[Python-Dev] Pickling w/ low overhead

[David Ascher]

An issue which has dogged the NumPy project is that there is (to my
knowledge) no way to pickle very large arrays without creating strings
which contain all of the data.  This can be a problem given that NumPy
arrays tend to be very large -- often several megabytes, sometimes much
bigger.  This slows things down, sometimes a lot, depending on the
platform. It seems that it should be possible to do something more
efficient.

Two alternatives come to mind:

 -- define a new pickling protocol which passes a file-like object to the
    instance and have the instance write itself to that file, being as
    efficient or inefficient as it cares to.  This protocol is used only
    if the instance/type defines the appropriate slot.  Alternatively,
    enrich the semantics of the getstate interaction, so that an object
    can return partial data and tell the pickling mechanism to come back
    for more.

 -- make pickling of objects which support the buffer interface use that
    inteface's notion of segments and use that 'chunk' size to do
    something more efficient if not necessarily most efficient.  (oh, and
    make NumPy arrays support the buffer interface =).  This is simple
    for NumPy arrays since we want to pickle "everything", but may not be
    what other buffer-supporting objects want. 

Thoughts?  Alternatives?

--david