Sometimes a constructor or function can naturally accept multiple types. The classic solution to this is overloading. The problem is overloading can combinatorially expand for every multi-typed parameter.

A common example is the database connection. .Net offers a strongly typed DbConnection type, but the connection is usually stored as a string. There may also be framework-specific representations. Different users will have different opinions about how they want to represent the connection string and forcing users to convert every time would be frustrating.

The overload types may not always be directly interchangeable. Configuration is a common source of this pattern force. For example, when a user might just want to specify the number of concurrent connections, or they may need to be specific about concurrency, lifetime scope, resource division, and more.

## Multiple Input Types: Classic Solution

The typical solution in C-like languages is overloading, offering the action with multiple type signatures

public DbContext(string connection){
//...
}

public DbContext(DbConnection connection){
//...
}


This is tidy for a single overload, but every multi-type parameter creates a combinatorial explosion of necessary overloads. A function with three parameters and two options per parameter would need $2^3=8$ signatures to be complete.

public void ConfigureMessageBus(string connectionString, int maxThreads, int prefetch);

// ... and every cross between these two


Overloads also don’t work for properties of a data structure. Consider a configuration object with a connection string property. The only options are to offer two fields and implicitly leave one unused, or to force users into one type.

class Config{
public DbConnection DbConnection {get; set;}
public string ConnectionString {get; set;} // confusing duplicate. Behavior unclear
}



## Functional Foundations

Functional programming is rooted in math concepts, and every math function has a range and a domain.

$Domain \rarr function \rarr Range$

• Domain = input values
• Range = output values

In a programming sense, the domain is the valid input type(s) and the range is valid output types.

Domains and ranges can be combined different ways in math, and they can in functional programming too.

## Multiplying Type-spaces

First consider tuples. Tuples, triples, and the like are a collection of n values. For example, (5, "sam"). That looks familiar. This is the same as a vector in math.

This means that tuples “multiply” two type spaces. The tuple example before is an (int, string). It can contain any int value paired with any string value. It’s like making a graph with ints on one axis and strings on the other. Using it as input or output is like passing a point on that graph.

If we can multiply type-spaces, then why not add?

Functional languages allow this with a discriminated union. They can hold any one value from a determined set of types. You can also thing of it as an OR type.

// Truthy takes a bool, int, or string
type Truthy =
| Bool of bool
| Int of int
| String of string


## Multiple Input Types: Functional Approach

Discriminated unions allow a single argument to be any single choice out of a set of types. This works very well in overload-like situations. Each multi-type parameter is independent, so there is only one signature, no matter how many types each parameter allows.

It does require users must instantiate their value as the discriminated union, but that is generally simple compared to the clarity and maintainability.

Functional languages can also use overload-like behavior, but union-types accomplish the same task without an explosion in signatures. The single signature also more clearly documents its different uses cases all at once.

## Back to C#

Union-types have no convenient analog in most non-functional languages. However, some programmers have used generic typing with linq-style function chaining to mimic unions. One example is called OneOf.

## Summary

Overloads aren’t so bad. They enable flexible and direct use of a function with limited type variability. However, union types from functional languages push that flexibility even further, helping us to clearly and safely represent interchangeable type values.