One of the principles of good Haskell, and in general good Typed Functional Programming, is the principle of making invalid states irrepresentable. What does this mean? We use the typesystem to craft types that impose constraints on our data and our state, so that it's impossible to represent these states that should not exist. Now that, at the type level, we managed to banish invalid states, the typesystem will step in and give us trouble every time we try to construct an invalid state. If we can't construct an invalid state, it's very hard for our program to end up in an invalid state, because in order to reach that invalid state the program should have followed a chain of actions that construct the invalid state. But such program would be invalid at the type level, and the typechecker would happily step in and tell us we are doing something wrong. This is great, because the typesystem will happily remember for us the constraints our data has, so we don't have to trust our flaky memory to remember them.

Fortunately, many of the results of this technique can be adapted to other programming languages, and today we are going to experiment with it in Typescript.

A sample problem

Let's work on a sample problem so we can try to understand how we can use this. We are going to constraint a type for a function using Algebraic Data Types, so that we can prevent invalid parameters to it. Our toy problem is as follows:

We have a function that accepts a single parameter: an object with potentially two fields, called field1 and field2 .

and . The object may not have neither of the two fields.

The object may have only field1 , and not field2 .

, and not . Only if the object has field1 , then it can have field2 .

, then it can have . Therefore, an object with field2 , but not field1 , is invalid.

, but not , is invalid. For simplicity, when field1 or field2 exist, they will be of type string , but they could be of any type.

Naive solution

Let's start with the simplest approach. Because both field1 and field2 can exist, or not, we just make them optional.

interface Fields { field1 ? : string ; field2 ? : string ; } ; function receiver ( f : Fields ) { if ( f . field1 === undefined && f . field2 !== undefined ) { throw new Error ( "Oh noes, this should be impossible!" ) ; } }

Unfortunately, this doesn't prevent anything at compile time, and requires checking for that possible error at runtime.

receiver ( { field2 : "Hahaha, I didn't put a field1!" } )

Basic ADT solution

So we called receiver with the wrong fields several times in a row, our application exploded in flames, and we are not happy. Time to do something about it. Let's enumerate the cases again, so that we can see if we can make a type with the right shape:

The object may not have neither of the two fields.

The object may have only field1 , and not field2 .

, and not . Only if the object has field1 , then it can have field2 . Therefore, in this case, the object has both field1 and field2 .

, then it can have . Therefore, in this case, the object has both and . An object with field2 , but not field1 , is invalid.

Let's transcribe this into types:

interface NoFields { } ; interface Field1Only { field1 : string ; } ; interface BothField1AndField2 { field1 : string ; field2 : string ; } ; interface InvalidObject { field2 : string ; } ;

We decided to also include here InvalidObject , but it's a bit silly writing it, because we don't want it to really exist. We may keep it around as documentation, or we may remove it so that to affirm even more that it is not supposed to exist. Now let's write a type for Fields :

type Fields = NoFields | Field1Only | BothField1AndField2 ;

With this disposition, it's harder to send to receiver an InvalidObject :

receiver ( { field2 : "Hahaha, I didn't put a field1!" } ) ;

We also need to tweak the receiver function a little bit, mostly because the fields may not exist now, and the typechecker now requires proof that you are going to read fields that actually exist:

function receiver ( f : Fields ) { if ( "field1" in f ) { if ( "field2" in f ) { } else { } } else { } }

Limitations of structural typing

Unfortunately, for good or for bad, Typescript is a structural type system, and this allows us to bypass some of the safety if we are not careful. The NoFields type (empty object, {} ), in Typescript, means something totally different to what we want it to do. Actually when we write:

interface Foo { field : string ; } ;

Typescript understands that any object with a field of type string is good, except for the case where we create a new object, like:

const myFoo : Foo = { field : "asdf" } ;

But, on assignment, Typescript tests using structural typing, and that means our objects may end with more fields that what we would like them to have:

const getReady = { field : "asdf" , unexpectedField : "hehehe" } ; const myFoo : Foo = getReady ;

So, when we extend this idea to the empty object {} , turns out that on assignment, Typescript will accept any value as long as that value is an object, and has all the fields demanded. Because the type demands no fields, this second condition succeeds trivially for any object , which is totally not what we wanted it to do.

Banning unexpected fields

Let's try to make a type for objects with no fields, so that we actually have to go out of our way to fool the typechecker. We already know never , the type that can never be satisfied. Now we need another ingredient to say "every possible field". And this ingredient is: [key: string]: type . With these two we can construct the object with no fields:

type NoFields = { [ key : string ] : never ; } ;

This type means: this is an object, whose fields are of type never . Because you can't construct a never , there is no way to make valid values for the fields of this object. Therefore, the only solution is an object with no fields. Now, we have to be more deliberate to break the types:

type NoFields = { [ key : string ] : never ; } ; interface Field1Only { field1 : string ; } ; interface BothField1AndField2 { field1 : string ; field2 : string ; } ; type Fields = NoFields | Field1Only | BothField1AndField2 ; const broken = { field2 : "asdf" } ; const bypass1 : { } = broken ; const brokenThroughBypass1 : Fields = bypass1 ; const bypass2 : any = broken ; const brokenThroughBypass2 : Fields = bypass2 ;

It looks like now we need two very specific steps to break the system, so it will be definitely quite harder to do it, and we should notice something wrong if we have to go to such deep ways to construct a program.

Conclusion

Today we saw an approach to the great promise of program correctness through types, applied to a more mainstream language: Typescript. Although Typescript can't promise the same level of safety as Haskell, that doesn't prevent us from applying a few ideas from Haskell to Typescript.