Data Types

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  1. Introduction
  2. Set Theory in Programming
  3. Data Types
  4. Type Theory
    1. Weak Types
    2. Strong Types
    3. Algebraic Types
  5. Conclusion

Introduction

Computing is composed of two categories of information: instructions, and data. If computers speak a language, instructons are verbs and adverbs, and data are nouns and adjectives.

Computer science has a concept called type theory that extends from the mathematical concept of set theory, which itself is a formal definition of the natural thought process by which we categorize things into groups.

Humans can do this instinctively, and we can categorize even complicated objects by one or more attributes. Imagine a collection of toys, which are all various colors, shapes, and textures. We can arbitrarily sort and separate those toys by any or all of their categories with ease. It helps that the attributes I described were all clearly distinct and could be measured by different senses: color perception is purely sight, texture is purely touch, and geometry is a mixture of the two that also requires complex calculation.

Computers only have one sense, though: voltage level. So for them, determining data type is more complex.

In mathematics, where everything is abstract and unreal, set theory describes a rule set by which we can describe and categorize arbitrary elements in arbitrary ways. The classic example of this is the Venn Diagram, a visual depiction of two sets and (the circles) inside a universe (the area in which the diagram is drawn). These sets are given names, and items which satisfy membership in none, one, or both are placed accordingly. Things that are in neither nor go outside the circles, things that are either in or in go in the outer area of their respective circle, and things that are both in and in go in the intersection in the middle.

Set Theory in Programming

In mathematics, we have freer room to say what elements belong to what sets because we have an infinite symbol space that need never collide. This is untrue for computers, where we have finite symbol space that collides often, and so we must be able to distinguish between symbols somehow. Consider a single byte: 0b1010_0101 (also written 0xA5).

What does this byte mean?

It depends on context.

If we are treating it as an unsigned integer, it is a representation of the number 165. If we are treating it as a signed integer, it is a representation of the number -91. If we assume it is a piece of text, it gets even stranger. It’s not valid ASCII, and in UTF-8 it is a continuation byte of a longer character sequence. It could also be an index into memory that informs us where the data we really seek can be found.

It’s impossible to tell purely by inspection, because bits are bits and carry no meta information. To describe bits, we encode that information in yet more bits, and anyone looking at the system has to agree to abide by arbitrary rules about interpreting meaning. Some examples of these arbitrary rules are text encodings such as ASCII, UTF-8, or the myriad other standards, memory models (also known as Application Binary Interface, or ABI for short), or transmission protocols used to operate communication lines like Ethernet, telephone, serial, or more.

Data Types

As I briefly touched on above, computing has a concept of data types that governs how bits are interpreted. All programming languages have some kind of type system that determines how data is interpreted. In some languages, the type of a variable is just a number that lives alongside the variable data itself, whereas in some others the type is known by the compiler and does not need to be visible at runtime if the compiler knows it has arranged the code in such a way as to not need it.

Scripting languages such as Python and Ruby are often dynamically typed, where the type of data is stored alongside the data, while compiled languages such as C and Rust are more often statically typed, where data types are tied to points in the instruction stream, and data that doesn’t match expectations causes problems to arise. Both methods have pros and cons, which I do not plan to discuss here.

Let us imagine a 32-bit integer in C. The exact value doesn’t matter, so we’ll say it is 0x12345678. This could be an integer (sign doesn’t matter; it’s positive in both), four ASCII letters "␒4Vx" (the first byte, 0x12, is the control character DC2 and does not have a true visible form), a memory address, or anything else. Merely by looking at the memory, we don’t know, because C uses static typing and doesn’t encode type information into data. In C, types are stored in the source code and evaporate during compilation.

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int i = 0x12345678;
printf("%i", i);
//  305419896

Now we know that the data value is an integer.

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int* pv = i;
printf("%p", pv);
//  0x12345678
printf("%i", *i);
//  Prints ... whatever currently lives
//  at that address. Could be anything.

This is perfectly legal (on a 32-bit system, pointers are 32-bits wide; on a 64-bit system, it is assumed that the missing digits are all zero), because C’s type system is also very weak. The variable pv has the exact same value as the variable i, but a very different meaning. i is a number, but pv is a memory location where some other variable (here, also an int) is stored.

We can also do stranger things, like this:

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char bytes[4];
*(int*)bytes = i;
printf(
  "%x %x %x %x",
  bytes[0],
  bytes[1],
  bytes[2],
  bytes[3]
);
//  '12 34 56 78' (big endian) or
//  '78 56 34 12' (little endian)

This is fine: C’s type system is very permissive, and as long as we guarantee that the sizes of everything we’re throwing around are sufficient, it won’t cause catastrophes merely by writing data to memory locations.

C also lets us build complex data structures, like so:

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//  Make a struct Foo that takes up four bytes
struct Foo {
    short s;
    char c1;
    char c2;
} foo;

//  Assign four bytes into foo
(int)foo = i; // i is still 0x12345678

printf("%i, %i, %i", foo.s, foo.c1, foo.c2);
//  4660 86 120 (big endian) or
//  30806 52 18 (little endian)

In every single case above, the data value found in memory is exactly the same. What changes is how that run of data is interpreted.

Type Theory

It’s important to remember that bare bits have no type whatsoever. Data is inherently untyped, and humans and programs give meaning to data by how they act on it.

Types are a property of behavior, not of existence.

Types are abstract, and only exist in our minds. We choose how to interpret data and how our programs should interpret it. Things break down when our interpretations are wrong (for instance, treating a bare integer as a memory address, or as text), and so some languages and environments like to store type alongside the data it describes to ensure that it is interpreted correctly. Of course, since this practice encodes the type information in raw bits, it works only so long as everything working with that data agrees on the metadata as well, and as long as it is kept in sync with the data it describes.

This doesn’t change the premise that types are abstract, not concrete. When data is tagged with its type, the type is read and used to select among behavior. The type itself is just a number that the code treats as significant.

Weak Types

C is a classical example of a weak type system: it is only concerned with memory logistics. If a symbol has a certain bit width, it can store any value that will fit in that width. As I showed above, C will gladly permit redefining what the type of a symbol is, and will only raise a complaint if the memory size doesn’t match up.

This permits programmers a great deal of freedom, but leaves lots of room for mistakes.

Strong Types

By contrast, the Rust language has an incredibly strong type system. It matches C’s level of memory efficiency by not storing type metadata in memory wherever possible, but enforces type soundness by refusing to compile programs that are perfectly valid in C, do not break memory, but merely irk the abstract mathematics powering the type system.

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struct Foo(i32);
struct Bar(i32);

let foo = Foo(0x12345678);
let bar = Bar(0x12345678);

If we hook a debugger to this code and inspect the memory, we will see two identical 32-bit integers. There is no difference, whatsoever, between them, and certainly no trace of the Foo or Bar wrapper types.

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if foo == bar { do_something(); }

This will cause a compilation failure. I have informed the compiler that the variable foo is of Foo type, and bar is of Bar type, and the compiler treats these as completely separate, independent, never-the-twain-shall-meet, groups. Unless I give the compiler instructions permitting it, Foo and Bar types are as Capulets and Montagues, and aspiring Romeos find themselves mercilessly shut down at compile time, even though an inspection of the raw memory would consider this comparison successful. Mathematically, this means that the compiler sees a set and a set , and elements of one are not elements of the other.

In set theory terms, these plain types are fully disjoint and cannot mix. Rust does offer a way around this, however.

Algebraic Types

The term algebraic types refers to treating types as abstract mathematical sets, rather than blocks of memory. In addition to being abstract, these types define an algebra (hence the name) for manipulating them, including basic mathematical terms like addition and multiplication.

Let us consider nullable values. In C, this mostly refers to pointers. One specific pointer value, NULL, is the universal symbol for invalid, and any other value is considered valid. However, NULL takes the same memory layout as a real pointer, and so in C, they are the same type.

Rust has a different opinion. In Rust, values that exist are not members of the set of things that do not exist, and NULL is the sole member of the set of nonexistent items and is not a member of the set of existing items.

Rust solves this by using sum types: types which are the sum of the component elements.

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enum Option<T> {
    Some(T),
    None,
}

This first declares two types: Some, which can have anything as a contained value, and None, which is empty. Some is essentially a nearly infinite set, of which many things are elements, but None is not.

Option is a type which includes Some and None both. All Somes are Options, and all Nones are Options, and all Options are either a Some or a None but there is no Option that is both a Some and a None at the same time. Let’s put that in mathematical terms: the set is the sum of the sets and , which are two sets such that their intersection is empty.

(The character above means “where”, and this states that the left side is only true when the right side is satisfied. If there exists an element that is both and , then the whole expression is invalid.)

Normally, this distinction would require an extra bit alongside the data to state what flavor of Option some value was. However, Rust knows that for many data types (specifically, pointers and references), the memory value of a None is unique and will never be the memory value of a Some, so it makes that special value the discriminant.

C pointers use the NULL value to indicate invalidity. This value is often presumed to be 0, but might not be, depending on the hardware.

In Rust, the exact value is hidden from us, but for Option<Pointer>, the None variant is just the platform’s NULL value, and the Some(Pointer) variant is anything else. This permits memory that looks in Rust to be identical to C, but is treated by the code as being two completely different types. The compiler will refuse to treat a None as a Some, and if it can’t prove it at compile time, it will inject code that will enforce this behavior at runtime.

In Rust’s type theory, enums are sum types, since the set of all enum values is the sum of all the values of each set within it.

There also exists a product (mulitiplicative) type: the struct, seen above.

The set of all values of a struct is the product of all values of each member within the struct. The Foo and Bar structs above only had one member, so the set of all Foo values is . This is also true for Bar, but the sets Foo and Bar do not intersect at all.

A more complex structure has many more possible values.

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struct Baz {
    a: i32,
    b: Option<i32>,
}

There exists a Baz struct for every value of a and for every value of b, but multiplied rather than added.

Conclusion

In daily life, we organize information by type. In the real world, type is often an innate quality things have (like weight or strength or color), but can also be a quality we give them (like monetary value or beauty or relevance).

This same process of categorization occurs in computing, but with a slew of unique challenges due to the utter lack of intrinsic information carried by bits.

In computing, as in human decision-making, type is a property of the behavior chosen in response to a datum. Just as the same sentence can be taken in very different ways when heard by two different people, so can the same bits be taken in different ways when processed by two different instructions.

If you want a one-sentence summary of this article, though, it’s the snippet I gave above:

Type is not what the data is, but how we act on it.

That’s an important concept, and one that is often forgotten.