path: root/Doc/library/multiprocessing.shared_memory.rst

:mod:`multiprocessing.shared_memory` --- Shared memory for direct access across processes
=========================================================================================

.. module:: multiprocessing.shared_memory
   :synopsis: Provides shared memory for direct access across processes.

**Source code:** :source:`Lib/multiprocessing/shared_memory.py`

.. versionadded:: 3.8

.. index::
   single: Shared Memory
   single: POSIX Shared Memory
   single: Named Shared Memory

--------------

This module provides a class, :class:`SharedMemory`, for the allocation
and management of shared memory to be accessed by one or more processes
on a multicore or symmetric multiprocessor (SMP) machine.  To assist with
the life-cycle management of shared memory especially across distinct
processes, a :class:`~multiprocessing.managers.BaseManager` subclass,
:class:`SharedMemoryManager`, is also provided in the
``multiprocessing.managers`` module.

In this module, shared memory refers to "System V style" shared memory blocks
(though is not necessarily implemented explicitly as such) and does not refer
to "distributed shared memory".  This style of shared memory permits distinct
processes to potentially read and write to a common (or shared) region of
volatile memory.  Processes are conventionally limited to only have access to
their own process memory space but shared memory permits the sharing
of data between processes, avoiding the need to instead send messages between
processes containing that data.  Sharing data directly via memory can provide
significant performance benefits compared to sharing data via disk or socket
or other communications requiring the serialization/deserialization and
copying of data.


.. class:: SharedMemory(name=None, create=False, size=0)

   Creates a new shared memory block or attaches to an existing shared
   memory block.  Each shared memory block is assigned a unique name.
   In this way, one process can create a shared memory block with a
   particular name and a different process can attach to that same shared
   memory block using that same name.

   As a resource for sharing data across processes, shared memory blocks
   may outlive the original process that created them.  When one process
   no longer needs access to a shared memory block that might still be
   needed by other processes, the :meth:`close()` method should be called.
   When a shared memory block is no longer needed by any process, the
   :meth:`unlink()` method should be called to ensure proper cleanup.

   *name* is the unique name for the requested shared memory, specified as
   a string.  When creating a new shared memory block, if ``None`` (the
   default) is supplied for the name, a novel name will be generated.

   *create* controls whether a new shared memory block is created (``True``)
   or an existing shared memory block is attached (``False``).

   *size* specifies the requested number of bytes when creating a new shared
   memory block.  Because some platforms choose to allocate chunks of memory
   based upon that platform's memory page size, the exact size of the shared
   memory block may be larger or equal to the size requested.  When attaching
   to an existing shared memory block, the ``size`` parameter is ignored.

   .. method:: close()

      Closes access to the shared memory from this instance.  In order to
      ensure proper cleanup of resources, all instances should call
      ``close()`` once the instance is no longer needed.  Note that calling
      ``close()`` does not cause the shared memory block itself to be
      destroyed.

   .. method:: unlink()

      Requests that the underlying shared memory block be destroyed.  In
      order to ensure proper cleanup of resources, ``unlink()`` should be
      called once (and only once) across all processes which have need
      for the shared memory block.  After requesting its destruction, a
      shared memory block may or may not be immediately destroyed and
      this behavior may differ across platforms.  Attempts to access data
      inside the shared memory block after ``unlink()`` has been called may
      result in memory access errors.  Note: the last process relinquishing
      its hold on a shared memory block may call ``unlink()`` and
      :meth:`close()` in either order.

   .. attribute:: buf

      A memoryview of contents of the shared memory block.

   .. attribute:: name

      Read-only access to the unique name of the shared memory block.

   .. attribute:: size

      Read-only access to size in bytes of the shared memory block.


The following example demonstrates low-level use of :class:`SharedMemory`
instances::

   >>> from multiprocessing import shared_memory
   >>> shm_a = shared_memory.SharedMemory(create=True, size=10)
   >>> type(shm_a.buf)
   <class 'memoryview'>
   >>> buffer = shm_a.buf
   >>> len(buffer)
   10
   >>> buffer[:4] = bytearray([22, 33, 44, 55])  # Modify multiple at once
   >>> buffer[4] = 100                           # Modify single byte at a time
   >>> # Attach to an existing shared memory block
   >>> shm_b = shared_memory.SharedMemory(shm_a.name)
   >>> import array
   >>> array.array('b', shm_b.buf[:5])  # Copy the data into a new array.array
   array('b', [22, 33, 44, 55, 100])
   >>> shm_b.buf[:5] = b'howdy'  # Modify via shm_b using bytes
   >>> bytes(shm_a.buf[:5])      # Access via shm_a
   b'howdy'
   >>> shm_b.close()   # Close each SharedMemory instance
   >>> shm_a.close()
   >>> shm_a.unlink()  # Call unlink only once to release the shared memory



The following example demonstrates a practical use of the :class:`SharedMemory`
class with `NumPy arrays <https://numpy.org/>`_, accessing the
same ``numpy.ndarray`` from two distinct Python shells:

.. doctest::
   :options: +SKIP

   >>> # In the first Python interactive shell
   >>> import numpy as np
   >>> a = np.array([1, 1, 2, 3, 5, 8])  # Start with an existing NumPy array
   >>> from multiprocessing import shared_memory
   >>> shm = shared_memory.SharedMemory(create=True, size=a.nbytes)
   >>> # Now create a NumPy array backed by shared memory
   >>> b = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)
   >>> b[:] = a[:]  # Copy the original data into shared memory
   >>> b
   array([1, 1, 2, 3, 5, 8])
   >>> type(b)
   <class 'numpy.ndarray'>
   >>> type(a)
   <class 'numpy.ndarray'>
   >>> shm.name  # We did not specify a name so one was chosen for us
   'psm_21467_46075'

   >>> # In either the same shell or a new Python shell on the same machine
   >>> import numpy as np
   >>> from multiprocessing import shared_memory
   >>> # Attach to the existing shared memory block
   >>> existing_shm = shared_memory.SharedMemory(name='psm_21467_46075')
   >>> # Note that a.shape is (6,) and a.dtype is np.int64 in this example
   >>> c = np.ndarray((6,), dtype=np.int64, buffer=existing_shm.buf)
   >>> c
   array([1, 1, 2, 3, 5, 8])
   >>> c[-1] = 888
   >>> c
   array([  1,   1,   2,   3,   5, 888])

   >>> # Back in the first Python interactive shell, b reflects this change
   >>> b
   array([  1,   1,   2,   3,   5, 888])

   >>> # Clean up from within the second Python shell
   >>> del c  # Unnecessary; merely emphasizing the array is no longer used
   >>> existing_shm.close()

   >>> # Clean up from within the first Python shell
   >>> del b  # Unnecessary; merely emphasizing the array is no longer used
   >>> shm.close()
   >>> shm.unlink()  # Free and release the shared memory block at the very end


.. class:: SharedMemoryManager([address[, authkey]])
   :module: multiprocessing.managers

   A subclass of :class:`~multiprocessing.managers.BaseManager` which can be
   used for the management of shared memory blocks across processes.

   A call to :meth:`~multiprocessing.managers.BaseManager.start` on a
   :class:`SharedMemoryManager` instance causes a new process to be started.
   This new process's sole purpose is to manage the life cycle
   of all shared memory blocks created through it.  To trigger the release
   of all shared memory blocks managed by that process, call
   :meth:`~multiprocessing.managers.BaseManager.shutdown()` on the instance.
   This triggers a :meth:`SharedMemory.unlink()` call on all of the
   :class:`SharedMemory` objects managed by that process and then
   stops the process itself.  By creating ``SharedMemory`` instances
   through a ``SharedMemoryManager``, we avoid the need to manually track
   and trigger the freeing of shared memory resources.

   This class provides methods for creating and returning :class:`SharedMemory`
   instances and for creating a list-like object (:class:`ShareableList`)
   backed by shared memory.

   Refer to :class:`multiprocessing.managers.BaseManager` for a description
   of the inherited *address* and *authkey* optional input arguments and how
   they may be used to connect to an existing ``SharedMemoryManager`` service
   from other processes.

   .. method:: SharedMemory(size)

      Create and return a new :class:`SharedMemory` object with the
      specified ``size`` in bytes.

   .. method:: ShareableList(sequence)

      Create and return a new :class:`ShareableList` object, initialized
      by the values from the input ``sequence``.


The following example demonstrates the basic mechanisms of a
:class:`SharedMemoryManager`:

.. doctest::
   :options: +SKIP

   >>> from multiprocessing.managers import SharedMemoryManager
   >>> smm = SharedMemoryManager()
   >>> smm.start()  # Start the process that manages the shared memory blocks
   >>> sl = smm.ShareableList(range(4))
   >>> sl
   ShareableList([0, 1, 2, 3], name='psm_6572_7512')
   >>> raw_shm = smm.SharedMemory(size=128)
   >>> another_sl = smm.ShareableList('alpha')
   >>> another_sl
   ShareableList(['a', 'l', 'p', 'h', 'a'], name='psm_6572_12221')
   >>> smm.shutdown()  # Calls unlink() on sl, raw_shm, and another_sl

The following example depicts a potentially more convenient pattern for using
:class:`SharedMemoryManager` objects via the :keyword:`with` statement to
ensure that all shared memory blocks are released after they are no longer
needed:

.. doctest::
   :options: +SKIP

   >>> with SharedMemoryManager() as smm:
   ...     sl = smm.ShareableList(range(2000))
   ...     # Divide the work among two processes, storing partial results in sl
   ...     p1 = Process(target=do_work, args=(sl, 0, 1000))
   ...     p2 = Process(target=do_work, args=(sl, 1000, 2000))
   ...     p1.start()
   ...     p2.start()  # A multiprocessing.Pool might be more efficient
   ...     p1.join()
   ...     p2.join()   # Wait for all work to complete in both processes
   ...     total_result = sum(sl)  # Consolidate the partial results now in sl

When using a :class:`SharedMemoryManager` in a :keyword:`with` statement, the
shared memory blocks created using that manager are all released when the
:keyword:`with` statement's code block finishes execution.


.. class:: ShareableList(sequence=None, *, name=None)

   Provides a mutable list-like object where all values stored within are
   stored in a shared memory block.  This constrains storable values to
   only the ``int``, ``float``, ``bool``, ``str`` (less than 10M bytes each),
   ``bytes`` (less than 10M bytes each), and ``None`` built-in data types.
   It also notably differs from the built-in ``list`` type in that these
   lists can not change their overall length (i.e. no append, insert, etc.)
   and do not support the dynamic creation of new :class:`ShareableList`
   instances via slicing.

   *sequence* is used in populating a new ``ShareableList`` full of values.
   Set to ``None`` to instead attach to an already existing
   ``ShareableList`` by its unique shared memory name.

   *name* is the unique name for the requested shared memory, as described
   in the definition for :class:`SharedMemory`.  When attaching to an
   existing ``ShareableList``, specify its shared memory block's unique
   name while leaving ``sequence`` set to ``None``.

   .. method:: count(value)

      Returns the number of occurrences of ``value``.

   .. method:: index(value)

      Returns first index position of ``value``.  Raises :exc:`ValueError` if
      ``value`` is not present.

   .. attribute:: format

      Read-only attribute containing the :mod:`struct` packing format used by
      all currently stored values.

   .. attribute:: shm

      The :class:`SharedMemory` instance where the values are stored.


The following example demonstrates basic use of a :class:`ShareableList`
instance:

   >>> from multiprocessing import shared_memory
   >>> a = shared_memory.ShareableList(['howdy', b'HoWdY', -273.154, 100, None, True, 42])
   >>> [ type(entry) for entry in a ]
   [<class 'str'>, <class 'bytes'>, <class 'float'>, <class 'int'>, <class 'NoneType'>, <class 'bool'>, <class 'int'>]
   >>> a[2]
   -273.154
   >>> a[2] = -78.5
   >>> a[2]
   -78.5
   >>> a[2] = 'dry ice'  # Changing data types is supported as well
   >>> a[2]
   'dry ice'
   >>> a[2] = 'larger than previously allocated storage space'
   Traceback (most recent call last):
     ...
   ValueError: exceeds available storage for existing str
   >>> a[2]
   'dry ice'
   >>> len(a)
   7
   >>> a.index(42)
   6
   >>> a.count(b'howdy')
   0
   >>> a.count(b'HoWdY')
   1
   >>> a.shm.close()
   >>> a.shm.unlink()
   >>> del a  # Use of a ShareableList after call to unlink() is unsupported

The following example depicts how one, two, or many processes may access the
same :class:`ShareableList` by supplying the name of the shared memory block
behind it:

   >>> b = shared_memory.ShareableList(range(5))         # In a first process
   >>> c = shared_memory.ShareableList(name=b.shm.name)  # In a second process
   >>> c
   ShareableList([0, 1, 2, 3, 4], name='...')
   >>> c[-1] = -999
   >>> b[-1]
   -999
   >>> b.shm.close()
   >>> c.shm.close()
   >>> c.shm.unlink()

The following examples demonstrates that ``ShareableList``
(and underlying ``SharedMemory``) objects
can be pickled and unpickled if needed.
Note, that it will still be the same shared object.
This happens, because the deserialized object has
the same unique name and is just attached to an existing
object with the same name (if the object is still alive):

   >>> import pickle
   >>> from multiprocessing import shared_memory
   >>> sl = shared_memory.ShareableList(range(10))
   >>> list(sl)
   [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

   >>> deserialized_sl = pickle.loads(pickle.dumps(sl))
   >>> list(deserialized_sl)
   [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

   >>> sl[0] = -1
   >>> deserialized_sl[1] = -2
   >>> list(sl)
   [-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]
   >>> list(deserialized_sl)
   [-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]

   >>> sl.shm.close()
   >>> sl.shm.unlink()