Harlan Haskins

Software Engineer | About | Projects | Resume | Contact

On Opinionated Tools

I’ve recently begun a love affair with Haskell that’s caused me to rethink how I approach problem solving at a fundamental level.

Haskell, for those uninitiated, is a functional programming language that looks, reads, and behaves like pure mathematics. This rigid, well-defined structure has, in some ways, caused me to rethink my stance on the standard ways people try to decrease development time.

Programmers are lazy. It’s core to how we can effectively do our jobs, because we’ve always got efficiency on the mind. However, this means our decision-making process errs on what we think will lead to the least work.

Think of it as a greedy approach to problem solving – that is, we tend to prefer the solution that will get in our way the least right now, but neglect that the composition of many such soltions becomes a liability itself.

We trade convenience now for inconvenience later.

Programming Language Design

Some languages allow for ultimate flexibility when writing. They allow for very rapid development without handling edge cases right away. This can be really useful when prototyping or writing simple, one-off programs.

This convenience breaks down when running in production.

Let’s look at some examples. Say, for example, you’re writing an integer arithmetic library. A contrived example, I’m aware, but bear with me. Dynamic languages like Python mean you can get started really quickly!

def add(first, second):
    return first + second

def multiply(first, second):
    return first * second

def modulo(first, second):
    return first % second

Great! These functions work as intended when given two integers. However, you have experience writing libraries. You know that you need to handle cases where misuse might cause a nondescript error, and you need to handle those cases within your library.

For example, Python will not complain when writing this code.

import arithmetic
import random

if random.randint(1, 3) == 2:
    arithmetic.add(4, 'hello!')

The code above has a 33% chance of failing miserably. Worse, the error is tied to the internal implementation.

Traceback (most recent call last):
  File "arithmetic.py" line 1, in arithmetic
  File "<stdin>", line 1, in add
TypeError: unsupported operand type(s) for +: 'int' and 'str'

So since you cannot stop your users from misusing your library, you make sure that your clients get descriptive errors if they do.

def add(first, second):
    if first is None or second is None:
        raise ValueError('Cannot add None')

    if not isinstance(first, int) or not isinstance(second, int):
        raise ValueError(('Cannot add two non-integer types." +
                          'Expected (<type \'int\', \'int\'>), got ' +
                          '(%s, %s)') % (type(first), type(second)))

    return first + second

def multiply(first, second):
    if first is None or second is None:
        raise ValueError('Cannot multiply None')

    if not isinstance(first, int) or not isinstance(second, int):
        raise ValueError(('Cannot multiply two non-integer types." +
                          'Expected (<type \'int\', \'int\'>),' +
                          'got (%s, %s)') % (type(first), type(second)))

    return first * second

def modulo(first, second):
    if first is None or second is None:
        raise ValueError('Cannot take remainder of None')

    if not isinstance(first, int) or not isinstance(second, int):
        raise ValueError(('Cannot take remainder of two non-integer' +
                          'types. Expected (<type \'int\', \'int\'>),' +
                          'got (%s, %s)') % (type(first), type(second))))

    return first % second

Yikes.

And of course, you have to write the requisite unit tests to make sure the library gracefully and descriptively handles the errors.

In order to ensure safety and correctness, your codebase explodes in complexity.

Even worse, your library now does not work with floats, strs, or anything else that would respond to add, multiply, and modulo.

Compare that to the compatible Haskell implementation (with type signatures, too! Sorry, no point-free here.)

add :: Num a => a -> a -> a
add x y = x + y

multiply :: Num a => a -> a -> a
multiply x y = x * y

modulo :: Integral a => a -> a -> a
modulo x y = x `mod` y

Let’s consider what we didn’t have to do:

  • Check for None or null. Haskell’s type system is such that the non-existence of a value is expressed at the type level. There is a type, Maybe, that encompasses the possibility of not having a value. As such, handling None is always explicit, and completely ignorable unless necessary.

    Apple has also shown support for this kind of type-level safety. Swift’s type system is a dramatic improvement over Objective-C, and it enables you to write incredibly expressive and safe code, much easier.

  • Check the type of the inputs. This is a gimme: Haskell is a (very) strictly typed language, and typeclasses like Num and Integral allow us to write incredibly generic implementations that work on a wide variety of types.

  • Write unit tests. Our implementations are mathematically provably correct. There is no chance for side-effects. In the Python example, invalid values break control flow! They must be handled by the API client, with all manner of try/except/finally clauses. In Haskell, any side-effects are, like null, handled at the type level.

‘Garbage in, garbage out.’

Unopinionaed languages handle this by accepting all sorts of garbage – and letting the requisite garbage flow in response.

Opinionated languages stop the garbage from getting in.

Databases

I’ve been working with MongoDB for a while, and, while I really appreciate the ability to reason with the database as JSON, I’ve really come to despise the lack of schema validation. When writing an API around MongoDB, the work of validating the data falls on either the server or the individual clients. This leads to repeated work on many different platforms, to ensure data consistency throughout. It also leads to programmers making assumptions about the available data. When values can come back null in JSON, and that behavior is not explicitly defined at the database-level, that leads to hundreds of null checks in each of the clients.

Sure, it’s more flexible to just throw data into a Mongo document, and in some unfortunate cases, that may be completely required, but the lack of schema validation does little more than cause crashes, UI inconsistency, and silent-failures months down the line. In production.

Again, garbage in, garbage out.

Opinionated tools, though inconvenient, allow the programmers to focus on correctness of their implementation, not extra code to handle invalid input.