Speed

Our main executable comes with a Just-in-Time compiler. It is really fast in running most benchmarks – including very large and complicated Python applications, not just 10-liners.

There are two cases that you should be aware where PyPy will not be able to speed up your code:

• Short-running processes: if it doesn't run for at least a few seconds, then the JIT compiler won't have enough time to warm up.
• If all the time is spent in run-time libraries (i.e. in C functions), and not actually running Python code, the JIT compiler will not help.

So the case where PyPy works best is when executing long-running programs where a significant fraction of the time is spent executing Python code. This is the case covered by the majority of our benchmarks, but not all of them – the goal of PyPy is to get speed but still support (ideally) any Python program.

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Memory usage

Memory-hungry Python programs (several hundreds of MBs or more) might end up taking less space than they do in CPython. It is not always the case, though, as it depends on a lot of details. Also note that the baseline is higher than CPython's.

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Stackless

Support for Stackless and greenlets are now integrated in the normal PyPy. More detailed information is available here.

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Other features

PyPy has many secondary features and semi-independent projects. We will mention here:

• Other languages: we also implemented other languages that makes use of our RPython toolchain: Prolog (almost complete), as well as Smalltalk, JavaScript, Io, Scheme and Gameboy.
• There is also a Ruby implementation called Topaz and a PHP implementation called HippyVM.

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Sandboxing

PyPy's sandboxing is a working prototype for the idea of running untrusted user programs. Unlike other sandboxing approaches for Python, PyPy's does not try to limit language features considered “unsafe”. Instead we replace all calls to external libraries (C or platform) with a stub that communicates with an external process handling the policy.

Please be aware that it is a prototype only.
It needs work to become more complete, and you are welcome to help. In particular, almost none of the extension modules work (not even time ), and pypy_interact is merely a demo. Also, a more complete system would include a way to do the same as pypy_interact from other languages than Python, to embed a sandboxed interpreter inside programs written in other languages.

To run the sandboxed process, you need to get the full sources and build pypy-sandbox from it (see Building from source). These instructions give you a pypy-c that you should rename to pypy-sandbox to avoid future confusion. Then run:

cd pypy/sandbox
pypy_interact.py path/to/pypy-sandbox
# don't confuse it with pypy/goal/pyinteractive.py!
                

You get a fully sandboxed interpreter, in its own filesystem hierarchy (try os.listdir('/')). For example, you would run an untrusted script as follows:

mkdir virtualtmp
cp untrusted.py virtualtmp/
pypy_interact.py --tmp=virtualtmp pypy-sandbox /tmp/untrusted.py
                

Note that the path /tmp/untrusted.py is a path inside the sandboxed filesystem. You don't have to put untrusted.py in the real /tmp directory at all.

To read more about its features, try pypy_interact.py --helpor go to our documentation site.