David Mertz, Ph.D.
Your Obedient Writer, Gnosis Software, Inc.
The Python Enterprise Application Kit (PEAK) is a Python framework for rapidly developing and reusing application components. While Python itself is already a very-high-level language, PEAK provides even higher abstractions. One fairly recent capability added to PEAK is the capability to create generic functions, and specifically to dispatch them on predicates, not simply on type. Sounds mysterious? Read on.
The easiest way to describe PEAK is as "whatever wild ideas Phillip J. Eby has studied most recently." Despite the tone, I am only half joking in that characterization. While PEAK attracts about as many contributors as other Free Software Python projects of moderate size, at heart the direction of PEAK is driven by the evolving goals and interests of its original creator.
The obvious consequence of PEAK following evolving interest is that it
will probably be somewhat "experimental" for the forseeable future.
That said, do not worry too much about the issue--every version of
PEAK I have tried has been stable, and has provided concretely useful
features. Moreover, you can now obtain an automatically updated
tarball of the latest PEAK snapshot, complete with a friendly
disutils installation script, with little fuss (see Resources)
In the year since I last looked at PEAK, the most interesting new idea
introduced in PEAK is generic functions. This article will look at
this capability, even though it forms just one corner of PEAK as a
whole. Still, given the natural association of this idea with my own
multiple dispatch module,
multimethods, I am excited to look at
PEAK's extensions to dispatch styles. But before proceeding to the
discussion of generic functions, it is worth glancing at the diagram
of the components of PEAK given on the PEAK Wiki front page (check
that page for latest status, see Resources).
A quick note: the term "predicate dispatch" is used more often than "predicative" despite being less grammatically accurate. If you do a web or library search, try the shorter version.
Readers who read my earlier installment on the Gnosis Utilities module
gnosis.magic.multimethods have had an introduction to the idea of
multiple dispatch. In recap, most OOP programming is single dispatch;
that is, just one designated object determines which code path is
taken. In a call like
foo.doIt(other,args,here)) the class of
argument before the dot--say
Foo--determines which code is run; the
other, etc. might be tested in
if blocks of
but are not directly involved in code dispatch.
Conceptually, a more general technique is to allow all of the
arguments to a function/method to determine its specialization in
equal measure. Under a multiple dispatch system, a generic function
doIt() can be specialized to handle various type signatures, of
various specificity. In the
gnosis.magic.multimethods() API this
might look like:
doIt.add_rule((Foo1, Other2, int), func1) doIt.add_rule((Foo2, Other1, str), func2) doIt.add_rule((Foo1, Other1, float), func3) doIt(foo, other, args) # 'foo' is just one co-equal specializer
dispatch also has a type-based dispatcher, but it is
somewhat trivial currently since it only handles single dispatch--the
dispatch.on() wrapper gives you exceedingly little beyond normal
Python class-based dispatch. Nonetheless, looking at PEAK's
type-based dispatch warms us up to the syntax for full predicative
dispatch. Note here that the examples all take advantage of Python
2.4's new decorator syntax which is used to modify defined functions
or methods. You can use PEAK generic functions with earlier
versions of Python, but the syntax accomodation is less elegant:
import dispatch @dispatch.on('foo') def doIt(foo, other, args): "Base generic function of 'doIt()'" @doIt.when(int) def doIt(foo, other, args): print "foo is an int |", other, args @doIt.when(str) def doIt(foo, other, args): print "foo is a str |", other, args doIt( 1, 'this','that') # -> foo is an int | this that doIt('x','this','that') # -> foo is a str | this that
It is true enough that
dispatch.on() generic function signatures for
new types can be added without changing the prior code. For example, I
could add a
the prior code, and later calls would utilize this if relevant. But
there are a number of ways to accomplish largely the same thing
without the PEAK
Types are a funny thing. What most programmers think about when they
think about variable or object type owes a lot to the vagaries of
computer CPUs--even among programmers of high level languages like
Python. The difference between an int, float, and long are not
compelling distinctions among the numbers themselves, but simply
accidents of how chip designers have used registers and operands. But
at a theoretical level the type of integers between 3 and 17 is just
as sound as the type of integers between
latter being what the type
int is in Python, on 32-bit machines at
least). In fact, it makes sense to call
IntBetween3And17 a -subtype
int, at least informally, whether or not the inheritance tree
We can, of course, create our own types in Python by means of the
class statement, and arrange our types in whatever hierarchies we
like. But then, we can also enforce the collection of values that a
particular class may hold. The class
IntBetween3And17 is not too
difficult to implement in Python; nor is, for example, returning an
instance of the more general
IntBetween0And1000 in case you try to
add 100 to an object of the more restrictive class.
dispatch module has done--though Eby and other
contributors may or may not think of it this way--is create a rich
parametric type system to enhance the relatively meager built-in types
in raw Python. However, rather than provide wrappers for creating
various restricted-membership values of other classes--builtin or user
defined--[dispatch] provides a way for generic functions to do their
own fine-tuned "duck typing." The phrase "duck typing" is often used
in Python (and Ruby) circles in reference to the expression: "If it
walks like a duck and quacks like a duck, let's treat it like a duck":
We do not generally care what an object is. As long as it tastes as
good as a duck, Python will happily swallow it when in a mood for
Let us take a look at our simple
doIt() example with a bit of
parametric typing thrown in:
import dispatch @dispatch.generic() def doIt(foo, other): "Base generic function of 'doIt()'" @doIt.when("isinstance(foo,int) and isinstance(other,str)") def doIt(foo, other): print "foo is an unrestricted int |", foo, other @doIt.when("isinstance(foo,str) and isinstance(other,int)") def doIt(foo, other): print "foo is str, other an int |", foo, other @doIt.when("isinstance(foo,int) and 3<=foo<=17 and isinstance(other,str)") def doIt(foo, other): print "foo is between 3 and 17 |", foo, other @doIt.when("isinstance(foo,int) and 0<=foo<=1000 and isinstance(other,str)") def doIt(foo, other): print "foo is between 0 and 1000 |", foo, other doIt( 1, 'this') # -> foo is between 0 and 1000 | 1 this doIt('x', 1234) # -> foo is str, other an int | x 1234 doIt(10, 'this') # -> foo is between 3 and 17 | 10 this doIt(20, 'this') # -> foo is between 0 and 1000 | 20 this doIt(-7, 'this') # -> foo is an unrestricted int | -7 this try: doIt(2222, 66) except dispatch.interfaces.NoApplicableMethods: print "No Applicable Methods" # -> No Applicable Methods
Notice that the predicates you can specify (as strings) in
@doIt.when() conditions are exactly the same Python code you might
if block had you written the logic procedurally. However,
generic functions are much better; generic functions will order
themselves from more to less specific, so there is no question of
elif in the wrong place. And perhaps most powerful of
all, you can add more
@doIt.when() conditions in any subsequent
doIt() will thereafter begin evaluating that new condition
as a candidate to fulfill a particular call.
Unfortunately, once you start writing general predicates describing the values you would like to handle within a particular function body, it becomes easy to create amiguous conditions. Or at least that is exactly what I did quite innocently in trying to write the above example. For example, as a first brush, I naively wrote:
@doIt.when("isinstance(foo,int) and isinstance(other,str)") def doIt(foo, other): print "foo is an unrestricted int |", foo, other @doIt.when("3<=foo<=17 and isinstance(other,str)") def doIt(foo, other): print "foo is between 3 and 17 |", foo, other
Each condition is perfectly reasonable in itself. But then, both are
equally true for the call
doIt(10,"flaz"). Rather than guess what
you might have meant, PEAK raises the exception
Fair enough, we might say. After all, we really ought to specify unique conditions, or at least conditions that can be arranged by specificity. But I can very easily imagine a collection of defined predicative generic functions working happily for a long time before the ambiguous case is encountered. PEAK only complains when a particular call creates amibiguity, not when the potentially ambiguous functions are defined.
Moroever, even though the above example seems easy to catch, it can
get worse. After all, we are just stating two overlapping conditions
foo. But ambiguity need not be restricted to conditions of one
variable. They can be about multiple variables, and the ambiguity
might only exist with particular combinations of values. For example:
@doIt.when('foo < 10 and bar < 100') def doIt(foo, bar): print "Condition 1 |", foo, bar @doIt.when('foo < 100 and bar < 10') def doIt(foo, bar): print "Condition 2 |", foo, bar doIt(50,5) # -> Condition 2 | 50 5 doIt(5,50) # -> Condition 1 | 5 50 doIt(5,5) # -> raises dispatch.interfaces.AmbiguousMethod
It starts to look, potentially, like difficult program logic to debug.
One way to ease the possible pain of predicate ambiguity is by
breaking down applications of conditions into separate generic
dispatch gives you a similar ability to explicitly
dispatch to a
gnosis.magic.multimethods or CLOS
We will not go into the details of
next_method here; instead, let us
focus on the more general techniques of wrapping the primary generic
functions in pre-conditions and/or post-conditions--methods that are
called before or after a primary function. Moreover, unlike with
doIt.after() conditions are
happy to execute multiple satisfied predicates. This matches our
concept of a number of pre-conditions we want to make sure of before
calling the main code. In case of ambiguity, order of execution is
essentially arbitrary (it uses the order of definition).
A variation on our little
doIt() example makes this more clear:
import dispatch @dispatch.generic() def doIt(foo, other): "Base generic function of 'doIt()'" @doIt.before("isinstance(foo,int)") def sayType_int(foo, other): print "foo is an int |", @doIt.before("isinstance(foo,float)") def sayType_float(foo, other): print "foo is a float |", @doIt.when("3<=foo<=17") def doIt(foo, other): print "foo is between 3 and 17 |", @doIt.when("0<=foo<=1000") def doIt(foo, other): print "foo is between 0 and 1000 |", @doIt.when(dispatch.strategy.default) def doNothing(foo, other): pass @doIt.after("True") def sayValues(foo, other): print foo, other doIt(-17, 'x') # -> foo is an int | -17 x doIt(1.1, 'x') # -> foo is a float | foo is between 0 and 1000 | 1.1 x doIt( 9, 'x') # -> foo is an int | foo is between 3 and 17 | 9 x
If you are able to state invariant pre- and post-conditions, you can greatly reduce the danger of ambiguity sneaking in.
There is one more thing to notice in this last example. In prior
examples, we always used the same
doIt() name to define
specializations of the generic function
doIt() However, there is no
requirement for that naming pattern. Each specialization may be named
however you like, and descriptive names are a good idea when
applicable. The generic function itself must be named in the
decorators to specialization and in the ultimate call that gets
dispatched. Moreover, specialization functions are perfectly well
usable outside of the generic framework. For example, if you like, you
sayValues("blah","bloo") by itself; in this case, the
effect is the same as calling
doIt("blah","bloo") (but only because
neither pre-condition nor primary conditions are satisfied by these
Generic functions--especially the wild idea of predicatively dispatched functions--can be a little hard to reason about at first. But predicative dispatch is a very elegant extension of multiple dispatch on (narrow) types alone.
The greatest benefit in PEAK's
dispatch package is the possibility
it offers for a much more accurate and concise modularization of code.
Once you define a generic function and a collection of
specializations, you remain free to add as many additional
specializations as you want later on--all without so much as touching
the original (hopefully well-tested) code. For large scale
collaboration, or simply for applications that are adjusted for a
family of related versions, this package looks extremely promising.
I have not written about it here, but Phillip J. Eby has put considerable work into optimizing this dispatch framework; so you need not worry about these powerful facilities significantly impacting the speed of your programs that use them. You just get cleaner code, for free.
For the latest installable snapshot of the PEAK system in CVS, simply download:
Officially stable versions lag, but the snapshots seem eminently usable.
The home page for PEAK itself is the place to start for an introduction to the library as a whole.
The front page of the PEAK Wiki contains, among other things, a very nice diagram laying out the components of PEAK, along with their statuses:
Phillip J. Eby gave a nice summary presentation on generic functions at PYCON '05. The slides are available at:
A prior Charming Python developed and presented a library to enable multiple dispatch.
gnosis.magic.multimethods is perfectly usable by itself if
you wish to download it from:
multimethods can also be obtained as part of the overall
Gnosis Utilities package, see:
David Mertz programs generically and is dispatched multiply. David may be reached at [email protected]; his life pored over at http://gnosis.cx/publish/. Check out David's book Text Processing in Python (http://gnosis.cx/TPiP/).