Bartosz Witkowski - Blog.
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“Bad programmers worry about the code. Good programmers worry about data structures and their relationships.”

The above quote from Linus Torvalds made so much sense for me the first time I read it. Getting the key data structures right makes the rest of the task flow naturally.

While this quote still makes sense to me, recently I have begun to appreciate how using a good type makes everything much easier both by virtue of better reasoning and better documentation.

Note - this blog entry is language agnostic but the examples will be in scala.

# Being honest

How often do you see something like:

Let’s assume that people always have a name and surname - which may not be true in real life see. There’s really not much to that class and the code seems innocuous.

But that class is a horrible lie. Do these instances make sense?

What does it mean for a person to have a name "" or an empty surname? What is the meaning of a negative age?

If something cannot happen we should use a type that disallows it.

Taking the name field as an example I can think of two ways for a type to enforce a precondition (in this case “there are no names that are empty strings”).

1) Create a data structure that makes lying impossible by construction e.g

2) Create a data structure with a smart “partial” constructor that doesn’t allow lying.

“Traditionally”, the second example might be done with assertions (require (!string.empty)) but throwing exceptions makes the code hard to reason about.

A better Person class could look like this:

While impossible instances of the Person now cannot be constructed this class is still not perfect. Does it makes sense for some person to be UInt.MaxValue years old? 2000? 500? Does it make sense to limit the age to 200? We can add further limitations to Age to make sure that we operate on data that is not only possible but also makes sense.

Thinking about what values should be allowed in a data type makes one aware that commonly we ignore the “bad apples” and nonsensical cases. While it is an extereme position I think that in most cases using primitive data types like Int, Double, String, Long etc should not be seen in domain specific data types.

# Keeping count.

We say that some type $A$ has $n$ inhabitants - concrete values that a varible of that type may have. In scala the boolean type has two inhabitants true and false. Longs have $2^{64}$ values. For a more in-depth article see this: The algebra of algebraic data types

Keeping count of the inhabitants of the type and using as much inhabitants as needed makes the usage clear.

The java Comparable interface is uniquely horrible. Why does an operation that only has three values

• a > b, a = b, a < b returns a type with $2^{32}$ inhabitants? Why does a < b have 2147483648 possible values? Having more inhabitants then there are possibilities detracts from the real meaning.

Other common deficiency is using an Int to pass a Char and signaling -1 by end of file. Why not use:

The meaning is clear and we don’t have to worry about millions of unused values.

Another place where it’s worth to count the inhabitants are data types with optional values. Keeping to the Person example I’ve often seen code that looks like:

Looking only at maleSpecificData and femaleSpecificData fields we can count 4 variants (None, None), (None, Some(y)), (Some(x), None), (Some(x), Some(y)).

The way I’ve seen classes like these used the option fields were a lie and the intent was to encode either:

or

Thinking about the possible inhabitants makes “lying” much harder.

# Holding a promise.

Sometimes we need to hold a more specific precondition then the general data structure allows. For example let’s think about an ordered list.

I’ve seen that precondition satisfied by meticulously running .sorted on a list which makes the precondition distributed throughout the codebase (kind of like the reverse of encapsulation).

While there is no OrderedList implementation in the scala library but we can easily create our own either through an intermediate class or through Tagging.

By creating a new class like:

we can encapsulate the logic and easily keep the predicate satisfied everywhere.

An ordered list is an obvious candidate for a new data structure because it is common enough to be used and reused again - but it is worth creating a new type even when the logic for is a one time thing.

Putting the logic directly into a new type (even if there would be one usage of that data type) keeps us honest.

# Types as documentation

Keeping rigor when creating a type makes it very obvious what values should and should not go into a data type. Using a NonEmptyString or NonEmptyList type is much better then documenting that through a comment // the list cannot be empty - the former is checked by the compiler while the latter is only wishful thinking.

Creating a “one off” data types like:

also helps documentation and helps type safety. Why use:

when this is possible:

And while both make mistakes possible:

the latter makes the mistake more obvious with a good choice of variable naming.