# Clean Code: Types

I recently revived my Clean Code tech talk which I put together a couple of years ago and with which I started this blog: Clean Code - Part 1 and Clean Code - Part 2. I took the opportunity to completely revamp the talk and ended up with 3 parts: Algorithms, Types, and State. The Algorithms is mostly covered by the Fibonacci post, so in this post we will talk about Types.

## Mars Climate Orbiter

The Mars Climate Orbiter crashed and disintegrated in the Mars atmosphere because a component developed by Lockheed provided momentum measured in pound-force seconds, while another component developed by NASA expected momentum as Newton seconds.

We can image the component developed by NASA being something like this:

// Will not disintegrate as long as momentum >= 2 N s
void trajectory_correction(double momentum)
{
if (momentum < 2 /* N s */)
{
disintegrate();
}
/* ... */
}


We can also imagine the Lockheed component calling into the above with:

void main()
{
trajectory_correction(1.5 /* lbf s */);
}


A pound-force second (lbfs) is about 4.448222 Newton seconds (Ns). So from Lockheed’s perspective, passing in 1.5 lbfs to trajectory_correction should be just fine: 1.5 lbfs is about 6.672333 Ns, way above the 2 Ns threshold.

The problem is the interpretation of the data. The NASA component ends up comparing lbfs to Ns without conversion, misinterpreting the lbfs input as Ns. Since 1.5 is less than 2, the orbiter disintegrates. This is a known anti-pattern called “primitive obsession”.

## Primitive Obsession

Primitive obsession happens when we use a primitive data type to represent a value in the problem’s domain and causes situations like the above. Representing zip codes as numbers, telephone numbers as strings, Ns and lbfs as double are all examples of this.

A more type safe solution would have defined a simple Ns type:

struct Ns
{
double value;
};

bool operator<(const Ns& a, const Ns& b)
{
return a.value < b.value;
}


We can similarly define a simple lbfs type:

struct lbfs
{
double value;
};

bool operator<(const lbfs& a, const lbfs& b)
{
return a.value < b.value;
}


Now we can implement a type safe trajectory_correction:

// Will not disintegrate as long as momentum >= 2 N s
void trajectory_correction(Ns momentum)
{
if (momentum < Ns{ 2 })
{
disintegrate();
}
/* ... */
}


Calling this with lbfs as below fails to compile as the types are incompatible:

void main()
{
trajectory_correction(lbfs{ 1.5 });
}


Note how the meaning of the values, which used to be specified in comments (2 /* Ns */, /* lbfs */) gets pulled into the type system and expressed in code (Ns{ 2 }, lbfs{ 1.5 }).

We can, of course, provide casting from lbfs to Ns as an explicit operator:

struct lbfs
{
double value;

explicit operator Ns()
{
return value * 4.448222;
}
};


Equipped with this, we can call trajectory_correction via a static cast:

void main()
{
trajectory_correction(static_cast<Ns>(lbfs{ 1.5 }));
}


This does the right thing of multiplying by the ratio. The cast can also be made implicit (by using the implicit keyword instead), in which case it is applied automatically. As a rule of thumb, it’s best to follow the Zen of Python:

Explicit is better than implicit

The moral of the story is that nowadays we have very sophisticated type checkers but we do need to provide them enough information to catch this type of errors. That information comes from declaring types to represent our problem domain. [1]

## State Space

Bad things happen when our programs end up in a bad state. Types help us narrow down the possibility of such bad states. One way to think about this is to look at types as sets of possible values. For example bool is the set {true, false} where a variable of the type can be one of the two values. Similarly, uint32_t is the set {0 ... 4294967295}. Looking at types like this, we can define the state space of our program as the product of the types of all live variables at a given point in time.

If we have a bool and an uint32_t, our state space is {true, false} X {0 ... 4294967295}. This simply means that the two variables can be in any of their possible states and since we have two of them, our program can be in any of their combined states.

This gets more interesting when we look at functions that initialize values:

bool get_momentum(Ns& momentum)
{
if (!some_condition()) return false;

momentum = Ns{ 3 };

return true;
}


In the above example we take a Ns by reference and initialize it if some condition is met. The function returns true if the value was properly initialized. If the function cannot, for whatever reason, set the value, it returns false.

Looking at this from the state space lens, our state space is the product bool X Ns. If the function returns true, then momentum was set and is in any one of the possible Ns values. The problem is that if the function returns false, then momentum was not set. It is still in any one of the possible Ns values, but it is not a valid value. Often times we have bugs where we accidentally propagate such invalid state:

void example()
{

get_momentum(momentum);

trajectory_correction(momentum);
}


What we should have done instead is:

void example()
{
Ns momentum;

if (get_momentum(momentum))
{
trajectory_correction(momentum);
}
}


There is a better way though, where this can be enforced:

std::optional<Ns> get_momentum()
{
if (!some_condition()) return std::nullopt;

return std::make_optional(Ns{ 3 });
}


Using an optional, this version of the function has a significantly smaller state space: instead of bool X Ns, we have Ns + 1. The function either returns a valid Ns value or nullopt to denote the absence of a value. Now it becomes impossible to have an invalid Ns that gets propagated throughout the system. We can also no longer forget to check the return value as an optional<Ns> is not implicitly convertible to an Ns - we need to explicitly unpack it:

void example()
{
auto maybeMomentum = get_momentum();

if (maybeMomentum)
{
trajectory_correction(*maybeMomentum);
}
}


In general, we want our functions to return result or error not result and error. This way we eliminate the states in which we have an error but also an invalid result which might make its way in further computation.

From this point of view, throwing exceptions is OK as this follows the same pattern: a function either returns a result or throws an exception.

## RAII

RAII stands for Resource Acquisition Is Initialization but has more to do with releasing resources. The name originated from C++ but the pattern can be implemented in any language (see, for example, .NET’s IDisposable). RAII ensures automatic cleanup of resources.

What are resources? A few examples: heap memory, database connections, OS handles. In general, a resource is something we acquire from the outside world and we need to release when it is no longer needed. That means executing some form of free, delete, close etc. on the resource.

Since these resources are external, they are not directly expressed into our type system. For example if we allocate some heap memory, we get a pointer on which we have to call delete:

struct Foo {};

void example()
{
Foo* foo = new Foo();

/* Use foo */

delete foo;
}


But what happens if we forget or something prevents us from calling delete?

void example()
{
Foo* foo = new Foo();

throw std::exception();

delete foo;
}


In this case we no longer call delete and we leak the resource. In general, we don’t want to perform such manual cleanup. For heap memory, we actually have unique_ptr to help us manage it:

void example()
{
auto foo = std::make_unique<Foo>();

throw std::exception();
}


The unique_ptr is a stack object so whenever it goes out of scope (when the function throws or during stack unwinding if an exception was thrown) its destructor gets called. It’s destructor implements the call to delete. This way, we no longer have to manually manage the memory resource - we hand it off to a wrapper which owns it and handles releasing it.

Similar wrappers exist or can be created for any of the other resources (for example a Windows OS HANDLE can be wrapped in a type where its destructor would call CloseHandle.

The key takeaway is never to do manual resource cleanup - either use an existing wrapper or, if none exists for your particular scenario, implement one.

## Summary

This post started with a famous example of why typing is important, and covered three important aspects of leveraging types to write safer code:

• Declaring and using stronger types (as opposed to primitive obsession).
• Reducing state space, returning result or error instead of result and error.
• RAII and automatic resource management.

Types are great tools for implementing safer, reusable code.

 [1] There is a great series of posts on Fluent C++ on Strong Typing.