Using threads allows a program to run multiple operations concurrently in the same process space
The simplest way to use a Thread is to instanticate it with a target function and call start() to let it begin working
In [1]:
import threading
In [4]:
def worker():
""""thread worker function"""
print("Worker\n")
threads = []
for i in range(5):
t = threading.Thread(target=worker)
threads.append(t)
t.start()
It is useful to be able to spawn a thread and pass it arguments to tell it what work to do. Any type of object can be passed as argument to the therad. This example passes a number, which the thread then prints.
In [7]:
import threading
def worker(num):
print('Worker: %s' % num)
threads = []
for i in range(5):
t = threading.Thread(target=worker, args=(i,))
threads.append(t)
t.start()
Using arguments to identify or name the thread is cumbersome and unnecessary. Each Thread instance has a name with a default value that can be changed as the thread is created. Naming threads is useful in server processes with multiple service threads handling different operations.
In [9]:
import threading
import time
def worker():
print(threading.current_thread().getName(), 'Starting')
time.sleep(0.2)
print(threading.current_thread().getName(), 'Exiting')
def my_service():
print(threading.current_thread().getName(), 'Starting')
time.sleep(0.3)
print(threading.current_thread().getName(), 'Exiting')
t = threading.Thread(name='my_service', target=my_service)
w = threading.Thread(name='worker', target=worker)
w2 = threading.Thread(target=worker) # use default name
w.start()
w2.start()
t.start()
Most programs do not use print to debug. The logging module supports embedding the thread name in every log message using the formatter code %(threadName)s. Including thread names in log messages makes it possible to trace those messages back to their source.
In [10]:
import logging
import threading
import time
def worker():
logging.debug('Starting')
time.sleep(0.2)
logging.debug('Exiting')
def my_service():
logging.debug('Starting')
time.sleep(0.3)
logging.debug('Exiting')
logging.basicConfig(
level=logging.DEBUG,
format='[%(levelname)s] (%(threadName)-10s) %(message)s',
)
t = threading.Thread(name='my_service', target=my_service)
w = threading.Thread(name='worker', target=worker)
w2 = threading.Thread(target=worker) # use default name
w.start()
w2.start()
t.start()
Up to this point, the example programs have implicitly waited to exit until all threads have completed their work. Sometimes programs spawn a thread as a daemon that runs without blocking the main program from exiting. Using daemon threads is useful for services where there may not be an easy way to interrupt the thread, or where letting the thread die in the middle of its work does not lose or corrupt data (for example, a thread that generates “heart beats” for a service monitoring tool). To mark a thread as a daemon, pass daemon=True when constructing it or call its set_daemon() method with True. The default is for threads to not be daemons.
In [12]:
import threading
import time
import logging
def daemon():
logging.debug('Starting')
time.sleep(10)
logging.debug('Exiting')
def non_daemon():
logging.debug('Starting')
logging.debug('Exiting')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
d = threading.Thread(name='daemon', target=daemon, daemon=True)
t = threading.Thread(name='non-daemon', target=non_daemon)
d.start()
t.start()
The output does not include the "Exiting" message from the daemon thread, since all of the non-daemon threads (including the main thread) exit before the daemon thread wakes up from the sleep() call.
To wait until a daemon thread has completed its work, use the join() metho
In [13]:
import threading
import time
import logging
def daemon():
logging.debug('Starting')
time.sleep(0.2)
logging.debug('Exiting')
def non_daemon():
logging.debug('Starting')
logging.debug('Exiting')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
d = threading.Thread(name='daemon', target=daemon, daemon=True)
t = threading.Thread(name='non-daemon', target=non_daemon)
d.start()
t.start()
d.join()
t.join()
By default, join() blocks indefinitely. It is also possible to pass a float value representing the number of seconds to wait for the thread to become inactive. If the thread does not complete within the timeout period, join() returns anyway.
In [16]:
import threading
import time
import logging
def daemon():
logging.debug('Starting')
time.sleep(3)
logging.debug('Exiting')
def non_daemon():
logging.debug('Starting')
logging.debug('Exiting')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
d = threading.Thread(name='daemon', target=daemon, daemon=True)
t = threading.Thread(name='non-daemon', target=non_daemon)
d.start()
t.start()
d.join(0.1)
print('d.isAlive()', d.isAlive())
t.join()
Since the timeout passed is less than the amount of time the daemon thread sleeps, the thread is still “alive” after join() returns.
It is not necessary to retain an explicit handle to all of the daemon threads in order to ensure they have completed before exiting the main process. enumerate() returns a list of active Thread instances. The list includes the current thread, and since joining the current thread introduces a deadlock situation, it must be skipped.
In [17]:
import random
import threading
import time
import logging
def worker():
"""thread worker function"""
pause = random.randint(1, 5) / 10
logging.debug('sleeping %0.2f', pause)
time.sleep(pause)
logging.debug('ending')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
for i in range(3):
t = threading.Thread(target=worker, daemon=True)
t.start()
main_thread = threading.main_thread()
for t in threading.enumerate():
if t is main_thread:
continue
logging.debug('joining %s', t.getName())
t.join()
At start-up, a Thread does some basic initialization and then calls its run() method, which calls the target function passed to the constructor. To create a subclass of Thread, override run() to do whatever is necessary.
In [18]:
import threading
import logging
class MyThread(threading.Thread):
def run(self):
logging.debug('running')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
for i in range(5):
t = MyThread()
t.start()
Because the args and kwargs values passed to the Thread constructor are saved in private variables using names prefixed with '__', they are not easily accessed from a subclass. To pass arguments to a custom thread type, redefine the constructor to save the values in an instance attribute that can be seen in the subclass.
In [19]:
import threading
import logging
class MyThreadWithArgs(threading.Thread):
def __init__(self, group=None, target=None, name=None,
args=(), kwargs=None, *, daemon=None):
super().__init__(group=group, target=target, name=name,
daemon=daemon)
self.args = args
self.kwargs = kwargs
def run(self):
logging.debug('running with %s and %s',
self.args, self.kwargs)
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
for i in range(5):
t = MyThreadWithArgs(args=(i,), kwargs={'a': 'A', 'b': 'B'})
t.start()
One example of a reason to subclass Thread is provided by Timer, also included in threading. A Timer starts its work after a delay, and can be canceled at any point within that delay time period.
In [20]:
import threading
import time
import logging
def delayed():
logging.debug('worker running')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
t1 = threading.Timer(0.3, delayed)
t1.setName('t1')
t2 = threading.Timer(0.3, delayed)
t2.setName('t2')
logging.debug('starting timers')
t1.start()
t2.start()
logging.debug('waiting before canceling %s', t2.getName())
time.sleep(0.2)
logging.debug('canceling %s', t2.getName())
t2.cancel()
logging.debug('done')
The second timer in this example is never run, and the first timer appears to run after the rest of the main program is done. Since it is not a daemon thread, it is joined implicitly when the main thread is done
Although the point of using multiple threads is to run separate operations concurrently, there are times when it is important to be able to synchronize the operations in two or more threads. Event objects are a simple way to communicate between threads safely. An Event manages an internal flag that callers can control with the set() and clear() methods. Other threads can use wait() to pause until the flag is set, effectively blocking progress until allowed to continue.
In [21]:
import logging
import threading
import time
def wait_for_event(e):
"""Wait for the event to be set before doing anything"""
logging.debug('wait_for_event starting')
event_is_set = e.wait()
logging.debug('event set: %s', event_is_set)
def wait_for_event_timeout(e, t):
"""Wait t seconds and then timeout"""
while not e.is_set():
logging.debug('wait_for_event_timeout starting')
event_is_set = e.wait(t)
logging.debug('event set: %s', event_is_set)
if event_is_set:
logging.debug('processing event')
else:
logging.debug('doing other work')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
e = threading.Event()
t1 = threading.Thread(
name='block',
target=wait_for_event,
args=(e,),
)
t1.start()
t2 = threading.Thread(
name='nonblock',
target=wait_for_event_timeout,
args=(e, 2),
)
t2.start()
logging.debug('Waiting before calling Event.set()')
time.sleep(0.3)
e.set()
logging.debug('Event is set')
The wait() method takes an argument representing the number of seconds to wait for the event before timing out. It returns a Boolean indicating whether or not the event is set, so the caller knows why wait() returned. The is_set() method can be used separately on the event without fear of blocking.
In this example, wait_for_event_timeout() checks the event status without blocking indefinitely. The wait_for_event() blocks on the call to wait(), which does not return until the event status changes.
in addition to synchronizing the operations of threads, it is also important to be able to control access to shared resources to prevent corruption or missed data. Python’s built-in data structures (lists, dictionaries, etc.) are thread-safe as a side-effect of having atomic byte-codes for manipulating them (the global interpreter lock used to protect Python’s internal data structures is not released in the middle of an update). Other data structures implemented in Python, or simpler types like integers and floats, do not have that protection. To guard against simultaneous access to an object, use a Lock object.
In [ ]:
# %load thread_lock.py
import logging
import random
import threading
import time
class Counter:
def __init__(self, start=0):
self.lock = threading.Lock()
self.value = start
def increment(self):
logging.debug('Waiting for lock')
self.lock.acquire()
try:
logging.debug('Acquired lock')
self.value = self.value + 1
finally:
logging.debug("Release lock")
self.lock.release()
def worker(c):
for i in range(2):
pause = random.random()
logging.debug('Sleeping %0.02f', pause)
time.sleep(pause)
c.increment()
logging.debug('Done')
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
counter = Counter()
for i in range(2):
t = threading.Thread(target=worker, args=(counter,))
t.start()
logging.debug('Waiting for worker threads')
main_thread = threading.main_thread()
for t in threading.enumerate():
logging.debug("processing thread")
if t is not main_thread:
logging.debug("start join")
t.join()
logging.debug('Counter: %d', counter.value)
In [3]:
!python thread_lock.py
To find out whether another thread has acquired the lock without holding up the current thread, pass False for the blocking argument to acquire(). In the next example, worker() tries to acquire the lock three separate times and counts how many attempts it has to make to do so. In the mean time, lock_holder() cycles between holding and releasing the lock, with short pauses in each state used to simulate load.
In [ ]:
# %load threading_lock_nolock.py
import logging
import threading
import time
def lock_holder(lock):
logging.debug('Starting')
while True:
lock.acquire()
try:
logging.debug('Holding')
time.sleep(0.5)
finally:
logging.debug('Not holding')
lock.release()
time.sleep(0.5)
def worker(lock):
logging.debug('Starting')
num_tries = 0
num_acquires = 0
while num_acquires < 3:
time.sleep(0.5)
logging.debug('Trying to acquire')
have_it = lock.acquire(0)
try:
num_tries += 1
if have_it:
logging.debug('Iteration %d: Acquired',
num_tries)
num_acquires += 1
else:
logging.debug('Iteration %d: Not acquired',
num_tries)
finally:
if have_it:
lock.release()
logging.debug('Done after %d iterations', num_tries)
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
lock = threading.Lock()
holder = threading.Thread(
target=lock_holder,
args=(lock,),
name='LockHolder',
daemon=True,
)
holder.start()
worker = threading.Thread(
target=worker,
args=(lock,),
name='Worker',
)
worker.start()
In [6]:
!python threading_lock_nolock.py
Normal Lock objects cannot be acquired more than once, even by the same thread. This can introduce undesirable side-effects if a lock is accessed by more than one function in the same call chain.
In [7]:
import threading
lock = threading.Lock()
print('First try :', lock.acquire())
print('Second try:', lock.acquire(0))
In this case, the second call to acquire() is given a zero timeout to prevent it from blocking because the lock has been obtained by the first call.
In a situation where separate code from the same thread needs to “re-acquire” the lock, use an RLock instead.
In [8]:
import threading
lock = threading.RLock()
print('First try :', lock.acquire())
print('Second try:', lock.acquire(0))
Locks implement the context manager API and are compatible with the with statement. Using with removes the need to explicitly acquire and release the lock.
In [9]:
import threading
import logging
def worker_with(lock):
with lock:
logging.debug('Lock acquired via with')
def worker_no_with(lock):
lock.acquire()
try:
logging.debug('Lock acquired directly')
finally:
lock.release()
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
lock = threading.Lock()
w = threading.Thread(target=worker_with, args=(lock,))
nw = threading.Thread(target=worker_no_with, args=(lock,))
w.start()
nw.start()
In addition to using Events, another way of synchronizing threads is through using a Condition object. Because the Condition uses a Lock, it can be tied to a shared resource, allowing multiple threads to wait for the resource to be updated. In this example, the consumer() threads wait for the Condition to be set before continuing. The producer() thread is responsible for setting the condition and notifying the other threads that they can continue.
In [11]:
import logging
import threading
import time
def consumer(cond):
"""wait for the condition and use the resource"""
logging.debug('Starting consumer thread')
with cond:
cond.wait()
logging.debug('Resource is available to consumer')
def producer(cond):
"""set up the resource to be used by the consumer"""
logging.debug('Starting producer thread')
with cond:
logging.debug('Making resource available')
cond.notifyAll()
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s (%(threadName)-2s) %(message)s',
)
condition = threading.Condition()
c1 = threading.Thread(name='c1', target=consumer,
args=(condition,))
c2 = threading.Thread(name='c2', target=consumer,
args=(condition,))
p = threading.Thread(name='p', target=producer,
args=(condition,))
c1.start()
time.sleep(0.2)
c2.start()
time.sleep(0.2)
p.start()
Barriers are another thread synchronization mechanism. A Barrier establishes a control point and all participating threads block until all of the participating “parties” have reached that point. It lets threads start up separately and then pause until they are all ready to proceed.
In [13]:
import threading
import time
def worker(barrier):
print(threading.current_thread().name,
'waiting for barrier with {} others\n'.format(
barrier.n_waiting))
worker_id = barrier.wait()
print(threading.current_thread().name, 'after barrier\n',
worker_id)
NUM_THREADS = 3
barrier = threading.Barrier(NUM_THREADS)
threads = [
threading.Thread(
name='worker-%s\n' % i,
target=worker,
args=(barrier,),
)
for i in range(NUM_THREADS)
]
for t in threads:
print(t.name, 'starting\n')
t.start()
time.sleep(0.1)
for t in threads:
t.join()
In this example, the Barrier is configured to block until three threads are waiting. When the condition is met, all of the threads are released past the control point at the same time. The return value from wait() indicates the number of the party being released, and can be used to limit some threads from taking an action like cleaning up a shared resource.
The abort() method of Barrier causes all of the waiting threads to receive a BrokenBarrierError. This allows threads to clean up if processing is stopped while they are blocked on wait().
In [14]:
import threading
import time
def worker(barrier):
print(threading.current_thread().name,
'waiting for barrier with {} others'.format(
barrier.n_waiting))
try:
worker_id = barrier.wait()
except threading.BrokenBarrierError:
print(threading.current_thread().name, 'aborting')
else:
print(threading.current_thread().name, 'after barrier',
worker_id)
NUM_THREADS = 3
barrier = threading.Barrier(NUM_THREADS + 1)
threads = [
threading.Thread(
name='worker-%s' % i,
target=worker,
args=(barrier,),
)
for i in range(NUM_THREADS)
]
for t in threads:
print(t.name, 'starting')
t.start()
time.sleep(0.1)
barrier.abort()
for t in threads:
t.join()
Sometimes it is useful to allow more than one worker access to a resource at a time, while still limiting the overall number. For example, a connection pool might support a fixed number of simultaneous connections, or a network application might support a fixed number of concurrent downloads. A Semaphore is one way to manage those connections.
In [15]:
import logging
import random
import threading
import time
class ActivePool:
def __init__(self):
super(ActivePool, self).__init__()
self.active = []
self.lock = threading.Lock()
def makeActive(self, name):
with self.lock:
self.active.append(name)
logging.debug('Running: %s', self.active)
def makeInactive(self, name):
with self.lock:
self.active.remove(name)
logging.debug('Running: %s', self.active)
def worker(s, pool):
logging.debug('Waiting to join the pool')
with s:
name = threading.current_thread().getName()
pool.makeActive(name)
time.sleep(0.1)
pool.makeInactive(name)
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s (%(threadName)-2s) %(message)s',
)
pool = ActivePool()
s = threading.Semaphore(2)
for i in range(4):
t = threading.Thread(
target=worker,
name=str(i),
args=(s, pool),
)
t.start()
While some resources need to be locked so multiple threads can use them, others need to be protected so that they are hidden from threads that do not own them. The local() class creates an object capable of hiding values from view in separate threads.
In [16]:
import random
import threading
import logging
def show_value(data):
try:
val = data.value
except AttributeError:
logging.debug('No value yet')
else:
logging.debug('value=%s', val)
def worker(data):
show_value(data)
data.value = random.randint(1, 100)
show_value(data)
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
local_data = threading.local()
show_value(local_data)
local_data.value = 1000
show_value(local_data)
for i in range(2):
t = threading.Thread(target=worker, args=(local_data,))
t.start()
To initialize the settings so all threads start with the same value, use a subclass and set the attributes in init().
In [21]:
import random
import threading
import logging
def show_value(data):
try:
val = data.value
except AttributeError:
logging.debug('No value yet')
else:
logging.debug('value=%s', val)
def worker(data):
show_value(data)
data.value = random.randint(1, 100)
show_value(data)
class MyLocal(threading.local):
def __init__(self, value):
super().__init__()
logging.debug('Initializing %r', self)
self.value = value
logging.basicConfig(
level=logging.DEBUG,
format='(%(threadName)-10s) %(message)s',
)
local_data = MyLocal(1000)
show_value(local_data)
for i in range(2):
t = threading.Thread(target=worker, args=(local_data,))
t.start()
If a exception occures in child thread, the thread return immediataly. the trace info which is in the context of spawn context is gone. Following is a workaround of this problem
In [7]:
import sys
import threading
import queue
class ExcThread(threading.Thread):
def __init__(self, bucket):
threading.Thread.__init__(self)
self.bucket = bucket
def run(self):
try:
raise Exception('An error occured here.')
except Exception:
self.bucket.put(sys.exc_info())
def main():
bucket = queue.Queue()
thread_obj = ExcThread(bucket)
thread_obj.start()
while True:
try:
exc = bucket.get(block=False)
except Queue.Empty:
pass
else:
exc_type, exc_obj, exc_trace = exc
# deal with the exception
print(exc_type)
print(exc_obj)
print(exc_trace)
thread_obj.join(0.1)
if thread_obj.isAlive():
continue
else:
break
if __name__ == '__main__':
main()
In [ ]: