Related
My question refers specifically to why it was designed that way, due to the unnecessary performance implication.
When thread T1 has this code:
cv.acquire()
cv.wait()
cv.release()
and thread T2 has this code:
cv.acquire()
cv.notify() # requires that lock be held
cv.release()
what happens is that T1 waits and releases the lock, then T2 acquires it, notifies cv which wakes up T1. Now, there is a race-condition between T2's release and T1's reacquiring after returning from wait(). If T1 tries to reacquire first, it will be unnecessarily resuspended until T2's release() is completed.
Note: I'm intentionally not using the with statement, to better illustrate the race with explicit calls.
This seems like a design flaw. Is there any rationale known for this, or am I missing something?
This is not a definitive answer, but it's supposed to cover the relevant details I've managed to gather about this problem.
First, Python's threading implementation is based on Java's. Java's Condition.signal() documentation reads:
An implementation may (and typically does) require that the current thread hold the lock associated with this Condition when this method is called.
Now, the question was why enforce this behavior in Python in particular. But first I want to cover the pros and cons of each approach.
As to why some think it's often a better idea to hold the lock, I found two main arguments:
From the minute a waiter acquire()s the lock—that is, before releasing it on wait()—it is guaranteed to be notified of signals. If the corresponding release() happened prior to signalling, this would allow the sequence(where P=Producer and C=Consumer) P: release(); C: acquire(); P: notify(); C: wait() in which case the wait() corresponding to the acquire() of the same flow would miss the signal. There are cases where this doesn't matter (and could even be considered to be more accurate), but there are cases where that's undesirable. This is one argument.
When you notify() outside a lock, this may cause a scheduling priority inversion; that is, a low-priority thread might end up taking priority over a high-priority thread. Consider a work queue with one producer and two consumers (LC=Low-priority consumer and HC=High-priority consumer), where LC is currently executing a work item and HC is blocked in wait().
The following sequence may occur:
P LC HC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
execute(item) (in wait())
lock()
wq.push(item)
release()
acquire()
item = wq.pop()
release();
notify()
(wake-up)
while (wq.empty())
wait();
Whereas if the notify() happened before release(), LC wouldn't have been able to acquire() before HC had been woken-up. This is where the priority inversion occurred. This is the second argument.
The argument in favor of notifying outside of the lock is for high-performance threading, where a thread need not go back to sleep just to wake-up again the very next time-slice it gets—which was already explained how it might happen in my question.
Python's threading Module
In Python, as I said, you must hold the lock while notifying. The irony is that the internal implementation does not allow the underlying OS to avoid priority inversion, because it enforces a FIFO order on the waiters. Of course, the fact that the order of waiters is deterministic could come in handy, but the question remains why enforce such a thing when it could be argued that it would be more precise to differentiate between the lock and the condition variable, for that in some flows that require optimized concurrency and minimal blocking, acquire() should not by itself register a preceding waiting state, but only the wait() call itself.
Arguably, Python programmers would not care about performance to this extent anyway—although that still doesn't answer the question of why, when implementing a standard library, one should not allow several standard behaviors to be possible.
One thing which remains to be said is that the developers of the threading module might have specifically wanted a FIFO order for some reason, and found that this was somehow the best way of achieving it, and wanted to establish that as a Condition at the expense of the other (probably more prevalent) approaches. For this, they deserve the benefit of the doubt until they might account for it themselves.
There are several reasons which are compelling (when taken together).
1. The notifier needs to take a lock
Pretend that Condition.notifyUnlocked() exists.
The standard producer/consumer arrangement requires taking locks on both sides:
def unlocked(qu,cv): # qu is a thread-safe queue
qu.push(make_stuff())
cv.notifyUnlocked()
def consume(qu,cv):
with cv:
while True: # vs. other consumers or spurious wakeups
if qu: break
cv.wait()
x=qu.pop()
use_stuff(x)
This fails because both the push() and the notifyUnlocked() can intervene between the if qu: and the wait().
Writing either of
def lockedNotify(qu,cv):
qu.push(make_stuff())
with cv: cv.notify()
def lockedPush(qu,cv):
x=make_stuff() # don't hold the lock here
with cv: qu.push(x)
cv.notifyUnlocked()
works (which is an interesting exercise to demonstrate). The second form has the advantage of removing the requirement that qu be thread-safe, but it costs no more locks to take it around the call to notify() as well.
It remains to explain the preference for doing so, especially given that (as you observed) CPython does wake up the notified thread to have it switch to waiting on the mutex (rather than simply moving it to that wait queue).
2. The condition variable itself needs a lock
The Condition has internal data that must be protected in case of concurrent waits/notifications. (Glancing at the CPython implementation, I see the possibility that two unsynchronized notify()s could erroneously target the same waiting thread, which could cause reduced throughput or even deadlock.) It could protect that data with a dedicated lock, of course; since we need a user-visible lock already, using that one avoids additional synchronization costs.
3. Multiple wake conditions can need the lock
(Adapted from a comment on the blog post linked below.)
def setSignal(box,cv):
signal=False
with cv:
if not box.val:
box.val=True
signal=True
if signal: cv.notifyUnlocked()
def waitFor(box,v,cv):
v=bool(v) # to use ==
while True:
with cv:
if box.val==v: break
cv.wait()
Suppose box.val is False and thread #1 is waiting in waitFor(box,True,cv). Thread #2 calls setSignal; when it releases cv, #1 is still blocked on the condition. Thread #3 then calls waitFor(box,False,cv), finds that box.val is True, and waits. Then #2 calls notify(), waking #3, which is still unsatisfied and blocks again. Now #1 and #3 are both waiting, despite the fact that one of them must have its condition satisfied.
def setTrue(box,cv):
with cv:
if not box.val:
box.val=True
cv.notify()
Now that situation cannot arise: either #3 arrives before the update and never waits, or it arrives during or after the update and has not yet waited, guaranteeing that the notification goes to #1, which returns from waitFor.
4. The hardware might need a lock
With wait morphing and no GIL (in some alternate or future implementation of Python), the memory ordering (cf. Java's rules) imposed by the lock-release after notify() and the lock-acquire on return from wait() might be the only guarantee of the notifying thread's updates being visible to the waiting thread.
5. Real-time systems might need it
Immediately after the POSIX text you quoted we find:
however, if predictable scheduling behavior is required, then that mutex
shall be locked by the thread calling pthread_cond_broadcast() or
pthread_cond_signal().
One blog post contains further discussion of the rationale and history of this recommendation (as well as of some of the other issues here).
A couple of months ago exactly the same question occurred to me. But since I had ipython opened, looking at threading.Condition.wait?? result (the source for the method) didn't take long to answer it myself.
In short, the wait method creates another lock called waiter, acquires it, appends it to a list and then, surprise, releases the lock on itself. After that it acquires the waiter once again, that is it starts to wait until someone releases the waiter. Then it acquires the lock on itself again and returns.
The notify method pops a waiter from the waiter list (waiter is a lock, as we remember) and releases it allowing the corresponding wait method to continue.
That is the trick is that the wait method is not holding the lock on the condition itself while waiting for the notify method to release the waiter.
UPD1: I seem to have misunderstood the question. Is it correct that you are bothered that T1 might try to reacquire the lock on itself before the T2 release it?
But is it possible in the context of python's GIL? Or you think that one can insert an IO call before releasing the condition, which would allow T1 to wake up and wait forever?
It's explained in Python 3 documentation: https://docs.python.org/3/library/threading.html#condition-objects.
Note: the notify() and notify_all() methods don’t release the lock; this means that the thread or threads awakened will not return from their wait() call immediately, but only when the thread that called notify() or notify_all() finally relinquishes ownership of the lock.
What happens is that T1 waits and releases the lock, then T2 acquires it, notifies cv which wakes up T1.
Not quite. The cv.notify() call does not wake the T1 thread: It only moves it to a different queue. Before the notify(), T1 was waiting for the condition to be true. After the notify(), T1 is waiting to acquire the lock. T2 does not release the lock, and T1 does not "wake up" until T2 explicitly calls cv.release().
There is no race condition, this is how condition variables work.
When wait() is called, then the underlying lock is released until a notification occurs. It is guaranteed that the caller of wait will reacquire the lock before the function returns (eg, after the wait completes).
You're right that there could be some inefficiency if T1 was directly woken up when notify() is called. However, condition variables are typically implemented via OS primitives, and the OS will often be smart enough to realize that T2 still has the lock, so it won't immediately wake up T1 but instead queue it to be woken.
Additionally, in python, this doesn't really matter anyways, as there's only a single thread due to the GIL, so the threads wouldn't be able to run concurrently anyways.
Additionally, it's preferred to use the following forms instead of calling acquire/release directly:
with cv:
cv.wait()
And:
with cv:
cv.notify()
This ensures that the underlying lock is released even if an exception occurs.
I'd like to ask about asyncio.Condition. I'm not familiar with the concept, but I know and understand locks, semaphores, and queues since my student years.
I could not find a good explanation or typical use cases, just this example. I looked at the source. The core fnctionality is achieved with a FIFO of futures. Each waiting coroutine adds a new future and awaits it. Another coroutine may call notify() which sets the result of one or optionally more futures from the FIFO and that wakes up the same number of waiting coroutines. Really simple up to this point.
However, the implementation and the usage is more complicated than this. A waiting coroutine must first acquire a lock associated with the condition in order to be able to wait (and the wait() releases it while waiting). Also the notifier must acquire a lock to be able to notify(). This leads to with statement before each operation:
async with condition:
# condition operation (wait or notify)
or else a RuntimeError occurrs.
I do not understand the point of having this lock. What resource do we need to protect with the lock? In asyncio there could be always only one coroutine executing in the event loop, there are no "critical sections" as known from threading.
Is this lock really needed (why?) or is it for compatibility with threading code only?
My first idea was it is for the compatibility, but in such case why didn't they remove the lock while preserving the usage? i.e. making
async with condition:
basically an optional no-op.
The answer for this is essentially the same as for threading.Condition vs threading.Event; a condition without a lock is an event, not a condition(*).
Conditions are used to signal that a resource is available. Whomever was waiting for the condition, can use that resource until they are done with it. To ensure that no-one else can use the resource, you need to lock the resource:
resource = get_some_resource()
async with resource.condition:
await resource.condition.wait()
# this resource is mine, no-one will touch it
await resource.do_something_async()
# lock released, resource is available again for the next user
Note how the lock is not released after wait() resumes! Until the lock is released, no other co-routine waiting for the same condition can proceed, access to the resource is made exclusive by virtue of the lock. Note that the lock is released while waiting, so other coroutines can add themselves to the queue, but for wait() to finally return the lock must first be re-acquired.
If you don't need to coordinate access to a shared resource, use an event; a condition is basically a lock and event combined into one primitive, avoiding common implementation pitfalls.
Note that multiple conditions can share locks. This would let you signal specific stages, and other coroutines can wait for that specific stage to arrive. The shared lock would coordinate access to a single resource, but different conditions are signalled when each stage is initiated.
For threading, the typical use-case for conditions offered is that of a single producer, and multiple consumers all waiting on items from the producer to process. The work queue is the shared resource, the producer acquires the condition lock to push an item into the queue and then call notify(), at which point the next consumer waiting on the condition is given the lock (as it returns from wait()) and can remove the item from the queue to work on. This doesn't quite translate to a coroutine-based application, as coroutines don't have the sitting-idle-waiting-for-work-to-be-done problems threading systems have, it's much easier to just spin up consumer co-routines as needed (with perhaps a semaphore to impose a ceiling).
Perhaps a better example is the aioimaplib library, which supports IMAP4 transactions in full. These transactions are asynchronous, but you need to have access to the shared connection resource. So the library uses a single Condition object and wait_for() to wait for a specific state to arrive and thus give exclusive connection access to the coroutine waiting for that transaction state.
(*): Events have a different use-case from conditions, and thus behave a little different from a condition without locking. Once set, an event needs to be cleared explicitly, while a condition 'auto-clears' when used, and is never 'set' when no-one is waiting on the condition. But if you want to signal between tasks and don't need to control access to a shared resource, then you probably wanted an event.
My question refers specifically to why it was designed that way, due to the unnecessary performance implication.
When thread T1 has this code:
cv.acquire()
cv.wait()
cv.release()
and thread T2 has this code:
cv.acquire()
cv.notify() # requires that lock be held
cv.release()
what happens is that T1 waits and releases the lock, then T2 acquires it, notifies cv which wakes up T1. Now, there is a race-condition between T2's release and T1's reacquiring after returning from wait(). If T1 tries to reacquire first, it will be unnecessarily resuspended until T2's release() is completed.
Note: I'm intentionally not using the with statement, to better illustrate the race with explicit calls.
This seems like a design flaw. Is there any rationale known for this, or am I missing something?
This is not a definitive answer, but it's supposed to cover the relevant details I've managed to gather about this problem.
First, Python's threading implementation is based on Java's. Java's Condition.signal() documentation reads:
An implementation may (and typically does) require that the current thread hold the lock associated with this Condition when this method is called.
Now, the question was why enforce this behavior in Python in particular. But first I want to cover the pros and cons of each approach.
As to why some think it's often a better idea to hold the lock, I found two main arguments:
From the minute a waiter acquire()s the lock—that is, before releasing it on wait()—it is guaranteed to be notified of signals. If the corresponding release() happened prior to signalling, this would allow the sequence(where P=Producer and C=Consumer) P: release(); C: acquire(); P: notify(); C: wait() in which case the wait() corresponding to the acquire() of the same flow would miss the signal. There are cases where this doesn't matter (and could even be considered to be more accurate), but there are cases where that's undesirable. This is one argument.
When you notify() outside a lock, this may cause a scheduling priority inversion; that is, a low-priority thread might end up taking priority over a high-priority thread. Consider a work queue with one producer and two consumers (LC=Low-priority consumer and HC=High-priority consumer), where LC is currently executing a work item and HC is blocked in wait().
The following sequence may occur:
P LC HC
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
execute(item) (in wait())
lock()
wq.push(item)
release()
acquire()
item = wq.pop()
release();
notify()
(wake-up)
while (wq.empty())
wait();
Whereas if the notify() happened before release(), LC wouldn't have been able to acquire() before HC had been woken-up. This is where the priority inversion occurred. This is the second argument.
The argument in favor of notifying outside of the lock is for high-performance threading, where a thread need not go back to sleep just to wake-up again the very next time-slice it gets—which was already explained how it might happen in my question.
Python's threading Module
In Python, as I said, you must hold the lock while notifying. The irony is that the internal implementation does not allow the underlying OS to avoid priority inversion, because it enforces a FIFO order on the waiters. Of course, the fact that the order of waiters is deterministic could come in handy, but the question remains why enforce such a thing when it could be argued that it would be more precise to differentiate between the lock and the condition variable, for that in some flows that require optimized concurrency and minimal blocking, acquire() should not by itself register a preceding waiting state, but only the wait() call itself.
Arguably, Python programmers would not care about performance to this extent anyway—although that still doesn't answer the question of why, when implementing a standard library, one should not allow several standard behaviors to be possible.
One thing which remains to be said is that the developers of the threading module might have specifically wanted a FIFO order for some reason, and found that this was somehow the best way of achieving it, and wanted to establish that as a Condition at the expense of the other (probably more prevalent) approaches. For this, they deserve the benefit of the doubt until they might account for it themselves.
There are several reasons which are compelling (when taken together).
1. The notifier needs to take a lock
Pretend that Condition.notifyUnlocked() exists.
The standard producer/consumer arrangement requires taking locks on both sides:
def unlocked(qu,cv): # qu is a thread-safe queue
qu.push(make_stuff())
cv.notifyUnlocked()
def consume(qu,cv):
with cv:
while True: # vs. other consumers or spurious wakeups
if qu: break
cv.wait()
x=qu.pop()
use_stuff(x)
This fails because both the push() and the notifyUnlocked() can intervene between the if qu: and the wait().
Writing either of
def lockedNotify(qu,cv):
qu.push(make_stuff())
with cv: cv.notify()
def lockedPush(qu,cv):
x=make_stuff() # don't hold the lock here
with cv: qu.push(x)
cv.notifyUnlocked()
works (which is an interesting exercise to demonstrate). The second form has the advantage of removing the requirement that qu be thread-safe, but it costs no more locks to take it around the call to notify() as well.
It remains to explain the preference for doing so, especially given that (as you observed) CPython does wake up the notified thread to have it switch to waiting on the mutex (rather than simply moving it to that wait queue).
2. The condition variable itself needs a lock
The Condition has internal data that must be protected in case of concurrent waits/notifications. (Glancing at the CPython implementation, I see the possibility that two unsynchronized notify()s could erroneously target the same waiting thread, which could cause reduced throughput or even deadlock.) It could protect that data with a dedicated lock, of course; since we need a user-visible lock already, using that one avoids additional synchronization costs.
3. Multiple wake conditions can need the lock
(Adapted from a comment on the blog post linked below.)
def setSignal(box,cv):
signal=False
with cv:
if not box.val:
box.val=True
signal=True
if signal: cv.notifyUnlocked()
def waitFor(box,v,cv):
v=bool(v) # to use ==
while True:
with cv:
if box.val==v: break
cv.wait()
Suppose box.val is False and thread #1 is waiting in waitFor(box,True,cv). Thread #2 calls setSignal; when it releases cv, #1 is still blocked on the condition. Thread #3 then calls waitFor(box,False,cv), finds that box.val is True, and waits. Then #2 calls notify(), waking #3, which is still unsatisfied and blocks again. Now #1 and #3 are both waiting, despite the fact that one of them must have its condition satisfied.
def setTrue(box,cv):
with cv:
if not box.val:
box.val=True
cv.notify()
Now that situation cannot arise: either #3 arrives before the update and never waits, or it arrives during or after the update and has not yet waited, guaranteeing that the notification goes to #1, which returns from waitFor.
4. The hardware might need a lock
With wait morphing and no GIL (in some alternate or future implementation of Python), the memory ordering (cf. Java's rules) imposed by the lock-release after notify() and the lock-acquire on return from wait() might be the only guarantee of the notifying thread's updates being visible to the waiting thread.
5. Real-time systems might need it
Immediately after the POSIX text you quoted we find:
however, if predictable scheduling behavior is required, then that mutex
shall be locked by the thread calling pthread_cond_broadcast() or
pthread_cond_signal().
One blog post contains further discussion of the rationale and history of this recommendation (as well as of some of the other issues here).
A couple of months ago exactly the same question occurred to me. But since I had ipython opened, looking at threading.Condition.wait?? result (the source for the method) didn't take long to answer it myself.
In short, the wait method creates another lock called waiter, acquires it, appends it to a list and then, surprise, releases the lock on itself. After that it acquires the waiter once again, that is it starts to wait until someone releases the waiter. Then it acquires the lock on itself again and returns.
The notify method pops a waiter from the waiter list (waiter is a lock, as we remember) and releases it allowing the corresponding wait method to continue.
That is the trick is that the wait method is not holding the lock on the condition itself while waiting for the notify method to release the waiter.
UPD1: I seem to have misunderstood the question. Is it correct that you are bothered that T1 might try to reacquire the lock on itself before the T2 release it?
But is it possible in the context of python's GIL? Or you think that one can insert an IO call before releasing the condition, which would allow T1 to wake up and wait forever?
It's explained in Python 3 documentation: https://docs.python.org/3/library/threading.html#condition-objects.
Note: the notify() and notify_all() methods don’t release the lock; this means that the thread or threads awakened will not return from their wait() call immediately, but only when the thread that called notify() or notify_all() finally relinquishes ownership of the lock.
What happens is that T1 waits and releases the lock, then T2 acquires it, notifies cv which wakes up T1.
Not quite. The cv.notify() call does not wake the T1 thread: It only moves it to a different queue. Before the notify(), T1 was waiting for the condition to be true. After the notify(), T1 is waiting to acquire the lock. T2 does not release the lock, and T1 does not "wake up" until T2 explicitly calls cv.release().
There is no race condition, this is how condition variables work.
When wait() is called, then the underlying lock is released until a notification occurs. It is guaranteed that the caller of wait will reacquire the lock before the function returns (eg, after the wait completes).
You're right that there could be some inefficiency if T1 was directly woken up when notify() is called. However, condition variables are typically implemented via OS primitives, and the OS will often be smart enough to realize that T2 still has the lock, so it won't immediately wake up T1 but instead queue it to be woken.
Additionally, in python, this doesn't really matter anyways, as there's only a single thread due to the GIL, so the threads wouldn't be able to run concurrently anyways.
Additionally, it's preferred to use the following forms instead of calling acquire/release directly:
with cv:
cv.wait()
And:
with cv:
cv.notify()
This ensures that the underlying lock is released even if an exception occurs.
In a multi-threaded Python process I have a number of non-daemon threads, by which I mean threads which keep the main process alive even after the main thread has exited / stopped.
My non-daemon threads hold weak references to certain objects in the main thread, but when the main thread ends (control falls off the bottom of the file) these objects do not appear to be garbage collected, and my weak reference finaliser callbacks don't fire.
Am I wrong to expect the main thread to be garbage collected? I would have expected that the thread-locals would be deallocated (i.e. garbage collected)...
What have I missed?
Supporting materials
Output from pprint.pprint( threading.enumerate() ) showing the main thread has stopped while others soldier on.
[<_MainThread(MainThread, stopped 139664516818688)>,
<LDQServer(testLogIOWorkerThread, started 139664479889152)>,
<_Timer(Thread-18, started 139663928870656)>,
<LDQServer(debugLogIOWorkerThread, started 139664437925632)>,
<_Timer(Thread-17, started 139664463103744)>,
<_Timer(Thread-19, started 139663937263360)>,
<LDQServer(testLogIOWorkerThread, started 139664471496448)>,
<LDQServer(debugLogIOWorkerThread, started 139664446318336)>]
And since someone always asks about the use-case...
My network service occasionally misses its real-time deadlines (which causes a total system failure in the worst case). This turned out to be because logging of (important) DEBUG data would block whenever the file-system has a tantrum. So I am attempting to retrofit a number of established specialised logging libraries to defer blocking I/O to a worker thread.
Sadly the established usage pattern is a mix of short-lived logging channels which log overlapping parallel transactions, and long-lived module-scope channels which are never explicitly closed.
So I created a decorator which defers method calls to a worker thread. The worker thread is non-daemon to ensure that all (slow) blocking I/O completes before the interpreter exits, and holds a weak reference to the client-side (where method calls get enqueued). When the client-side is garbage collected the weak reference's callback fires and the worker thread knows no more work will be enqueued, and so will exit at its next convenience.
This seems to work fine in all but one important use-case: when the logging channel is in the main thread. When the main thread stops / exits the logging channel is not finalised, and so my (non-daemon) worker thread lives on keeping the entire process alive.
It's a bad idea for your main thread to end without calling join on all non-daemon threads, or to make any assumptions about what happens if you don't.
If you don't do anything very unusual, CPython (at least 2.0-3.3) will cover for you by automatically calling join on all non-daemon threads as pair of _MainThread._exitfunc. This isn't actually documented, so you shouldn't rely on it, but it's what's happening to you.
Your main thread hasn't actually exited at all; it's blocking inside its _MainThread._exitfunc trying to join some arbitrary non-daemon thread. Its objects won't be finalized until the atexit handler is called, which doesn't happen until after it finishes joining all non-daemon threads.
Meanwhile, if you avoid this (e.g., by using thread/_thread directly, or by detaching the main thread from its object or forcing it into a normal Thread instance), what happens? It isn't defined. The threading module makes no reference to it at all, but in CPython 2.0-3.3, and likely in any other reasonable implementation, it falls to the thread/_thread module to decide. And, as the docs say:
When the main thread exits, it is system defined whether the other threads survive. On SGI IRIX using the native thread implementation, they survive. On most other systems, they are killed without executing try ... finally clauses or executing object destructors.
So, if you manage to avoid joining all of your non-daemon threads, you have to write code that can handle both having them hard-killed like daemon threads, and having them continue running until exit.
If they do continue running, at least in CPython 2.7 and 3.3 on POSIX systems, that the main thread's OS-level thread handle, and various higher-level Python objects representing it, may be still retained, and not get cleaned up by the GC.
On top of that, even if everything were released, you can't rely on the GC ever deleting anything. If your code depends on deterministic GC, there are many cases you can get away with it in CPython (although your code will then break in PyPy, Jython, IronPython, etc.), but at exit time is not one of them. CPython can, and will, leak objects at exit time and let the OS sort 'em out. (This is why writable files that you never close may lose the last few writes—the __del__ method never gets called, and therefore there's nobody to tell them to flush, and at least on POSIX the underlying FILE* doesn't automatically flush either.)
If you want something to be cleaned up when the main thread finishes, you have to use some kind of close function rather than relying on __del__, and you have to make sure it gets triggered via a with block around the main block of code, an atexit function, or some other mechanism.
One last thing:
I would have expected that the thread-locals would be deallocated (i.e. garbage collected)...
Do you actually have thread locals somewhere? Or do you just mean locals and/or globals that are only accessed in one thread?
I'm writing to many files in a threaded app and I'm creating one handler per file. I have HandlerFactory class that manages the distribution of these handlers. What I'd like to do is that
thread A requests and gets foo.txt's file handle from the HandlerFactory class
thread B requests foo.txt's file handler
handler class recognizes that this file handle has been checked out
handler class puts thread A to sleep
thread B closes file handle using a wrapper method from HandlerFactory
HandlerFactory notifies sleeping threads
thread B wakes and successfully gets foo.txt's file handle
This is what I have so far,
def get_handler(self, file_path, type):
self.lock.acquire()
if file_path not in self.handlers:
self.handlers[file_path] = open(file_path, type)
elif not self.handlers[file_path].closed:
time.sleep(1)
self.lock.release()
return self.handlers[file_path][type]
I believe this covers the sleeping and handler retrieval successfully, but I am unsure how to wake up all threads, or even better wake up a specific thread.
What you're looking for is known as a condition variable.
Condition Variables
Here is the Python 2 library reference.
For Python 3 it can be found here
Looks like you want a threading.Semaphore associated with each handler (other synchronization objects like Events and Conditions are also possible, but a Semaphore seems simplest for your needs). (Specifically, use a BoundedSemaphore: for your use case, that will raise an exception immediately for programming errors that erroneously release the semaphone more times than they acquire it -- and that's exactly the reason for being of the bounded version of semaphones;-).
Initialize each semaphore to a value of 1 when you build it (so that means the handler is available). Each using-thread calls acquire on the semaphore to get the handler (that may block it), and release on it when it's done with the handler (that will unblock exactly one of the waiting threads). That's simpler than the acquire/wait/notify/release lifecycle of a Condition, and more future-proof too, since as the docs for Condition say:
The current implementation wakes up
exactly one thread, if any are
waiting. However, it’s not safe to
rely on this behavior. A future,
optimized implementation may
occasionally wake up more than one
thread.
while with a Semaphore you're playing it safe (the semantics whereof are safe to rely on: if a semaphore is initialized to N, there are at all times between 0 and N-1 [[included]] threads that have successfully acquired the semaphore and not yet released it).
You do realize that Python has a giant lock, so that most of the benefits of multi-threading you do not get, right?
Unless there is some reason for the master thread to do something with the results of each worker, you may wish to consider just forking off another process for each request. You won't have to deal with locking issues then. Have the children do what they need to do, then die. If they do need to communicate back, do it over a pipe, with XMLRPC, or through a sqlite database (which is threadsafe).