# `threading` --- Thread-based parallelism

Code source : Lib/threading.py

This module constructs higher-level threading interfaces on top of the lower level `_thread` module.

Disponibilité: not WASI.

This module does not work or is not available on WebAssembly. See Plateformes WebAssembly for more information.

## Introduction

The `threading` module provides a way to run multiple threads (smaller units of a process) concurrently within a single process. It allows for the creation and management of threads, making it possible to execute tasks in parallel, sharing memory space. Threads are particularly useful when tasks are I/O bound, such as file operations or making network requests, where much of the time is spent waiting for external resources.

A typical use case for `threading` includes managing a pool of worker threads that can process multiple tasks concurrently. Here's a basic example of creating and starting threads using `Thread`:

    import threading
    import time

    def crawl(link, delay=3):
        print(f"crawl started for {link}")
        time.sleep(delay)  # Blocking I/O (simulating a network request)
        print(f"crawl ended for {link}")

    links = [
        "https://python.org",
        "https://docs.python.org",
        "https://peps.python.org",
    ]

    # Start threads for each link
    threads = []
    for link in links:
        # Using `args` to pass positional arguments and `kwargs` for keyword arguments
        t = threading.Thread(target=crawl, args=(link,), kwargs={"delay": 2})
        threads.append(t)

    # Start each thread
    for t in threads:
        t.start()

    # Wait for all threads to finish
    for t in threads:
        t.join()

Modifié dans la version 3.7: Ce module était auparavant optionnel, il est maintenant toujours disponible.

Voir aussi

`concurrent.futures.ThreadPoolExecutor` offers a higher level interface to push tasks to a background thread without blocking execution of the calling thread, while still being able to retrieve their results when needed.

`queue` provides a thread-safe interface for exchanging data between running threads.

`asyncio` offers an alternative approach to achieving task level concurrency without requiring the use of multiple operating system threads.

Note

In the Python 2.x series, this module contained `camelCase` names for some methods and functions. These are deprecated as of Python 3.10, but they are still supported for compatibility with Python 2.5 and lower.

Particularité de l'implémentation CPython : In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use `multiprocessing` or `concurrent.futures.ProcessPoolExecutor`. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.

## GIL and performance considerations

Unlike the `multiprocessing` module, which uses separate processes to bypass the global interpreter lock (GIL), the threading module operates within a single process, meaning that all threads share the same memory space. However, the GIL limits the performance gains of threading when it comes to CPU-bound tasks, as only one thread can execute Python bytecode at a time. Despite this, threads remain a useful tool for achieving concurrency in many scenarios.

As of Python 3.13, free-threaded builds can disable the GIL, enabling true parallel execution of threads, but this feature is not available by default (see PEP 703).

## Référence

Ce module définit les fonctions suivantes :

threading.active_count()

  Renvoie le nombre d'objets `Thread` actuellement vivants. Le compte renvoyé est égal à la longueur de la liste renvoyée par `enumerate()`.

  The function `activeCount` is a deprecated alias for this function.

threading.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.

  The function `currentThread` is a deprecated alias for this function.

threading.excepthook(args, /)

  Gère les exceptions non-attrapées levées par `Thread.run()`.

  L'argument arg a les attributs suivants :

  - exc_type : le type de l'exception ;

  - exc_value: la valeur de l'exception, peut être `None` ;

  - exc_traceback : la pile d'appels pour cette exception, peut être `None` ;

  - thread: le fil d'exécution ayant levé l'exception, peut être `None`.

  Si exc_type est `SystemExit`, l'exception est ignorée silencieusement. Toutes les autres sont affichées sur `sys.stderr`.

  Si cette fonction lève une exception, `sys.excepthook()` est appelée pour la gérer.

  La fonction `threading.excepthook()` peut être surchargée afin de contrôler comment les exceptions non-attrapées levées par `Thread.run()` sont gérées.

  Stocker exc_value en utilisant une fonction de rappel personnalisée peut créer un cycle de références. exc_value doit être nettoyée explicitement pour casser ce cycle lorsque l'exception n'est plus nécessaire.

  Stocker thread en utilisant une fonction de rappel personnalisée peut le ressusciter, si c'est un objet en cours de finalisation. Évitez de stocker thread après la fin de la fonction de rappel, pour éviter de ressusciter des objets.

  Voir aussi

  `sys.excepthook()` gère les exceptions qui n'ont pas été attrapées.

  Ajouté dans la version 3.8.

threading.__excepthook__

  Holds the original value of `threading.excepthook()`. It is saved so that the original value can be restored in case they happen to get replaced with broken or alternative objects.

  Ajouté dans la version 3.10.

threading.get_ident()

  Renvoie l'« identifiant de fil » du fil d'exécution courant. C'est un entier non nul. Sa valeur n'a pas de signification directe ; il est destiné à être utilisé comme valeur magique opaque, par exemple comme clef de dictionnaire de données pour chaque fil. Les identificateurs de fils peuvent être recyclés lorsqu'un fil se termine et qu'un autre fil est créé.

  Ajouté dans la version 3.3.

threading.get_native_id()

  Renvoie l'identifiant natif complet assigné par le noyau du fil d'exécution actuel. C'est un entier non négatif. Sa valeur peut uniquement être utilisée pour identifier ce fil d'exécution à l'échelle du système (jusqu'à ce que le fil d'exécution se termine, après quoi la valeur peut être recyclée par le système d'exploitation).

  Disponibilité: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX, DragonFlyBSD, GNU/kFreeBSD, Solaris.

  Ajouté dans la version 3.8.

  Modifié dans la version 3.13: Added support for GNU/kFreeBSD.

  Modifié dans la version 3.15: Added support for Solaris.

threading.enumerate()

  Return a list of all `Thread` objects currently active. The list includes daemonic threads and dummy thread objects created by `current_thread()`. It excludes terminated threads and threads that have not yet been started. However, the main thread is always part of the result, even when terminated.

threading.main_thread()

  Renvoie l'objet fil d'exécution `Thread` principal. Dans des conditions normales, le fil principal est le fil à partir duquel l'interpréteur Python a été lancé.

  Ajouté dans la version 3.4.

threading.settrace(func)

  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.

threading.settrace_all_threads(func)

  Set a trace function for all threads started from the `threading` module and all Python threads that are currently executing.

  The func will be passed to `sys.settrace()` for each thread, before its `run()` method is called.

  Ajouté dans la version 3.12.

threading.gettrace()

  Renvoie la fonction de traçage tel que définie par `settrace()`.

  Ajouté dans la version 3.10.

threading.setprofile(func)

  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.

threading.setprofile_all_threads(func)

  Set a profile function for all threads started from the `threading` module and all Python threads that are currently executing.

  The func will be passed to `sys.setprofile()` for each thread, before its `run()` method is called.

  Ajouté dans la version 3.12.

threading.getprofile()

  Renvoie la fonction de profilage tel que défini par `setprofile()`.

  Ajouté dans la version 3.10.

threading.stack_size([size])

  Renvoie la taille de la pile d'exécution utilisée lors de la création de nouveaux fils d'exécution. L'argument optionnel size spécifie la taille de pile à utiliser pour les fils créés ultérieurement, et doit être 0 (pour utiliser la taille de la plate-forme ou la valeur configurée par défaut) ou un entier positif supérieur ou égal à 32 768 (32 Kio). Si size n'est pas spécifié, 0 est utilisé. Si la modification de la taille de la pile de fils n'est pas prise en charge, une `RuntimeError` est levée. Si la taille de pile spécifiée n'est pas valide, une `ValueError` est levée et la taille de pile n'est pas modifiée. 32 Kio est actuellement la valeur minimale de taille de pile prise en charge pour garantir un espace de pile suffisant pour l'interpréteur lui-même. Notez que certaines plates-formes peuvent avoir des restrictions particulières sur les valeurs de taille de la pile, telles que l'exigence d'une taille de pile minimale > 32 Kio ou d'une allocation en multiples de la taille de page de la mémoire du système – la documentation de la plate-forme devrait être consultée pour plus d'informations (4 Kio sont courantes ; en l'absence de renseignements plus spécifiques, l'approche proposée est l'utilisation de multiples de 4 096 pour la taille de la pile).

  Disponibilité: Windows, pthreads.

  Unix platforms with POSIX threads support.

Ce module définit également la constante suivante :

threading.TIMEOUT_MAX

  La valeur maximale autorisée pour le paramètre timeout des fonctions bloquantes (`Lock.acquire()`, `RLock.acquire()`, `Condition.wait()`, etc.). Spécifier un délai d'attente supérieur à cette valeur lève une `OverflowError`.

  Ajouté dans la version 3.2.

Ce module définit un certain nombre de classes, qui sont détaillées dans les sections ci-dessous.

La conception de ce module est librement basée sur le modèle des fils d'exécution de Java. Cependant, là où Java fait des verrous et des variables de condition le comportement de base de chaque objet, ils sont des objets séparés en Python. La classe Python `Thread` prend en charge un sous-ensemble du comportement de la classe Thread de Java ; actuellement, il n'y a aucune priorité, aucun groupe de fils d'exécution, et les fils ne peuvent être détruits, arrêtés, suspendus, repris ni interrompus. Les méthodes statiques de la classe Thread de Java, lorsqu'elles sont implémentées, correspondent à des fonctions au niveau du module.

Toutes les méthodes décrites ci-dessous sont exécutées de manière atomique.

### Thread-local data

Thread-local data is data whose values are thread specific. If you have data that you want to be local to a thread, create a `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
    []

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 their `__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')

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

class threading.local

  Classe qui représente les données locales au fil d'exécution.

### Thread objects

The `Thread` 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.

Une fois qu'un objet fil d'exécution est créé, son activité doit être lancée en appelant la méthode `start()` du fil. Ceci invoque la méthode `run()` dans un fil d'exécution séparé.

Une fois que l'activité du fil d'exécution est lancée, le fil est considéré comme « vivant ». Il cesse d'être vivant lorsque sa méthode `run()` se termine – soit normalement, soit en levant une exception non gérée. La méthode `is_alive()` teste si le fil est vivant.

D'autres fils d'exécution peuvent appeler la méthode `join()` d'un fil. Ceci bloque le fil appelant jusqu'à ce que le fil dont la méthode `join()` est appelée soit terminé.

Un fil d'exécution a un nom. Le nom peut être passé au constructeur, et lu ou modifié via l'attribut `name`.

Si la méthode `run()` lève une exception, `threading.excepthook()` est appelée pour s'en occuper. Par défaut, `threading.excepthook()` ignore silencieusement `SystemExit`.

Un fil d'exécution peut être marqué comme « fil démon ». Un programme Python se termine quand il ne reste plus que des fils démons. La valeur initiale est héritée du fil d'exécution qui l'a créé. Cette option peut être définie par la propriété `daemon` ou par l'argument daemon du constructeur.

Note

Les fils d'exécution démons sont brusquement terminés à l'arrêt du programme Python. Leurs ressources (fichiers ouverts, transactions de base de données, etc.) peuvent ne pas être libérées correctement. Si vous voulez que vos fils s'arrêtent proprement, faites en sorte qu'ils ne soient pas démoniques et utilisez un mécanisme de signalisation approprié tel qu'un objet évènement `Event`.

Il y a un objet "fil principal", qui correspond au fil de contrôle initial dans le programme Python. Ce n'est pas un fil démon.

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 joined. They are never deleted, since it is impossible to detect the termination of alien threads.

class threading.Thread(group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None, context=None)

  Ce constructeur doit toujours être appelé avec des arguments nommés. Les arguments sont :

  group should be `None`; reserved for future extension when a `ThreadGroup` class is implemented.

  target est l'objet appelable qui doit être invoqué par la méthode `run()`. La valeur par défaut est `None`, ce qui signifie que rien n'est appelé.

  name is the thread name. By default, a unique name is constructed of the form "Thread-N" where N is a small decimal number, or "Thread-N (target)" where "target" is `target.__name__` if the target argument is specified.

  args is a list or tuple of arguments for the target invocation. Defaults to `()`.

  kwargs est un dictionnaire d'arguments nommés pour l'invocation de l'objet appelable. La valeur par défaut est `{}`.

  S'il ne vaut pas `None`, daemon définit explicitement si le fil d'exécution est démonique ou pas. S'il vaut `None` (par défaut), la valeur est héritée du fil courant.

  context is the `Context` value to use when starting the thread. The default value is `None` which indicates that the `sys.flags.thread_inherit_context` flag controls the behaviour. If the flag is true, threads will start with a copy of the context of the caller of `start()`. If false, they will start with an empty context. To explicitly start with an empty context, pass a new instance of `Context()`. To explicitly start with a copy of the current context, pass the value from `copy_context()`. The flag defaults true on free-threaded builds and false otherwise.

  Si la sous-classe réimplémente le constructeur, elle doit s'assurer d'appeler le constructeur de la classe de base (`Thread.__init__()`) avant de faire autre chose au fil d'exécution.

  Modifié dans la version 3.3: Added the daemon parameter.

  Modifié dans la version 3.10: Use the target name if name argument is omitted.

  Modifié dans la version 3.14: Added the context parameter.

  start()

    Lance l'activité du fil d'exécution.

    Elle ne doit être appelée qu'une fois par objet de fil. Elle fait en sorte que la méthode `run()` de l'objet soit invoquée dans un fil d'exécution.

    Cette méthode lève une `RuntimeError` si elle est appelée plus d'une fois sur le même objet fil d'exécution.

    If supported, set the operating system thread name to `threading.Thread.name`. The name can be truncated depending on the operating system thread name limits.

    Modifié dans la version 3.14: Set the operating system thread name.

  run()

    Méthode représentant l'activité du fil d'exécution.

    Vous pouvez remplacer cette méthode dans une sous-classe. La méthode standard `run()` invoque l'objet appelable passé au constructeur de l'objet en tant qu'argument target, le cas échéant, avec des arguments positionnels et des arguments nommés tirés respectivement des arguments args et kwargs.

    Using list or tuple as the args argument which passed to the `Thread` could achieve the same effect.

    Example:

        >>> from threading import Thread
        >>> t = Thread(target=print, args=[1])
        >>> t.run()
        1
        >>> t = Thread(target=print, args=(1,))
        >>> t.run()
        1

  join(timeout=None)

    Attend que le fil d'exécution se termine. Ceci bloque le fil appelant jusqu'à ce que le fil dont la méthode `join()` est appelée se termine – soit normalement, soit par une exception non gérée – ou jusqu'à ce que le délai optionnel timeout soit atteint.

    When the timeout argument is present and not `None`, it should be a real number specifying a timeout for the operation in seconds (or fractions thereof). As `join()` always returns `None`, you must call `is_alive()` after `join()` to decide whether a timeout happened -- if the thread is still alive, the `join()` call timed out.

    Lorsque l'argument timeout n'est pas présent ou vaut `None`, l'opération se bloque jusqu'à ce que le fil d'exécution se termine.

    A thread can be joined many times.

    `join()` lève une `RuntimeError` si une tentative est faite pour attendre le fil d'exécution courant car cela conduirait à un interblocage (deadlock en anglais). Attendre via `join()` un fil d'exécution avant son lancement est aussi une erreur et, si vous tentez de le faire, lève la même exception.

    If an attempt is made to join a running daemonic thread in late stages of Python finalization `join()` raises a `PythonFinalizationError`.

    Modifié dans la version 3.14: May raise `PythonFinalizationError`.

    Modifié dans la version 3.15: Accepts any real number as timeout, not only integer or float.

  name

    Une chaîne de caractères utilisée à des fins d'identification seulement. Elle n'a pas de sémantique. Plusieurs fils d'exécution peuvent porter le même nom. Le nom initial est défini par le constructeur.

    On some platforms, the thread name is set at the operating system level when the thread starts, so that it is visible in task managers. This name may be truncated to fit in a system-specific limit (for example, 15 bytes on Linux or 63 bytes on macOS).

    Changes to name are only reflected at the OS level when the currently running thread is renamed. (Setting the name attribute of a different thread only updates the Python Thread object.)

  getName()
  setName()

    Deprecated getter/setter API for `name`; use it directly as a property instead.

    Obsolète depuis la version 3.10.

  ident

    « L'identificateur de fil d'exécution » de ce fil ou `None` si le fil n'a pas été lancé. C'est un entier non nul. Voyez également la fonction `get_ident()`. Les identificateurs de fils peuvent être recyclés lorsqu'un fil se termine et qu'un autre fil est créé. L'identifiant est disponible même après que le fil ait terminé.

  native_id

    The Thread ID (`TID`) of this thread, as assigned by the OS (kernel). This is a non-negative integer, or `None` if the thread has not been started. See the `get_native_id()` function. This value may be used to uniquely identify this particular thread system-wide (until the thread terminates, after which the value may be recycled by the OS).

    Note

    Tout comme pour les Process IDs, les Thread IDs ne sont valides (garantis uniques sur le système) uniquement du démarrage du fil à sa fin.

    Disponibilité: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX, DragonFlyBSD.

    Ajouté dans la version 3.8.

  is_alive()

    Renvoie si le fil d'exécution est vivant ou pas.

    Cette méthode renvoie `True` depuis juste avant le démarrage de la méthode `run()` et jusqu'à juste après la terminaison de la méthode `run()`. La fonction `enumerate()` du module renvoie une liste de tous les fils d'exécution vivants.

  daemon

    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`.

    Le programme Python se termine lorsqu'il ne reste plus de fils d'exécution non-démons vivants.

  isDaemon()
  setDaemon()

    Deprecated getter/setter API for `daemon`; use it directly as a property instead.

    Obsolète depuis la version 3.10.

### Lock objects

Un verrou primitif n'appartient pas à un fil d'exécution lorsqu'il est verrouillé. En Python, c'est actuellement la méthode de synchronisation la plus bas-niveau qui soit disponible, implémentée directement par le module d'extension `_thread`.

Un verrou primitif est soit « verrouillé » soit « déverrouillé ». Il est créé dans un état déverrouillé. Il a deux méthodes, `acquire()` et `release()`. Lorsque l'état est déverrouillé, `acquire()` verrouille et se termine immédiatement. Lorsque l'état est verrouillé, `acquire()` bloque jusqu'à ce qu'un appel à `release()` provenant d'un autre fil d'exécution le déverrouille. À ce moment `acquire()` le verrouille à nouveau et rend la main. La méthode `release()` ne doit être appelée que si le verrou est verrouillé, elle le déverrouille alors et se termine immédiatement. Déverrouiller un verrou qui n'est pas verrouillé provoque une `RuntimeError`.

Locks also support the context management protocol.

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.

class threading.Lock

  The class implementing primitive lock objects. Once a thread has acquired a lock, subsequent attempts to acquire it block, until it is released; any thread may release it.

  Modifié dans la version 3.13: `Lock` is now a class. In earlier Pythons, `Lock` was a factory function which returned an instance of the underlying private lock type.

  acquire(blocking=True, timeout=-1)

    Acquiert un verrou, bloquant ou non bloquant.

    When invoked with the blocking argument set to `True` (the default), block until the lock is unlocked, then set it to locked and return `True`.

    When invoked with the blocking argument set to `False`, do not block. If a call with blocking set to `True` would block, return `False` immediately; otherwise, set the lock to locked and return `True`.

    When invoked with the timeout argument set to a positive value, block for at most the number of seconds specified by timeout and as long as the lock cannot be acquired. A timeout argument of `-1` specifies an unbounded wait. It is forbidden to specify a timeout when blocking is `False`.

    The return value is `True` if the lock is acquired successfully, `False` if not (for example if the timeout expired).

    Modifié dans la version 3.2: Le paramètre timeout est nouveau.

    Modifié dans la version 3.2: Lock acquisition can now be interrupted by signals on POSIX if the underlying threading implementation supports it.

    Modifié dans la version 3.14: Lock acquisition can now be interrupted by signals on Windows.

    Modifié dans la version 3.15: Accepts any real number as timeout, not only integer or float.

  release()

    Release a lock. This can be called from any thread, not only the thread which has acquired the 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.

    Lorsqu'elle est invoquée sur un verrou déverrouillé, une `RuntimeError` est levée.

    Il n'y a pas de valeur de retour.

  locked()

    Return `True` if the lock is acquired.

### RLock objects

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.

Threads call a lock's `acquire()` method to lock it, and its `release()` method to unlock it.

Note

Reentrant locks support the context management protocol, so it is recommended to use `with` instead of manually calling `acquire()` and `release()` to handle acquiring and releasing the lock for a block of code.

RLock's `acquire()`/`release()` call pairs may be nested, unlike Lock's `acquire()`/`release()`. Only the final `release()` (the `release()` of the outermost pair) resets the lock to an unlocked state and allows another thread blocked in `acquire()` to proceed.

`acquire()`/`release()` must be used in pairs: each acquire must have a release in the thread that has acquired the lock. Failing to call release as many times the lock has been acquired can lead to deadlock.

class threading.RLock

  This class implements reentrant lock objects. 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.

  Note that `RLock` is actually a factory function which returns an instance of the most efficient version of the concrete RLock class that is supported by the platform.

  acquire(blocking=True, timeout=-1)

    Acquiert un verrou, bloquant ou non bloquant.

    Voir aussi

    Using RLock as a context manager

      Recommended over manual `acquire()` and `release()` calls whenever practical.

    When invoked with the blocking argument set to `True` (the default):

    > - If no thread owns the lock, acquire the lock and return immediately.

    > - If another thread owns the lock, block until we are able to acquire lock, or timeout, if set to a positive value.

    > - If the same thread owns the lock, acquire the lock again, and return immediately. This is the difference between `Lock` and `RLock`; `Lock` handles this case the same as the previous, blocking until the lock can be acquired.

    When invoked with the blocking argument set to `False`:

    > - If no thread owns the lock, acquire the lock and return immediately.

    > - If another thread owns the lock, return immediately.

    > - If the same thread owns the lock, acquire the lock again and return immediately.

    In all cases, if the thread was able to acquire the lock, return `True`. If the thread was unable to acquire the lock (i.e. if not blocking or the timeout was reached) return `False`.

    If called multiple times, failing to call `release()` as many times may lead to deadlock. Consider using `RLock` as a context manager rather than calling acquire/release directly.

    Modifié dans la version 3.2: Le paramètre timeout est nouveau.

    Modifié dans la version 3.15: Accepts any real number as timeout, not only integer or float.

  release()

    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 not acquired.

    Il n'y a pas de valeur de retour.

  locked()

    Return a boolean indicating whether this object is locked right now.

    Ajouté dans la version 3.14.

### Condition objects

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. The lock is part of the condition object: you don't have to track it separately.

A condition variable obeys the context management protocol: using the `with` statement acquires the associated lock for the duration of the enclosed block. The `acquire()` and `release()` methods also call the corresponding methods of the associated lock.

Other methods must be called with the associated lock held. The `wait()` method releases the lock, and then blocks until another thread awakens it by calling `notify()` or `notify_all()`. Once awakened, `wait()` 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 `notify_all()` method wakes up all threads waiting for the condition variable.

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.

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 `notify_all()` 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
    with cv:
        while not an_item_is_available():
            cv.wait()
        get_an_available_item()

    # Produce one item
    with cv:
        make_an_item_available()
        cv.notify()

The `while` loop checking for the application's condition is necessary because `wait()` can return after an arbitrary long time, and the condition which prompted the `notify()` call may no longer hold true. This is inherent to multi-threaded programming. The `wait_for()` method can be used to automate the condition checking, and eases the computation of timeouts:

    # Consume an item
    with cv:
        cv.wait_for(an_item_is_available)
        get_an_available_item()

To choose between `notify()` and `notify_all()`, 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.

class threading.Condition(lock=None)

  This class implements condition variable objects. A condition variable allows one or more threads to wait until they are notified by another 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.

  Modifié dans la version 3.3: changed from a factory function to a class.

  acquire(*args)

    Acquire the underlying lock. This method calls the corresponding method on the underlying lock; the return value is whatever that method returns.

  release()

    Release the underlying lock. This method calls the corresponding method on the underlying lock; there is no return value.

  locked()

    Return a boolean indicating whether this object is locked right now.

    Ajouté dans la version 3.14.

  wait(timeout=None)

    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 `notify_all()` 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 real 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.

    The return value is `True` unless a given timeout expired, in which case it is `False`.

    Modifié dans la version 3.2: Previously, the method always returned `None`.

  wait_for(predicate, timeout=None)

    Wait until a condition evaluates to true. predicate should be a callable which result will be interpreted as a boolean value. A timeout may be provided giving the maximum time to wait.

    This utility method may call `wait()` repeatedly until the predicate is satisfied, or until a timeout occurs. The return value is the last return value of the predicate and will evaluate to `False` if the method timed out.

    Ignoring the timeout feature, calling this method is roughly equivalent to writing:

        while not predicate():
            cv.wait()

    Therefore, the same rules apply as with `wait()`: The lock must be held when called and is re-acquired on return. The predicate is evaluated with the lock held.

    Ajouté dans la version 3.2.

  notify(n=1)

    By default, wake up one 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 at most n of the threads waiting for the condition variable; it is a no-op if no threads are waiting.

    The current implementation wakes up exactly n threads, if at least n threads are waiting. However, it's not safe to rely on this behavior. A future, optimized implementation may occasionally wake up more than n threads.

    Note: an 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.

  notify_all()

    Wake up all threads waiting on this condition. This method acts like `notify()`, but wakes up all waiting threads instead of one. If the calling thread has not acquired the lock when this method is called, a `RuntimeError` is raised.

    The method `notifyAll` is a deprecated alias for this method.

### Semaphore objects

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 the names `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()`.

Semaphores also support the context management protocol.

class threading.Semaphore(value=1)

  This class implements semaphore objects. A semaphore manages an atomic counter representing the number of `release()` calls minus the number of `acquire()` calls, plus an initial value. The `acquire()` method blocks if necessary until it can return without making the counter negative. If not given, value defaults to 1.

  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.

  Modifié dans la version 3.3: changed from a factory function to a class.

  acquire(blocking=True, timeout=None)

    Acquiert un sémaphore.

    When invoked without arguments:

    - If the internal counter is larger than zero on entry, decrement it by one and return `True` immediately.

    - If the internal counter is zero on entry, block until awoken by a call to `release()`. Once awoken (and the counter is greater than 0), decrement the counter by 1 and return `True`. Exactly one thread will be awoken by each call to `release()`. The order in which threads are awoken should not be relied on.

    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`.

    When invoked with a timeout other than `None`, it will block for at most timeout seconds. If acquire does not complete successfully in that interval, return `False`. Return `True` otherwise.

    Modifié dans la version 3.2: Le paramètre timeout est nouveau.

    Modifié dans la version 3.15: Accepts any real number as timeout, not only integer or float.

  release(n=1)

    Release a semaphore, incrementing the internal counter by n. When it was zero on entry and other threads are waiting for it to become larger than zero again, wake up n of those threads.

    Modifié dans la version 3.9: Added the n parameter to release multiple waiting threads at once.

class threading.BoundedSemaphore(value=1)

  Class implementing bounded semaphore objects. A bounded semaphore checks to make sure its current value doesn't exceed its initial value. If it does, `ValueError` is raised. In most situations semaphores are used to guard resources with limited capacity. If the semaphore is released too many times it's a sign of a bug. If not given, value defaults to 1.

  Modifié dans la version 3.3: changed from a factory function to a class.

### `Semaphore` example

Semaphores are often used to guard resources with limited capacity, for example, a database server. In any situation where the size of the resource 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:

    with pool_sema:
        conn = connectdb()
        try:
            # ... use connection ...
        finally:
            conn.close()

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.

### Event objects

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.

class threading.Event

  Class implementing event objects. An event manages a 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 flag is initially false.

  Modifié dans la version 3.3: changed from a factory function to a class.

  is_set()

    Return `True` if and only if the internal flag is true.

    The method `isSet` is a deprecated alias for this method.

  set()

    Set the internal flag to true. All threads waiting for it to become true are awakened. Threads that call `wait()` once the flag is true will not block at all.

  clear()

    Reset the internal flag to false. Subsequently, threads calling `wait()` will block until `set()` is called to set the internal flag to true again.

  wait(timeout=None)

    Block as long as the internal flag is false and the timeout, if given, has not expired. The return value represents the reason that this blocking method returned; `True` if returning because the internal flag is set to true, or `False` if a timeout is given and the internal flag did not become true within the given wait time.

    When the timeout argument is present and not `None`, it should be a real number specifying a timeout for the operation in seconds, or fractions thereof.

    Modifié dans la version 3.1: Previously, the method always returned `None`.

### Timer objects

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 `Timer.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.

Par exemple :

    def hello():
        print("hello, world")

    t = Timer(30.0, hello)
    t.start()  # after 30 seconds, "hello, world" will be printed

class threading.Timer(interval, function, args=None, kwargs=None)

  Create a timer that will run function with arguments args and keyword arguments kwargs, after interval seconds have passed. If args is `None` (the default) then an empty list will be used. If kwargs is `None` (the default) then an empty dict will be used.

  Modifié dans la version 3.3: changed from a factory function to a class.

  cancel()

    Stop the timer, and cancel the execution of the timer's action. This will only work if the timer is still in its waiting stage.

### Barrier objects

Ajouté dans la version 3.2.

This class provides a simple synchronization primitive for use by a fixed number of threads that need to wait for each other. Each of the threads tries to pass the barrier by calling the `wait()` method and will block until all of the threads have made their `wait()` calls. At this point, the threads are released simultaneously.

The barrier can be reused any number of times for the same number of threads.

As an example, here is a simple way to synchronize a client and server thread:

    b = Barrier(2, timeout=5)

    def server():
        start_server()
        b.wait()
        while True:
            connection = accept_connection()
            process_server_connection(connection)

    def client():
        b.wait()
        while True:
            connection = make_connection()
            process_client_connection(connection)

class threading.Barrier(parties, action=None, timeout=None)

  Create a barrier object for parties number of threads. An action, when provided, is a callable to be called by one of the threads when they are released. timeout is the default timeout value if none is specified for the `wait()` method.

  wait(timeout=None)

    Pass the barrier. When all the threads party to the barrier have called this function, they are all released simultaneously. If a timeout is provided, it is used in preference to any that was supplied to the class constructor.

    The return value is an integer in the range 0 to parties -- 1, different for each thread. This can be used to select a thread to do some special housekeeping, e.g.:

        i = barrier.wait()
        if i == 0:
            # Only one thread needs to print this
            print("passed the barrier")

    If an action was provided to the constructor, one of the threads will have called it prior to being released. Should this call raise an error, the barrier is put into the broken state.

    If the call times out, the barrier is put into the broken state.

    This method may raise a `BrokenBarrierError` exception if the barrier is broken or reset while a thread is waiting.

  reset()

    Return the barrier to the default, empty state. Any threads waiting on it will receive the `BrokenBarrierError` exception.

    Note that using this function may require some external synchronization if there are other threads whose state is unknown. If a barrier is broken it may be better to just leave it and create a new one.

  abort()

    Put the barrier into a broken state. This causes any active or future calls to `wait()` to fail with the `BrokenBarrierError`. Use this for example if one of the threads needs to abort, to avoid deadlocking the application.

    It may be preferable to simply create the barrier with a sensible timeout value to automatically guard against one of the threads going awry.

  parties

    The number of threads required to pass the barrier.

  n_waiting

    The number of threads currently waiting in the barrier.

  broken

    Booléen qui vaut `True` si la barrière est rompue.

exception threading.BrokenBarrierError

  Cette exception, une sous-classe de `RuntimeError`, est déclenchée lorsque l'objet `Barrier` est réinitialisé ou cassé.

## Using locks, conditions, and semaphores in the `with` statement

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. Hence, the following snippet:

    with some_lock:
        # do something...

est équivalente à :

    some_lock.acquire()
    try:
        # do something...
    finally:
        some_lock.release()

Currently, `Lock`, `RLock`, `Condition`, `Semaphore`, and `BoundedSemaphore` objects may be used as `with` statement context managers.

## Iterator synchronization

By default, Python iterators do not support concurrent access. Most iterators make no guarantees when accessed simultaneously from multiple threads. Generator iterators, for example, raise `ValueError` if one of their iterator methods is called while the generator is already executing. The tools in this section allow reliable concurrency support to be added to ordinary iterators and iterator-producing callables.

The `serialize_iterator` wrapper lets multiple threads share a single iterator and take turns consuming from it. While one thread is running `__next__()`, the others block until the iterator becomes available. Each value produced by the underlying iterator is delivered to exactly one caller.

The `concurrent_tee()` function lets multiple threads each receive the full stream of values from one underlying iterator. It creates independent iterators that all draw from the same source. Values are buffered until consumed by all of the derived iterators.

class threading.serialize_iterator(iterable)

  Return an iterator wrapper that serializes concurrent calls to `__next__()` using a lock.

  If the wrapped iterator also defines `send()`, `throw()`, or `close()`, those calls are serialized as well.

  This makes it possible to share a single iterator, including a generator iterator, between multiple threads. A lock ensures that calls are handled one at a time. No values are duplicated or skipped by the wrapper itself. Each item from the underlying iterator is given to exactly one caller.

  This wrapper does not copy or buffer values. Threads that call `next()` while another thread is already advancing the iterator will block until the active call completes.

  Example:

      import threading

      def squares(n):
          for x in range(n):
              yield x * x

      def consume(name, iterable):
          for item in iterable:
              print(name, item)

      source = threading.serialize_iterator(squares(5))

      t1 = threading.Thread(target=consume, args=("left", source))
      t2 = threading.Thread(target=consume, args=("right", source))
      t1.start()
      t2.start()
      t1.join()
      t2.join()

  In this example, each number is printed exactly once, but the work is shared between the two threads.

  Ajouté dans la version 3.15.

threading.synchronized_iterator(func)

  Wrap an iterator-producing callable so that each iterator it returns is automatically passed through `serialize_iterator`.

  This is especially useful as a decorator for generator functions, allowing their generator-iterators to be consumed from multiple threads.

  Example:

      import threading

      @threading.synchronized_iterator
      def squares(n):
          for x in range(n):
              yield x * x

      def consume(name, iterable):
          for item in iterable:
              print(name, item)

      source = squares(5)

      t1 = threading.Thread(target=consume, args=("left", source))
      t2 = threading.Thread(target=consume, args=("right", source))
      t1.start()
      t2.start()
      t1.join()
      t2.join()

  The returned wrapper preserves the metadata of func, such as its name and wrapped function reference.

  Ajouté dans la version 3.15.

threading.concurrent_tee(iterable, n=2)

  Return n independent iterators from a single input iterable, with guaranteed behavior when the derived iterators are consumed concurrently.

  This function is similar to `itertools.tee()`, but is intended for cases where the source iterator may feed consumers running in different threads. Each returned iterator yields every value from the underlying iterable, in the same order.

  Internally, values are buffered until every derived iterator has consumed them.

  The returned iterators share the same underlying synchronization lock. Each individual derived iterator is intended to be consumed by one thread at a time. If a single derived iterator must itself be shared by multiple threads, wrap it with `serialize_iterator`.

  If n is `0`, return an empty tuple. If n is negative, raise `ValueError`.

  Example:

      import threading

      def squares(n):
          for x in range(n):
              yield x * x

      def consume(name, iterable):
          for item in iterable:
              print(name, item)

      source = squares(5)
      left, right = threading.concurrent_tee(source)

      t1 = threading.Thread(target=consume, args=("left", left))
      t2 = threading.Thread(target=consume, args=("right", right))
      t1.start()
      t2.start()
      t1.join()
      t2.join()

  In this example, both consumer threads see the full sequence of squares from a single generator expression.

  Ajouté dans la version 3.15.