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Thread-local objects. (Note that this module provides a Python version of the threading.local class. Depending on the version of Python you're using, there may be a faster one available. You should always import the `local` class from `threading`.) Thread-local objects support the management of thread-local data. If you have data that you want to be local to a thread, simply create a thread-local object and use its attributes: >>> mydata = local() >>> mydata.number = 42 >>> mydata.number 42 You can also access the local-object's dictionary: >>> mydata.__dict__ {'number': 42} >>> mydata.__dict__.setdefault('widgets', []) [] >>> mydata.widgets [] What's important about thread-local objects is that their data are local to a thread. If we access the data in a different thread: >>> log = [] >>> def f(): ... items = sorted(mydata.__dict__.items()) ... log.append(items) ... mydata.number = 11 ... log.append(mydata.number) >>> import threading >>> thread = threading.Thread(target=f) >>> thread.start() >>> thread.join() >>> log [[], 11] we get different data. Furthermore, changes made in the other thread don't affect data seen in this thread: >>> mydata.number 42 Of course, values you get from a local object, including a __dict__ attribute, are for whatever thread was current at the time the attribute was read. For that reason, you generally don't want to save these values across threads, as they apply only to the thread they came from. You can create custom local objects by subclassing the local class: >>> class MyLocal(local): ... number = 2 ... def __init__(self, /, **kw): ... self.__dict__.update(kw) ... def squared(self): ... return self.number ** 2 This can be useful to support default values, methods and initialization. Note that if you define an __init__ method, it will be called each time the local object is used in a separate thread. This is necessary to initialize each thread's dictionary. Now if we create a local object: >>> mydata = MyLocal(color='red') Now we have a default number: >>> mydata.number 2 an initial color: >>> mydata.color 'red' >>> del mydata.color And a method that operates on the data: >>> mydata.squared() 4 As before, we can access the data in a separate thread: >>> log = [] >>> thread = threading.Thread(target=f) >>> thread.start() >>> thread.join() >>> log [[('color', 'red')], 11] without affecting this thread's data: >>> mydata.number 2 >>> mydata.color Traceback (most recent call last): ... AttributeError: 'MyLocal' object has no attribute 'color' Note that subclasses can define slots, but they are not thread local. They are shared across threads: >>> class MyLocal(local): ... __slots__ = 'number' >>> mydata = MyLocal() >>> mydata.number = 42 >>> mydata.color = 'red' So, the separate thread: >>> thread = threading.Thread(target=f) >>> thread.start() >>> thread.join() affects what we see: >>> mydata.number 11 >>> del mydata
RLock(*args, **kwargs) Factory function that returns a new reentrant lock. A reentrant lock must be released by the thread that acquired it. Once a thread has acquired a reentrant lock, the same thread may acquire it again without blocking; the thread must release it once for each time it has acquired it.
contextmanager(func) @contextmanager decorator. Typical usage: @contextmanager def some_generator(<arguments>): <setup> try: yield <value> finally: <cleanup> This makes this: with some_generator(<arguments>) as <variable>: <body> equivalent to this: <setup> try: <variable> = <value> <body> finally: <cleanup>
current_thread() Return the current Thread object, corresponding to the caller's thread of control. If the caller's thread of control was not created through the threading module, a dummy thread object with limited functionality is returned.