On Sun, Jan 1, 2017 at 3:48 PM, Evan Goldschmidt <evan.goldschm...@gmail.com
> wrote:

> Is the advice in this thread, particularly with respect to generating C++
> message implementations, still valid for modern versions of the Python
> protobuf runtime?
>

> Brief spelunking through the Python codebase didn't yield a clear
> mechanism for how messages are automagically discovered.
>
Yes, it's still valid for Python protobuf runtime. The mechanism hasn't
changed. The message creation process is delegated to C++
DescriptorPool/MessageFactory which has a global registry of all loaded C++
protos.


>
> On Tuesday, December 7, 2010 at 10:49:43 PM UTC-8, Yang Zhang wrote:
>>
>> On Tue, Dec 7, 2010 at 9:40 PM, Kenton Varda <ken...@google.com> wrote:
>> > On Tue, Dec 7, 2010 at 9:19 PM, Yang Zhang <yangha...@gmail.com> wrote:
>> >>
>> >> > Also, note that if you explicitly compile C++ versions of your
>> >> > messages and link them into the process, they'll be even faster.  (If
>> >> > you
>> >> > don't, the library falls back to DynamicMessage which is not as fast
>> as
>> >> > generated code.)
>> >>
>> >> I'm trying to decipher that last hint, but having some trouble - what
>> >> exactly do you mean / how do I do that? I'm just using protoc
>> >> --py_out=... and PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp.
>> >
>> > I'm not completely sure what I mean, because I don't have much
>> experience
>> > with Python C Extensions.  Basically I'm saying you should additionally
>> > generate C++ code using protoc, the compile that into a C extension
>> (even
>> > with no interface), and then load it into your Python process.  Simply
>> > having the C++ code for your message types present will make them
>> faster.
>>
>> Ah, my understanding now is that:
>>
>> - Python code ordinarily (without
>> PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp) uses pure Python
>> (generated code) to parse/serialize messages.
>>
>> - Python code *with* PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp) uses
>> generic C++ code that dynamically parses/serializes messages (via
>> DynamicMessage/reflection), as opposed to using any pre-generated C++
>> code.
>>
>> - Python code with PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp actually
>> also *searches for the symbols for any pre-generated C++ code in the
>> current process*, and uses them if available instead of
>> DynamicMessage...? (This is via some global DescriptorPool magic?)
>>
>> Sounds like pretty weird behavior, but indeed, now I get even faster
>> processing. The following run shows ~68x and ~13x speedups vs. ~15x
>> and ~8x (my original speedup calculations were ~15x and ~8x, not ~12x
>> and ~7x...not sure how I got those, I probably was going off a
>> different set of measurements):
>>
>> $ PYTHONPATH=build/lib.linux-x86_64-2.6/:$PYTHONPATH
>> PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp python sandbox/pbbench.py
>> out.ini
>> noop: 1.6188621521e-07
>> ser: 6.39575719833e-06
>> parse: 4.55250144005e-05
>> msg size: 10730
>>
>> This was simple to do. I added a C extension to my setup.py:
>>
>> <<<
>> setup(
>>     ...
>>     ext_modules=[Extension('podpb',
>> sources=['cpp/podpb.c','cpp/main.pb.cc'], libraries=['protobuf'])],
>>     ...
>>     )
>> >>>
>>
>> Generate the second source file with `protoc --cpp_out=cpp`, and
>> create the first one to set up an empty Python module:
>>
>> <<<
>> #include <Python.h>
>>
>> static PyMethodDef PodMethods[] = {
>>   {NULL, NULL, 0, NULL}        /* Sentinel */
>> };
>>
>> PyMODINIT_FUNC
>> initpodpb(void)
>> {
>>   PyObject *m;
>>
>>   m = Py_InitModule("podpb", PodMethods);
>>   if (m == NULL)
>>     return;
>> }
>> >>>
>>
>> Now `python setup.py build` should build everything. Just import the
>> module (podpb in our case) and you're good.
>>
>> Awesome tip, thanks Kenton. I foresee additions to the documentation
>> in protobuf's near future.... :)
>> --
>> Yang Zhang
>> http://yz.mit.edu/
>>
>> --
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