PyPy 1.1: Compatibility & Consolidation¶
Welcome to the PyPy 1.1 release - the first release after the end of EU funding. This release focuses on making PyPy’s Python interpreter more compatible with CPython (currently CPython 2.5) and on making the interpreter more stable and bug-free.
PyPy’s Getting Started lives at:
Highlights of This Release¶
More of CPython’s standard library extension modules are supported, among them ctypes, sqlite3, csv, and many more. Most of these extension modules are fully supported under Windows as well.
Through a large number of tweaks, performance has been improved by 10%-50% since the 1.0 release. The Python interpreter is now between 0.8-2x (and in some corner case 3-4x) slower than CPython. A large part of these speed-ups come from our new generational garbage collectors.
Our Python interpreter now supports distutils as well as easy_install for pure-Python modules.
We have tested PyPy with a number of third-party libraries. PyPy can run now: Django, Pylons, BitTorrent, Twisted, SymPy, Pyglet, Nevow, Pinax:
https://morepypy.blogspot.com/2008/08/pypy-runs-unmodified-django-10-beta.html https://morepypy.blogspot.com/2008/07/pypys-python-runs-pinax-django.html https://morepypy.blogspot.com/2008/06/running-nevow-on-top-of-pypy.html
A buildbot was set up to run the various tests that PyPy is using nightly on Windows and Linux machines:
Sandboxing support: It is possible to translate the Python interpreter in a special way so that the result is fully sandboxed.
clrmodule was greatly improved. This module is used to interface with .NET libraries when translating the Python interpreter to the CLI.
Stackless improvements: PyPy’s
stacklessmodule is now more complete. We added channel preferences which change details of the scheduling semantics. In addition, the pickling of tasklets has been improved to work in more cases.
Classic classes are enabled by default now. In addition, they have been greatly optimized and debugged:
PyPy’s Python interpreter can be translated to Java bytecode now to produce a pypy-jvm. At the moment there is no integration with Java libraries yet, so this is not really useful.
We added cross-compilation machinery to our translation toolchain to make it possible to cross-compile our Python interpreter to Nokia’s Maemo platform:
Some effort was spent to make the Python interpreter more memory-efficient. This includes the implementation of a mark-compact GC which uses less memory than other GCs during collection. Additionally there were various optimizations that make Python objects smaller, e.g. class instances are often only 50% of the size of CPython.
The support for the trace hook in the Python interpreter was improved to be able to trace the execution of builtin functions and methods. With this, we implemented the
_lsprofmodule, which is the core of the
A number of rarely used features of PyPy were removed since the previous release because they were unmaintained and/or buggy. Those are: The LLVM and the JS backends, the aspect-oriented programming features, the logic object space, the extension compiler and the first incarnation of the JIT generator. The new JIT generator is in active development, but not included in the release.
What is PyPy?¶
Technically, PyPy is both a Python interpreter implementation and an advanced compiler, or more precisely a framework for implementing dynamic languages and generating virtual machines for them.
The framework allows for alternative frontends and for alternative backends, currently C, Java and .NET. For our main target “C”, we can “mix in” different garbage collectors and threading models, including micro-threads aka “Stackless”. The inherent complexity that arises from this ambitious approach is mostly kept away from the Python interpreter implementation, our main frontend.
Socially, PyPy is a collaborative effort of many individuals working together in a distributed and sprint-driven way since 2003. PyPy would not have gotten as far as it has without the coding, feedback and general support from numerous people.
the PyPy release team, [in alphabetical order]
Amaury Forgeot d’Arc, Anders Hammerquist, Antonio Cuni, Armin Rigo, Carl Friedrich Bolz, Christian Tismer, Holger Krekel, Maciek Fijalkowski, Samuele Pedroni
and many others: https://codespeak.net/pypy/dist/pypy/doc/contributor.html