执行shell命令
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通过“threading”创建一个线程
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性能问题 - GIL
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生产者和消费者
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线程池模板
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使用多处理ThreadPool
与“map” 性能比较 输出:
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互斥锁
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Deadlock
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实现“监控”
| Using RLock
# ref: An introduction to Python Concurrency - David Beazley
from threading import Thread
from threading import RLock
import time
class monitor(object):
lock = RLock()
def foo(self,tid):
with monitor.lock:
print "%d in foo" % tid
time.sleep(5)
self.ker(tid)
def ker(self,tid):
with monitor.lock:
print "%d in ker" % tid
m = monitor()
def task1(id):
m.foo(id)
def task2(id):
m.ker(id)
t1 = Thread(target=task1,args=(1,))
t2 = Thread(target=task2,args=(2,))
t1.start()
t2.start()
t1.join()
t2.join()
output: $ python monitor.py 1 in foo 1 in ker 2 in ker |
控制primitive资源
| 使用信号量
from threading import Thread
from threading import Semaphore
from random import random
import time
# limit resource to 3
sema = Semaphore(3)
def foo(tid):
with sema:
print "%d acquire sema" % tid
wt = random()*5
time.sleep(wt)
print "%d release sema" % tid
threads = []
for _t in range(5):
t = Thread(target=foo,args=(_t,))
threads.append(t)
t.start()
for _t in threads:
_t.join()
输出: python semaphore.py 0 acquire sema 1 acquire sema 2 acquire sema 0 release sema 3 acquire sema 2 release sema 4 acquire sema 1 release sema 4 release sema 3 release sema |
Ensure tasks has done
| Using ‘event’ from threading import Thread from threading import Event import time e = Event() def worker(id): print "%d wait event" % id e.wait() print "%d get event set" % id t1=Thread(target=worker,args=(1,)) t2=Thread(target=worker,args=(2,)) t3=Thread(target=worker,args=(3,)) t1.start() t2.start() t3.start() # wait sleep task(event) happen time.sleep(3) e.set() output: python event.py 1 wait event 2 wait event 3 wait event 2 get event set 3 get event set 1 get event set |
线程安全优先队列
| Using ‘condition’
import threading
import heapq
import time
import random
class PriorityQueue(object):
def __init__(self):
self._q = []
self._count = 0
self._cv = threading.Condition()
def __str__(self):
return str(self._q)
def __repr__(self):
return self._q
def put(self, item, priority):
with self._cv:
heapq.heappush(self._q, (-priority,self._count,item))
self._count += 1
self._cv.notify()
def pop(self):
with self._cv:
while len(self._q) == 0:
print("wait...")
self._cv.wait()
ret = heapq.heappop(self._q)[-1]
return ret
priq = PriorityQueue()
def producer():
while True:
print(priq.pop())
def consumer():
while True:
time.sleep(3)
print("consumer put value")
priority = random.random()
priq.put(priority,priority*10)
for _ in range(3):
priority = random.random()
priq.put(priority,priority*10)
t1=threading.Thread(target=producer)
t2=threading.Thread(target=consumer)
t1.start();t2.start()
t1.join();t2.join()
output: python3 thread_safe.py 0.6657491871045683 0.5278797439991247 0.20990624606296315 wait... consumer put value 0.09123101305407577 wait... |
Multiprocessing
| Solving GIL problem via processes >>> from multiprocessing import Pool >>> def fib(n): ... if n <= 2: ... return 1 ... return fib(n-1) + fib(n-2) ... >>> def profile(func): ... def wrapper(*args, **kwargs): ... import time ... start = time.time() ... func(*args, **kwargs) ... end = time.time() ... print end - start ... return wrapper ... >>> @profile ... def nomultiprocess(): ... map(fib,[35]*5) ... >>> @profile ... def hasmultiprocess(): ... pool = Pool(5) ... pool.map(fib,[35]*5) ... >>> nomultiprocess() 23.8454811573 >>> hasmultiprocess() 13.2433719635 |
自定义多处理映射
from multiprocessing import Process, Pipe
from itertools import izip
def spawn(f):
def fun(pipe,x):
pipe.send(f(x))
pipe.close()
return fun
def parmap(f,X):
pipe=[Pipe() for x in X]
proc=[Process(target=spawn(f),
args=(c,x))
for x,(p,c) in izip(X,pipe)]
[p.start() for p in proc]
[p.join() for p in proc]
return [p.recv() for (p,c) in pipe]
print parmap(lambda x:x**x,range(1,5)) |
优雅的方法来杀死所有的子进程
from __future__ import print_function
import signal
import os
import time
from multiprocessing import Process, Pipe
NUM_PROCESS = 10
def aurora(n):
while True:
time.sleep(n)
if __name__ == "__main__":
procs = [Process(target=aurora, args=(x,))
for x in range(NUM_PROCESS)]
try:
for p in procs:
p.daemon = True
p.start()
[p.join() for p in procs]
finally:
for p in procs:
if not p.is_alive(): continue
os.kill(p.pid, signal.SIGKILL) |
简单的循环调度程序
>>> def fib(n): ... if n <= 2: ... return 1 ... return fib(n-1)+fib(n-2) ... >>> def gen_fib(n): ... for _ in range(1,n+1): ... yield fib(_) ... >>> t=[gen_fib(5),gen_fib(3)] >>> from collections import deque >>> tasks = deque() >>> tasks.extend(t) >>> def run(tasks): ... while tasks: ... try: ... task = tasks.popleft() ... print task.next() ... tasks.append(task) ... except StopIteration: ... print "done" ... >>> run(tasks) 1 1 1 1 2 2 3 done 5 done |
具有阻塞功能的调度器
# ref: PyCon 2015 - David Beazley
import socket
from select import select
from collections import deque
tasks = deque()
r_wait = {}
s_wait = {}
def fib(n):
if n <= 2:
return 1
return fib(n-1)+fib(n-2)
def run():
while any([tasks,r_wait,s_wait]):
while not tasks:
# polling
rr, sr, _ = select(r_wait, s_wait, {})
for _ in rr:
tasks.append(r_wait.pop(_))
for _ in sr:
tasks.append(s_wait.pop(_))
try:
task = tasks.popleft()
why,what = task.next()
if why == 'recv':
r_wait[what] = task
elif why == 'send':
s_wait[what] = task
else:
raise RuntimeError
except StopIteration:
pass
def fib_server():
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR,1)
sock.bind(('localhost',5566))
sock.listen(5)
while True:
yield 'recv', sock
c, a = sock.accept()
tasks.append(fib_handler(c))
def fib_handler(client):
while True:
yield 'recv', client
req = client.recv(1024)
if not req:
break
resp = fib(int(req))
yield 'send', client
client.send(str(resp)+'\n')
client.close()
tasks.append(fib_server())
run()
output: (bash 1) $ nc loalhost 5566 20 6765 output: (bash 2) $ nc localhost 5566 10 55 |
PoolExecutor
# python2.x is module futures on PyPI
# new in Python3.2
>>> from concurrent.futures import \
... ThreadPoolExecutor
>>> def fib(n):
... if n<=2:
... return 1
... return fib(n-1) + fib(n-2)
...
>>> with ThreadPoolExecutor(3) as e:
... res= e.map(fib,[1,2,3,4,5])
... for _ in res:
... print(_, end=' ')
...
1 1 2 3 5 >>>
# result is generator?!
>>> with ThreadPoolExecutor(3) as e:
... res = e.map(fib, [1,2,3])
... inspect.isgenerator(res)
...
True
# demo GIL
from concurrent import futures
import time
def fib(n):
if n <= 2:
return 1
return fib(n-1) + fib(n-2)
def thread():
s = time.time()
with futures.ThreadPoolExecutor(2) as e:
res = e.map(fib, [35]*2)
for _ in res:
print(_)
e = time.time()
print("thread cost: {}".format(e-s))
def process():
s = time.time()
with futures.ProcessPoolExecutor(2) as e:
res = e.map(fib, [35]*2)
for _ in res:
print(_)
e = time.time()
print("pocess cost: {}".format(e-s))
# bash> python3 -i test.py
>>> thread()
9227465
9227465
thread cost: 12.550225019454956
>>> process()
9227465
9227465
pocess cost: 5.538189888000488 |
What “with ThreadPoolExecutor” doing?
from concurrent import futures
def fib(n):
if n <= 2:
return 1
return fib(n-1) + fib(n-2)
with futures.ThreadPoolExecutor(3) as e:
fut = e.submit(fib, 30)
res = fut.result()
print(res)
# equal to
e = futures.ThreadPoolExecutor(3)
fut = e.submit(fib, 30)
fut.result()
e.shutdown(wait=True)
print(res)
output: $ python3 thread_pool_exec.py 832040 832040 |
Future Object
# future: deferred computation
# add_done_callback
from concurrent import futures
def fib(n):
if n <= 2:
return 1
return fib(n-1) + fib(n-2)
def handler(future):
res = future.result()
print("res: {}".format(res))
def thread_v1():
with futures.ThreadPoolExecutor(3) as e:
for _ in range(3):
f = e.submit(fib, 30+_)
f.add_done_callback(handler)
print("end")
def thread_v2():
to_do = []
with futures.ThreadPoolExecutor(3) as e:
for _ in range(3):
fut = e.submit(fib, 30+_)
to_do.append(fut)
for _f in futures.as_completed(to_do):
res = _f.result()
print("res: {}".format(res))
print("end")
output: $ python3 -i fut.py >>> thread_v1() res: 832040 res: 1346269 res: 2178309 end >>> thread_v2() res: 832040 res: 1346269 res: 2178309 end |
Future error handling
from concurrent import futures
def spam():
raise RuntimeError
def handler(future):
print("callback handler")
try:
res = future.result()
except RuntimeError:
print("get RuntimeError")
def thread_spam():
with futures.ThreadPoolExecutor(2) as e:
f = e.submit(spam)
f.add_done_callback(handler)
output: $ python -i fut_err.py >>> thread_spam() callback handler get RuntimeError |