12.1 启动与停止线程

问题¶

You want to create and destroy threads for concurrent execution of code.

解决方案¶

The threading library can be used to execute any Python callable in its own thread. To do this, you create a Thread instance and supply the callable that you wish to execute as a target. Here is a simple example:

# Code to execute in an independent thread import time def countdown(n):

while n > 0:

print(‘T-minus’, n) n -= 1 time.sleep(5)

# Create and launch a thread from threading import Thread t = Thread(target=countdown, args=(10,)) t.start()

When you create a thread instance, it doesn’t start executing until you invoke its start() method (which invokes the target function with the arguments you supplied). Threads are executed in their own system-level thread (e.g., a POSIX thread or Windows threads) that is fully managed by the host operating system. Once started, threads run independently until the target function returns. You can query a thread instance to see if it’s still running:

if t.is_alive():

print(‘Still running’)

else:

print(‘Completed’)

You can also request to join with a thread, which waits for it to terminate:

t.join()

The interpreter remains running until all threads terminate. For long-running threads or background tasks that run forever, you should consider making the thread daemonic. For example:

t = Thread(target=countdown, args=(10,), daemon=True) t.start()

Daemonic threads can’t be joined. However, they are destroyed automatically when the main thread terminates. Beyond the two operations shown, there aren’t many other things you can do with threads. For example, there are no operations to terminate a thread, signal a thread, adjust its scheduling, or perform any other high-level operations. If you want these features, you need to build them yourself. If you want to be able to terminate threads, the thread must be programmed to poll for exit at selected points. For example, you might put your thread in a class such as this:

class CountdownTask:

def __init__(self):

self._running = True

def terminate(self):

self._running = False

def run(self, n):

while self._running and n > 0:

print(‘T-minus’, n) n -= 1 time.sleep(5)

c = CountdownTask() t = Thread(target=c.run, args=(10,)) t.start() ... c.terminate() # Signal termination t.join() # Wait for actual termination (if needed)

Polling for thread termination can be tricky to coordinate if threads perform blocking operations such as I/O. For example, a thread blocked indefinitely on an I/O operation may never return to check if it’s been killed. To correctly deal with this case, you’ll need to carefully program thread to utilize timeout loops. For example:

class IOTask:

def terminate(self):

self._running = False

def run(self, sock):

# sock is a socket sock.settimeout(5) # Set timeout period while self._running:

# Perform a blocking I/O operation w/ timeout try:

data = sock.recv(8192) break

except socket.timeout:

continue

# Continued processing ...

# Terminated return

讨论¶

Due to a global interpreter lock (GIL), Python threads are restricted to an execution model that only allows one thread to execute in the interpreter at any given time. For this reason, Python threads should generally not be used for computationally intensive tasks where you are trying to achieve parallelism on multiple CPUs. They are much better suited for I/O handling and handling concurrent execution in code that performs blocking operations (e.g., waiting for I/O, waiting for results from a database, etc.). Sometimes you will see threads defined via inheritance from the Thread class. For example:

from threading import Thread

class CountdownThread(Thread):

def __init__(self, n):

super().__init__() self.n = 0

def run(self):

while self.n > 0:

print(‘T-minus’, self.n) self.n -= 1 time.sleep(5)

c = CountdownThread(5) c.start()

Although this works, it introduces an extra dependency between the code and the threading library. That is, you can only use the resulting code in the context of threads, whereas the technique shown earlier involves writing code with no explicit dependency on threading. By freeing your code of such dependencies, it becomes usable in other contexts that may or may not involve threads. For instance, you might be able to execute your code in a separate process using the multiprocessing module using code like this:

import multiprocessing c = CountdownTask(5) p = multiprocessing.Process(target=c.run) p.start() ...

Again, this only works if the CountdownTask class has been written in a manner that is neutral to the actual means of concurrency (threads, processes, etc.).