1. Four Python Pains
Stefane Fermigier
Scripting Languages Workshop, IRILL, May 2011

2. Part1: Some context

10. Wrong
(on several points)

11. Python
(“Scripting Language”)
• Has built-in syntactical support for arbitrarily
complex data structures (lists, dictionaries)
• Supports significantly complex applications:
ERP5, Plone, Nuxeo CPS (~320 KLOC),
OpenERP (~150 KLOC), Komodo (~250
KLOC)
• Is compiled (to bytecode)

12. Scala
(“Sys. Prog. Language”)
• Is interpreted (it as a REPL)
• Is a high-level language - in other words, has a
high CPU instructions / statement ratio
(comparable or higher than Python)

13. Other counterexamples
• Java, pre-generics (Java SE < 5, i.e. at the time
of Ousterhout’s paper), had really weak
support for static typing
• Clojure (that many consider in the same
league as Scala) is not even statically typed

16. At which expense?

17. • Execution speed?
• Safety?
• Maintainability?
• What else?

18. Or maybe Python is a
scripting language after all ?

19. Uses of Python
• Education
• Small to medium sized sysadmin and
software engineering project
• Medium to large web frameworks (Zope,
Django...) and applications (Youtube...)
• Scientific computing (numpy, scipy...)
• Scripting of games, desktop apps, etc.

20. Recap: 4 pain points
• Speed
• Scalability
• Type-safety
• Adherence to external code bases, and an
implementation (CPython)

21. Part 2: Discussion

22. Speed
• Python (seems to be) slower by 10x than
Clojure and V8 (JavaScript)
• Both Clojure and JavaScript are dynamically
typed languages
• TODO: look at Clojure and V8 and see
how they do it

24. Scalability
• CPython multi-threading / multi-core
performance is constrained by “the GIL” (global
interpretor lock)
• It doesn’t have to be there (Jython doesn’t have
one) but removing it from Python is
controversial
• Python has support for some par prog
constructs (see: http://wiki.python.org/moin/
ParallelProcessing) but not the fancy new ones
(a la Scala, Clojure, Go, etc.)

26. Type Safety
• Static typic is good to create safer programs,
and also to ease refactoring (necessary
condition for agility)
• Since Python 3, type annotations are possible
on variables, parameters, etc.
• Some tools (e.g. Jetbrains’ PyCharm) have
support for type inference on Python, and
enable: code completion, errors detections,
refactoring...

28. Adherence to CPython
• There are at least 4 major Python
implementations: CPython, Jython, IronPython
and PyPy
• But only CPython is mainstream
• Why? Because all the “system programming
lang” extensions written for CPython cannot
be used with the others
• And we’re talking about hundreds of packages

29. Solution?
• There are also tools, languages and
extensions that ease up the task of writing a
CPython extension: SWIG, ctypes, Pyrex,
Cython...
• By encapsulating part of the complexity and
low-level details of creating a Python
extension, it could make is both easier, more
robust and easier to share extensions
between language implementations

30. Conclusion

31. • Python could maybe be made 10x faster by
applying ideas from Clojure and V8
• Python could be made more scalable by
removing the GIL (probably needs to
rewrite the interpreter, though) and adding
modern parallel programming paradigms

32. • Python programs could be made more
robust with the help of static typing analysis
tools + a little help from the developers
(i.e. add a few type annotations in their
programs and libraries)
• Python is a scripting language after all (but
not only), and could benefit from a way of
writing extensions that is higher level than
the common one (but then: how to convert
the existing code base?)