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Python 2.3-to-3.2 test compatibility
========================================
Separating Concerns Using Object Add-Ons
========================================
(NEW in version 0.6: the``Registry`` base class, and the
``ClassAddOn.for_frame()`` classmethod.)
In any sufficiently-sized application or framework, it's common to end up
lumping a lot of different concerns into the same class. For example, you
may have business logic, persistence code, and UI all jammed into a single
class. Attribute and method names for all sorts of different operations get
shoved into a single namespace -- even when using mixin classes.
Separating concerns into different objects, however, makes it easier to write
reusable and separately-testable components. The AddOns package
(``peak.util.addons``) lets you manage concerns using ``AddOn`` classes.
``AddOn`` classes are like dynamic mixins, but with their own private attribute
and method namespaces. A concern implemented using add-ons can be added at
runtime to any object that either has a writable ``__dict__`` attribute, or
is weak-referenceable.
``AddOn`` classes are also like adapters, but rather than creating a new
instance each time you ask for one, an existing instance is returned if
possible. In this way, add-ons can keep track of ongoing state. For example,
a ``Persistence`` add-on might keep track of whether its subject has been saved
to disk yet::
>>> from peak.util.addons import AddOn
>>> class Persistence(AddOn):
... saved = True
... def changed(self):
... self.saved = False
... def save_if_needed(self):
... if not self.saved:
... print("saving")
... self.saved = True
>>> class Thing: pass
>>> aThing = Thing()
>>> Persistence(aThing).saved
True
>>> Persistence(aThing).changed()
>>> Persistence(aThing).saved
False
>>> Persistence(aThing).save_if_needed()
saving
>>> Persistence(aThing).save_if_needed() # no action taken
This makes it easy for us to, for example, write a loop that saves a bunch of
objects, because we don't need to concern ourselves with initializing the
state of the persistence add-on. A class doesn't need to inherit from a
special base in order to be able to have this state tracked, and it doesn't
need to know *how* to initialize it, either.
Of course, in the case of persistence, a class does need to know *when* to call
the persistence methods, to indicate changedness and to request saving.
However, a library providing such an add-on can also provide decorators and
other tools to make this easier, while still remaining largely independent of
the objects involved.
Indeed, the AddOns library was actually created to make it easier to implement
functionality using function or method decorators. For example, one can create
a ``@synchronized`` decorator that safely locks an object -- see the example
below under `Threading Concerns`_.
In summary, the AddOns library provides you with a basic form of AOP, that lets
you attach (or "introduce", in AspectJ terminology) additional attributes and
methods to an object, using a private namespace. (If you also want to do
AspectJ-style "advice", the PEAK-Rules package can be used to do "before",
"after", and "around" advice in combination with add-ons.)
.. contents:: **Table of Contents**
Basic API
---------
If you need to, you can query for the existence of an add-on::
>>> Persistence.exists_for(aThing)
True
And by default, it won't exist::
>>> anotherThing = Thing()
>>> Persistence.exists_for(anotherThing)
False
Until you refer to it directly, e.g.::
>>> Persistence(aThing) is Persistence(anotherThing)
False
At which point it will of course exist::
>>> Persistence.exists_for(anotherThing)
True
And maintain its state, linked to its subject::
>>> Persistence(anotherThing) is Persistence(anotherThing)
True
Until/unless you delete it (or its subject is garbage collected)::
>>> Persistence.delete_from(anotherThing)
>>> Persistence.exists_for(anotherThing)
False
AddOn Keys and Instances
------------------------
Add-ons are stored either in their subject's ``__dict__``, or if it does not
have one (or is a type object with a read-only ``__dict__``), they are
stored in a special dictionary linked to the subject via a weak reference.
By default, the dictionary key is the add-on class, so there is exactly one
add-on instance per subject::
>>> aThing.__dict__
{<class 'Persistence'>: <Persistence object at...>}
But in some cases, you may wish to have more than one instance of a given
add-on class for a subject. (For example, PEAK-Rules uses add-ons to represent
indexes on different expressions contained within rules.) For this purpose,
you can redefine your AddOn's ``__init__`` method to accept additional
arguments besides its subject. The additional arguments become part of the key
that instances are stored under, such that more than one add-on instance can
exist for a given object::
>>> class Index(AddOn, dict):
... def __init__(self, subject, expression):
... self.expression = expression
>>> something = Thing()
>>> Index(something, "x>y")["a"] = "b"
>>> [key for key in something.__dict__ if not isinstance(key, str)]
[(<class 'Index'>, 'x>y')]
>>> "a" in Index(something, "z<22")
False
>>> Index(something, "x>y")
{'a': 'b'}
>>> Index(something, "x>y").expression
'x>y'
>>> keys = [key for key in something.__dict__ if not isinstance(key, str)]
>>> keys.sort()
>>> keys
[(<class 'Index'>, 'x>y'), (<class 'Index'>, 'z<22')]
>>> Index.exists_for(something, 'x>y')
True
>>> Index.exists_for(anotherThing, 'q==42')
False
By default, an add-on class' key is either the class by itself, or a tuple
containing the class, followed by any arguments that appeared in the
constructor call after the add-on's subject. However, you can redefine the
``addon_key()`` classmethod in your subclass, and change it to do something
different. For example, you could make different add-on classes generate
overlapping keys, or you could use attributes of the arguments to generate the
key. You could even generate a string key, to cause the add-on to be attached
as an attribute!::
>>> class Leech(AddOn):
... def addon_key(cls):
... return "__leech__"
... addon_key = classmethod(addon_key)
>>> something = Thing()
>>> Leech(something) is something.__leech__
True
The ``addon_key`` method only receives the arguments that appear *after* the
subject in the constructor call. So, in the case above, it receives no
arguments. Had we called it with additional arguments, we'd have gotten an
error::
>>> Leech(something, 42)
Traceback (most recent call last):
...
TypeError: addon_key() takes exactly 1 ...argument (2 given)
Naturally, your ``addon_key()`` and ``__init__()`` (and/or ``__new__()``)
methods should also agree on how many arguments there can be, and what they
mean!
In general, you should include your add-on class (or some add-on class) as part
of your key, so as to make collisions with other people's add-on classes
impossible. Keys should also be designed for thread-safety, where applicable.
(See the section below on `Threading Concerns`_ for more details.)
Role Storage and Garbage Collection
-----------------------------------
By the way, the approach above of using an string as an add-on key won't always
make the add-on into an attribute of the subject! If an object doesn't have a
``__dict__``, or that ``__dict__`` isn't writable (as in the case of type
objects), then the add-on is stored in a weakly-keyed dictionary, maintained
elsewhere::
>>> class NoDict(object):
... __slots__ = '__weakref__'
>>> dictless = NoDict()
>>> Leech(dictless)
<Leech object at ...>
>>> dictless.__leech__
Traceback (most recent call last):
...
AttributeError: 'NoDict' object has no attribute '__leech__'
Of course, if an object doesn't have a dictionary *and* isn't
weak-referenceable, there's simply no way to store an add-on for it::
>>> ob = object()
>>> Leech(ob)
Traceback (most recent call last):
...
TypeError: cannot create weak reference to 'object' object
However, there is an ``addons_for()`` function in the ``peak.util.addons``
module that you can extend using PEAK-Rules advice. Once you add a method to
support a type that otherwise can't be used with add-ons, you should be able to
use any and all kinds of add-on objects with that type. (Assuming, of course,
that you can implement a suitable storage mechanism!)
Finally, a few words regarding garbage collection. If you don't want to create
a reference cycle, don't store a reference to your subject in your add-on. Even
though the ``__init__`` and ``__new__`` messages get the subject passed in, you
are not under any obligation to *store* the subject, and often won't need to.
Usually, the code that is accessing the add-on knows what subject is in use,
and can pass the subject to the add-on's methods if needed. It's rare that the
add-on really needs to keep a reference to the subject past the ``__new__()``
and ``__init__()`` calls.
Add-on instances will usually be garbage collected at the same time as their
subject, unless there is some other reference to them. If they keep a
reference to their subject, their garbage collection may be delayed until
Python's cycle collector is run. But if they don't keep a reference, they will
usually be deleted as soon as the subject is::
>>> def deleting(r):
... print("deleting "+str(r))
>>> from weakref import ref
>>> r = ref(Leech(something), deleting)
>>> del something
deleting <weakref at ...; dead>
(Add-ons that are stored outside the instance dictionary of their subject,
however, may take slightly longer, as Python processes weak reference
callbacks.)
It is also *not* recommended that you have ``__del__`` methods on your add-on
objects, especially if you keep a reference to your subject. In such a case,
garbage collection may become impossible, and both the add-on and its subject
would "leak" (i.e., take up memory forever without being recoverable).
Class Add-Ons
-------------
Sometimes, it's useful to attach add-ons to classes instead of instances. You
could use normal ``AddOn`` classes, of course, as they work just fine with both
classic classes and new-style types -- even built-ins::
>>> Persistence.exists_for(int)
False
>>> Persistence(int) is Persistence(int)
True
>>> Persistence.exists_for(int)
True
>>> class X: pass
>>> Persistence.exists_for(X)
False
>>> Persistence(X) is Persistence(X)
True
>>> Persistence.exists_for(X)
True
But, sometimes you have add-ons that are specifically intended for adding
metadata to classes -- perhaps by way of class or method decorators. In such
a case, you need a way to access the add-on *before* its subject even exists!
The ``ClassAddOn`` base class provides a mechanism for this. It adds an extra
classmethod, ``for_enclosing_class()``, that you can use to access the add-on
for the class that is currently being defined in the scope that invoked the
caller. For example, suppose we want to have a method decorator that adds
the method to some class-level registry::
>>> from peak.util.addons import ClassAddOn
>>> class SpecialMethodRegistry(ClassAddOn):
... def __init__(self, subject):
... self.special_methods = {}
... super(SpecialMethodRegistry, self).__init__(subject)
>>> def specialmethod(func):
... smr = SpecialMethodRegistry.for_enclosing_class()
... smr.special_methods[func.__name__] = func
... return func
>>> class Demo:
... def dummy(self, foo):
... pass
... dummy = specialmethod(dummy)
>>> SpecialMethodRegistry(Demo).special_methods
{'dummy': <function dummy at ...>}
>>> class Demo2(object):
... def dummy(self, foo):
... pass
... dummy = specialmethod(dummy)
>>> SpecialMethodRegistry(Demo2).special_methods
{'dummy': <function dummy at ...>}
You can of course use the usual add-on API for class add-ons::
>>> SpecialMethodRegistry.exists_for(int)
False
>>> SpecialMethodRegistry(int).special_methods['x'] = 123
>>> SpecialMethodRegistry.exists_for(int)
True
Except that you cannot explicitly delete them, they must be garbage collected
naturally::
>>> SpecialMethodRegistry.delete_from(Demo)
Traceback (most recent call last):
...
TypeError: ClassAddOns cannot be deleted
Delayed Initialization
~~~~~~~~~~~~~~~~~~~~~~
When a class add-on is initialized, the class may not exist yet. In this case,
``None`` is passed as the first argument to the ``__new__`` and ``__init__``
methods. You must be able to handle this case correctly, if your add-on will
be accessed inside a class definition with ``for_enclosing_class()``.
You can, however, define a ``created_for()`` instance method that will be
called as soon as the actual class is available. It is also called by the
default ``__init__`` method, if the add-on is initially created for a class
that already exists. Either way, the ``created_for()`` method should be called
at most once for any given add-on instance. For example::
>>> class SpecialMethodRegistry(ClassAddOn):
... def __init__(self, subject):
... print("init called for "+str(subject))
... self.special_methods = {}
... super(SpecialMethodRegistry, self).__init__(subject)
...
... def created_for(self, cls):
... print("created for "+cls.__name__)
>>> class Demo:
... def dummy(self, foo):
... pass
... dummy = specialmethod(dummy)
init called for None
created for Demo
Above, ``__init__`` was called with ``None`` since the type didn't exist yet.
However, accessing the add-on for an existing type (that doesn't have the add-
on yet) will call ``__init__`` with the type, and the default implementation of
``ClassAddOn.__init__`` will also call ``created_for()`` for us, when it sees
the subject is not ``None``::
>>> SpecialMethodRegistry(float)
init called for <... 'float'>
created for float
<SpecialMethodRegistry object at ...>
>>> SpecialMethodRegistry(float) # created_for doesn't get called again
<SpecialMethodRegistry object at ...>
One of the most useful features of having this ``created_for()`` method is
that it allows you to set up class-level metadata that involves inherited
settings from base classes. In ``created_for()``, you have access to the
class' ``__bases__`` and or ``__mro__``, and you can just ask for an instance
of the same add-on for those base classes, then incorporate their data into
your own instance as appropriate. You are guaranteed that any such add-ons you
access will already be initialized, including having their ``created_for()``
method called.
Since this works recursively, and because class add-ons can be attached even to
built-in types like ``object``, the work of creating a correct class metadata
registry is immensely simplified, compared to having to special case such base
classes, check for bases where no metadata was added or defined, etc.
Instead, classes that didn't define any metadata will just have an add-on
instance containing whatever was setup by your add-on's ``__init__()`` method,
plus whatever additional data was added by its ``created_for()`` method.
Thus, metadata accumulation using class add-ons can actually be simpler than
doing the same things with metaclasses, since metaclasses can't be
retroactively added to existing classes. Of course, class add-ons can't
entirely replace metaclasses or base class mixins, but for the things they
*can* do, they are much easier to implement correctly.
Keys, Decoration, and ``for_enclosing_class()``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Class add-ons can have add-on keys, just like regular add-ons, and they're
implemented in the same way. And, you can pass the extra arguments as
positional arguments to ``for_enclosing_class()``. For example::
>>> class Index(ClassAddOn):
... def __init__(self, subject, expr):
... self.expr = expr
... self.funcs = []
... super(Index, self).__init__(subject)
>>> def indexedmethod(expr):
... def decorate(func):
... Index.for_enclosing_class(expr).funcs.append(func)
... return func
... return decorate
>>> class Demo:
... def dummy(self, foo):
... pass
... dummy = indexedmethod("x*y")(dummy)
>>> Index(Demo, "x*y").funcs
[<function dummy at ...>]
>>> Index(Demo, "y+z").funcs
[]
Note, by the way, that you do not need to use a function decorator to add
metadata to a class. You just need to be calling ``for_enclosing_class()``
in a function called directly from the class body::
>>> def special_methods(**kw):
... smr = SpecialMethodRegistry.for_enclosing_class()
... smr.special_methods.update(kw)
>>> class Demo:
... special_methods(x=23, y=55)
init called for None
created for Demo
>>> SpecialMethodRegistry(Demo).special_methods
{'y': 55, 'x': 23}
By default, the ``for_enclosing_class()`` method assumes is it being called by
a function that is being called directly from the class suite, such as a
method decorator, or a standalone function call as shown above. But if you
make a call from somewhere else, such as outside a class statement, you will
get an error::
>>> special_methods(z=42)
Traceback (most recent call last):
...
SyntaxError: Class decorators may only be used inside a class statement
Similarly, if you have a function that calls ``for_enclosing_class()``, but
then you call that function from another function, it will still fail::
>>> def sm(**kw):
... special_methods(**kw)
>>> class Demo:
... sm(x=23, y=55)
Traceback (most recent call last):
...
SyntaxError: Class decorators may only be used inside a class statement
This is because ``for_enclosing_class()`` assumes the class is being defined
two stack levels above its frame. You can change this assumption, however,
by using the ``level`` keyword argument::
>>> def special_methods(level=2, **kw):
... smr = SpecialMethodRegistry.for_enclosing_class(level=level)
... smr.special_methods.update(kw)
>>> def sm(**kw):
... special_methods(level=3, **kw)
>>> class Demo:
... sm(x=23)
... special_methods(y=55)
init called for None
created for Demo
>>> SpecialMethodRegistry(Demo).special_methods
{'y': 55, 'x': 23}
Alternately, you can pass a specific Python frame object via the ``frame``
keyword argument to ``for_enclosing_class()``, or use the ``for_frame()``
classmethod instead. ``for_frame()`` takes a Python stack frame, followed by
any extra positional arguments needed to create the key.
Class Registries (NEW in version 0.6)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For many of common class add-on use cases, you just want a dict-like object
with "inheritance" for the values in base classes. The ``Registry`` base class
provides this behavior, by subclassing ``ClassAddOn`` and the Python ``dict``
builtin type, to create a class add-on that's also a dictionary. It then
overrides the ``created_for()`` method to automatically populate itself with
any inherited values from base classes.
Let's define a ``MethodGoodness`` registry that will store a "goodness"
rating for methods::
>>> from peak.util.addons import Registry
>>> class MethodGoodness(Registry):
... """Dictionary of method goodness"""
>>> def goodness(value):
... def decorate(func):
... MethodGoodness.for_enclosing_class()[func.__name__]=value
... return func
... return decorate
>>> class Demo(object):
... def aMethod(self, foo):
... pass
... aMethod = goodness(17)(aMethod)
... def another_method(whinge, spam):
... woohoo
... another_method = goodness(-99)(another_method)
>>> MethodGoodness(Demo)
{'aMethod': 17, 'another_method': -99}
So far, so good. Let's see what happens with a subclass::
>>> class Demo2(Demo):
... def another_method(self, fixed):
... pass
... another_method = goodness(42)(another_method)
>>> MethodGoodness(Demo2)
{'another_method': 42, 'aMethod': 17}
Values set in base class registries are automatically added to the current
class' registry of the same type and key, if the current class doesn't have
an entry defined. Python's new-style method resolution order is used to
determine the precedence of inherited attributes. (For classic classes, a
temporary new-style class is created that inherits from the classic class, in
order to determine the resolution order, then discarded.)
Once the class in question has been created, the registry gets an extra
attribute, ``defined_in_class``, which is a dictionary listing the entries that
were actually defined in the corresponding class, e.g.::
>>> MethodGoodness(Demo).defined_in_class
{'aMethod': 17, 'another_method': -99}
>>> MethodGoodness(Demo2).defined_in_class
{'another_method': 42}
As you can see, this second dictionary contains only the values registered in
that class, and not any inherited values.
Finally, note that ``Registry`` objects have one additional method that can
be useful to call from a decorator: ``set(key, value)``. This method will
raise an error if a different value already exists for the given key, and is
useful for catching errors in class definitions, e.g.:
>>> def goodness(value):
... def decorate(func):
... MethodGoodness.for_enclosing_class().set(func.__name__, value)
... return func
... return decorate
>>> class Demo3(object):
... def aMethod(self, foo):
... pass
... aMethod = goodness(17)(aMethod)
... def aMethod(self, foo):
... pass
... aMethod = goodness(27)(aMethod)
Traceback (most recent call last):
...
ValueError: MethodGoodness['aMethod'] already contains 17; can't set to 27
Threading Concerns
------------------
Add-on lookup and creation is thread-safe (i.e. race-condition free), so long
as the add-on key contains no objects with ``__hash__`` or ``__equals__``
methods involve any Python code (as opposed to being pure C code that doesn't
call any Python code). So, unkeyed add-ons, or add-ons whose keys consist only
of instances of built-in types (recursively, in the case of tuples) or types
that inherit their ``__hash__`` and ``__equals__`` methods from built-in types,
can be initialized in a thread-safe manner.
This does *not* mean, however, that two or more add-on instances can't be
created for the same subject at the same time! Code in an add-on class'
``__new__`` or ``__init__`` methods **must not** assume that it will in fact be
the only add-on instance attached to its subject, if you wish the code to be
thread-safe.
This is because the ``AddOn`` access machinery allows multiple threads to
*create* an add-on instance at the same time, but only one of those objects
will *win* the race to become "the" add-on instance, and no thread can know in
advance whether it will win. Thus, if you wish your ``AddOn`` instances to do
something *to* their constructor arguments at initialization time, you must
either give up on your add-on being thread-safe, or use some other locking
mechanism.
Of course, add-on initialization is only one small part of the overall thread-
safety puzzle. Unless your add-on exists only to compute some immutable
metadata about its subject, the rest of your add-on's methods need to be
thread-safe also.
One way to do that, is to use a ``@synchronized`` decorator, combined with a
``Locking`` add-on::
>>> class Locking(AddOn):
... def __init__(self, subject):
... from threading import RLock
... self.lock = RLock()
... def acquire(self):
... print("acquiring")
... self.lock.acquire()
... def release(self):
... self.lock.release()
... print("released")
>>> def synchronized(func):
... def wrapper(self, *__args,**__kw):
... Locking(self).acquire()
... try:
... func(self, *__args,**__kw)
... finally:
... Locking(self).release()
...
... from peak.util.decorators import rewrap
... return rewrap(func, wrapper)
>>> class AnotherThing:
... def ping(self):
... print("ping")
... ping = synchronized(ping)
>>> AnotherThing().ping()
acquiring
ping
released
If the ``Locking()`` add-on constructor were not thread-safe, this decorator
would not be able to do its job correctly, because two threads accessing an
object that didn't *have* the add-on yet, could end up locking two different
locks, and proceeding to run the supposedly-"synchronized" method at the same
time!
(In general, thread-safety is harder than it looks. But at least you don't have
to worry about this one tiny part of correctly implementing it.)
Of course, synchronized methods will be slower than normal methods, which is
why AddOns doesn't do anything besides that one small part of the thread-safety
puzzle, to avoid penalizing non-threaded code. As the PEAK motto says,
STASCTAP! (Simple Things Are Simple, Complex Things Are Possible.)
Mailing List
------------
Questions, discussion, and bug reports for this software should be directed to
the PEAK mailing list; see http://www.eby-sarna.com/mailman/listinfo/PEAK/
for details.
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