September 16, 2016
September 02, 2016
August 23, 2016
In the programs that I wrote I used to know every line, every character even. Then they got bigger, but I still knew every file. Then they got bigger yet but at least I knew all the dependencies not only by name but by virtue.
Now a static single page web site in React comes with over 100000 files, some of which, as you know, are left pads and hot pockets and there are all new contests. Such complexity is beyond anyone's reach. Even the authors find it overwhelming.
We have lost the understanding of what's happening.
Today's software development is not engineering as such. Given the quality of the outcome it is not even a professional activity. Software development has become a recreational game for the young. The target audience for the programmer became not the users but other programmers. Stackoverflow and Github saw to that. This is now the most active social network in the world.
To impress one's peers it's no longer necessary to build some quality software. Bah ! You can't even tell what quality means. But to ride bicycle without hands - that's something ! And if you could do it blindfolded ! And backwards !
And so we see thousands of exercises in making things backwards. Without understanding the purpose or the reason, pick up a new tool, play for a month then move on to a new stimuli. Worse yet if it leaves behind another backwards-backwards-backwards-backwards tool. This adds another layer of useless and not understood complexity and provides positive feedback to the loop.
I remember well one day in 2005, when something out of the ordinary happened. At the time I was working under supervision of a great software engineer. He was always talking about "architecture", you know. Back then I didn't understand it at all, despite having already worked as a programmer for some 8-9 years. I thought it was all managerial talk. And then I was sitting in a conference room alone thinking how to organize a UI for some application and it dawned on me. I knew what architecture was, not burdened with details, my mind went to the next level, it was almost like I could fly. That I could not forget or unlearn, and I'm happy that this knowledge is with me, because it would not have happened today.
Today I would have just be dabbling in a Sea Of Complexity, pleasing my mind with details. May be I would have been happy about that, who knows.
July 15, 2016
And I'm sick and tired of its effects.
It is an outstanding example of what happens when people that don't have the faintest come to control technology, in this case the Internet. For each web site to prompt the user about cookies is a terrible idea.
2. The users don't take it seriously. Even when the warning is straightforward (we need cookies to do something you may not like) it is a matter of a single click to close the annoying window.
3. It does not improve privacy. At all. From privacy standpoint, cookies are not the villains but the most innocent messengers that are being shot.
4. It makes the Internet more stressful. As if we had not enough banners, one-time offers, subscription popups, landing pages, paywalls and so on, now we have these noisy popups.
5. Technically, cookie consent is a catch-22 situation - to know whether to accept a cookie from a site you need to own a cookie from it. Therefore if you refuse, the site will ask again. Moreover, even if you accept, each browser on each device manages its own cookies, and only a limited number of them. So the questions will continue ad nauseam.
May 01, 2016
I really love Python, but unfortunately don't have to use it in my current daily job. So now I have to practice it in my spare time, making something generally useful and hopefully suitable for improving my Python application framework.
1. The idea
I already had a method signature checking decorator written years ago, and it turned out enormously useful, so in the same line I started thinking about whether it would be possible to implement declarative function guards that select one version out of many to be executed depending on the actual call arguments. In pseudo-Python, I would like to write something like this:
def foo(a, b) when a > b: ... def foo(a, b) when a < b: ... foo(2, 1) # executes the first foo
2. Proof of concept
At the first sight it looks impossible, because the second function kind of shadows the second one:
def foo(a, b): print "first" def foo(a, b): print "second" foo(2, 1) # second
but this is not exactly so. Technically, the above piece of code looks something like this:
new_function = def (a, b): print "first" local_namespace['foo'] = new_function new_function = def (a, b): print "second" local_namespace['foo'] = new_function
and so the problem is not the function itself which is overwritten, but its identically named reference entry in current namespace. If you manage to save the reference in between, nothing stops you from calling it:
def foo(a, b): print("first") old_foo = foo def foo(a, b): if a > b: old_foo(a, b) elif a < b: print("second") foo(2, 1) # first foo(1, 2) # second
so there you have it, what's left is to automate the process and it's done.
There is no question as to how the guard should be attached to the guarded function - it would be done by terms of a decorator:
@guard def foo(): # hey, I'm now being guarded ! ... @guard def foo(): # and so am I ...
but the question remains where the guarding expression should appear. I see six ways of doing it:
A) as a parameter to the decorator itself:
@guard("a > b") def foo(a, b): ...
B) as a default value for some predefined parameter:
@guard def foo(a, b, _when = "a > b"): ...
C) as an annotation to some predefined parameter:
@guard def foo(a, b, _when: "a > b"): ...
D) as an annotation to return value:
@guard def foo(a, b) -> "a > b": ...
E) as a docstring
@guard def foo(a, b): "a > b" ...
F) as a comment
@guard def foo(a, b): # when a > b ...
Now I will dismiss them one by one until the winner is determined.
Method F (as a comment) is the first to go because implementing it would require serious parsing, access to source code and be semantically misleading as the comments are treated as something insignificant which can be omitted or ignored. The rest of the methods at least depend on the runtime information only and work on compiled modules.
Method A (as a parameter to the decorator) looks attractive, but is dismissed because it moves the decision from the function to the wrapper. So the function alone can't have guard expression and therefore it would not be possible to separate declaration from guarding:
def foo(a, b): # I want to be guarded ... # but it is this guard here that knows how foo = guard("a > b")(foo)
The rest of the methods are more or less equivalent and the choice is to personal taste. Nevertheless, I discard method E (docstring) because there is just one docstring per function and it has other uses. Besides, to me it looks like it describes the insides of the function, not the outsides.
So the final choice is between having the guarding expression as annotation and as default value. The real difference is this: a parameter with a default value can always be put last, but a parameter with annotation alone can not:
def foo(a, b = 0, _when: "a > b") # syntax error ...
This and the fact that aforementioned typecheck decorator already makes use of annotations tips the decision towards default value:
@guard def foo(a, b, _when = "a > b"): ... @guard @typecheck def foo(a: int, b: int, _when = "a > b") -> int: ...The choice of a name for the parameter containing the guard expression is arbitrary, but it has to be simple, clear and not conflicting at the same time. "_when" looks like a reasonable choice.
With a few exceptions, the semantics of a guarded function is straightforward:
@guard def foo(a, b, _when = "a > b"): ... @guard def foo(a, b, _when = "a < b"): ... foo(2, 1) # executes the first version foo(1, 2) # executes the second version foo(1, 1) # throws
Except when there really is a question which version to invoke:
@guard def foo(a, b, _when = "a > 0"): ... @guard def foo(a, b, _when = "b > 0"): ... foo(2, 1) # now what ?
and if there is a default version, which is the one without the guarding expression:
@guard def foo(a, b): # default ... @guard def foo(a, b, _when = "a > b"): ... foo(2, 1) # uh ?
and the way it seems logical to me is this: the expressions are evaluated from top to bottom one by one until the match is found, except for the default version, which is always considered last.
Therefore here is how it should work:
@guard def foo(a, b): print("default") @guard def foo(a, b, _when = "a > 0"): print("a > 0") @guard def foo(a, b, _when = "a > 0 and b > 0"): print("never gets to execute") @guard def foo(a, b, _when = "b > 0"): print("b > 0") foo(1, 1) # a > 0 foo(1, -1) # a > 0 foo(-1, 1) # b > 0 foo(-1, -1) # default
5. Function compatibility
So far we have only seen the case of identical function versions being guarded. But what about functions that have the same name but different signatures ?
@guard def foo(a): ... @guard def foo(a, b): ...
Should we even consider to have these guarded as versions of one function ? In my opinion - no, because it creates an impression of a different concept - function overloading, which is not supported by Python in the first place. Besides, it would be impossible to map the arguments across the versions.
Another question is the behavior of default arguments:
@guard def foo(a = 1, _when = "a > 0"): ... @guard def foo(a = -1, _when = "a < 0"): ...
Guarding these as one could work, but would be confusing as to which value the argument has upon which call. So this case I also reject.
What about a simplest case of different names for the same positional arguments ?
@guard def foo(a, b): ... @guard def foo(b, a): ...
Technically, those have identical signatures, and can be guarded as one, but is likely to be another source of confusion, possibly from a mistake, typo or a bad copy/paste.
Therefore the way I implement it is this: all the guarded functions with the same name need to have identical signatures, down to parameter names, order and default values, except for the _when meta-parameter and annotations. The annotations are excused so that guard decorator could be compatible with typecheck decorator. So the following is about as far as two compatible versions can diverge:
@guard @typecheck def foo(a: int, _when = "isinstance(a, int)", *args, b, **kwargs): ... @guard @typecheck def foo(a: str, *args, b, _when = "isinstance(a, str)", **kwargs): ...Note how the _when parameter can be positional as well as keyword. This way it can be always put at the end of the parameter list in the declaration.
6. Function naming
Before we used simple functions, presumably declared at module level. But how about this:
@guard def foo(): ... def bar(): @guard def foo(): ... class C: @guard def foo(self): ...
those three are obviously not versions of the same function, but they are called foo() so how do we tell them apart ?
In Python 3.2 and later the answer is this: f.__qualname__ contains a fully qualified name of the function, kind of a "a path" to it:
foo bar.<locals>.foo C.foo
respectively. It doesn't matter much what exactly is in the __qualname__, but that they are different, just what we need. Prior to Python 3.3 there is no __qualname__ and we need to fallback to a hacky implementation of qualname.
7. Special cases
Lambdas are unnamed functions. Their __qualname__ has <lambda> in it but no own name. They would be impossible to guard:
foo = lambda: ... foo = guard(foo) bar = lambda: ... bar = guard(bar)
because from the guard's point of view they are not "foo" and "bar", but the same "<lambda>".
An interesting glitch allows guarding classmethods and staticmethods. See, classmethod/staticmethod are not regular decorator functions but objects and therefore cannot be stacked with guard decorator
class C: @guard # this won't work @classmethod def foo(cls): ...
because classmethod can't be seen through to the original function foo. But it gets interesting when you swap the decorators around:
class C: @classmethod @guard def foo(cls, _when = "..."): ... @classmethod @guard def foo(cls, _when = "..."): ...
the way it works now is that guard decorator attaches to the original function foo, before it's wrapped with classmethod. Therefore the guarded chain of versions contains only the original functions, not classmethods. But when it comes to the actual call to it, it goes through a classmethod decorator before it gets to guard, the classmethod does it argument binding magic and whichever foo is matched by guard to be executed, gets its first argument bound to class as expected.
8. The register
Here is one final question: when a guarded function is encountered:
@guard def foo(...): ...
where should the decorator look for previously declared versions of foo() ? There must exist some global state that maps function names to their previous implementations.
The most obvious solution is to attach a state dict to the guard decorator itself. The dict would then map (module_name, function_name) tuples to lists of previous functions versions. This approach certainly works but has a downside, especially considering I'm going to use it with Pythomnic3k framework. The reason is that in Pythomnic3k modules are reloaded automatically whenever source files containing them change. Having a separate global structure holding references to expired modules would be bad, but having a chain of function versions cross different identically named modules from the past would be a disaster.
There is a better solution of making the register slightly less global and attach the state dict to the module in which a function is encountered. This dict would map just function names to the lists of versions. Then all the information about the module's guarded functions disappear with the module with no additional effort.
The implementation works.
I'm integrating it with Pythomnic3k framework so that all public method functions are instrumented with it automatically, although it is tricky, because when you have a text of just a
def foo(...): ... def foo(...): ...
and you need to turn it into
@guard @typecheck def foo(...): ... @guard @typecheck def foo(...): ...
it requires modification of the parsed syntax tree. I will have to write a follow-up post on that.
That's all and thanks for reading.