
``queue`` --- A synchronized queue class
****************************************

The ``queue`` module implements multi-producer, multi-consumer queues.
It is especially useful in threaded programming when information must
be exchanged safely between multiple threads.  The ``Queue`` class in
this module implements all the required locking semantics.  It depends
on the availability of thread support in Python; see the ``threading``
module.

Implements three types of queue whose only difference is the order
that the entries are retrieved.  In a FIFO queue, the first tasks
added are the first retrieved. In a LIFO queue, the most recently
added entry is the first retrieved (operating like a stack).  With a
priority queue, the entries are kept sorted (using the ``heapq``
module) and the lowest valued entry is retrieved first.

The ``queue`` module defines the following classes and exceptions:

class class queue.Queue(maxsize)

   Constructor for a FIFO queue.  *maxsize* is an integer that sets
   the upperbound limit on the number of items that can be placed in
   the queue.  Insertion will block once this size has been reached,
   until queue items are consumed.  If *maxsize* is less than or equal
   to zero, the queue size is infinite.

class class queue.LifoQueue(maxsize)

   Constructor for a LIFO queue.  *maxsize* is an integer that sets
   the upperbound limit on the number of items that can be placed in
   the queue.  Insertion will block once this size has been reached,
   until queue items are consumed.  If *maxsize* is less than or equal
   to zero, the queue size is infinite.

class class queue.PriorityQueue(maxsize)

   Constructor for a priority queue.  *maxsize* is an integer that
   sets the upperbound limit on the number of items that can be placed
   in the queue.  Insertion will block once this size has been
   reached, until queue items are consumed.  If *maxsize* is less than
   or equal to zero, the queue size is infinite.

   The lowest valued entries are retrieved first (the lowest valued
   entry is the one returned by ``sorted(list(entries))[0]``).  A
   typical pattern for entries is a tuple in the form:
   ``(priority_number, data)``.

exception exception queue.Empty

   Exception raised when non-blocking ``get()`` (or ``get_nowait()``)
   is called on a ``Queue`` object which is empty.

exception exception queue.Full

   Exception raised when non-blocking ``put()`` (or ``put_nowait()``)
   is called on a ``Queue`` object which is full.

See also:

   ``collections.deque`` is an alternative implementation of unbounded
   queues with fast atomic ``append()`` and ``popleft()`` operations
   that do not require locking.


Queue Objects
=============

Queue objects (``Queue``, ``LifoQueue``, or ``PriorityQueue``) provide
the public methods described below.

Queue.qsize()

   Return the approximate size of the queue.  Note, qsize() > 0
   doesn't guarantee that a subsequent get() will not block, nor will
   qsize() < maxsize guarantee that put() will not block.

Queue.empty()

   Return ``True`` if the queue is empty, ``False`` otherwise.  If
   empty() returns ``True`` it doesn't guarantee that a subsequent
   call to put() will not block.  Similarly, if empty() returns
   ``False`` it doesn't guarantee that a subsequent call to get() will
   not block.

Queue.full()

   Return ``True`` if the queue is full, ``False`` otherwise.  If
   full() returns ``True`` it doesn't guarantee that a subsequent call
   to get() will not block.  Similarly, if full() returns ``False`` it
   doesn't guarantee that a subsequent call to put() will not block.

Queue.put(item[, block[, timeout]])

   Put *item* into the queue. If optional args *block* is true and
   *timeout* is None (the default), block if necessary until a free
   slot is available. If *timeout* is a positive number, it blocks at
   most *timeout* seconds and raises the ``Full`` exception if no free
   slot was available within that time. Otherwise (*block* is false),
   put an item on the queue if a free slot is immediately available,
   else raise the ``Full`` exception (*timeout* is ignored in that
   case).

Queue.put_nowait(item)

   Equivalent to ``put(item, False)``.

Queue.get([block[, timeout]])

   Remove and return an item from the queue. If optional args *block*
   is true and *timeout* is None (the default), block if necessary
   until an item is available. If *timeout* is a positive number, it
   blocks at most *timeout* seconds and raises the ``Empty`` exception
   if no item was available within that time. Otherwise (*block* is
   false), return an item if one is immediately available, else raise
   the ``Empty`` exception (*timeout* is ignored in that case).

Queue.get_nowait()

   Equivalent to ``get(False)``.

Two methods are offered to support tracking whether enqueued tasks
have been fully processed by daemon consumer threads.

Queue.task_done()

   Indicate that a formerly enqueued task is complete.  Used by queue
   consumer threads.  For each ``get()`` used to fetch a task, a
   subsequent call to ``task_done()`` tells the queue that the
   processing on the task is complete.

   If a ``join()`` is currently blocking, it will resume when all
   items have been processed (meaning that a ``task_done()`` call was
   received for every item that had been ``put()`` into the queue).

   Raises a ``ValueError`` if called more times than there were items
   placed in the queue.

Queue.join()

   Blocks until all items in the queue have been gotten and processed.

   The count of unfinished tasks goes up whenever an item is added to
   the queue. The count goes down whenever a consumer thread calls
   ``task_done()`` to indicate that the item was retrieved and all
   work on it is complete. When the count of unfinished tasks drops to
   zero, ``join()`` unblocks.

Example of how to wait for enqueued tasks to be completed:

   def worker():
       while True:
           item = q.get()
           do_work(item)
           q.task_done()

   q = Queue()
   for i in range(num_worker_threads):
        t = Thread(target=worker)
        t.daemon = True
        t.start()

   for item in source():
       q.put(item)

   q.join()       # block until all tasks are done
