python3 分布式进程(跨机器)BaseManager(multiprocessing.managers)

A机器负责发送任务和接受结果:

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#task_master.py
import  random,time,queue
from  multiprocessing.managers  import  BaseManager
 
task_queue  =  queue.Queue()
result_queue  =  queue.Queue()
 
class  QueueManager(BaseManager):
     pass
 
if  __name__  = =  '__main__' :
     print ( "master start." )
     QueueManager.register( 'get_task_queue' , callable  =  lambda :task_queue)
     QueueManager.register( 'get_result_queue' , callable  =  lambda :result_queue)
     manager  =  QueueManager(address  =  ( '10.10.100.11' , 9833 ),authkey = b 'abc' )
     manager.start()
     task  =  manager.get_task_queue()
     result  =  manager.get_result_queue()
 
     for  in  range ( 10 ):
         =  random.randint( 0 , 1000 )
         print ( 'put task %d ...'  %  n)
         task.put(n)
     print ( 'try get results...' )
 
     for  in  range ( 10 ):
         =  result.get(timeout  =  100 )
         print ( 'Result:%s'  %  r)
     manager.shutdown()
     print ( 'master exit.' )

B机器负责处理任务和发送结果:

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#task_worker.py
import  sys,time,queue
from  multiprocessing.managers  import  BaseManager
 
class  QueueManager(BaseManager):
     pass
 
QueueManager.register( 'get_task_queue' )
QueueManager.register( 'get_result_queue' )
 
server_addr  =  '10.10.100.11'
print ( 'connect to server %s...'  %  server_addr)
 
=  QueueManager(address = (server_addr, 9833 ),authkey = b 'abc' )
m.connect()
 
task  =  m.get_task_queue()
result  =  m.get_result_queue()
 
for  in  range ( 10 ):
     try :
         =  task.get(timeout  =  10 )
         print ( 'run task %d * %d'  % (n,n))
         =  '%d * %d = %d'  % (n,n,n * n)
         time.sleep( 1 )
         result.put(r)
     except  Queue.Empty:
         print ( 'task queue is empty' )
 
print ( 'worker exit'

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转载自www.cnblogs.com/ExMan/p/10187599.html