zeroMQ初体验-3.分而治之模式(push/pull)

http://iyuan.iteye.com/blog/974040

push/pull模式： 

 

模型描述： 
1.上游(任务发布) 
2.工人(中间，具体工作) 
3.下游(信号采集或者工作结果收集) 

上游代码： 

import zmq
import random
import time

context = zmq.Context()

# Socket to send messages on
sender = context.socket(zmq.PUSH)
sender.bind("tcp://*:5557")

print "Press Enter when the workers are ready: "
_ = raw_input()
print "Sending tasks to workers..."

# The first message is "0" and signals start of batch
sender.send('0')

# Initialize random number generator
random.seed()

# Send 100 tasks
total_msec = 0
for task_nbr in range(100):
    # Random workload from 1 to 100 msecs
    workload = random.randint(1, 100)
    total_msec += workload
    sender.send(str(workload))
print "Total expected cost: %s msec" % total_msec

工作代码： 

import sys
import time
import zmq

context = zmq.Context()

# Socket to receive messages on
receiver = context.socket(zmq.PULL)
receiver.connect("tcp://localhost:5557")

# Socket to send messages to
sender = context.socket(zmq.PUSH)
sender.connect("tcp://localhost:5558")

# Process tasks forever
while True:
    s = receiver.recv()

    # Simple progress indicator for the viewer
    sys.stdout.write('.')
    sys.stdout.flush()

    # Do the work
    time.sleep(int(s)*0.001)

    # Send results to sink
    sender.send('')

下游代码： 

import sys
import time
import zmq

context = zmq.Context()

# Socket to receive messages on
receiver = context.socket(zmq.PULL)
receiver.bind("tcp://*:5558")

# Wait for start of batch
s = receiver.recv()

# Start our clock now
tstart = time.time()

# Process 100 confirmations
total_msec = 0
for task_nbr in range(100):
    s = receiver.recv()
    if task_nbr % 10 == 0:
        sys.stdout.write(':')
    else:
        sys.stdout.write('.')

# Calculate and report duration of batch
tend = time.time()
print "Total elapsed time: %d msec" % ((tend-tstart)*1000)

注意点： 
这种模式与pub/sub模式一样都是单向的，区别有两点： 
1，该模式下在没有消费者的情况下，发布者的信息是不会消耗的(由发布者进程维护) 
2，多个消费者消费的是同一列信息，假设A得到了一条信息，则B将不再得到 

这种模式主要针对在消费者能力不够的情况下，提供的多消费者并行消费解决方案(也算是之前的pub/sub模式的那个"堵塞问题"的一个解决策略吧) 

由上面的模型图可以看出，这是一个N:N的模式，在1:N的情况下，各消费者并不是平均消费的，而在N:1的情况下，则有所不同，如下图： 
 


这种模式主要关注点在于，可以扩展中间worker，来到达并发的目的。