def _consume_messages(cls, host, port):
kafka = KafkaClient(cls.server.host + ":" + str(cls.server.port))
consumer = MultiProcessConsumer(kafka,
None,
cls.beaver_config.get('kafka_topic'),
num_procs=5)
return consumer.get_messages(count=100, block=True, timeout=5)
def cluster(self):
rc = 1
# Test cluster as a whole
self._client = DevopsSimpleClient(self._broker)
# Use multiprocessing for parallel consumers
from kafka import MultiProcessConsumer
# This will split the number of partitions among two processes
consumer = MultiProcessConsumer(self._client, "devops-group", "devopstest1", num_procs=2)
# This will spawn processes such that each handles 2 partitions max
consumer = MultiProcessConsumer(self._client, "devops-group", "devopstest1",
partitions_per_proc=2)
for message in consumer:
if self._pattern.match(message.message.value):
if self._debug:
print(message.message.value)
rc = 0
for message in consumer.get_messages(count=5, block=True, timeout=4):
if self._pattern.match(message.message.value):
if self._debug:
print(message.message.value)
rc = 0
else:
rc = 2
self._client.close()
return (rc, None)
def multiprocess_consumer():
'''multiprocess consumer'''
from kafka import KafkaClient, MultiProcessConsumer
kafka = KafkaClient(KAFKA_SERVER)
# This will split the number of partitions among two processes
consumer = MultiProcessConsumer(kafka, b'my-group', b'topic1', num_procs=2)
# This will spawn processes such that each handles 2 partitions max
consumer = MultiProcessConsumer(kafka, b'my-group', b'topic1',
partitions_per_proc=2)
for message in consumer:
print message
for message in consumer.get_messages(count=5, block=True, timeout=4):
print message
#!/usr/bin/env python
####Takes two arguments topic n
#### n is the expected number of messages after which the app will stop reading
####Read the messages from the topic and print each message on a new line
import uuid
import sys
from kafka import KafkaClient, MultiProcessConsumer
topic=sys.argv[1]
n=int(sys.argv[2])
kafka = KafkaClient("192.168.4.40:9092")
consumer = MultiProcessConsumer(kafka, str(uuid.uuid4()), topic)
for msg in consumer.get_messages(count=n, block=True, timeout=60000):
print(msg)
kafka.close()
#!/usr/bin/env python
####Takes two arguments topic n
#### n is the expected number of messages after which the app will stop reading
####Read the messages from the topic and print each message on a new line
import uuid
import sys
from kafka import KafkaClient, MultiProcessConsumer
topic = sys.argv[1]
n = int(sys.argv[2])
kafka = KafkaClient("192.168.4.40:9092")
consumer = MultiProcessConsumer(kafka, str(uuid.uuid4()), topic)
for msg in consumer.get_messages(count=n, block=True, timeout=60000):
print(msg)
kafka.close()
def _consume_messages(cls, host, port):
kafka = KafkaClient(cls.server.host + ":" + str(cls.server.port))
consumer = MultiProcessConsumer(kafka, None, cls.beaver_config.get('kafka_topic'), num_procs=5)
return consumer.get_messages(count=100, block=True, timeout=5)
from kafka import KafkaClient, MultiProcessConsumer
kafka = KafkaClient('localhost:9092')
# This will split the number of partitions among two processes
consumer = MultiProcessConsumer(kafka, b'my-group', b'my-topic', num_procs=2)
# This will spawn processes such that each handles 2 partitions max
consumer = MultiProcessConsumer(kafka, b'my-group', b'my-topic',
partitions_per_proc=2)
for message in consumer:
print(message)
for message in consumer.get_messages(count=5, block=True, timeout=4):
print(message)
