"threading" --- 執行緒層級的平行處理
************************************

**原始碼：**Lib/threading.py

======================================================================

此模組在較低層的 "_thread" 模組之上，建構了較高層的執行緒（threading）
介面。

可用性: not WASI.

此模組在 WebAssembly 平台上不起作用或無法使用。更多資訊請參閱
WebAssembly 平台。


介紹
====

模組 "threading" 提供了一種方式，能在單一進程（process）中同時執行多個
執行緒 （一個進程中的較小執行單元）。它允許創造與管理執行緒，使多個任
務可以平行執行並共享同一個記憶體空間。當任務屬於 I/O 密集型（I/O bound
）時，執行緒特別有用，例如檔案操作或網路請求，因為這類任務的大部分時間
都花在等待外部資源。

模組 "threading" 的一個典型使用情境是管理一個工作執行緒（worker
threads）池，讓多個任務能夠同時被處理。以下是一個使用 "Thread": 來創造
並啟動執行緒的基本範例：

   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()

在 3.7 版的變更: This module used to be optional, it is now always
available.

也參考:

  "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.

備註:

  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 實作細節：** 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
=========

此模組定義了以下函式：

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.

   "currentThread" 函式是此函式的已棄用別名。

threading.excepthook(args, /)

   處理由 "Thread.run()" 引發且未被捕捉到的例外。

   *args* 引數有下列屬性：

   * *exc_type*：例外型別。

   * *exc_value*: Exception value, can be "None".

   * *exc_traceback*: Exception traceback, can be "None".

   * *thread*：引發例外的執行緒，可以是 "None"。

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

   如果這個函式引發例外，則會呼叫 "sys.excepthook()" 來處理它。

   "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.

   也參考: "sys.excepthook()" 處理未被捕捉到的例外。

   在 3.8 版被加入.

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.

   在 3.10 版被加入.

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.

   在 3.3 版被加入.

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).

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

   在 3.8 版被加入.

   在 3.13 版的變更: 新增對 GNU/kFreeBSD 的支援。

   在 3.15 版的變更: 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.

   在 3.4 版被加入.

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.

   在 3.12 版被加入.

threading.gettrace()

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

   在 3.10 版被加入.

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.

   在 3.12 版被加入.

threading.getprofile()

   取得由 "setprofile()" 設定的剖析函式。

   在 3.10 版被加入.

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).

   可用性: Windows, pthreads.

   Unix platforms with POSIX threads support.

此模組也定義了以下常數：

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".

   在 3.2 版被加入.

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.

現在如果我們建立一個 "local" 物件：

   >>> mydata = MyLocal(color='red')

會有一個預設的數字：

   >>> mydata.number
   2

和一個初始顏色：

   >>> mydata.color
   'red'
   >>> del mydata.color

和一個操作資料的方法：

   >>> 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 物件
-----------

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.

備註:

  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.

   在 3.3 版的變更: 新增 *daemon* 參數。

   在 3.10 版的變更: Use the *target* name if *name* argument is
   omitted.

   在 3.14 版的變更: 新增 *context* 參數。

   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.

      在 3.14 版的變更: 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.

      舉例來說：

         >>> 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".

      在 3.14 版的變更: 可能引發 "PythonFinalizationError"。

      在 3.15 版的變更: 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.

      在 3.10 版之後被棄用.

   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).

      備註:

        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.

      可用性: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX,
      DragonFlyBSD.

      在 3.8 版被加入.

   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.

      在 3.10 版之後被棄用.


Lock 物件
---------

原語鎖 (primitive lock) 是一種同步原語 (synchronization primitive)，在
鎖定時不屬於特定執行緒。在 Python 中，它是目前可用的最低階同步原語，直
接由 "_thread" 擴充模組實作。

原語鎖會處於兩種狀態之一：「鎖定 (locked)」或「未鎖定 (unclocked)」，
建立時會處於未鎖定狀態。它有兩個基本方法 "acquire()" 和 "release()"。
當狀態為未鎖定時，"acquire()" 會將狀態變更為鎖定並立即回傳。當狀態被鎖
定時，"acquire()" 會阻塞 (block)，直到另一個執行緒中對 "release()" 的
呼叫將其更改為未鎖定狀態，然後 "acquire()" 呼叫會將其重置為鎖定並回傳
。"release()" 方法只能在鎖定狀態下呼叫；它將狀態更改為未鎖定並立即回傳
。如果嘗試釋放未鎖定的鎖，則會引發 "RuntimeError"。

鎖也支援情境管理協定。

當多個執行緒阻塞在 "acquire()" 中等待狀態轉變為未鎖定，此時若呼叫
"release()" 將狀態重置為未鎖定，則只會有一個執行緒繼續進行；哪一個等待
執行緒會繼續進行是未定義的，並且可能因實作而異。

所有方法均以最小不可分割的操作方式 (atomically) 執行。

class threading.Lock

   實作原語鎖物件的類別。一旦執行緒獲得了鎖，後續再嘗試獲得它就會被阻
   塞，直到鎖被釋放；任何執行緒都可以去釋放它。

   在 3.13 版的變更: "Lock" 現在是一個類別。在早期的 Python 中，"Lock"
   是一個會回傳底層私有鎖型別實例的工廠函式。

   acquire(blocking=True, timeout=-1)

      阻塞或非阻塞地取得鎖。

      當以 *blocking* 引數設為 "True"（預設值）來叫用，將會阻塞直到鎖
      被解鎖，然後將其設為鎖定並回傳 "True"。

      當以 *blocking* 引數設為 "False" 叫用則不會阻塞。如果 *blocking*
      設定為 "True" 的呼叫會阻塞，則立即回傳 "False"；否則將鎖設為鎖定
      並回傳 "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".

      如果成功取得鎖，則回傳值為 "True"，否則回傳值為 "False"（例如像
      是 *timeout* 已逾期）。

      在 3.2 版的變更: 新的 *timeout* 參數。

      在 3.2 版的變更: 如果底層執行緒實作支援的話，鎖的取得現在可以被
      POSIX 上的訊號中斷。

      在 3.14 版的變更: Lock acquisition can now be interrupted by
      signals on Windows.

      在 3.15 版的變更: Accepts any real number as *timeout*, not only
      integer or float.

   release()

      釋放鎖。這可以從任何執行緒呼叫，而不是只有獲得鎖的執行緒。

      當鎖被鎖定時，將其重置為未鎖定然後回傳。如果任何其他執行緒在等待
      鎖被解鎖時被阻塞，只允許其中一個執行緒繼續進行。

      當在未鎖定的鎖上叫用時，會引發 "RuntimeError"

      沒有回傳值。

   locked()

      如果有取得了鎖，則回傳 "True"。


RLock 物件
----------

可重入鎖 (reentrant lock) 是一種同步原語，同一執行緒可以多次取得它。在
內部，除了原語鎖使用的鎖定/未鎖定狀態之外，它還使用「所屬執行緒
(owning thread)」和「遞迴等級 (recursion level)」的概念。在鎖定狀態下
，某個執行緒會擁有鎖；在未鎖定狀態下則沒有執行緒擁有它。

執行緒呼叫鎖的 "acquire()" 方法來鎖定它，並呼叫它的 "release()" 方法來
解鎖它。

備註:

  可重入鎖支援情境管理協定，因此建議使用 "with" 而不是手動呼叫
  "acquire()" 和 "release()" 來對程式碼區塊處理鎖的獲得和釋放。

RLock 的 "acquire()"/"release()" 呼叫成對組合可以嵌套使用，這與 Lock
的 "acquire()"/"release()" 不同。只有最後一個 "release()"（最外面一對
的 "release()"）會將鎖重置為未鎖定狀態，並允許在 "acquire()" 中阻塞的
另一個執行緒繼續進行。

"acquire()"/"release()" 必須成對使用：每次獲得都必須在已獲得鎖的執行緒
中有一個釋放。如果鎖釋放的次數不能和取得的次數一樣的話，可能會導致死鎖
(deadlock)。

class threading.RLock

   此類別實作了可重入鎖物件。可重入鎖必須由獲得它的執行緒釋放。一旦一
   個執行緒獲得了可重入鎖，同一個執行緒可以再次獲得它而不會阻塞；執行
   緒每次獲得它也都必須釋放它一次。

   請注意，"RLock" 實際上是一個工廠函式，它會回傳平台有支援的特定
   RLock 類別的最高效率版本的實例。

   acquire(blocking=True, timeout=-1)

      阻塞或非阻塞地取得鎖。

      也參考:

        將 RLock 用作為情境管理器
           若是使用場景合理，和手動呼叫 "acquire()" 和 "release()" 相
           比，會是更為推薦的使用方式。

      當以 *blocking* 引數設為 "True"（預設值）來呼叫：

         * 如果沒有執行緒擁有鎖，則獲得鎖並立即回傳。

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

         * 如果同一個執行緒擁有鎖，則再次取得鎖，並立即回傳。這就是
           "Lock" 和 "RLock" 之間的差別；"Lock" 處理方式與上一種情況相
           同，會阻塞直到能夠取得鎖。

      當以 *blocking* 引數設為 "False" 來呼叫：

         * 如果沒有執行緒擁有鎖，則獲得鎖並立即回傳。

         * 如果另一個執行緒擁有該鎖，則立即回傳。

         * 如果同一個執行緒擁有鎖，則再次取得鎖並立即回傳。

      在所有情況下，如果執行緒能夠取得鎖則回傳 "True"。如果執行緒無法
      取得鎖（即沒有阻塞或已達超時限制）則回傳 "False"。

      如果多次呼叫，又未能呼叫相同次數的 "release()"，則可能會導致死鎖
      。考慮將 "RLock" 作為情境管理器使用，而不是直接呼叫
      acquire/release。

      在 3.2 版的變更: 新的 *timeout* 參數。

      在 3.15 版的變更: Accepts any real number as *timeout*, not only
      integer or float.

   release()

      釋放鎖並減少遞迴等級。如果被減至零，則將鎖重置為未鎖定（不屬於任
      何執行緒），並且如果任何其他執行緒被阻塞以等待鎖變成未鎖定狀態，
      則僅允許其中一個執行緒繼續進行。如果遞減後遞迴等級仍然非零，則鎖
      會保持鎖定並由呼叫它的執行緒所擁有。

      僅當呼叫的執行緒擁有鎖時才能呼叫此方法。如果在未取得鎖時呼叫此方
      法則會引發 "RuntimeError"。

      沒有回傳值。

   locked()

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

      在 3.14 版被加入.


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.

   在 3.3 版的變更: 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.

      在 3.14 版被加入.

   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".

      在 3.2 版的變更: 先前此方法總是回傳 "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.

      在 3.2 版被加入.

   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 物件
--------------

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.

   在 3.3 版的變更: 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.

      在 3.2 版的變更: 新的 *timeout* 參數。

      在 3.15 版的變更: 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.

      在 3.9 版的變更: 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.

   在 3.3 版的變更: changed from a factory function to a class.


"Semaphore" 範例
----------------

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:
           # ... 使用該連線 ...
       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.

   在 3.3 版的變更: 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.

      在 3.1 版的變更: 先前此方法總是回傳 "None"。


Timer 物件
----------

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.

舉例來說：

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

   t = Timer(30.0, hello)
   t.start()  # 30 秒後會印出 "hello, world"

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.

   在 3.3 版的變更: 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 物件
------------

在 3.2 版被加入.

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:
             # 只會有一個執行緒會印出這個
             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:
       # 做某些事情...

is equivalent to:

   some_lock.acquire()
   try:
       # 做某些事情...
   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.

   在 3.15 版被加入.

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.

   在 3.15 版被加入.

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.

   在 3.15 版被加入.
