(or: why I won’t write anything longer than a page in a dynamically-typed langauge)
Reading and understanding code is a huge part of being a developer.
When I’m at work, I probably spend most of my time reading code, rather than writing it. Pretty much anything you do that involves existing code will require you to read some of it. At some point, you’re going to need to answer a question like:
- What can I do with X?
- What kind of thing is X?
- How do I do X with Y?
and then you’re going to need to read some code. If you use, fix, or interface with existing code, then these questions are always going to crop up.1
The amount of reading that you need to do will vary depending on what you’re trying to do and how much you already know about the code in question. If you’re fixing a small bug in code that you wrote yourself, chances are that you won’t need to do much reading. If you’re making extensive changes to someone else’s code, then you may well need to read and understand a significant fraction of it.
Even when you’re writing totally fresh code, you’re likely to need to do some reading. Once you’ve put down a project for a while, it’s pretty easy to forget the details of even code that you wrote yourself. Hopefully it comes back to you a bit quicker, but there will still be corners that are as fresh to you as if they were written by a stranger.
Given that reading code is such a big part of my experience of being a programmer, I think it’s pretty important to figure out the factors make reading and understanding code easier or harder. Let’s start with an example.
The mysteries of Python
Suppose you want to make a really quick alteration to a routine that processes messages in some format. There’s something funny with the normalization process, and you want to print the message header each time so you can see if anything sticks out at you.
def process(message): normalized = normalize(message) if not normalized.isValid: raise new MessageFormatError("Invalid message!") handle(normalized)
Alright, we just need to insert a
might allow me to get the header? (Question 1) In fact, maybe it has some class
methods! But what’s the class of
message? (Question 2)
Now things get tricky. I have a couple of options, in decreasing order of
- Maybe I have my editor set up with some fancy Python type-inferencing thing like rope. Then, so long as it’s not too complicated, it might be able to work out the type for me.
- I can run the code in a REPL or directly; break in the function; and then inspect the object directly.
- I can guess that there might be a class called
Message, and have a look for that.
- I can try and find callers of
process, and work my way back up various hypothetical call stacks until I can find something that lets me pin it down.
However, all of these have pretty severe disadvantages:
rope gets confused pretty
easily; running the code can be prohibitively expensive, if the component you
are interested in is in the middle of a routine; guessing is, well, guesssing;
and working up the call stack is tedious and tricky.
At least once you’ve got the name of the class, you can usually find its definition pretty quickly, and then you’re probably in safe territory. Assuming you don’t have to do anything more than call a single method to get the header. And assuming that it’s not such “dynamic” code that message might have many classes.
In a relatively unfamiliar codebase, this process can easily take 10-15 minutes, and in the worse case, a lot longer.
(The attributes are kept in a map that’s populated with a schema that’s taken from a JSON blob that you get from the internet whose constitution depends on the version of the API that you’re talking to… )
A bad answer: documentation
I’ve been deliberately assuming that there is absolutely no documentation anywhere in this hypothetical codebase. That’s both somewhat unfair, and a depressingly realistic assumption. Useful documentation is rare, although you’re more likely to find it in open-source code than in that module your coworker whipped up last week in a hurry.
But what kind of documentation would actualy help here? What definitely does not help is a kind of verbose, English description of what the function does.
“This function processes a message, normalizing it to ensure that any special characters due to BLAH are removed.”
Great, that’s actually pretty useful in general, since it told me why the
normalization is necessary. It’s totally useless for telling me how to work with
message. All it’s told me is that it can be described in English as a
“message”. You don’t say.
What would help is to tell me what class
message is expected to have. I do
see that in some Python libraries, and it’s really helpful for figuring how to
actually do stuff.
Depressingly, even knowing the class might not be enough. If the function I need
is actually just a standalone function, then it’s another painful hunt to try
and find functions that take
A better answer: static types and resolution
It’s telling that the kind of documentation that I find most useful when actually working with Python is essentially a neutered type signature.
Having static types and resolution makes a lot of these problems easier. You know (always), at the press of a button, what kind of thing a variable is. You know, at the press of a button, what kind of methods it has, if it has a class. You know with maybe a single command (or a Hoogle) what functions operate on that type. You know, at the press of a button, all the callers of a function in your codebase.
I use these features all the time when I’m working on reasonably-sized Java codebases. When working on Python I pay the (amortized) 10 minute cost. Which wastes my time, encourages me to guess and pray, and is just plain painful.
Note that the types are only half of this. Static resolution of calls is just as useful, because you can just go, at the press of a button, to the code that will definitely be run. And yes, dynamic dispatch in object-oriented languages breaks this - and it can be extremely difficult to figure out what is going on in code that abuses dispatch!2
The best bit about delegating this to the compiler is that it’s maintenance free, and always up to date. Okay, I lied, if you’re writing Java it’s a massive pain, but all sane langauges have type inference these days.
But isn’t Python supposed to be very readable?
I would say that Python is superficially readable.
If you glance at a function you can probably describe in English what it’s doing. That’s great for a high-level overview, but then you get into the details and you realise you have no idea what’s going on at the operational level. Which is where you work when you actually write Python. It’s great that you know that it’s a message-processor, but that doesn’t tell you how to get the message header.
On the other hand, something like Java is superficially a mess, with all the
type annotations everywhere, but when you need to work with it you’ve got the
information you need at your fingertips. Need the message header?
message.<C-SPC>hea<TAB><CR>, done, move on to the next thing. Being constantly
dragged out of flow to go and hunt down information that should be trivially
available is horrendous.
Obviously, I think that languages with static typing and type inference have a strict improvement in readability over both Python and Java. You do need the compiler around a bit more to tell you the inferred types, but it’s better than having all the redundant noise that Java forces on you. However, I highly doubt that such languages are the supremum of readability - no doubt there is further to go.
But when it comes down to it, I would rather read Java than Python any day, and I don’t want to inflict it on anyone else either. And that, more than anything to do with expressiveness, or even safety, is the real reason I won’t use Python for anything longer than a page.