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I'm Brandon Smith, a programmer in Austin, Texas. More about me.

 

Three Types of Data

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In my work I've developed a mental framework related to data modeling, which has helped greatly both when coming up with a model and when making decisions down the road about how to use that model. Here I will establish three different categories of data in software: Constants, State, and Cached Values. By "data" I generally mean "variables in code", but the same principles could be applied to files on a disk, or tables in a database, or whatever else.

These three categories are disjoint: that is, if a piece of data falls into one of them, it should not also be treated like one of the others. Different languages will vary in their ability to express this constraint via the type system or otherwise, so it's better to think of it as a convention or a frame of mind (though if you can actually enforce it, that is of course all the better). Always ask yourself which category a given piece of data belongs to, and then adhere to the rules around that category when working with it. In my experience, any program's data needs can be roughly expressed in terms of these three categories alone.

It's important to note that these are high-level categories of usage: what purpose is served, what operations should or should not be allowed, what assumptions can be made. These are not directly equivalent to other uses of the terms "constant", "state", and "cache", and while concepts like immutability may be loosely relevant, I'm purposely expressing these ideas in a language-agnostic and style-agnostic way.

I also want to note that there's a lot of philosophical overlap between this and my previous post ("Procedures, Functions, Data"). That one categorized logic where this one categorizes data, but the ideas are very similar. Still, I felt that the ideas contained here were independently useful enough to deserve their own post.

Let's get started.

Constants

A Constant, in this context, is information that doesn't change during the course of running the program.

This may correspond to the const keyword in many languages, though some, like JavaScript, can't enforce it recursively down the object tree. Immutable data structures could be used to help enforce it, although it's not quite what they're designed for (more on this under the State heading). It could also take the form of a configuration file, or a command-line argument. A Constant may be changed over the course of development, but not over the course of runtime.

State

State is information that naturally changes during the course of running the program.

Often this consists of mutable values. It can also consist of immutable data structures, whose express purpose is to allow you to have State without mutability (in them, new States are derived from the previous State combined with some new information). Even monad-based I/O, in languages like Haskell, falls into this category.

Any program that does more than convert a set of inputs to a set of outputs has State. That includes not only graphical interfaces and video games, but web servers, operating systems, and control software. Even programs written in languages like Clojure and Haskell can have state; they just massage it into a form where it's as un-stateful as possible.

Here's why they do that, though: State is toxic. Even though it is necessary. A program should have as little State as it can possibly get away with. In a perfect world, that means no piece of information should ever be represented in two different pieces of State. If you find yourself writing code to "synchronize State" between different variables, that's an enormous code smell and you should scrutinize it closely.

Cached Values

Cached values are information that is derived directly from Constants and/or State.

A Cached Value is similar to a Constant, but in practice will probably use the same language feature as your State, because at the top level, it can and will change (otherwise it would be a Constant!).

A Cached Value is like State that should only ever changed in one specific way: the re-computation of its value as a result of a change in actual State (usually via a pure function). It should never be mutated, only replaced. It is a good use-case for an immutable data structure, with one important stipulation: unlike State, its new value should never depend on its previous value.

Cached Values are an optimization over pure function calls (or stateless API requests, database reads, etc.). In most cases you could decide not to use them at all and always make the pure calls directly, you would just repeat work and/or have to wait on the network again. As such, they should be treated as always disposable. Unlike State, which embodies an untraceable accumulation of past things that have happened to it (or in Haskell's case a traceable accumulation), Cached Values are trash. They can disappear and be re-computed at any time, for any reason. This is why they must never be relied upon as State: changes to them can and will be thrown away without notice.

They are in some sense a duplication of information - which we said in the case of State is always to be avoided - but it's okay because they're a special, disposable duplication of State. "Synchronizing" them is always as simple as a single, controlled operation. It can happen anywhere, at any time, with no concerns to anything except performance (wasting effort).

Summary

To summarize:

Every piece of data in a program can be framed as a member of one of these categories (technically every piece of data could be framed as State, but that's what we're trying to minimize). But what does this net us?

A Constant:

A Cached Value can be:

By adding constraints we subtract possiblities, which means both we and our code can do certain things that would be intractably risky otherwise.

Any piece of State that can be converted to a Cached Value should be. Any piece of State or Cached Value that can be converted to a Constant, should be. By ushering parts of our program into more and more constrained possibility spaces we simplify it, and with simplicity comes fewer bugs, easier refactoring, and better understandability.