This module constructs higher-level threading interfaces on top of the lower level thread module. See also the mutex and Queue modules.
The dummy_threading module is provided for situations where threading cannot be used because thread is missing.
Note
Starting with Python 2.6, this module provides PEP 8 compliant aliases and properties to replace the camelCase names that were inspired by Java’s threading API. This updated API is compatible with that of the multiprocessing module. However, no schedule has been set for the deprecation of the camelCase names and they remain fully supported in both Python 2.x and 3.x.
Note
Starting with Python 2.5, several Thread methods raise RuntimeError instead of AssertionError if called erroneously.
This module defines the following functions and objects:
A class that represents thread-local data. Thread-local data are data whose values are thread specific. To manage thread-local data, just create an instance of local (or a subclass) and store attributes on it:
mydata = threading.local()
mydata.x = 1
The instance’s values will be different for separate threads.
For more details and extensive examples, see the documentation string of the _threading_local module.
New in version 2.4.
Set a trace function for all threads started from the threading module. The func will be passed to sys.settrace() for each thread, before its run() method is called.
New in version 2.3.
Set a profile function for all threads started from the threading module. The func will be passed to sys.setprofile() for each thread, before its run() method is called.
New in version 2.3.
Return the thread stack size used when creating new threads. The optional size argument specifies the stack size to be used for subsequently created threads, and must be 0 (use platform or configured default) or a positive integer value of at least 32,768 (32kB). If changing the thread stack size is unsupported, a ThreadError is raised. If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified. 32kB is currently the minimum supported stack size value to guarantee sufficient stack space for the interpreter itself. Note that some platforms may have particular restrictions on values for the stack size, such as requiring a minimum stack size > 32kB or requiring allocation in multiples of the system memory page size - platform documentation should be referred to for more information (4kB pages are common; using multiples of 4096 for the stack size is the suggested approach in the absence of more specific information). Availability: Windows, systems with POSIX threads.
New in version 2.5.
Detailed interfaces for the objects are documented below.
The design of this module is loosely based on Java’s threading model. However, where Java makes locks and condition variables basic behavior of every object, they are separate objects in Python. Python’s Thread class supports a subset of the behavior of Java’s Thread class; currently, there are no priorities, no thread groups, and threads cannot be destroyed, stopped, suspended, resumed, or interrupted. The static methods of Java’s Thread class, when implemented, are mapped to module-level functions.
All of the methods described below are executed atomically.
This class represents an activity that is run in a separate thread of control. There are two ways to specify the activity: by passing a callable object to the constructor, or by overriding the run() method in a subclass. No other methods (except for the constructor) should be overridden in a subclass. In other words, only override the __init__() and run() methods of this class.
Once a thread object is created, its activity must be started by calling the thread’s start() method. This invokes the run() method in a separate thread of control.
Once the thread’s activity is started, the thread is considered ‘alive’. It stops being alive when its run() method terminates – either normally, or by raising an unhandled exception. The is_alive() method tests whether the thread is alive.
Other threads can call a thread’s join() method. This blocks the calling thread until the thread whose join() method is called is terminated.
A thread has a name. The name can be passed to the constructor, and read or changed through the name attribute.
A thread can be flagged as a “daemon thread”. The significance of this flag is that the entire Python program exits when only daemon threads are left. The initial value is inherited from the creating thread. The flag can be set through the daemon property.
There is a “main thread” object; this corresponds to the initial thread of control in the Python program. It is not a daemon thread.
There is the possibility that “dummy thread objects” are created. These are thread objects corresponding to “alien threads”, which are threads of control started outside the threading module, such as directly from C code. Dummy thread objects have limited functionality; they are always considered alive and daemonic, and cannot be join()ed. They are never deleted, since it is impossible to detect the termination of alien threads.
This constructor should always be called with keyword arguments. Arguments are:
group should be None; reserved for future extension when a ThreadGroup class is implemented.
target is the callable object to be invoked by the run() method. Defaults to None, meaning nothing is called.
name is the thread name. By default, a unique name is constructed of the form “Thread-N” where N is a small decimal number.
args is the argument tuple for the target invocation. Defaults to ().
kwargs is a dictionary of keyword arguments for the target invocation. Defaults to {}.
If the subclass overrides the constructor, it must make sure to invoke the base class constructor (Thread.__init__()) before doing anything else to the thread.
Start the thread’s activity.
It must be called at most once per thread object. It arranges for the object’s run() method to be invoked in a separate thread of control.
This method will raise a RuntimeException if called more than once on the same thread object.
Method representing the thread’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.
Wait until the thread terminates. This blocks the calling thread until the thread whose join() method is called terminates – either normally or through an unhandled exception – or until the optional timeout occurs.
When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof). As join() always returns None, you must call isAlive() after join() to decide whether a timeout happened – if the thread is still alive, the join() call timed out.
When the timeout argument is not present or None, the operation will block until the thread terminates.
A thread can be join()ed many times.
join() raises a RuntimeError if an attempt is made to join the current thread as that would cause a deadlock. It is also an error to join() a thread before it has been started and attempts to do so raises the same exception.
The ‘thread identifier’ of this thread or None if the thread has not been started. This is a nonzero integer. See the thread.get_ident() function. Thread identifiers may be recycled when a thread exits and another thread is created. The identifier is available even after the thread has exited.
New in version 2.6.
Return whether the thread is alive.
Roughly, a thread is alive from the moment the start() method returns until its run() method terminates. The module function enumerate() returns a list of all alive threads.
A boolean value indicating whether this thread is a daemon thread (True) or not (False). This must be set before start() is called, otherwise RuntimeError is raised. Its initial value is inherited from the creating thread; the main thread is not a daemon thread and therefore all threads created in the main thread default to daemon = False.
The entire Python program exits when no alive non-daemon threads are left.
A primitive lock is a synchronization primitive that is not owned by a particular thread when locked. In Python, it is currently the lowest level synchronization primitive available, implemented directly by the thread extension module.
A primitive lock is in one of two states, “locked” or “unlocked”. It is created in the unlocked state. It has two basic methods, acquire() and release(). When the state is unlocked, acquire() changes the state to locked and returns immediately. When the state is locked, acquire() blocks until a call to release() in another thread changes it to unlocked, then the acquire() call resets it to locked and returns. The release() method should only be called in the locked state; it changes the state to unlocked and returns immediately. If an attempt is made to release an unlocked lock, a RuntimeError will be raised.
When more than one thread is blocked in acquire() waiting for the state to turn to unlocked, only one thread proceeds when a release() call resets the state to unlocked; which one of the waiting threads proceeds is not defined, and may vary across implementations.
All methods are executed atomically.
Acquire a lock, blocking or non-blocking.
When invoked without arguments, block until the lock is unlocked, then set it to locked, and return true.
When invoked with the blocking argument set to true, do the same thing as when called without arguments, and return true.
When invoked with the blocking argument set to false, do not block. If a call without an argument would block, return false immediately; otherwise, do the same thing as when called without arguments, and return true.
Release a lock.
When the lock is locked, reset it to unlocked, and return. If any other threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed.
Do not call this method when the lock is unlocked.
There is no return value.
A reentrant lock is a synchronization primitive that may be acquired multiple times by the same thread. Internally, it uses the concepts of “owning thread” and “recursion level” in addition to the locked/unlocked state used by primitive locks. In the locked state, some thread owns the lock; in the unlocked state, no thread owns it.
To lock the lock, a thread calls its acquire() method; this returns once the thread owns the lock. To unlock the lock, a thread calls its release() method. acquire()/release() call pairs may be nested; only the final release() (the release() of the outermost pair) resets the lock to unlocked and allows another thread blocked in acquire() to proceed.
Acquire a lock, blocking or non-blocking.
When invoked without arguments: if this thread already owns the lock, increment the recursion level by one, and return immediately. Otherwise, if another thread owns the lock, block until the lock is unlocked. Once the lock is unlocked (not owned by any thread), then grab ownership, set the recursion level to one, and return. If more than one thread is blocked waiting until the lock is unlocked, only one at a time will be able to grab ownership of the lock. There is no return value in this case.
When invoked with the blocking argument set to true, do the same thing as when called without arguments, and return true.
When invoked with the blocking argument set to false, do not block. If a call without an argument would block, return false immediately; otherwise, do the same thing as when called without arguments, and return true.
Release a lock, decrementing the recursion level. If after the decrement it is zero, reset the lock to unlocked (not owned by any thread), and if any other threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling thread.
Only call this method when the calling thread owns the lock. A RuntimeError is raised if this method is called when the lock is unlocked.
There is no return value.
A condition variable is always associated with some kind of lock; this can be passed in or one will be created by default. (Passing one in is useful when several condition variables must share the same lock.)
A condition variable has acquire() and release() methods that call the corresponding methods of the associated lock. It also has a wait() method, and notify() and notifyAll() methods. These three must only be called when the calling thread has acquired the lock, otherwise a RuntimeError is raised.
The wait() method releases the lock, and then blocks until it is awakened by a notify() or notifyAll() call for the same condition variable in another thread. Once awakened, it re-acquires the lock and returns. It is also possible to specify a timeout.
The notify() method wakes up one of the threads waiting for the condition variable, if any are waiting. The notifyAll() method wakes up all threads waiting for the condition variable.
Note: the notify() and notifyAll() 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 notifyAll() finally relinquishes ownership of the lock.
Tip: the typical programming style using condition variables uses the lock to synchronize access to some shared state; threads that are interested in a particular change of state call wait() repeatedly until they see the desired state, while threads that modify the state call notify() or notifyAll() when they change the state in such a way that it could possibly be a desired state for one of the waiters. For example, the following code is a generic producer-consumer situation with unlimited buffer capacity:
# Consume one item
cv.acquire()
while not an_item_is_available():
cv.wait()
get_an_available_item()
cv.release()
# Produce one item
cv.acquire()
make_an_item_available()
cv.notify()
cv.release()
To choose between notify() and notifyAll(), consider whether one state change can be interesting for only one or several waiting threads. E.g. in a typical producer-consumer situation, adding one item to the buffer only needs to wake up one consumer thread.
If the lock argument is given and not None, it must be a Lock or RLock object, and it is used as the underlying lock. Otherwise, a new RLock object is created and used as the underlying lock.
Wait until notified or until a timeout occurs. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.
This method releases the underlying lock, and then blocks until it is awakened by a notify() or notifyAll() call for the same condition variable in another thread, or until the optional timeout occurs. Once awakened or timed out, it re-acquires the lock and returns.
When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof).
When the underlying lock is an RLock, it is not released using its release() method, since this may not actually unlock the lock when it was acquired multiple times recursively. Instead, an internal interface of the RLock class is used, which really unlocks it even when it has been recursively acquired several times. Another internal interface is then used to restore the recursion level when the lock is reacquired.
Wake up a thread waiting on this condition, if any. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.
This method wakes up one of the threads waiting for the condition variable, if any are waiting; it is a no-op if no threads are waiting.
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.
Note: the awakened thread does not actually return from its wait() call until it can reacquire the lock. Since notify() does not release the lock, its caller should.
This is one of the oldest synchronization primitives in the history of computer science, invented by the early Dutch computer scientist Edsger W. Dijkstra (he used P() and V() instead of acquire() and release()).
A semaphore manages an internal counter which is decremented by each acquire() call and incremented by each release() call. The counter can never go below zero; when acquire() finds that it is zero, it blocks, waiting until some other thread calls release().
The optional argument gives the initial value for the internal counter; it defaults to 1. If the value given is less than 0, ValueError is raised.
Acquire a semaphore.
When invoked without arguments: if the internal counter is larger than zero on entry, decrement it by one and return immediately. If it is zero on entry, block, waiting until some other thread has called release() to make it larger than zero. This is done with proper interlocking so that if multiple acquire() calls are blocked, release() will wake exactly one of them up. The implementation may pick one at random, so the order in which blocked threads are awakened should not be relied on. There is no return value in this case.
When invoked with blocking set to true, do the same thing as when called without arguments, and return true.
When invoked with blocking set to false, do not block. If a call without an argument would block, return false immediately; otherwise, do the same thing as when called without arguments, and return true.
Semaphores are often used to guard resources with limited capacity, for example, a database server. In any situation where the size of the resource size is fixed, you should use a bounded semaphore. Before spawning any worker threads, your main thread would initialize the semaphore:
maxconnections = 5
...
pool_sema = BoundedSemaphore(value=maxconnections)
Once spawned, worker threads call the semaphore’s acquire and release methods when they need to connect to the server:
pool_sema.acquire()
conn = connectdb()
... use connection ...
conn.close()
pool_sema.release()
The use of a bounded semaphore reduces the chance that a programming error which causes the semaphore to be released more than it’s acquired will go undetected.
This is one of the simplest mechanisms for communication between threads: one thread signals an event and other threads wait for it.
An event object manages an internal flag that can be set to true with the set() method and reset to false with the clear() method. The wait() method blocks until the flag is true.
The internal flag is initially false.
Return true if and only if the internal flag is true.
Changed in version 2.6: The is_set() syntax is new.
Block until the internal flag is true. If the internal flag is true on entry, return immediately. Otherwise, block until another thread calls set() to set the flag to true, or until the optional timeout occurs.
When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof).
This method always returns None.
This class represents an action that should be run only after a certain amount of time has passed — a timer. Timer is a subclass of Thread and as such also functions as an example of creating custom threads.
Timers are started, as with threads, by calling their start() method. The timer can be stopped (before its action has begun) by calling the cancel() method. The interval the timer will wait before executing its action may not be exactly the same as the interval specified by the user.
For example:
def hello():
print "hello, world"
t = Timer(30.0, hello)
t.start() # after 30 seconds, "hello, world" will be printed
Create a timer that will run function with arguments args and keyword arguments kwargs, after interval seconds have passed.
All of the objects provided by this module that have acquire() and release() methods can be used as context managers for a with statement. The acquire() method will be called when the block is entered, and release() will be called when the block is exited.
Currently, Lock, RLock, Condition, Semaphore, and BoundedSemaphore objects may be used as with statement context managers. For example:
import threading
some_rlock = threading.RLock()
with some_rlock:
print "some_rlock is locked while this executes"
While the import machinery is thread safe, there are two key restrictions on threaded imports due to inherent limitations in the way that thread safety is provided: