Abstract

This PEP proposes to standardize a new pickle protocol version, and accompanying APIs to take full advantage of it:

1. A new pickle protocol version (5) to cover the extra metadata needed for out-of-band data buffers.
2. A new PickleBuffer type for __reduce_ex__ implementations to return out-of-band data buffers.
3. A new buffer_callback parameter when pickling, to handle out-of-band data buffers.
4. A new buffers parameter when unpickling to provide out-of-band data buffers.

The PEP guarantees unchanged behaviour for anyone not using the new APIs.

Rationale

The pickle protocol was originally designed in 1995 for on-disk persistency of arbitrary Python objects. The performance of a 1995-era storage medium probably made it irrelevant to focus on performance metrics such as use of RAM bandwidth when copying temporary data before writing it to disk.

Nowadays the pickle protocol sees a growing use in applications where most of the data isn’t ever persisted to disk (or, when it is, it uses a portable format instead of Python-specific). Instead, pickle is being used to transmit data and commands from one process to another, either on the same machine or on multiple machines. Those applications will sometimes deal with very large data (such as Numpy arrays or Pandas dataframes) that need to be transferred around. For those applications, pickle is currently wasteful as it imposes spurious memory copies of the data being serialized.

As a matter of fact, the standard multiprocessing module uses pickle for serialization, and therefore also suffers from this problem when sending large data to another process.

Third-party Python libraries, such as Dask [1], PyArrow [4] and IPyParallel [3], have started implementing alternative serialization schemes with the explicit goal of avoiding copies on large data. Implementing a new serialization scheme is difficult and often leads to reduced generality (since many Python objects support pickle but not the new serialization scheme). Falling back on pickle for unsupported types is an option, but then you get back the spurious memory copies you wanted to avoid in the first place. For example, dask is able to avoid memory copies for Numpy arrays and built-in containers thereof (such as lists or dicts containing Numpy arrays), but if a large Numpy array is an attribute of a user-defined object, dask will serialize the user-defined object as a pickle stream, leading to memory copies.

The common theme of these third-party serialization efforts is to generate a stream of object metadata (which contains pickle-like information about the objects being serialized) and a separate stream of zero-copy buffer objects for the payloads of large objects. Note that, in this scheme, small objects such as ints, etc. can be dumped together with the metadata stream. Refinements can include opportunistic compression of large data depending on its type and layout, like dask does.

This PEP aims to make pickle usable in a way where large data is handled as a separate stream of zero-copy buffers, letting the application handle those buffers optimally.

Example

To keep the example simple and avoid requiring knowledge of third-party libraries, we will focus here on a bytearray object (but the issue is conceptually the same with more sophisticated objects such as Numpy arrays). Like most objects, the bytearray object isn’t immediately understood by the pickle module and must therefore specify its decomposition scheme.

Here is how a bytearray object currently decomposes for pickling:

>>> b.__reduce_ex__(4)
(<class 'bytearray'>, (b'abc',), None)

This is because the bytearray.__reduce_ex__ implementation reads morally as follows:

class bytearray:

   def __reduce_ex__(self, protocol):
      if protocol == 4:
         return type(self), bytes(self), None
      # Legacy code for earlier protocols omitted

In turn it produces the following pickle code:

>>> pickletools.dis(pickletools.optimize(pickle.dumps(b, protocol=4)))
    0: \x80 PROTO      4
    2: \x95 FRAME      30
   11: \x8c SHORT_BINUNICODE 'builtins'
   21: \x8c SHORT_BINUNICODE 'bytearray'
   32: \x93 STACK_GLOBAL
   33: C    SHORT_BINBYTES b'abc'
   38: \x85 TUPLE1
   39: R    REDUCE
   40: .    STOP

(the call to pickletools.optimize above is only meant to make the pickle stream more readable by removing the MEMOIZE opcodes)

We can notice several things about the bytearray’s payload (the sequence of bytes b'abc'):

• bytearray.__reduce_ex__ produces a first copy by instantiating a new bytes object from the bytearray’s data.
• pickle.dumps produces a second copy when inserting the contents of that bytes object into the pickle stream, after the SHORT_BINBYTES opcode.
• Furthermore, when deserializing the pickle stream, a temporary bytes object is created when the SHORT_BINBYTES opcode is encountered (inducing a data copy).

What we really want is something like the following:

• bytearray.__reduce_ex__ produces a view of the bytearray’s data.
• pickle.dumps doesn’t try to copy that data into the pickle stream but instead passes the buffer view to its caller (which can decide on the most efficient handling of that buffer).
• When deserializing, pickle.loads takes the pickle stream and the buffer view separately, and passes the buffer view directly to the bytearray constructor.

We see that several conditions are required for the above to work:

• __reduce__ or __reduce_ex__ must be able to return something that indicates a serializable no-copy buffer view.
• The pickle protocol must be able to represent references to such buffer views, instructing the unpickler that it may have to get the actual buffer out of band.
• The pickle.Pickler API must provide its caller with a way to receive such buffer views while serializing.
• The pickle.Unpickler API must similarly allow its caller to provide the buffer views required for deserialization.
• For compatibility, the pickle protocol must also be able to contain direct serializations of such buffer views, such that current uses of the pickle API don’t have to be modified if they are not concerned with memory copies.

Producer API

We are introducing a new type pickle.PickleBuffer which can be instantiated from any buffer-supporting object, and is specifically meant to be returned from __reduce__ implementations:

class bytearray:

   def __reduce_ex__(self, protocol):
      if protocol >= 5:
         return type(self), (PickleBuffer(self),), None
      # Legacy code for earlier protocols omitted

PickleBuffer is a simple wrapper that doesn’t have all the memoryview semantics and functionality, but is specifically recognized by the pickle module if protocol 5 or higher is enabled. It is an error to try to serialize a PickleBuffer with pickle protocol version 4 or earlier.

Only the raw data of the PickleBuffer will be considered by the pickle module. Any type-specific metadata (such as shapes or datatype) must be returned separately by the type’s __reduce__ implementation, as is already the case.

Consumer API

pickle.Pickler.__init__ and pickle.dumps are augmented with an additional buffer_callback parameter:

class Pickler:
   def __init__(self, file, protocol=None, ..., buffer_callback=None):
      """
      If *buffer_callback* is None (the default), buffer views are
      serialized into *file* as part of the pickle stream.

      If *buffer_callback* is not None, then it can be called any number
      of times with a buffer view.  If the callback returns a false value
      (such as None), the given buffer is out-of-band; otherwise the
      buffer is serialized in-band, i.e. inside the pickle stream.

      The callback should arrange to store or transmit out-of-band buffers
      without changing their order.

      It is an error if *buffer_callback* is not None and *protocol* is
      None or smaller than 5.
      """

def pickle.dumps(obj, protocol=None, *, ..., buffer_callback=None):
   """
   See above for *buffer_callback*.
   """

pickle.Unpickler.__init__ and pickle.loads are augmented with an additional buffers parameter:

class Unpickler:
   def __init__(file, *, ..., buffers=None):
      """
      If *buffers* is not None, it should be an iterable of buffer-enabled
      objects that is consumed each time the pickle stream references
      an out-of-band buffer view.  Such buffers have been given in order
      to the *buffer_callback* of a Pickler object.

      If *buffers* is None (the default), then the buffers are taken
      from the pickle stream, assuming they are serialized there.
      It is an error for *buffers* to be None if the pickle stream
      was produced with a non-None *buffer_callback*.
      """

def pickle.loads(data, *, ..., buffers=None):
   """
   See above for *buffers*.
   """

Protocol changes

Three new opcodes are introduced:

• BYTEARRAY8 creates a bytearray from the data following it in the pickle stream and pushes it on the stack (just like BINBYTES8 does for bytes objects);
• NEXT_BUFFER fetches a buffer from the buffers iterable and pushes it on the stack.
• READONLY_BUFFER makes a readonly view of the top of the stack.

When pickling encounters a PickleBuffer, that buffer can be considered in-band or out-of-band depending on the following conditions:

• if no buffer_callback is given, the buffer is in-band;
• if a buffer_callback is given, it is called with the buffer. If the callback returns a true value, the buffer is in-band; if the callback returns a false value, the buffer is out-of-band.

An in-band buffer is serialized as follows:

• If the buffer is writable, it is serialized into the pickle stream as if it were a bytearray object.
• If the buffer is readonly, it is serialized into the pickle stream as if it were a bytes object.

An out-of-band buffer is serialized as follows:

• If the buffer is writable, a NEXT_BUFFER opcode is appended to the pickle stream.
• If the buffer is readonly, a NEXT_BUFFER opcode is appended to the pickle stream, followed by a READONLY_BUFFER opcode.

The distinction between readonly and writable buffers is motivated below (see “Mutability”).

Side effects

Caveats

class ndarray:

   def __reduce_ex__(self, protocol):
      if protocol == 5:
         return numpy.frombuffer, (PickleBuffer(self), self.dtype)
      # Legacy code for earlier protocols omitted

>>> import numpy as np
>>> a = np.zeros(10)
>>> a[0]
0.0
>>> buffers = []
>>> data = pickle.dumps(a, protocol=5, buffer_callback=buffers.append)
>>> b = pickle.loads(data, buffers=buffers)
>>> b[0] = 42
>>> a[0]
42.0

Rejected alternatives

class MyPickle(pickle.Pickler):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.buffers = []

    def persistent_id(self, obj):
        if type(obj) is not bytearray:
            return None
        else:
            index = len(self.buffers)
            self.buffers.append(obj)
            return ('bytearray', index)


class MyUnpickle(pickle.Unpickler):

    def __init__(self, *args, buffers, **kwargs):
        super().__init__(*args, **kwargs)
        self.buffers = buffers

    def persistent_load(self, pid):
        type_tag, index = pid
        if type_tag == 'bytearray':
            return self.buffers[index]
        else:
            assert 0  # unexpected type

Implementation

The PEP was initially implemented in the author’s GitHub fork [6]. It was later merged into Python 3.8 [7].

A backport for Python 3.6 and 3.7 is downloadable from PyPI [8].

Support for pickle protocol 5 and out-of-band buffers was added to Numpy [11].

Support for pickle protocol 5 and out-of-band buffers was added to the Apache Arrow Python bindings [9].

Related work

Dask.distributed implements a custom zero-copy serialization with fallback to pickle [2].

PyArrow implements zero-copy component-based serialization for a few selected types [5].

PEP 554 proposes hosting multiple interpreters in a single process, with provisions for transferring buffers between interpreters as a communication scheme.

Acknowledgements

Thanks to the following people for early feedback: Nick Coghlan, Olivier Grisel, Stefan Krah, MinRK, Matt Rocklin, Eric Snow.

Thanks to Pierre Glaser and Olivier Grisel for experimenting with the implementation.

References

Copyright

This document has been placed into the public domain.