![]() 1 1 2 3 5 > # result is generator?! > with ThreadPoolExecutor ( 3 ) as e. Figure 3.10 The use of a queue for communication between processes. if n > with ThreadPoolExecutor ( 3 ) as e. Figure 3.4 Using a Pool to perform the example of Figure 3.3. ![]() # python2.x is module futures on PyPI # new in Python3.2 > from concurrent.futures import \ recv ( 1024 ) if not req : break resp = fib ( int ( req )) yield 'send', client client. append ( fib_handler ( c )) def fib_handler ( client ): while True : yield 'recv', client req = client. listen ( 5 ) while True : yield 'recv', sock c, a = sock. next () if why = 'recv' : r_wait = task elif why = 'send' : s_wait = task else : raise RuntimeError except StopIteration : pass def fib_server (): sock = socket. # ref: P圜on 2015 - David Beazley import socket from select import select from collections import deque tasks = deque () r_wait = ) for _ in rr : tasks. Thread ( target = producer ) t2 = threading. put ( priority, priority * 10 ) t1 = threading. ![]() put ( priority, priority * 10 ) for _ in range ( 3 ): priority = random. sleep ( 3 ) print ( "consumer put value" ) priority = random. Queue() Set up a pool to process a bunch of stuff in parallel poolmp.Pool() valuesrange(20). pop ()) def consumer (): while True : time. Let us take an example of a book library to understand thread pools better. _q ) return ret priq = PriorityQueue () def producer (): while True : print ( priq. Applications can queue work items, associate work with waitable handles. ![]() _q def put ( self, item, priority ): with self. Before we can begin explaining it to you, let’s take an example of Pool- an object, a way to parallelize executing a function across input values and distributing input data across processes. Condition () def _str_ ( self ): return str ( self. Import threading import heapq import time import random class PriorityQueue ( object ): def _init_ ( self ): self. ![]()
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