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Mutability
----------
PEP 3118 buffers [#pep-3118]_ can be readonly or writable. Some objects,
such as Numpy arrays, need to be backed by a mutable buffer for full
operation. Pickle consumers that use the ``buffer_callback`` and ``buffers``
arguments will have to be careful to recreate mutable buffers. When doing
I/O, this implies using buffer-passing API variants such as ``readinto``
(which are also often preferrable for performance).
Data sharing
------------
If you pickle and then unpickle an object in the same process, passing
out-of-band buffer views, then the unpickled object may be backed by the
same buffer as the original pickled object.
For example, it might be reasonable to implement reduction of a Numpy array
as follows (crucial metadata such as shapes is omitted for simplicity)::
class ndarray:
def __reduce_ex__(self, protocol):
if protocol == 5:
return numpy.frombuffer, (PickleBuffer(self), self.dtype)
# Legacy code for earlier protocols omitted
Then simply passing the PickleBuffer around from ``dumps`` to ``loads``
will produce a new Numpy array sharing the same underlying memory as the
original Numpy object (and, incidentally, keeping it alive)::
This seems incompatible with v4 semantics. There, a loads plus dumps combination is approximately a deep copy. This isn't. Sometimes. Sometimes it is.
Other than that way, I like it.
Rob