Positronic Vibrations

I was doing my usual research on Python when I ran across this recipe to make tail recursive calls not blow the stack in Python: Tail Call Optimization Decorator. I read through the code and thought, "Wow! This is devilishly clever!" As a thought exercise, I think it's awesome. But, it got me thinking about clever and production code. In my opinion, clever is never good or wanted in production code. It's great to learn and understand clever code though. It's a great mental workout to keep you sharp.

So, what's my point with all of this besides to say that clever is bad? The example in the above link is factorial (which as everyone knows is hated by me, but that's another story). But, the amazing thing about factorial examples are that they are dead simple, yet there's several ways to get the answer. Here's a few that I came up with:

def reg_fact(x):
    if x is 1:
        return 1
    return reg_fact(x - 1) * x

def tail_fact(x):
    def func(acc, x):
        if x is 1:
            return acc
        return func(acc * x, x - 1)
    return func(1, x)

def not_rec_fact(x):
    result = 1
    for each in range(2, x + 1):
        result *= each
    return result

def not_rec_fact_fancy(x):
    return reduce(lambda result, each: result * each, range(1, x + 1))

import operator
def not_rec_fact_super_fancy(x):
    return reduce(operator.mul, range(1, x + 1))

Each of these compute factorial. Amazingly, there's even more ways than what I listed (the math wizards in the audience know what they are). Now, think about this: Computing factorial is dead simple. What happens when we get to harder problems? Being clever can actually get in the way of making the code easy to understand. It might even kill performance. Let's think back to the devilishly clever code. The performance of making Python tail recursive is awful. Sure, it's pure and tail recursive, but in production code that is deadly. What we want is simple and to the point. It's why I generally like solutions that need less code. There's less noise to get in the way of understanding and generally can mean better performance as well.

Real world problems are hardly ever as straightforward as factorial. The balancing act comes when you drop a solution because it's not working for whatever reason. Raising and catching exceptions so you can have a tail recursive factorial is overkill. It took more code than the non-recursive version. It begs for the programmer to know their tool set and to know how to solve problems in that tool set that are straightforward. Tail recursion is powerful in languages like Haskell and Erlang. But, there's always another way of doing things that can make more sense in the language you are using. In our case, the other ways were just as easy yet more scalable for our tool set. Food for thought the next time you go down the path of clever and end up writing more noise than solution.

Labels: design, functional programming, Python

posted by Blaine at 8:17 PM |  2 comments |  Post a Comment