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Python: Design and Implementation Guido van Rossum guido@google

Python: Design and Implementation Guido van Rossum guido@google.com CS242, Stanford University, 10/9/06. Overview. Brief history lesson Python’s design and design process Python’s implementation Conclusion; questions. Timeline. Project started, name picked - Dec 1989

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Python: Design and Implementation Guido van Rossum guido@google

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  1. Python: Design and Implementation Guido van Rossumguido@google.com CS242, Stanford University, 10/9/06

  2. Overview • Brief history lesson • Python’s design and design process • Python’s implementation • Conclusion; questions

  3. Timeline • Project started, name picked - Dec 1989 • First public release (USENET) - Feb 1991 • python.org website - 1996 or 1997 • 2.0 released - 2000 • Python Software Foundation - 2001 • … • 2.4 released - 2004 • 2.5 released - 2006

  4. Why I Designed Python • ABC - teaching language created at CWI • designed in late 70s, early 80s (not me) • I was on the implementation team until 86 • had my own thoughts about its failing • Amoeba - distributed microkernel OS • almost completely, but not quite, unlike Unix • only languages available were C and sh • I decided to try to bridge the gap • Other influences: C, Modula-3, Icon, A68

  5. Original Design Process • Consider a desirable feature (either user feedback or my own needs while using it) • Only if there is absolutely no way to implement it as a library module or C extension, consider changing the compiler • Make the absolutely minimal changes necessary to the compiler; reuse existing functionality as much as possible • This gave us explicit self, for example

  6. Modern Design Process • Pretty much the same, but also worrying about backwards compatibility • Set the bar really high by only accepting new language features that will be of use to a wide variety of users • Prevent repeating early mistakes, where sometimes shortcuts were taken that were too extreme, and had to be changed at great cost (e.g. int/long, classic classes)

  7. Counterforce: Readability • No hack so clever or it must be readable! • Require at least one of: • Does it read naturally, like English? • Does it resemble well-known math notation? • Does it resemble a similar feature in another (popular) programming language? • Is it a logical extension of existing syntax? • Of course this is highly subjective…

  8. Community Process • PEP: Python Enhancement Proposal • Like JSR (Java), RFC (IETF) • Though a bit less formal (smaller community) • Not just for language changes • Common APIs (e.g. db-API, WSGI) • Sometimes library design (often not needed) • Bookkeeping (e.g. release schedules) • Typically not for performance enhancements • Not a democracy!

  9. Original Design Goals • Simple implementation (1 person) • Typical Very-High-Level Language goals • Cross-platform (hardware & OS) • Readability and expressive power • Easy to learn and remember; predictability • Safety: bugs don’t crash interpreter • Don’t compete with C; complement it • Extensibility through C extension modules • This makes Python an ideal glue language

  10. “Standard” VHLL Features • Automatic memory management • Small number of powerful data types • Bytecode interpreter • Built-in serialization (marshal, pickle) • KISS • Even indentation was borrowed! • Lots of other stuff, too • Python doesn’t have NIH syndrome :-)

  11. Python’s “Big Ideas” • No type checking in the compiler • Dynamic typing, static scoping • Everything is an object • Everything has a namespace (or multiple!) • Everything can be introspected, printed • System has few privileges over user • Interactive prompt >>> • Simplicity of implementation still matters!

  12. How Python Is Compiled • Lexer -> token stream • Uses a stack to parse whitespace! • Parser -> concrete syntax tree • Simple & stupid LL(1) parsing engine • Filter -> abstract syntax tree (uses ASDL) • Pass 1 -> symbol table • Pass 2 -> bytecode • Bytecode is in-memory objects • Saving to disk (.pyc file) is just a speed hack

  13. How Python Is Executed • Virtual machine executes bytecode • Simple stack machine • Stack frames allocated on the heap • C stack frames point to heap stack frames • Additional stack for try blocks • VM calls out to “abstract” operations • e.g. “PyNumber_Add(a, b)” • Some bytecodes represent “support” calls • e.g. import, print (since these have syntax)

  14. def gcd(a, b):while b: a, b = b, a%breturn a >>> dis.dis(gcd) 2 0 SETUP_LOOP 29 (to 32) >> 3 LOAD_FAST 1 (b) 6 JUMP_IF_FALSE 21 (to 30) 9 POP_TOP 3 10 LOAD_FAST 1 (b) 13 LOAD_FAST 0 (a) 16 LOAD_FAST 1 (b) 19 BINARY_MODULO 20 ROT_TWO 21 STORE_FAST 0 (a) 24 STORE_FAST 1 (b) 27 JUMP_ABSOLUTE 3 >> 30 POP_TOP 31 POP_BLOCK 4 >> 32 LOAD_FAST 0 (a) 35 RETURN_VALUE >>> Example Bytecode

  15. Separation of Concerns • VM knows very little about objects • Just their abstract API • Occasionally “cheats” for performance hacks • e.g. int+int, list[int], function calls • Some objects are “part” of the VM • e.g. code, frame, traceback; not function! • Namespaces implemented as dictionaries • Objects know nothing about VM • Support infrastructure ties things together • import bookkeeping, parser, interactive prompt

  16. Object Header • typedef struct { int ob_refcnt; PyTypeObject *ob_type;} PyObject; • Example (in reality this uses macros): • typedef struct {int ob_refcnt; PyTypeObject *ob_type; long ob_ival; PyIntObject;

  17. Type Object • typedef struct {<HEAD>char *tp_name; … destructor tp_dealloc; printfunc tp_print; getattrfunc tp_getattr; setattrfunc tp_setattr; … } PyTypeObject;

  18. Other Python Implementations • Jython - compiles to Java bytecode • IronPython - compiles to .NET IL Why? • Because we can! • Keeps Python language definition honest • No implementation specific hacks allowed • Side bet in case Java or .NET “wins” • Fills important gap on those platforms

  19. Jython Details • 100% Pure Java™ certified • Language differs in few details • e.g. strings are always Unicode (UTF-16) • Library differs where C code is wrapped • e.g. no Tkinter • Fully automated wrapping of Java classes • thanks to Java’s reflection API • Needs to load significant “Jython Runtime” • Executes 2-5x slower than CPython

  20. IronPython Details • Very similar in architecture to Jython • Sometimes faster than CPython! • Supported by Microsoft • but open source license!

  21. Questions • And answers • PS skip to next slide now!

  22. [Google Events at Stanford] • Career Fair: October 10 • Tech Talk: October 18 • On-campus interviews: • November (multiple dates) • Submit your resume by October 19 • Apply through: • www.google.com/jobs/students/fulltime • Questions: • Davidson Young <davidsony@google.com>

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