# `threading` — Thread-based parallelism

Source code: Lib/threading.py

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

Availability: not WASI.

This module does not work or is not available on WebAssembly. See WebAssembly platforms 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()

Changed in version 3.7: This module used to be optional, it is now always available.

See also

`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.

CPython implementation detail: 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).

## Reference

This module defines the following functions:

threading.active_count()

  Return the number of `Thread` objects currently alive. The returned count is equal to the length of the list returned by `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, /)

  Handle uncaught exception raised by `Thread.run()`.

  The args argument has the following attributes:

  - exc_type: Exception type.

  - exc_value: Exception value, can be `None`.

  - exc_traceback: Exception traceback, can be `None`.

  - thread: Thread which raised the exception, can be `None`.

  If exc_type is `SystemExit`, the exception is silently ignored. Otherwise, the exception is printed out on `sys.stderr`.

  If this function raises an exception, `sys.excepthook()` is called to handle it.

  `threading.excepthook()` can be overridden to control how uncaught exceptions raised by `Thread.run()` are handled.

  Storing exc_value using a custom hook can create a reference cycle. It should be cleared explicitly to break the reference cycle when the exception is no longer needed.

  Storing thread using a custom hook can resurrect it if it is set to an object which is being finalized. Avoid storing thread after the custom hook completes to avoid resurrecting objects.

  See also

  `sys.excepthook()` handles uncaught exceptions.

  Added in 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.

  Added in version 3.10.

threading.get_ident()

  Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data. Thread identifiers may be recycled when a thread exits and another thread is created.

  Added in version 3.3.

threading.get_native_id()

  Return the native integral Thread ID of the current thread assigned by the kernel. This is a non-negative integer. Its 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).

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

  Added in version 3.8.

  Changed in version 3.13: Added support for GNU/kFreeBSD.

  Changed in 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()

  Return the main `Thread` object. In normal conditions, the main thread is the thread from which the Python interpreter was started.

  Added in 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.

  Added in version 3.12.

threading.gettrace()

  Get the trace function as set by `settrace()`.

  Added in 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.

  Added in version 3.12.

threading.getprofile()

  Get the profiler function as set by `setprofile()`.

  Added in version 3.10.

threading.stack_size([size])

  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 (32 KiB). If size is not specified, 0 is used. If changing the thread stack size is unsupported, a `RuntimeError` is raised. If the specified stack size is invalid, a `ValueError` is raised and the stack size is unmodified. 32 KiB 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 > 32 KiB or requiring allocation in multiples of the system memory page size - platform documentation should be referred to for more information (4 KiB pages are common; using multiples of 4096 for the stack size is the suggested approach in the absence of more specific information).

  Availability: Windows, pthreads.

  Unix platforms with POSIX threads support.

This module also defines the following constant:

threading.TIMEOUT_MAX

  The maximum value allowed for the timeout parameter of blocking functions (`Lock.acquire()`, `RLock.acquire()`, `Condition.wait()`, etc.). Specifying a timeout greater than this value will raise an `OverflowError`.

  Added in version 3.2.

This module defines a number of classes, which are detailed in the sections 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.

### 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

  A class that represents thread-local data.

### 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.

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.

If the `run()` method raises an exception, `threading.excepthook()` is called to handle it. By default, `threading.excepthook()` ignores silently `SystemExit`.

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 or the daemon constructor argument.

Note

Daemon threads are abruptly stopped at shutdown. Their resources (such as open files, database transactions, etc.) may not be released properly. If you want your threads to stop gracefully, make them non-daemonic and use a suitable signalling mechanism such as an `Event`.

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 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)

  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, 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 is a dictionary of keyword arguments for the target invocation. Defaults to `{}`.

  If not `None`, daemon explicitly sets whether the thread is daemonic. If `None` (the default), the daemonic property is inherited from the current thread.

  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.

  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.

  Changed in version 3.3: Added the daemon parameter.

  Changed in version 3.10: Use the target name if name argument is omitted.

  Changed in version 3.14: Added the context parameter.

  start()

    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 `RuntimeError` if called more than once on the same thread object.

    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.

    Changed in version 3.14: Set the operating system thread name.

  run()

    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 positional and keyword arguments taken from the args and kwargs arguments, respectively.

    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)

    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 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.

    When the timeout argument is not present or `None`, the operation will block until the thread terminates.

    A thread can be joined 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 raise the same exception.

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

    Changed in version 3.14: May raise `PythonFinalizationError`.

    Changed in version 3.15: Accepts any real number as timeout, not only integer or float.

  name

    A string used for identification purposes only. It has no semantics. Multiple threads may be given the same name. The initial name is set by the constructor.

    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.

    Deprecated since version 3.10.

  ident

    The ‘thread identifier’ of this thread or `None` if the thread has not been started. This is a nonzero integer. See the `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.

  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

    Similar to Process IDs, Thread IDs are only valid (guaranteed unique system-wide) from the time the thread is created until the thread has been terminated.

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

    Added in version 3.8.

  is_alive()

    Return whether the thread is alive.

    This method returns `True` just before the `run()` method starts until just after the `run()` method terminates. The module function `enumerate()` returns a list of all alive threads.

  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`.

    The entire Python program exits when no alive non-daemon threads are left.

  isDaemon()
  setDaemon()

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

    Deprecated since version 3.10.

### Lock objects

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.

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.

  Changed in 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)

    Acquire a lock, blocking or non-blocking.

    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).

    Changed in version 3.2: The timeout parameter is new.

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

    Changed in version 3.14: Lock acquisition can now be interrupted by signals on Windows.

    Changed in 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.

    When invoked on an unlocked lock, a `RuntimeError` is raised.

    There is no return value.

  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)

    Acquire a lock, blocking or non-blocking.

    See also

    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.

    Changed in version 3.2: The timeout parameter is new.

    Changed in 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.

    There is no return value.

  locked()

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

    Added in 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.

  Changed in 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.

    Added in 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`.

    Changed in 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.

    Added in 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.

  Changed in version 3.3: changed from a factory function to a class.

  acquire(blocking=True, timeout=None)

    Acquire a semaphore.

    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.

    Changed in version 3.2: The timeout parameter is new.

    Changed in 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.

    Changed in 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.

  Changed in 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.

  Changed in 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.

    Changed in 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.

For example:

    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.

  Changed in 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

Added in 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

    A boolean that is `True` if the barrier is in the broken state.

exception threading.BrokenBarrierError

  This exception, a subclass of `RuntimeError`, is raised when the `Barrier` object is reset or broken.

## 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...

is equivalent to:

    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.

  Added in 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.

  Added in 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.

  Added in version 3.15.