cpy2py 0.15.1

Framework for combining different python interpeters

Multi-intepreter execution environment

cpy2py allows multiple interpreters to act as one application. In parallel to the main interpreter, other interpreters are run to execute parts of the application.

Table of Contents

• Quick Guide

  • Twinterpreters and TwinMasters

  • TwinObjects

  • Debugging

• Current Status

  • Features

  • Gotchas/Limitations

  • Performance

Quick Guide

Twinterpreters and TwinMasters

A twinterpreter is simply another interpreter running as a subprocess - with some glue and magic sprinkled on it. You can control and create them using a cpy2py.TwinMaster.

You should only ever worry about two methods: TwinMaster.start launches the twinterpreter. TwinMaster.execute executes an arbitrary callable in the twinterpreter.

from my_module import my_function
twinterpreter = TwinMaster('pypy')
twinterpreter.start()
twinterpreter.execute(my_function, 1, 2, 3, 'ka-pow!', doctor="who?")

TwinObjects

The real power of cpy2py are Twins - objects living in one twinterpreter and being represented by proxies in any other interpeter. Using twins, you can seamlessly split your application across multiple twins.

You create twins by inheriting from cpy2py.TwinObject instead of object and setting a __twin_id__. That’s it.

from cpy2py import TwinObject
class SuperComputer(TwinObject):
    __twin_id__ = 'pypy'  # makes class native to pypy twinterpeter

    def megaloop(self, x, y):
        return sum(a+b for a in range(x) for b in range(y))

class CWrapper(TwinObject):
    __twin_id__ = 'python'  # makes class native to python twinterpeter

    def callme(self, who, what="buy milk"):
        return some_clib.c_fcn_cll_cplx_xmpl(who, what)

If you don’t set __twin_id__ on a child of cpy2py.TwinObject, the class will always be native to the main interpreter. Handy for all the stuff that’s needed everywhere but really doesn’t belong anywhere.

note:

At the moment, you have to explicitly start a class’s native twinterpreter before instantiating the class. Only the main interpreter is always available, of course.

Debugging

The core of cpy2py supports some logging facilities. All such loggers are children of the __cpy2py__ logger. By default, no active handlers are attached and propagation is disabled. If needed, you reconfigure them like any other logging logger to suit your needs.

For small scale debugging, one can set the environment variable CPY2PY_DEBUG. If it is defined and not empty, logging output is written to stderr.

Note that loggers are meant for development and only address the internal state. Your application should not depend on this information. Unless cpy2py misbehaves (or you suspect it to), ignore its logging.

Current Status

CPy2Py is stable at its core, but still has some features missing. What’s there is more than sufficient to significantly enhance your applications.

Features

• Any pickle’able callable can be dispatched to another interpreter.

• Objects can natively support twinterpreters.

  • Objects may be set to reside in any interpreter.

  • Once created, objects use cpy2py completely transparently.

  • Both class and instance attributes work as expected. Methods, classmethods, staticmethods and descriptors are fully supported.

  • Inheritance is fully supported, including multiple inheritance. Affiliation to interpreters can be changed freely.

• A wide range of interpeters is supported.

  • Pure python, no dependencies means perfect portability.

  • Any interpeter compatible with python 2.6 to 3.5 is supported.

  • Virtual Environments work out of the box.

  • Tested with cpython and pypy, on Linux and Mac OSX.

Gotchas/Limitations

• Calls across interpreters are blocking and not threadsafe. If recursion switches between twinterpreters, cpy2py.TwinMaster must use the 'async' kernel.

• Module level settings are not synchronized. For example, configuration of logging is not applied to twinterpreters. Use cpy2py.twinterpreter.group_state.TwinGroupState for initialisation, or write modules aware of twinterpreters.

• A weakref to objects only takes local references into account, not cross-interpreter references.

Performance

Dispatching to another twinterpreter adds about 200 - 300 us of overhead. This is mainly due to serialization for the IPC between the interpreters.

In general, twinterpreters get faster the shorter they have to wait between requests. pypy twinterpreters benefit from a high number of requests, allowing their JIT to warm up. Python3 connections are the fastest, provided that both twinterpreters support pickle protocol 4.

You can benchmark the overhead yourself using the cpy2py_benchmark tools.

pypy2	15x15k	30x5k	300x1
pypy2	187 ± 1.5 us	228 ± 2.5 us	505 ± 51.8 us
pypy3	165 ± 1.3 us	209 ± 2.4 us	402 ± 8.0 us
python2.7	178 ± 0.6 us	139 ± 0.3 us	239 ± 7.6 us
python3.4	149 ± 0.4 us	118 ± 0.2 us	258 ± 8.0 us