Michele Simionato & David Mertz
Simplistic Writers, developerWorks
Too much cleverness in programming makes designs more complicated, code more fragile, learning curves steeper, and worst of all, it makes debugging harder. Michele and David feel, in part, responsible for some excesses of cleverness that followed the enthusiastic reception of their earlier articles on Python metaclasses. In this article, they attempt to make amends, by helping programmers eschew cleverness.
Recently, co-author Michele attended the EuroPython 2006 conference. The conference was good, the organization perfect, the talks of very high level, the people extremely nice. Nonetheless, he noticed something of a distrurbing trend in the Python community that prompted this paper. About simultaneously, co-author David, was reflecting on a similar issue with some submitted patches to Gnosis Utilities. The trend at issue is the trend towards cleverness. Unfortunately, whereas once cleverness in the Python community was once largely confined to Zope and Twisted, it now is appearing everywhere.
We have nothing against cleverness in experimental projects and learning exercises. Our gripe is with cleverness in production frameworks that we are forced to cope with as users. This article makes a small contribution against cleverness, at least in a case where we have some expertise, i.e. against metaclass abuses.
For this article, at least, we take a ruthless stance: we consider metaclass abuse any usage of a metaclass where you could have solved the same problem equally well without a custom metaclass. Of course, the guilt of the authors is obvious here: our earlier installments on metaclasses in Python helped popularize their usage. Nostra culpa.
One of the most common metaprogramming scenarios is the creation of classes with attributes and methods which are dynamically generated. Contrarily to popular belief, this is a job where most of the time you do not need and you do not want a custom metaclass; or so we argue in this installment.
The paper is intended for two sets of readers: average programmers who would benefit from knowing a few meta-programming tricks, but are scared off by brain melting concepts; clever programmers who are too clever and should know better. The problem for the latter is that it is easy to be clever, whereas it takes a lot of time to become unclever. For instance, it took us a few months to understand how to use metaclasses, but a few years to understand how not to use them.
During class creation attributes and methods of classes are set once and for all. Or rather, in Python, methods and attributes can be changed at nearly any point, but only if naughty programmers sacrifice transparency.
In several various common situations, a programmer may want to create
classes in more dynamic ways than simply running static code for their
creation. For instance, she may want to set some default class
attributes according to parameters read from a configuration file; or
she may want to set class properties according to the fields in a
database table. The easiest way to dynamically customize class
behavior uses an imperative style: first creates the class, then add
methods and attributes. For example, an excellent programmer of our
acquaintance, Anand Pillai has proposed a path to Gnosis Utilities'
gnosis.xml.objectify that does exactly this. A base class
gnosis.xml.objectify._XO_ that is specialized (at runtime) to
hold "xml node objects" is "decorated" with a number of enhanced
setattr(_XO_, 'orig_tagname', orig_tagname) setattr(_XO_, 'findelem', findelem) setattr(_XO_, 'XPath', XPath) setattr(_XO_, 'change_pcdata', change_pcdata) setattr(_XO_,'addChild',addChild)
One might suggest, reasonably enough, that the same enhancement can be
accomplished simply by subclassing the
XO base class. True, in one
sense, but Anand has provided about two dozen possible enhancements,
and particular users might want some of them, but not others. There
are too many permutations to easily create subclasses for every
enhancement scenario. Still, the above code is not exactly pretty.
One could accomplish the above sort of job with a custom metaclass,
XO, but with behavior determined dynamically, but that
brings us back to the excessive cleverness (and opacity) that we hope
A clean, and non-ugly, solution to the above need might be to add class decorators to Python. If we had those, we might write code similar to:
features = [('XPath',XPath), ('addChild',addChild), ('is_root',is_root)] @enhance(features) class _XO_plus(gnosis.xml.objectify._XO_): pass gnosis.xml.objectify._XO_ = _XO_plus
That syntax, however, is a thing of the future, if it becomes available at all.
It might seem like all the fuss in this paper, so far is about
nothing. Why not just, e.g. define the metaclass of
Enhance, and be done with it.
Enhance.__init__() can happily add
whatever capabilities are needed for the particular use in question.
This might look like, e.g.:
class _XO_plus(gnosis.xml.objectify._XO_): __metaclass__ = Enhance features = [('XPath',XPath), ('addChild',addChild)] gnosis.xml.objectify._XO_ = _XO_plus
Unfortunately, things are not so simple once you start to worry about inheritance. Once you have defined a custom metaclass for your base class, all the derived classes will inherit the metaclass, so the initialization code will be run on all derived classes, magically and implicitly. This may be fine in specific circumstances (for instance, suppose you have to register in your framework all the classes you define: using a metaclass ensures that you cannot forget to register a derived class), however, in many cases you may not like this behavior because:
1. You believe that explicit is better than implicit;
2. The derived classes have the same dynamic class attributes of the base class. Setting them again for each derived class is a waste since they would be available anyway by inheritance; this may be an especially significant issue if the initialization code is slow or computationally expensive. You might add a check in the metaclass code to see if the attributes were already set in a parent class, but this adds plumbing and it does not give real control on a per-class basis;
3. A custom metaclass will make your classes somewhat magic and nonstandard: you may not want to increase your chances to incur in metaclass conflicts, issues with ``__slots__``, fights with (Zope) extension classes, and other guru-level intricacies. Metaclasses are more fragile than many people realize. We have rarely used them for production code, even after four years of usage in experimental code.
4. You feel that a custom metaclasses is overkill for the simple job of class initialization and you would rather use a simpler solution.
In other words,you should use a custom metaclass only when your real intention is to have code running on derived classes without users of those classes noticing it. If this is not your case, skip the metaclass and make your life (and that of your users) happier.
What we present in the rest of this paper might be accused of cleverness. But the cleverness need not burden users, just us authors. Readers can do something much akin to the hypothetical (non-ugly) class decorator we propose, but without enountering the inheritance and metaclass conflict issues the metaclass approach raises. The "deep magic" decorator we give in full later generally just enhances the straightforward (but slightly ugly) imperative approach, and is "morally equivalent" to:
def Enhance(cls, **kw): for k, v in kw.iteritems(): setattr(cls, k, v) class ClassToBeInitialized(object): pass Enhance(ClassToBeInitialized, a=1, b=2)
The above imperative enhancer is not so bad. But it has a few drawbacks: It make you repeat the class name; readability is suboptimal since class definition and class initialization are separated--for long class definitions you can miss the last line; it feels wrong to first define something and then immediately mutate it.
classinitializer decorator provides a declarative solution. The
Enhance(cls,**kw) into a method that can be used
in a class definition:
>>> @classinitializer # add magic to Enhance ... def Enhance(cls, **kw): ... for k, v in kw.iteritems(): ... setattr(cls, k, v) >>> class ClassToBeInitialized(object): ... Enhance(a=1, b=2) >>> ClassToBeInitialized.a 1 >>> ClassToBeInitialized.b 2
If you have used Zope interfaces, you may have seen examples of class
zope.interface.implements). In fact,
classinitializer is implemented by using a trick copied from
zope.interface.advice, which credits Phillip J. Eby. The trick uses
the ``__metaclass__`` hook, but it does not use a custom metaclass.
ClassToBeInitialized keeps its original metaclass, i.e. the plain
type of new style classes:
>>> type(ClassToBeInitialized) <type 'type'>
In principle, the trick also works for old style classes, and it would be easy to write an implementation keeping old style classes old style. However, since according to Guido "old style classes are morally deprecated", the current implementation converts old style classes into new style classes:
>>> class WasOldStyle: ... Enhance(a=1, b=2) >>> WasOldStyle.a, WasOldStyle.b (1, 2) >>> type(WasOldStyle) <type 'type'>
One of the motivations for the
classinitializer decorator, is to
hide the plumbing, and to make mere mortals able to implements their
own class initializers in an easy way, without knowing the details of
how class creation works and the secrets of the
The other motivation, is that even for Python wizards it is very
inconvenient to rewrite the code managing the
every time one writes a new class initializer.
As a final note, let us point out that the decorated version of
Enhance is smart enough to continue to work as a non-decorated
version outside a class scope, provided that you pass to it an
explicit class argument:
>>> Enhance(WasOldStyle, a=2) >>> WasOldStyle.a 2
Here is the code for
classinitializer. You do not need to understand
it to use the decorator:
import sys def classinitializer(proc): # basic idea stolen from zope.interface.advice, P.J. Eby def newproc(*args, **kw): frame = sys._getframe(1) if '__module__' in frame.f_locals and not \ '__module__' in frame.f_code.co_varnames: # we are in a class if '__metaclass__' in frame.f_locals: raise SyntaxError("Don't use two class initializers or\n" "a class initializer together with a __metaclass__ hook") def makecls(name, bases, dic): try: cls = type(name, bases, dic) except TypeError, e: if "can't have only classic bases" in str(e): cls = type(name, bases + (object,), dic) else: # other strange errs, e.g. __slots__ conflicts raise proc(cls, *args, **kw) return cls frame.f_locals["__metaclass__"] = makecls else: proc(*args, **kw) newproc.__name__ = proc.__name__ newproc.__module__ = proc.__module__ newproc.__doc__ = proc.__doc__ newproc.__dict__ = proc.__dict__ return newproc
From the implementation it is clear how class initializers work: when
you call a class initializer inside a class, your are actually
_metaclass_ hook that will be called by the class'
type). The metaclass will create the class
(as a new style one) and will pass it to the class initializer
Since class initializers (re)define the
_metaclass_ hook, they
don't play well with classes that define a
explicitly (as opposed to implicitly inheriting one). If a
_metaclass_ hook is defined after the class initializer, it
silently overrides it.
>>> class C: ... Enhance(a=1) ... def __metaclass__(name, bases, dic): ... cls = type(name, bases, dic) ... print 'Enhance is silently ignored' ... return cls ... Enhance is silently ignored >>> C.a Traceback (most recent call last): ... AttributeError: type object 'C' has no attribute 'a'
While unfortunate, there is no general solution to this issue; we
simply document it. On the other hand, if you call a class initializer
_metaclass_ hook, you will get an exception:
>>> class C: ... def __metaclass__(name, bases, dic): ... cls = type(name, bases, dic) ... print 'calling explicit __metaclass__' ... return cls ... Enhance(a=1) ... Traceback (most recent call last): ... SyntaxError: Don't use two class initializers or a class initializer together with a __metaclass__ hook
Raising an error is preferable to silently overriding your
_metaclass_ hook. As a consequence, you will get an
error if you try to use two class initializers at the same time, or if
you call twice the same one:
>>> class C: ... Enhance(a=1) ... Enhance(b=2) Traceback (most recent call last): ... SyntaxError: Don't use two class initializers or a class initializer together with a__metaclass__ hook
On the plus side, all issues for inherited
_metaclass_ hooks and
for custom metaclasses are handled:
>>> class B: # a base class with a custom metaclass ... class __metaclass__(type): ... pass >>> class C(B): # class with both custom metaclass AND class initializer ... Enhance(a=1) >>> C.a 1 >>> type(C) <class '_main.__metaclass__'>
The class initializer does not disturb the metaclass of
C, which is
the one inherited by base
B, and the inherited metaclass does not
disturb the class initializer, which does its job just fine. You would
have run into trouble, instead, if you tried to call
directly in the base class.
With all this machinery defined, customizing class initialization becomes rather straightforward, and elegant looking. It might be something as simple as:
class _XO_plus(gnosis.xml.objectify._XO_): Enhance(XPath=XPath, addChild=addChild, is_root=is_root) gnosis.xml.objectify._XO_ = _XO_plus
This example still uses the "injection" which is somewhat superfluous
to the general case; i.e. we put the enhanced class back into a
specific name in the module namespace. It is necessary for the
particular module, but will not be needed most of the time. In any
case, the argument to to
Enhance() need not be fixed in code as
above, you can equally use
Enhance(**feature_set) for something
The other point to keep in mind is that your
Enhance() function can
do rather more than the simple version suggested above. The decorator
is more than happy to tweak more sophisticated enhancement functions.
For example, here is one that adds "records" to a class:
@classinitializer def def_properties(cls, schema): """ Add properties to cls, according to the schema, which is a list of pairs (fieldname, typecast). A typecast is a callable converting the field value into a Python type. The initializer saves the attribute names in a list cls.fields and the typecasts in a list cls.types. Instances of cls are expected to have private attributes with names determined by the field names. """ cls.fields =  cls.types =  for name, typecast in schema: if hasattr(cls, name): # avoid accidental overriding raise AttributeError('You are overriding %s!' % name) def getter(self, name=name): return getattr(self, '_' + name) def setter(self, value, name=name, typecast=typecast): setattr(self, '_' + name, typecast(value)) setattr(cls, name, property(getter, setter)) cls.fields.append(name) cls.types.append(typecast)
The differing concerns of (a) what is enhanced; (b) how the magic works; and (c) what the basic class itself does are kept orthogonal:
>>> class Article(object): ... # fields and types are dynamically set by the initializer ... def_properties([('title', str), ('author', str), ('date', date)]) ... def __init__(self, values): # add error checking if you like ... for field, cast, value in zip(self.fields, self.types, values): ... setattr(self, '_' + field, cast(value)) >>> a=Article(['How to use class initializers', 'M. Simionato', '2006-07-10']) >>> a.title 'How to use class initializers' >>> a.author 'M. Simionato' >>> a.date datetime.date(2006, 7, 10)
The authors' first two articles on metaclasses are at:
Metaclass programming in Python: Pushing object-oriented programming to the next level: http://www-128.ibm.com/developerworks/linux/library/l-pymeta.html
Metaclass programming in Python, Part 2: Understanding the arcana of inheritance and instance creation: http://www-128.ibm.com/developerworks/linux/library/l-pymeta2
Co-author David recently wrote a Charming Python installment about using decorators instead of metaclasses. Take a look at "Decorators make magic easy A look at the newest Python facility for meta-programming" at:
The code from which everything was born:
which contains the source code in this article, and a doctester
python doctester.py classinitializer.txt to extract the
self-test code from the paper.
Michele's recipe "A simple and useful doctester for your documentation":
Michele Simionato is a plain, ordinary, theoretical physicist who was driven to Python by a quantum fluctuation that could well have passed without consequences, had he not met David Mertz. Now he has been trapped in Python's gravitational field. He will let his readers judge the final outcome. You can contact Michele at email@example.com, or you can read his web site (http://www.phyast.pitt.edu/~micheles/).
David Mertz almost enjoys problems because of the solutions they enable. David may be reached at firstname.lastname@example.org; his life pored over athttp://gnosis.cx/publish/. Check out David's book Text Processing in Python (http://gnosis.cx/TPiP/).