config.WORKFLOW_RESULTS_KAFKA_CONFIG
self.producer = Producer(kafka_config)
self.execution_db = execution_db
self.topic = walkoff.config.Config.WORKFLOW_RESULTS_KAFKA_TOPIC
self.message_converter = message_converter
if self.check_status():
self._ready = True
def shutdown(self):
self.producer.flush()
@staticmethod
def _delivery_callback(err, msg):
if err is not None:
logger.error('Kafka message delivery failed: {}'.format(err))
def _format_topic(self, event):
return '{}.{}'.format(self.topic, event.name)
def handle_event(self, workflow, sender, **kwargs):
"""Listens for the data_sent callback, which signifies that an execution element needs to trigger a
callback in the main thread.
Args:
workflow (Workflow): The Workflow object that triggered the event
sender (ExecutionElement): The execution element that sent the signal.
kwargs (dict): Any extra data to send.
"""
event = kwargs['event']
if event in [WalkoffEvent.TriggerActionAwaitingData, WalkoffEvent.WorkflowPaused]:
saved_workflow = SavedWorkflow.from_workflow(workflow)
self.execution_db.session.add(saved_workflow)
self.execution_db.session.commit()
elif event == WalkoffEvent.ConsoleLog:
action = workflow.get_executing_action()
sender = action
if self.id_:
packet_bytes = self.message_converter.event_to_protobuf(sender, workflow, **kwargs)
self.producer.produce(self._format_topic(event), packet_bytes, callback=self._delivery_callback)
else:
event.send(sender, data=kwargs.get('data', None))
def is_ready(self):
return self._ready
def check_status(self):
if self.producer is not None:
return True
return False
def send_ready_message(self):
WalkoffEvent.CommonWorkflowSignal.send(sender={'id': '1'}, event=WalkoffEvent.WorkerReady)
def create_workflow_request_message(self, workflow_id, workflow_execution_id, start=None, start_arguments=None,
resume=False, environment_variables=None, user=None):
return self.message_converter.create_workflow_request_message(workflow_id, workflow_execution_id, start,
start_arguments, resume, environment_variables,
user)
them into python code"""
with gzip.open(filename, "r") as tweetfile:
for row in tweetfile:
yield json.loads(row)
def delivery_callback (self, err, msg):
if err:
print('%% Message failed delivery: %s\n' % err)
else:
print('%% Message delivered to %s [%d]\n' % \
(msg.topic(), msg.partition()))
if __name__ == '__main__':
kfk = Producer({
'bootstrap.servers': "kafka:2181",
'group.id': "json_producer"
})
time.sleep(10)
def delivery_callback (err, msg):
if err:
sys.stderr.write('%% Message failed delivery: %s\n' % err)
else:
sys.stderr.write('%% Message delivered to %s [%d]\n' % \
(msg.topic(), msg.partition()))
for tweet in get_tweet('examples/tweets-200k.txt.gz'):
# if len(tweet['entities']['urls']) > 0 and \
# any(tweet['lang'] in l for l in ['es', 'en']):
try:
print("%s: %s" % (tweet['user']['screen_name'], tweet['text']))
class ProducerServer:
"""
Setup Basic Kafka Servers
"""
# Tracks existing topics across all Producer instances
existing_topics = set([])
def __init__(self, conf):
self.conf = conf
self.producer = Producer({
"bootstrap.servers": conf.get("producer", "bootstrap.servers")
})
self.client = AdminClient({
"bootstrap.servers": conf.get("producer", "bootstrap.servers")
})
if conf.get("producer", "topic_name") not in ProducerServer.existing_topics:
self.create_topic()
ProducerServer.existing_topics.add(conf.get("producer", "topic_name"))
def topic_exists(self, topic_name):
"""Checks if the given topic exists"""
topics = self.client.list_topics(timeout=5)
return topics.topics.get(topic_name) is not None
def create_topic(self):
"""
Create topic if it doesn't exists
"""
exists = self.topic_exists(self.conf.get("producer", "topic_name"))
if exists:
logger.info(f"topic already exists: {self.conf.get('producer', 'topic_name')}")
else:
futures = self.client.create_topics([
NewTopic(
topic=self.conf.get("producer", "topic_name"),
num_partitions=self.conf.getint("producer", "num_partitions"),
replication_factor=self.conf.getint("producer", "replication_factor")
)
])
for topic, future in futures.items():
try:
future.result()
logger.info(f"topic created: {self.conf.get('producer', 'topic_name')}")
except Exception as e:
logger.error(f"failed to create topic {self.conf.get('producer', 'topic_name')}: {e}")
@staticmethod
def serialize_json(json_data):
"""
Take JSON dictionary object and convert that to string(serialize)
:param json_data: JSON dictionary object
:return: JSON string
"""
return json.dumps(json_data)
def run(self):
"""
Read JSON data and produce serialized rows to Kafa Topic
"""
try:
with open(self.conf.get("producer","input_file")) as f:
data = json.loads(f.read())
logger.info(f"Reading {len(data)} lines from {self.conf.get('producer','input_file')}")
for idx, row in tqdm(enumerate(data), total=len(data), desc="Producer:> "):
self.producer.poll(timeout=self.conf.getfloat("producer", "consume_timeout"))
message = self.serialize_json(row)
logger.info(f"Serialized Data: {message}")
self.producer.produce(
topic=self.conf.get("producer","topic_name"),
value=message,
callback=self.delivery_callback
)
time.sleep(0.2)
logger.info("Processing complete \n Cleaning Producer!")
self.close()
except KeyboardInterrupt as e:
self.close()
@staticmethod
def delivery_callback(err, msg):
"""
Callback triggered by produce function to check successful delivery
of message to broker
"""
if err is not None:
logger.error(f"Failed to deliver message: {err}")
else:
logger.info(f"Successfully produced message to topic {msg.topic()}")
def close(self):
"""Prepares the producer for exit by cleaning up the producer"""
try:
if self.conf.get("producer","topic_name"):
self.producer.flush()
logger.info("Producer Shutdown!!! ")
except Exception as e:
logger.error("producer close incomplete - skipping")
#if (success + fail) % 10000 == 0:
# print("Success: " + str(success) + " Failed: " + str(fail))
def printStats():
global sent
global success
global fail
print("Sent: " + str(sent))
print("Delivered: " + str(success))
print("Failed: " + str(fail))
acks_mode = sys.argv[3]
p = Producer({'bootstrap.servers': '172.17.0.3:9092,172.17.0.4:9093,172.17.0.5:9094',
'message.send.max.retries': 0,
#'batch.num.messages': 1000,
#'stats_cb': my_stats_callback,
#'statistics.interval.ms': 1000,
'default.topic.config': { 'request.required.acks': acks_mode }})
topic = sys.argv[4]
count = int(sys.argv[1]) + 1
wait_period = float(sys.argv[2])
success = 0
fail = 0
sent = 0
atexit.register(printStats)
for data in range(1, count):
# Trigger any available delivery report callbacks from previous produce() calls
p.poll(0)
key = data['ALPACA_API_KEY']
secret_key = data['ALPACA_SECRET_KEY']
base_url = data['PAPER_URL']
yamlfile.close()
api = REST(
key,
secret_key,
base_url
)
proc = mp.Process(target=alpaca_process_run)
proc.start()
conf = {'bootstrap.servers': 'localhost:9093'}
request_producer = Producer(**conf)
symbol_brain_dic = {}
while True:
symbol = input()
if symbol != 'STOP' and symbol != 'STOP_ALL':
req_symbols = symbol.split(',')
print(req_symbols)
for symbol in req_symbols:
data = api.get_asset(symbol)
if data.shortable and data.easy_to_borrow:
cmd = Command()
cmd.command_type = CommandType.START_LIVE
sc = StrategyConfiguration()
sc.type = StrategyType.TICK
sc.interval = 50
import ccloud_lib
if __name__ == '__main__':
# Initialization
args = ccloud_lib.parse_args()
config_file = args.config_file
topic = args.topic
conf = ccloud_lib.read_ccloud_config(config_file)
# Create Producer instance
p = Producer({
'bootstrap.servers': conf['bootstrap.servers'],
'sasl.mechanisms': 'PLAIN',
'security.protocol': 'SASL_SSL',
'sasl.username': conf['sasl.username'],
'sasl.password': conf['sasl.password']
})
# Create topic if needed
# Examples of additional admin API functionality:
# https://github.com/confluentinc/confluent-kafka-python/blob/master/examples/adminapi.py
a = AdminClient({
'bootstrap.servers': conf['bootstrap.servers'],
'sasl.mechanisms': 'PLAIN',
'security.protocol': 'SASL_SSL',
'sasl.username': conf['sasl.username'],
'sasl.password': conf['sasl.password']
})
fs = a.create_topics([NewTopic(
print("nb hours: " + str(nb_hours))
# create a table of node instances
thr = sumM3Thresholder(nb_hours)
# Create Kafka Consumer instance
c = Consumer({
'bootstrap.servers': sys.argv[1],
'group.id': 'sumM3Consumer'
})
# Subscribe to topic 'm3Analysis'
c.subscribe(['m3Analysis'])
# Create a provider instance.
p = Producer({'bootstrap.servers': sys.argv[1]})
# Process messages
try:
while True:
try:
s3msg_in = sumM3Message()
s3msg_in.poll_kafka(c, 300.0)
if s3msg_in.topic == "":
print("No good message for 300 sec.")
else:
# Check whether this message triggers a threshold
if thr.checkList(s3msg_in):
# this message needs re-broadcasting
msg = thr.node_list[s3msg_in.node_dns].to_string()
print("Sending: " + msg)
# $ python -m venv ccloud_example
# $ source ccloud_example/bin/activate
# $ pip install confluent_kafka
# $ python confluent_cloud.py
# $ deactivate
import uuid
from confluent_kafka import Producer, Consumer
p = Producer({
'bootstrap.servers': '<ccloud bootstrap servers>',
'broker.version.fallback': '0.10.0.0',
'api.version.fallback.ms': 0,
'sasl.mechanisms': 'PLAIN',
'security.protocol': 'SASL_SSL',
'sasl.username': '******',
'sasl.password': '******'
})
def acked(err, msg):
"""Delivery report callback called (from flush()) on successful or failed delivery of the message."""
if err is not None:
print("failed to deliver message: {}".format(err.str()))
else:
print("produced to: {} [{}] @ {}".format(msg.topic(), msg.partition(), msg.offset()))
p.produce('python-test-topic', value='python test value', callback=acked)
"Sydney-NSW-AU": range(45, 75),
"Mumbai-NH-IN": range(70, 100),
"London-UK-GB": range(45, 75)
}
location_hour_offset = [0, -3, 14, 9, 6]
weather_types = ["Sunny", "Cloudy", "Fog", "Rain", "Lightning", "Windy"]
humidities = range(30, 100)
wind_speed_mph = range(0, 20)
ET = tz.gettz("America/New_York")
starting_datetime = datetime(2018, 6, 1, 0, 0, 0, 0, tzinfo=ET)
# Create Kafka Producer
# https://docs.confluent.io/current/clients/confluent-kafka-python/#confluent_kafka.Producer.Producer
# https://docs.confluent.io/current/installation/configuration/producer-configs.html
producer = Producer({"bootstrap.servers": "localhost:9092"})
def acked(err, msg):
if err is not None:
print(f"Failed to deliver message: {msg.value()}: {err.str()}")
else:
print(f"Message produced: {msg.value()}")
def generate_weather_strings(current_generation: int,
current_datetime: datetime) -> Tuple[str, str]:
for index, (location,
temps) in enumerate(location_mapped_to_temp_in_ranges.items()):
current_day = current_generation / 24
base_current_hour = fmod(
help='A TSV file')
parser.add_argument(
'kafka_hosts',
metavar='h',
type=str,
nargs=1,
help='A comma separated list of Kafka hosts, e.g. 127.0.0.1:9092.')
parsed_args = parser.parse_args()
tsv_path = parsed_args.tsv_path[0]
in_tsv = open(tsv_path, 'r')
hosts = parsed_args.kafka_hosts[0]
log.info(f'Connecting to Kafka broker(s): {hosts}')
reader = csv.DictReader(in_tsv, delimiter='\t')
start = time.monotonic()
log.info('Beginning production of messages')
producer = Producer({'bootstrap.servers': hosts})
count = 0
for idx, row in enumerate(reader):
count = idx
encoded = _parse_row(row)
while True:
try:
producer.produce('inbound_images', encoded)
except BufferError:
# Give the producer time to catch up before retrying
producer.poll(1)
break
if idx % 10000 == 0:
log.info(f'Produced {idx} messages so far')
print(f'Produced {count} at rate {count / (time.monotonic() - start)}/s')
in_tsv.close()
def publish(request):
elasticsearch_server = 'http://localhost:9200/clincoded'
return_object = {'status': 'Fail',
'message': 'Unable to deliver message'}
# Check that required parameters have been provided
if not('type' in request.params and 'uuid' in request.params):
return_object['message'] = 'Required parameters missing in request'
return return_object
# Attempt to retrieve data (from Elasticsearch)
try:
searchRes = requests.get('{}/{}/{}'.format(elasticsearch_server, request.params['type'], request.params['uuid']), timeout=10)
if searchRes.status_code != requests.codes.ok:
return_object['message'] = 'Data search failed'
return return_object
except Exception as e:
return_object['message'] = 'Data search could not be completed'
return return_object
# Store JSON-encoded content of search result(s)
try:
resultJSON = searchRes.json()
except Exception as e:
return_object['message'] = 'Retrieved data not in expected format'
return return_object
# Check that search found data
if 'found' not in resultJSON or not(resultJSON['found']):
return_object['message'] = 'Requested data could not be found'
return return_object
# Check that data has expected elements
try:
data_type_to_publish = resultJSON['_source']['embedded']['resourceType']
if data_type_to_publish == 'classification':
evidence_to_publish = resultJSON['_source']['embedded']['resourceParent']['gdm']
publishing_affiliation = resultJSON['_source']['embedded']['resource']['affiliation']
evidence_counts_to_publish = resultJSON['_source']['embedded']['resource']['classificationPoints']
elif data_type_to_publish == 'interpretation':
evidence_to_publish = resultJSON['_source']['embedded']['resourceParent']['interpretation']
else:
raise Exception
except Exception as e:
return_object['message'] = 'Retrieved data missing expected elements'
return return_object
# Check that message should be sent? (approved status? permission to publish?)
# Construct message
try:
if data_type_to_publish == 'interpretation':
message_template = deepcopy(clincoded.messaging.templates.vci_to_dx.message_template)
data_to_remove = clincoded.messaging.templates.vci_to_dx.data_to_remove
add_data_to_msg_template(resultJSON['_source']['embedded'], None, None, message_template)
else:
message_template = deepcopy(clincoded.messaging.templates.gci_to_dx.message_template)
classification_points = deepcopy(evidence_counts_to_publish)
add_data_to_msg_template(resultJSON['_source']['embedded'], gather_evidence(evidence_to_publish, publishing_affiliation),
gather_evidence_counts(classification_points, True), message_template)
message = json.dumps(message_template, separators=(',', ':'))
except Exception as e:
return_object['message'] = 'Failed to build complete message'
return return_object
# Transform message (if necessary, via independent service)
try:
if data_type_to_publish == 'interpretation':
remove_data_from_msg_template(data_to_remove, message_template['interpretation'])
message_template['interpretation'] = transform_interpretation(message_template['interpretation'], request.host)
message = json.dumps(message_template, separators=(',', ':'))
except Exception as e:
if e.args:
return_object['message'] = e.args
else:
return_object['message'] = 'Failed to build complete message'
return return_object
# Configure message delivery parameters
kafka_cert_pw = ''
if 'KAFKA_CERT_PW' in os.environ:
kafka_cert_pw = os.environ['KAFKA_CERT_PW']
kafka_conf = {'bootstrap.servers': 'localhost:9093',
'log_level': 0,
'security.protocol': 'ssl',
'ssl.key.location': 'etc/certs/client.key',
'ssl.key.password': kafka_cert_pw,
'ssl.certificate.location': 'etc/certs/client.crt',
'ssl.ca.location': 'etc/certs/server.crt'}
kafka_topic = 'test'
kafka_timeout = 10
if request.host != 'localhost:6543':
kafka_conf = {'bootstrap.servers': 'exchange.clinicalgenome.org:9093',
'log_level': 0,
'security.protocol': 'ssl',
'ssl.key.location': 'etc/certs/dataexchange/client.key',
'ssl.key.password': kafka_cert_pw,
'ssl.certificate.location': 'etc/certs/dataexchange/client.crt',
'ssl.ca.location': 'etc/certs/dataexchange/server.crt'}
if data_type_to_publish == 'interpretation':
kafka_topic = 'variant_interpretation'
else:
kafka_topic = 'gene_validity'
if request.host != 'curation.clinicalgenome.org':
kafka_topic += '_dev'
# Send message
p = Producer(**kafka_conf)
def delivery_callback(err, msg):
nonlocal return_object
if err:
return_object['message'] = err
else:
return_object = {'status': 'Success',
'message': message,
'partition': msg.partition(),
'offset': msg.offset()}
try:
p.produce(kafka_topic, message, callback=delivery_callback)
p.flush(kafka_timeout)
return return_object
except Exception as e:
return_object['message'] = 'Message delivery failed'
return return_object
def produce(self):
# debug
debuger = dt2ts(self.start) * 1000
stream = self.getBGPStream()
topic = self._create_topic()
producer = Producer({
'bootstrap.servers':
self.config['DEFAULT']['KafkaServer'],
'default.topic.config': {
'compression.codec': 'snappy',
'queue.buffering.max.messages': 1000000,
}
})
rec = BGPRecord()
stream.start()
logging.info(f"[{topic}] start producing")
try:
while stream and stream.get_next_record(rec):
if rec.time > dt2ts(self.end):
logging.error(f"[{topic}] time window exceed")
break
if rec.status != "valid":
continue
completeRecord = dict()
completeRecord["rec"] = getRecordDict(rec)
recordTimeStamp = int(rec.time) * 1000
completeRecord["elements"] = []
elem = rec.get_next_elem()
count = 0
while (elem):
elementDict = getElementDict(elem)
completeRecord["elements"].append(elementDict)
count += 1
if count > MAX_ELEMENT:
producer.produce(topic,
msgpack.packb(
{'record': completeRecord},
use_bin_type=True),
callback=self._delivery_report,
timestamp=recordTimeStamp)
producer.poll(0)
completeRecord["elements"] = []
count = 0
elem = rec.get_next_elem()
if len(completeRecord['elements']):
producer.produce(topic,
msgpack.packb({'record': completeRecord},
use_bin_type=True),
callback=self._delivery_report,
timestamp=recordTimeStamp)
producer.poll(0)
# debug
if recordTimeStamp > debuger:
logging.debug(
f"[{topic}] produced at {ts2dt(debuger//1000)}")
debuger += 3600000
except Exception as e:
logging.error(f"[{topic}] exit with error : {e}")
return
finally:
producer.produce(topic,
msgpack.packb({'end': True}, use_bin_type=True),
callback=self._delivery_report,
timestamp=dt2ts(self.end) * 1000)
producer.poll(0)
producer.flush()
logging.info(f"[{topic}] done producing")
def test_produce_headers():
""" Test produce() with timestamp arg """
p = Producer({'socket.timeout.ms': 10,
'error_cb': error_cb,
'message.timeout.ms': 10})
binval = pack('hhl', 1, 2, 3)
headers_to_test = [
[('headerkey', 'headervalue')],
[('dupkey', 'dupvalue'), ('empty', ''), ('dupkey', 'dupvalue')],
[('dupkey', 'dupvalue'), ('dupkey', 'diffvalue')],
[('key_with_null_value', None)],
[('binaryval', binval)],
[('alreadyutf8', u'Småland'.encode('utf-8'))],
[('isunicode', 'Jämtland')],
{'headerkey': 'headervalue'},
{'dupkey': 'dupvalue', 'empty': '', 'dupkey': 'dupvalue'}, # noqa: F601
{'dupkey': 'dupvalue', 'dupkey': 'diffvalue'}, # noqa: F601
{'key_with_null_value': None},
{'binaryval': binval},
{'alreadyutf8': u'Småland'.encode('utf-8')},
{'isunicode': 'Jämtland'}
]
for headers in headers_to_test:
print('headers', type(headers), headers)
p.produce('mytopic', value='somedata', key='a key', headers=headers)
p.produce('mytopic', value='somedata', headers=headers)
with pytest.raises(TypeError):
p.produce('mytopic', value='somedata', key='a key', headers=('a', 'b'))
with pytest.raises(TypeError):
p.produce('mytopic', value='somedata', key='a key', headers=[('malformed_header')])
with pytest.raises(TypeError):
p.produce('mytopic', value='somedata', headers={'anint': 1234})
p.flush()
def main():
producer = Producer({'bootstrap.servers': 'localhost:9092'})
producer.produce('test-topic', b'Hello, Kafka.')
# producer.poll(0)
producer.flush()