Other posts in this series:

Applicative functors

I'll start this post off with a tantalizing quote that I first heard from a former colleague/mentor Kris Nuttycombe:

In functional programming, applicatives are the essence of parallel processing, and monads are the essence of sequential processing.

In this post about applicative functors (aka applicatives), and my next planned post about monads, I hope to dig into this notion, and try to impart some intuition as to why this is true.

When I first learned about monads and applicatives, it took me longer to grok applicatives, even though monads are an abstraction built on top of applicatives. I was more used to the idea of monads from using things like flatMap on lists and arrays (even though flatMap for list/array doesn't really impart the feeling of sequential processing), and from using various async types like Promises and Futures. Also, when writing basic application code, you tend to do a lot of sequential processing (in both OO/imperative and FP styles), which tend to relate more to monadic concepts - you often have workflows where you compute a value, then use that value in the next computation, and so on. Additionally, the apply / ap / <*> function (which we'll soon see) is not something you tend to run into as much in its base form, so it's not as immediately recognizable. However, you do see applicative behavior when using things like JavaScript's Promise.all function, where you pass in an array of Promises and get back and array of results, assuming all the promises succeed, or a failed Promise if any of them fail.

The apply / ap / <*> function itself is just so strangely simple yet mysterious, and to me, it was not immediately obvious how this humble function lends itself to parallel processing.

The plan

I'm going to approach the topic of applicatives in the way that I think would have helped me to more quickly understand and appreciate it:

Introduce the apply / ap / <*> function and the APPLY typeclass to show what it is, how simple it is, and to immediately dispel any notions of magic Introduce the pure function and the full-on APPLICATIVE typeclass Mention why apply and pure are in separate typeclasses Show some example implementations of APPLICATIVE , to re-iterate how simple and unmagical it is Show how apply and pure relate to FUNCTOR 's map Demonstrate how map and apply / ap / <*> functions can be re-formulated into functions that makes the parallel capability more clear ( tuple2 , map2 , etc.) Short but detailed walkthrough of how apply works Introduce the map/ap/ap pattern, as I like to call it Show the variation on the map/ap/ap pattern: pure(f)/ap/ap Talk about applicative “effects” Talk about APPLY extensions Talk about applicative validation Talk about the APPLICATIVE laws

Applicative Programming with Effects paper

Before we jump in, I'll just put a link to the paper where I believe the concept of applicative functors was first introduced in 2007. The paper is called Applicative Programming with Effects by Conor McBride and Ross Paterson. As far as academic papers go, it's very readable, so I'd recommend checking it out at some point. That said, if you're new to FP, it's important to remember that with academic papers you may not understand much or any of it initially. If that's the case, don't worry about it - try to plant a few seeds then come back to it in the future. If you're never are able to understand it, that's okay too! You don't need to understand the full scope of everything to make use of the parts you do understand, and you don't need to fully grok all the underlying math and academics. FP is not an all-or-nothing paradigm.

APPLY typeclass

The APPLY typeclass is an extension of the FUNCTOR typeclass that I covered in my blog post about functors Being an extension of FUNCTOR simply means that APPLY is everything that FUNCTOR is, and must abide by the same laws as FUNCTOR , but it also adds something new to the mix, along with some new laws. APPLY adds one new function which is often called apply , ap , or as an operator <*> . I'll try to use the name apply here, but might also refer to ap or <*> . I'll cover the laws at the end of the article to avoid getting lost before we even get started.

We can define the APPLY typeclass as a ReasonML module type like this:

module type APPLY = { type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; } ;

As you can see, APPLY has the type t('a) and the map function we know from FUNCTOR , and adds the new function apply . The apply function is the only difference compared to FUNCTOR .

Using the module include mechanism from OCaml/ReasonML, we can define APPLY like this too:

module type FUNCTOR = { type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLY = { include FUNCTOR ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; } ;

The include here more clearly illustrates the relationship between FUNCTOR and APPLY . If you haven't seen include , it's basically like a module-level copy or spread - it takes whatever's inside the module you're including and spreads it into the module that has the include . So in this case, we're including the type t('a) and let map = ... from FUNCTOR , and then we just add our apply after that.

If you looked closely at the first example, you might have noticed that I aligned the 'a and 'b parameters to illustrate a similarity between map and apply :

let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ;

map applies a pure function to a value that's inside a functor context, while apply can apply a function that's inside a functor context to a value that's inside another functor context. This curiously simple function is what unlocks the power of parallel processing, but if you don't see it yet, that's okay, I didn't either!

APPLICATIVE typeclass

I'm going to introduce one more small concept now, because it's quite simple and it makes sense to just explain it together with apply . This new concept is the pure function. All pure does it take a pure value of type 'a , and stick it in a functor context t('a) . I say “pure value” to differentiate it from an “effectful value,” which is discussed later.

let pure : ' a = > t ( ' a ) ;

Here, 'a can be any type of value you want - the key point is that our other functions just see it as an 'a - they don't know nor care what it is.

The APPLICATIVE typeclass is simply an extension of APPLY that adds this pure function, and its corresponding laws:

module type APPLICATIVE = { // From FUNCTOR type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; // FROM APPLY let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; // For APPLICATIVE let pure : ' a = > t ( ' a ) ; } ;

Or we can write it using include like this:

module type FUNCTOR = { type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLY = { include FUNCTOR ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLICATIVE = { include APPLY ; let pure : ' a = > t ( ' a ) ; } ;

pure is a simple, but interesting function in that we now have the ability to actually put a value into our functor context, whereas before, we could only operate on values that were already in the context, using map and apply . Also note that we do not have a way to get a value out of our applicative functor context once it's in there, i.e. we don't have a t('a) => 'a function here.

APPLY and APPLICATIVE historical notes

For some historical context from Haskell, I believe that when the applicative functor was first identified as a distinct abstraction, the key parts had already been sort of “identified” as functions or concepts in the Monad typeclass. Some work has been done to split off an Applicative typeclass, which includes pure and <*> (the apply operator). Languages and FP libraries that are newer and that don't have the burden of maintaining backwards compatibility seem to be separating the concept of APPLY and APPLICATIVE up front. The reason to split these is that there are things that can conform to APPLY , but not APPLICATIVE , so it makes sense to keep those abstractions separate.

For the rest of the article, we're going to just focus on APPLICATIVE as a whole because it's useful to have both apply and pure at our disposal. However, this is a good time to mention that when faced with a problem, you should always try to follow the rule of least power. Use the abstraction that does what you need with the least amount of power - this makes your code more abstract, which means there are fewer ways for it to do the wrong thing or be used incorrectly, and makes it more general so that more things can use it, or be used with it.

Let's see what APPLICATIVE looks like in some real examples:

Option applicative

Let's implement APPLICATIVE for option('a) . This one is pretty easy, you just “follow the types.” I'm going to implement all the functions at the top-level of Option for convenience, then just alias the functions in the typeclass instances. I'm also going to use include to deal with the hierarchy from FUNCTOR up to APPLICATIVE . include works for both module types and modules, so we can use it in both the typeclasses and the instances.

module type FUNCTOR = { type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLY = { include FUNCTOR ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLICATIVE = { include APPLY ; let pure : ' a = > t ( ' a ) ; } ; module Option = { type t ( a' ) = option ( ' a ) ; let map = ( aToB : ' a = > ' b , optionA : option ( ' a ) ) = > switch ( optionA ) { | Some ( a ) = > Some ( aToB ( a ) ) ; | None = > None ; } ; let apply = ( optionAToB : option ( ' a = > ' b ) , optionA : option ( ' a ) ) = > switch ( optionAToB , optionA ) { | ( Some ( aToB ) , Some ( a ) ) = > Some ( aToB ( a ) ) | ( Some ( aToB ) , None ) = > None | ( None , Some ( a ) ) = > None | ( None , None ) = > None } ; let pure = a = > Some ( a ) ; module Functor : FUNCTOR with type t ( ' a ) = t ( ' a ) = { type nonrec t ( ' a ) = t ( ' a ) ; let map = map ; } ; module Apply : APPLY with type t ( ' a ) = t ( ' a ) = { include Functor ; let apply = apply ; } ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a ) = { include Apply ; let pure = pure ; } ; } ;

apply is pretty straightforward, because there aren't too many ways of implementing it. You could of course just return None in all cases, but that would violate the APPLY laws, which I'll cover at the end of the article. You can only ever get a 'b value if you have both the 'a => 'b function and the 'a value, so the cases where we only have the function or only the value, or neither just have to return None .

As for pure , it just takes a value and sticks it in Some .

Once I have map , apply , and pure , I can just create my instance modules Functor , Apply , and Applicative by aliasing the functions in the right places. As you can see, I'm defining each typeclass separately, even though Applicative can do everything Apply and Functor can do. In ReasonML, this is not strictly necessary to do, but I like to do it anyway, because it helps to quickly identify what a particular type can do, and it serves as a constant reminder of what each typeclass does - kind of like in-code, type-checked documentation about a type.

In fact, the Option module itself actually conforms to APPLICATIVE already because it has all the necessary parts, so it's not even strictly necessary to define Functor , Apply and Applicative , but I like to do it anyway to be explicit, and it feels more like Haskell/PureScript typeclass instances. Also, I like to annotate the module types, just to make it clear what my intentions are with these modules, even though the compiler can infer the module types.

That's it for option !

Js.Promise applicative

Now let's try implementing apply for a more complex type: Js.Promise.t('a) .

First think about the type signature, and substitute Js.Promise.t('a) for t('a) :

let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; let apply : ( Js.Promise.t ( ' a = > ' b ) , Js.Promise.t ( ' a ) ) = > Js.Promise.t ( ' b )

If you've worked with promises, it's probably not too hard to see how this is implemented - you just need to wait for the 'a => 'b function and the 'a value, and then resolve with the function applied to the value.

One quick observation here is that in theory, we should be able to fire off both of these promises at the same time, as they are not dependent on one-another - the promise of 'a => 'b doesn't care about the promise of 'a and vice versa - neither of them need to wait for the other to do its job. Our apply function has to wait for both of these promises, because we need the 'a => 'b function and the 'a value at the same time to use them together to get the 'b , but the input promises themselves are independent.

That said, these promises have already been “fired off” before we even get our hands on them in apply , because that's what JS promises do - they start running as soon as you construct them! So in apply we get two already-running “hot” promises, and we just need to wait for both of them to finish.

module Promise = { let apply = ( promiseAToB : Js.Promise.t ( ' a = > ' b ) , promiseA : Js.Promise.t ( ' a ) ) = > { promiseAToB | > Js.Promise.then_ ( aToB = > promiseA | > Js.Promise.then_ ( a = > Js.Promise.resolve ( aToB ( a ) ) ) ) ; } ; } ;

We're using Js.Promise.then_ here to just wait for the promise of the 'a => 'b function to resolve, then we wait for the promise of the 'a value to resolve, and then we finally resolve the chain with aToB(a) to get our 'b value. This chain might look like we're making this operation sequential, but remember that both promises are already running when we get them so the inner promise might finish before the outer, but it doesn't matter to us. We need them both to resolve before we can get our 'b value. If we were constructing the inner promise here, it would matter, but the promise was already constructed and running when we got it.

Another thing to note is that if either of the Js.Promises fail, the apply function will fail. Again, we can only succeed if both inputs succeed and give us our 'a => 'b function and our 'a value. With applicatives, we actually have a few different options for handling errors, but here I'm going to keep it simple and just fail fast using the default failure mechanism of chained Promises .

Example usage:

Promise.apply ( Js.Promise.resolve ( a = > a * 2 ) , Js.Promise.resolve ( 42 ) ) | > Js.Promise.then_ ( a = > Js.Promise.resolve ( Js.log ( a ) ) ) ; // 84

We could have also “cheated” and just used Js.Promise.all2 here, because someone has already implemented for us:

let apply = ( promiseAToB , promiseA ) = > Js.Promise.all2 ( ( promiseAToB , promiseA ) ) | > Js.Promise.then_ ( ( ( aToB , a ) ) = > Js.Promise.resolve ( aToB ( a ) ) ) ;

This isn't actually cheating - it's perfectly fine to use functions that already exist to implement typeclasses, assuming they follow the laws. Speaking of all2 , we'll soon see how to implement this ourselves for any applicative, not just Js.Promise , and we'll also see that by implementing apply and pure for a type, we can get a ton of other stuff for free!

As for pure , we just need to take a value of type 'a and get it into the Js.Promise context, so we can use Js.Promise.resolve .

Here is the full Promise module:

module Promise = { type t ( ' a ) = Js.Promise.t ( ' a ) ; let map = ( aToB , promiseA ) = > { promiseA | > Js.Promise.then_ ( a = > Js.Promise.resolve ( aToB ( a ) ) ) ; } ; let apply = ( promiseAToB : Js.Promise.t ( ' a = > ' b ) , promiseA : Js.Promise.t ( ' a ) ) = > { promiseAToB | > Js.Promise.then_ ( aToB = > promiseA | > Js.Promise.then_ ( a = > Js.Promise.resolve ( aToB ( a ) ) ) ) ; } ; let pure = a = > Js.Promise.resolve ( a ) ; module Functor : FUNCTOR with type t ( ' a ) = t ( ' a ) = { type nonrec t ( ' a ) = t ( ' a ) ; let map = map ; } ; module Apply : APPLY with type t ( ' a ) = t ( ' a ) = { include Functor ; let apply = apply ; } ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a ) = { include Apply ; let pure = pure ; } ; } ;

The implementation of apply for Js.Promise gives us a first glimpse as to why applicatives are associated with parallel processing - we get two independent “effectful values” (the promises), and we wait for both of them to finish independently before we use them to do our final computation. I'll talk about what “effectful values” means a little later.

List/array/tree applicative

The types list('a) , array('a) , and other “multi-value data types” like binary trees, etc. have applicative instances, but I'm going to skip these in this article, as I don't personally think they are immediately helpful in gaining an initial intuition about applicatives.

If you think about the signature of apply for a list('a) , you'd get the following:

let apply = ( list ( ' a = > ' b ) , list ( ' a ) ) = > list ( ' b ) ;

Basically, you have a list of functions and a list of values, and you need to apply some or all of the functions to some or all the values. You can probably imagine how you might end up with a cartesian product where all the functions are applied to all the values to produce a new longer list of un-obvious utility. The same idea applies for array('a) . For a binary tree, imagine a binary tree of functions Tree.t('a => 'b) applied to a binary tree of values Tree.t('a) . These applicatives can be implemented, but it's not something you run into as much in day-to-day use, so I'll not get into these at this time, but you are welcome to give it a shot yourself!

Result applicative

Now let's try to implement APPLICATIVE for Result.t('a, 'e) . Since FUNCTOR / APPLY / APPLICATIVE want to operate on a type t('a) , we'll use the module functor trick (see the functors article) again to “lock-in” our error type:

module type TYPE = { type t ; } ; module type FUNCTOR = { type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLY = { include FUNCTOR ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; } ; module type APPLICATIVE = { include APPLY ; let pure : ' a = > t ( ' a ) ; } ; module Result = { type t ( ' a , ' e ) = | Ok ( ' a ) | Error ( ' e ) ; let map = ( aToB , resultA ) = > switch ( resultA ) { | Ok ( a ) = > Ok ( aToB ( a ) ) | Error ( e ) = > Error ( e ) } ; let apply = ( resultAToB , resultA ) = > switch ( ( resultAToB , resultA ) ) { | ( Ok ( aToB ) , Ok ( a ) ) = > Ok ( aToB ( a ) ) | ( Error ( e1 ) , Ok ( a ) ) = > Error ( e1 ) | ( Ok ( aToB ) , Error ( e2 ) ) = > Error ( e2 ) | ( Error ( e1 ) , Error ( e2 ) ) = > Error ( e1 ) // !! ! } ; let pure = a = > Ok ( a ) ; module WithError = ( E : TYPE ) = > { module Functor : FUNCTOR with type t ( ' a ) = t ( ' a , E.t ) = { type nonrec t ( ' a ) = t ( ' a , E.t ) ; let map = map ; } ; module Apply : APPLY with type t ( ' a ) = t ( ' a , E.t ) = { include Functor ; let apply = apply ; } ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a , E.t ) = { include Apply ; let pure = pure ; } ; } ; } ;

The implementation of pure is obvious because there's only one way to turn a value of type 'a into a Result.t('a, 'e) ; however, the implementation of apply poses an interesting conundrum in the | (Error(e1), Error(e2)) case - we have two errors (of the same type 'e ), but we need to return a single error value in the resulting Error('e) constructor. We don't know what the 'e value is, so we don't have enough information to know how to pick one error or the other, or combine them, so we'll just pick one side ( e1 in this case), and fail the function. Just like with option and Js.Promise , the only way we can succeed is in the | (Ok(aToB), Ok(a)) case - we need both the function and the value in order to produce the result of type 'b with Ok(aToB(a)) .

We'll leave Result like this for now, but we'll revisit this error handling issue later in the section on “applicative validation”.

Function applicative

As we saw in the functor article, we could implement FUNCTOR for the function of type 'x => 'a . Let's see how to implement APPLICATIVE for this type:

module Function = { type t ( ' x , ' a ) = ' x = > ' a ; let map = ( aToB : ' a = > ' b , xToA : ' x = > ' a ) : ( ' x = > ' b ) = > { x = > aToB ( xToA ( x ) ) ; } ; let apply = ( xToAToB : ' x = > ( ' a = > ' b ) , xToA : ' x = > ' a ) : ( ' x = > ' b ) = > { x = > { let aToB = xToAToB ( x ) ; let a = xToA ( x ) ; aToB ( a ) ; } ; } ; let pure = ( a : ' a ) : ( ' x = > ' a ) = > { _ = > a ; } ; module WithArgument = ( X : TYPE ) = > { module Functor : FUNCTOR with type t ( ' a ) = t ( X.t , ' a ) = { type nonrec t ( ' a ) = t ( X.t , ' a ) ; let map = map ; } ; module Apply : APPLY with type t ( ' a ) = t ( X.t , ' a ) = { include Functor ; let apply = apply ; } ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( X.t , ' a ) = { include Apply ; let pure = pure ; } ; } ; } ;

The apply function here is given a function 'x => ('a => 'b) , and a function 'x => 'a , and needs to return a function 'x => 'b . So in this resulting function, we are given an 'x , and need to return a 'b . To do this, we use the 'x to get our 'a => 'b function from the first argument, and the same 'x to get our 'a value from the second argument, and just apply the function. This is a great exercise in “following the types” - we don't know up-front what we're doing, but we just look at the types and work it out. If you do this in FP, you'll often find that there's only one valid way to actually implement something, like in this case.

The pure function is just given a value 'a and needs to return a function 'x => 'a , but we already have our 'a , so we just return a function that throws away the input and returns our 'a . This function is commonly called const :

let const = ( a , _ ) = > a ;

This looks funny in ReasonML syntax, because it looks like a function that takes an a argument and another ignored argument, and then just returns the a , but if you think about it in terms of partial application, if you supply the a argument, you now have a function _ => a . This is useful for creating a function that just produces a constant value regardless of the input, hence the name const . It's another one of those utilities like identity: 'a => 'a that is so trivially simple, but sometimes it's exactly what you need.

Overall, the usefulness of this APPLICATIVE instance for 'x => 'a is not immediately obvious, but we'll explore it more when we talk about the reader monad.

Also, I wanted to show this as an example of an APPLICATIVE that's not just a static data value, to demonstrate that these concepts can apply to functions too.

JSON decoder applicative

Let's show a more real-world example of an applicative: the JSON decoder function we saw in the functor article. I'll just jump right into it, and explain after, but I encourage you to try it yourself too.

module Decoder = { module Error = { type t = | ExpectedBool ( Js.Json.t ) | ExpectedString ( Js.Json.t ) | Other ; } ; type t ( ' a ) = | Decode ( Js.Json.t = > Result.t ( ' a , Error.t ) ) ; let map = ( aToB , ( Decode ( jsonToResultA ) ) ) = > { Decode ( json = > jsonToResultA ( json ) | > Result.map ( aToB ) ) ; } ; let apply = ( Decode ( jsonToResultAToB ) , Decode ( jsonToResultA ) ) = > { Decode ( json = > { let resultAToB : Result.t ( ' a = > ' b , Error.t ) = jsonToResultAToB ( json ) ; let resultA : Result.t ( ' a , Error.t ) = jsonToResultA ( json ) ; Result.apply ( resultAToB , resultA ) ; } ) } ; let pure = a = > Decode ( _ = > Result.pure ( a ) ) ; module Functor : FUNCTOR with type t ( ' a ) = t ( ' a ) = { type nonrec t ( ' a ) = t ( ' a ) ; let map = map ; } ; module Apply : APPLY with type t ( ' a ) = t ( ' a ) = { include Functor ; let apply = apply ; } ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a ) = { include Apply ; let pure = pure ; } ; } ;

The apply function here feels a lot like the apply function we saw for 'x => 'a , and that's because it is very similar: Js.Json.t => Result.t('a, Error.t)) is of a similar form to 'x => 'a , except the 'a value is just buried in another (applicative) type Result . In Decoder.apply we have a decoder of a function, and a decoder of a value. Recall that a decoder is just a function that takes a Js.Json.t value and produces a Result , so we feed the input Js.Json.t value into each decoder argument to get a Result.t('a => 'b, Error.t) and a Result.t('a, Error.t) . Here we have a function 'a => 'b buried in a Result and a value 'a buried in a Result - so we can take advantage of the fact that Result is also an applicative functor, and just use Result.apply to apply the wrapped function to our wrapped value to get our Result.t('b, Error.t) ! Take note that we've again not actually done any work at this point - no JSON has been decoded - we've simply composed some functions to turn a decoder of 'a into a decoder of 'b . Think about how you might do something like this in an imperative or OO language - I'm pretty sure I would have done it in a much less elegant way!

This example also serves to show that you don't always need to completely understand what you're doing (or even the end result) to create the typeclass instances for a type. As long as you can do it, the types line up, and your implementation follows the laws, there's a good chance you did it correctly. Sometimes all you need to do is just follow the types! We're kind of glossing over the laws, and we haven't yet seen how to test the laws, but we'll hopefully try that out in another blog post. The concept of a “decoder of a function” 'a => 'b doesn't make much intuitive sense, but we'll soon see where this comes into play, and maybe it will become clear as to why we're doing these things.

map in terms of apply and pure

We've now seen that APPLICATIVE is an extension of FUNCTOR - it adds the functions apply and pure to the t('a) and map function we have from FUNCTOR . One interesting thing to note at this point is that you can actually implement map in terms of just apply and pure :

// ( pseudocode ) let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) = .. . ; let pure = ' a = > t ( ' a ) = .. . ; let map = ( aToB , fa ) = > apply ( pure ( aToB ) , fa ) ;

Here, our map function is given a function 'a => 'b and a value of type t('a) . We “lift” the function into our applicative context t using pure(aToB) , which gives us a value of type t('a => 'b) , then we apply that “wrapped” function to our t('a) value using apply , because that's what apply does:

let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ;

When we get into monads, we'll also see that you can implement apply in terms of the monadic function flatMap (aka bind or >>= ).

I just wanted to point this out, as sometimes it's actually easier or more convenient to implement one of the higher-level functions for a type, and then just implement the lower-level functions in terms of the “more powerful” ones.

Reformulation of apply as tuple2

Let's now look at a reformulation of apply that better demonstrates the parallel nature of applicatives. If this section bends your brain too far, I'd highly recommend just trying it for yourself - it can take some time and practice for this to sink in, especially if you're not used to working with functions with curried arguments.

Our goal will be to come up with a function of the following type, using only the powers offered to us by APPLICATIVE : t('a) , map , apply , and pure , and nothing more. To be honest, we're actually just going to just use APPLY here - we don't actually need pure for this.

let tuple2 : ( t ( ' a ) , t ( ' b ) ) = > t ( ( ' a , ' b ) ) ;

Given two “effectful” values t('a) and t('b) , let see if we can “run” them both to get at the values 'a and 'b , then combine those values in a “effectful” tuple. We're going to assume the result is also wrapped in our applicative context t , because given the types of map , apply , and pure , it doesn't look like we have any way to “get rid of” or “get out of” our t context - we can only put things into it using pure , and operate on things inside of our context using map and apply .

Let's start with a function that can create our tuple:

let makeTuple2 = ( a , b ) = > ( a , b ) ;

Since we're in a curried language, another way to think about this function is like this:

let makeTuple2 = a = > { b = > ( a , b ) ; } ;

These are both the same thing, but the second form might help with the intuition - given an 'a , we can return a function 'b => ('a, 'b) . The first makeTuple2 can also do this just as well, but it's not as clear with the ReasonML syntax of (a, b) => (a, b) - this looks like a function of arity 2 that just returns a tuple, but in reality, it's a curried function in ReasonML - a function of a single argument that returns another function of a single argument.

So back to our tuple2 challenge:

let tuple2 : ( t ( ' a ) , t ( ' b ) ) = > t ( ( ' a , ' b ) ) ;

We're given a t('a) and a t('b) , and we have a function makeTuple2: 'a => ('b => ('a, 'b)) . We have no way to get the 'a value out of our t('a) , we can only operate on the value inside the context using map and apply . We again have a value of the form t('a) (and a t('b) ), but we don't have a function of the form t('a => 'b) so apply doesn't seem immediately applicable, but we do have our “pure” makeTuple2 function, so let's just try the only thing that seems to make sense, map: ('a => 'b, t('a)) => t('b)

Let's quickly consider our function makeTuple2: 'a => ('b => ('a, 'b)) again. If we write it like this, we can sort of fuzz the right hand side into an opaque type like this:

' a = > ( ' b = > ( ' a , ' b ) ) ; ' a = > ' c where ' c represents our ' b = > ( ' a , ' b ) function

Given this type 'c , we can write our map like this:

let map : ( ' a = > ' c , t ( ' a ) ) = > t ( ' c ) ;

and if we substitute 'c with what it actually is:

let map : ( ' a = > ' c , , t ( ' a ) ) = > t ( ' c ) ; let map : ( ' a = > ( ' b = > ( ' a , ' b ) ) , t ( ' a ) ) = > t ( ' b = > ( ' a , ' b ) ) ;

If it's not clear, I'm just trying to demonstrate what happens when you map a function of more than one argument over a functor whose type matches the type of the first argument of our function - you end up with a function inside your functor context. And this function has one fewer argument than what you started with.

Let's try mapping makeTuple2 over our fa , which is a t('a) :

let fa : t ( ' a ) = .. . ; let fBToAB : t ( ' b = > ( ' a , ' b ) ) = map ( makeTuple2 , fa ) ;

Our fBToAB has the type t('b => ('a, 'b)) - we mapped a function of multiple arguments over our functor and ended up with a function of one fewer arguments inside our functor context. This ability to map and apply a function of multiple arguments over a bunch of individual functor values is the key to how applicatives work.

After this map over the first value t('a) , we now have a function of the form t('x => 'y) (actually t('b => ('a, 'b)) ). map can't deal with this because map wants a pure/non-wrapped function, but apply knows what to do with a wrapped function:

let fa : t ( ' a ) = .. . ; let fb : t ( ' b ) = .. . ; // map first - now we have a function inside a functor let fBToAB : t ( ' b = > ( ' a , ' b ) ) = map ( makeTuple2 , fa ) ; // then apply this wrapped function to the wrapped ' b value let fAB : t ( ( ' a , ' b ) ) = apply ( fBToAB , fb ) ;

We've now “filled” all the arguments of makeTuple2 , and we get our final result - a tuple wrapped in our applicative context: t(('a, 'b)) .

To wrap up, we'll now write our tuple2 function in terms of map and apply . (Stay tuned to see a cleaner, more intuitive way to do this below):

let tuple2 = ( fa : t ( ' a ) , fb : t ( ' b ) ) = > { let makeTuple2 = ( a , b ) = > ( a , b ) ; apply ( map ( makeTuple2 , fa ) , fb ) ; } ;

Let's run through a quick demonstration of this using options to make it more concrete:

let makeTuple2 = ( a , b ) = > ( a , b ) ; let optionA = Some ( 42 ) ; let optionB = Some ( " hi " ) ; // Map the pure function on our optionA // This results in a function ' b = > ( ' a , ' b ) * * that is inside an option * * let optionBToTuple : option ( ' b = > ( int , ' b ) ) = Option.map ( makeTuple2 , optionA ) ; // Now we have a function inside an option , and a value ` b inside // an option , and that's what apply deals with : let optionTuple : option ( ( int , string ) ) = Option.apply ( optionBToTuple , optionB ) ; // optionTuple = = Some ( ( 42 , " hi " ) )

If you're not getting it just by looking, try working through it it yourself, and look carefully at the types along the way. Basically, we're using map to apply the pure function to our option('a) for the first step, then using apply to apply a function (which is now inside an option ) to our option('b) .

One final note on this example: in the section where we implemented map in terms of apply and pure , we saw that if we lifted a function into the applicative context using pure , we could then just use apply to apply the function to our effectful value. We could also implement tuple2 in this same style - we can lift the pure function into our context first, then just apply it, then apply the resulting wrapped function to get the final result:

let tuple2 = ( fa : t ( ' a ) , fb : t ( ' b ) ) = > { let makeTuple2 = ( a , b ) = > ( a , b ) ; apply ( apply ( pure ( makeTuple2 ) , fa ) , fb ) ; } ;

Apply multiple times

Surprisingly, this pattern of map and apply works for any number of arguments - we can just keep apply ing the resulting functions to the next values until we run out of arguments in our function and arrive at our final result. That said, we will only succeed in getting the final result if each step along the way is also successful. If any step fails, the whole computation will fail, but we actually have some cool options for how we can deal with errors.

If you think about how apply is implemented, it gets a wrapped function, which basically carries information about the previous computations, and a wrapped value, which is the “current value” on which we want to operate. The previous computations may have already failed, but apply will still go through the motion of considering each input value, regardless of what happened in the past. That said, apply doesn't give us the ability to “recover” a computation that failed - we can't create a successful result unless we have both the 'a => 'b function and the 'a value, so all we can do is propagate the previous errors, or possibly add our own error to the mix (which we'll see later). This inability to fork or recover a computation is one of the reasons that applicatives are strictly less powerful than monads. However, being less powerful sometimes has its advantages - for example, because an applicative can't fork the flow of a computation (i.e. recover from an error, or create some new processing branch), we are forced to feed it all the information we want to process at once. Knowing the full-scope of the problem up-front can sometimes unlock certain optimizations and allow us to make certain assumptions about how the computation will occur.

Anyway, to demonstrate how to apply the pattern to functions of more arguments, below is an example of the map / apply pattern being used with a (curried) arity 3 function:

let makeTuple3 = ( a , b , c ) = > ( a , b , c ) ; let optionA = Some ( 42 ) ; let optionB = Some ( " hi " ) ; let optionC = Some ( true ) ; let optionBToCToTuple : option ( ( ' b , ' c ) = > ( int , ' b , ' c ) ) = Option.map ( makeTuple3 , optionA ) ; let optionCToTuple : option ( ' c = > ( int , string , ' c ) ) = Option.apply ( optionBToCToTuple , optionB ) ; let optionTuple : option ( ( int , string , bool ) ) = Option.apply ( optionCToTuple , optionC ) ; // optionTuple = Some ( ( 42 , " hi " , true ) )

More succinctly, we could write it like this:

let tuple3 = ( fa : t ( ' a ) , fb : t ( ' b ) , fc : t ( ' c ) ) = > { let f = ( a , b , c ) = > ( a , b , c ) ; apply ( apply ( map ( f , fa ) , fb ) , fc ) ; } ; // or using pure to immediately lift our function into the applicative context : let tuple3 = ( fa : t ( ' a ) , fb : t ( ' b ) , fc : t ( ' c ) ) = > { let f = ( a , b , c ) = > ( a , b , c ) ; apply ( apply ( apply ( pure ( f ) , fa ) , fb ) , fc ) ; } ;

Again, this pattern works for any number of arguments - just keep apply ing until you “fill” all the arguments of your function. Try it yourself with tuple4 and so-on.

The tuple2: (t('a), t('b)) => t(('a, 'b)) function is sometimes called product , product2 or zip , because it takes two effectful values and combines them into the most basic product type - a tuple. I hope to write about product and sum types in a later post. Looking at tuple2 it's likely more clear as to why applicatives are associated with parallel operations - we start with two independent “effectful” values t('a) , and t('b) , and we produce a value t(('a, 'b)) that's a combination of our two inputs (assuming they both “succeed”).

Well, that was my attempt at describing how applicatives work, but not sure how successful it was. If you got lost somewhere along the way, I'd again recommend trying it yourself with a concrete type like option . It's hard to explain, but once you slog through it enough times, you'll get it. Try implementing these functions too, to see how the patterns expands to more values:

let tuple4 : ( t ( ' a ) , t ( ' b ) , t ( ' c ) , t ( ' d ) ) = > t ( ( ' a , ' b , ' c , ' d ) ) ; let tuple5 : ( t ( ' a ) , t ( ' b ) , t ( ' c ) , t ( ' d ) , t ( ' e ) ) = > t ( ( ' a , ' b , ' c , ' d , ' e ) ) ; // etc .

If you haven't recognized it yet, these look at lot like the Js.Promise.allN functions, but there's no specific mention of Js.Promise here - we're just using the functions from our APPLY abstraction.

map2, map3, etc.

Now let's generalize our tuple2 , tuple3 , etc. functions. Recall the defintion we made for tuple2 :

let tuple2 = ( fa : t ( ' a ) , fb : t ( ' b ) ) = > { let makeTuple2 = ( a , b ) = > ( a , b ) ; apply ( map ( makeTuple2 , fa ) , fb ) ; } ;

The makeTuple2 here is just a function that basically takes two inputs, and “combines” them into something else. We can allow the caller to pass in this function, so they can combine the values however they want, rather than having to deal with a tuple. We'll call this function map2 :

let map2 = ( f : ( ' a , ' b ) = > ' c , fa : t ( ' a ) , fb : t ( ' b ) ) : t ( ' c ) = > { apply ( map ( f , fa ) , fb ) ; } ;

We call this map2 because it looks a lot like map - it applies a pure function to some independent values that are each inside their own functor context.

let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; let map2 : ( ( ' a , ' b ) = > ' c , t ( ' a ) , t ( ' b ) ) = > t ( ' c ) ;

It's important to note that the result of both of these is still inside our functor context t - we don't have a way to get it out of that context. You can also create map3 , map4 , etc. in the same way. These functions are sometimes called liftA2 , liftA3 , etc., as they act to “lift” a pure function ('a, 'b) => 'c into the applicative context (t('a), t('b)) => t('c) (using the power of the APPLY typeclass).

For one final side note, we can also now implement our tuple2 function in terms of map2 , and the same applies for map3 / tuple3 , etc.

let tuple2 = ( fa : t ( ' a ) , fb : t ( ' b ) ) : t ( ( ' a , ' b ) ) = > map2 ( ( a , b ) = > ( a , b ) , fa , fb ) ;

These mapN functions are useful for running a bunch of independent applicative effects and combining the results however we please.

The map/ap/ap pattern

Let's get super fancy, and cast off our fear of weird operators, and do the above using some Haskell-style infix operators. Once you grok the pattern (and memorize the operators), the infix-based approach actually becomes quite beautiful. The usual caveat with operators applies - they add a level of opacity and abstraction that can be quite hostile to newcomers, so it's best to introduce them with some hand-holding, and not just dump them on people without the prerequisite setup.

Let's first define an operator for map. We're going to use <$> because it's the conventional operator for map in many other FP languages, and it's useful to start to recognize it for what it is.

let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) = .. . ; let ( < $> ) = map ;

Remember that the function is on the left, and the effectful value on the right, so you'd use this like so:

let f = a = > a * 2 ; let fa = Some ( 42 ) ; // The following are all the same : let _ = f < $> fa ; let _ = map ( f , fa ) ; let _ = fa | > map ( f ) ;

Now let's define the operator <*> as apply , again the “function” part is on the left (even though the function for apply is wrapped), and the effectful value is on the right. Again, we're using <*> for apply , because that's what many other languages do, and it helps to get used to it.

let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) = .. . ; let ( < * > ) = apply ;

Now let's take a look back at our map3 function:

let map3 = ( f : ( ' a , ' b , ' c ) = > ' d , fa : t ( ' a ) , fb : t ( ' b ) , fc : t ( ' c ) ) : t ( ' d ) = > { apply ( apply ( map ( f , fa ) , fb ) , fc ) ; } ;

Infix operators let you move the name of a function between the two arguments, so we could write this like below - just start from the innermost function application ( map ) and move the name of the function between the args, and work your way out:

apply ( apply ( map ( f , fa ) , fb ) , fc ) ; apply ( apply ( f ` map ` fa ) , fb , fc ) ; apply ( f ` map ` fa ` apply ` fb , fc ) ; f ` map ` fa ` apply ` fb ` apply ` fc ;

Now just replace map with <$> and apply with <*> :

let map3 = ( f , fa , fb , fc ) = > f < $> fa < * > fb < * > fc ;

If you read it left-to-right, we start with our function f , and we map it over our first effectful value fa: t('a) , so now we have a wrapped function, which we apply to our next effectful value fb: t('b) , and so on. The two things below are the same thing - one just uses infix operators, and the other uses normal named functions:

let _ = f < $> fa < * > fb < * > fc ; let _ = apply ( apply ( map ( f , fa ) , fb ) , fc ) ;

The operators work for any number of arguments, so we could implement map4 , map5 , etc. like this too:

let map4 = ( f , fa , fb , fc , fd ) = > f < $> fa < * > fb < * > fc < * > fd ; let map5 = ( f , fa , fb , fc , fd , fe ) = > f < $> fa < * > fb < * > fc < * > fd < * > fe ; // etc .

This approach of applying a pure function to a series of “effectful” values is very common in FP languages like Haskell and PureScript, so you'll likely run into this at some point. I like to call this this map/ap/ap pattern, because you just start with a pure function, you map it over the first value, then you just ap it over all the remaining values.

You may run into the alternate form of this pattern, like this:

let map3 = ( f , fa , fb , fc ) = > pure ( f ) < * > fa < * > fb < * > fc ;

We've seen before how we can lift a pure function into our applicative context using pure and then just apply it, so this is just another way of achieving the same thing as map/ap/ap. The difference is that we immediately lift our function into the applicative context, so we can't use map ( <$> ) to apply it the first time - we have to go straight to apply ( <*> ) for the first application, and for all the rest.

We'll see some more concrete uses of these operators below in “applicative validation”.

Applicative “effects”

Let's take a break from code to talk about the concept of “effects” and “effectful” values. These concepts are a bit overloaded in functional programming, so let's take a look at a few examples. Let's assume our functions can't throw exceptions, and we're in a language that doesn't have the concept of a null value.

The function 'a => 'b is an example of what appears to be a pure/non-effectful function. Based on the types, there's no indication that the function can fail to produce a value or fail for any other reason. There's also no indication that the computation will be asynchronous - it appears to just map an argument of type 'a to a result value of type 'b .

Let's now consider the function 'a => option('b) . In this case, we are now made aware (via the type system) that this function might either produce a value of type 'b ( Some(b) ), or might fail to produce a value ( None ). In terms of “effects” we can say that this function has the “effect” of being unable to produce a value in some, or possibly all cases. This isn't a “side effect” like writing to STDOUT or reading the system time, but a behavior of the function where we're no longer just mapping inputs to nice and clean outputs. Just like option , it turns out we can actually model real-world side effects via the type system, but we'll not get into that now.

How about the function 'a => Result.t('b, 'e) ? Here we can observe the “effect” of possible failure - the function can either succeed and produce a value of type Ok(b) , or fail and produce an error of type Error(e) . This effect of possible failure is represented by the type Result.t('a, 'e) .

Let's quickly mention the function 'a => list('b) . We've ignored lists/arrays/trees/etc. to this point, but this type of function can also be seen as an effect - the effect of indeterminate results. Here we could get no results, a single result, or any number of results, and we can think about this effect similarly to how we think about other effects.

Now consider the RationalJS/future Future type. The way this type is defined and implemented, it represents an asynchronous computation that cannot fail. Being asynchronous, it's possible that it will just never complete, but it has no way of representing a computation that completed but failed. We can call this an “async effect.” With this library, if you need to represent an asynchronous computation that can fail, you're advised to use the type Future.t(Result.t('a, 'e)) - here we're combining the effect of possible failure with a separate async effect. This is a good demonstration of how effects can be “stacked” via the type system, and it's also a good example to show that it's possible to “run” or “remove” a single effect, while leaving other effects intact for separate or later processing. For example, if you were to allow the Future to complete, you're given a value of type Result.t('a, 'e) which still has the effect of possible failure. This failure effect can be “removed” by attempting to map or flatMap the value, and handling the case of Error , either by converting the error to a successful value, or handling it in some other way.

The Js.Promise.t('a) type also has two effects: an async effect and the effect of possibile failure. With this type, the possibility of failure is not directly observable by the type alone. This is okay - it just means that the type has an implicit/hidden/non-polymorphic way of representing the failure condition, but there's nothing inherently wrong with that, you just have to know that with a Js.Promise , there is a possibility of failure. You might run into code that uses Js.Promise.t(Result.t('a, 'e)) , but this is problematic in that we now have two ways to represent failure - the “hidden” Js.Promise error type, and the 'e type from the Result . I hope to talk about Js.Promise more in another blog post, but I'll leave it at that for now.

As a final example, if we look at the JSON decoder type like the Decoder.t('a) we defined above, we have a value that represents the effect of parsing a JSON value into some type, and the effect of possible of failure (which again is not represented by a polymorphic error type, but by a fixed error type that we've defined with the decoder). This decoding effect is a little different than the others in that it's more of a deferred computation, but the same idea applies - the effectful value itself doesn't do anything until we “run it” (by giving it a JSON value), and letting it produce a Result , which is how the effect of possible failure manifests itself, and can be handled or “run” separately from the decoding effect.

Before we move on, let's look back at the “pure” function 'a => 'b . In many languages, including ReasonML, this type of function can actually have all sorts of effects which do not manifest themselves as “effectful values,” but just as plain old side effects. We can do I/O, read the system time, and even launch the nukes, and nobody would be the wiser. It turns out you can actually encapsulate these kinds of “side effects” in the type system using a wide variety of different techniques, but we won't get into that now. The topic of effect management is a very actively evolving and quite fascinating topic in FP right now.

In summary, when we talk about applicatives (and monads), it's common to talk about “effectful” values and “running” said effects. The concept of “running” an effect sometimes can result in the “removal” of the effect, which indicates that the effect has been handled, processed, or executed, but this can mean different things for different types of effects. One key aspect of functional programming is the separation of describing or encoding work to do and the actual execution or interpretation of that work. If you're coming from a more imperative language or style, where effects kind of just happen when they happen, and are manifested by null values, exceptions, or spaghetti code that just does whatever it wants whenever it wants, the functional approach will take a little getting used to, but it unlocks a great deal of control and power.

Apply extensions

Now that we've seen some of the things that you can do with APPLICATIVE , let's try to capture those ideas so we can reuse them for every applicative. In the functor article, we saw that we could use a module functor to add “extensions” or “freebies” for any instance of FUNCTOR , and now we'll do the same thing for APPLY . Note that all of these extensions just need APPLY and not APPLICATIVE , because these things only need map and apply , and not pure .

module ApplyExtensions = ( A : APPLY ) = > { let applyFirst = ( fa : A.t ( ' a ) , fb : A.t ( ' b ) ) : A.t ( ' a ) = > { let f = ( a , _ ) = > a ; // const A.apply ( A.map ( f , fa ) , fb ) ; } ; let applySecond = ( fa : A.t ( ' a ) , fb : A.t ( ' b ) ) : A.t ( ' b ) = > { let f = ( _ , b ) = > b ; // const ( id ) A.apply ( A.map ( f , fa ) , fb ) ; } ; let map2 = ( f : ( ' a , ' b ) = > ' c , fa : A.t ( ' a ) , fb : A.t ( ' b ) ) : A.t ( ' c ) = > { A.apply ( A.map ( f , fa ) , fb ) ; } ; let map3 = ( f : ( ' a , ' b , ' c ) = > ' d , fa : A.t ( ' a ) , fb : A.t ( ' b ) , fc : A.t ( ' c ) ) : A.t ( ' d ) = > { A.apply ( A.apply ( A.map ( f , fa ) , fb ) , fc ) ; } ; // TODO : map4 , map5 , etc . let mapTuple2 = ( f : ( ' a , ' b ) = > ' c , ( fa : A.t ( ' a ) , fb : A.t ( ' b ) ) ) : A.t ( ' c ) = > { map2 ( f , fa , fb ) ; } ; let mapTuple3 = ( f : ( ' a , ' b , ' c ) = > ' d , ( fa : A.t ( ' a ) , fb : A.t ( ' b ) , fc : A.t ( ' c ) ) ) : A.t ( ' d ) = > { map3 ( f , fa , fb , fc ) ; } ; // TODO : mapTuple4 , mapTuple5 , etc . let tuple2 = ( fa : A.t ( ' a ) , fb : A.t ( ' b ) ) = > map2 ( ( a , b ) = > ( a , b ) , fa , fb ) ; let tuple3 = ( fa : A.t ( ' a ) , fb : A.t ( ' b ) , A.t ( ' c ) ) = > map3 ( ( a , b , c ) = > ( a , b , c ) , fa , fb , fc ) ; // TODO : tuple4 , tuple5 , etc . - as many as you want } ; module ApplyInfix = ( A : APPLY ) = > { module AE = ApplyExtensions ( A ) ; let ( < * > ) = A.apply ; let ( < * ) = AE.applyFirst ; let (* > ) = AE.applySecond; };

There's a lot going on here, so let's break it down. We're creating a module functor ApplyExtensions that takes an instance of APPLY and uses that instance to define a bunch of extension functions. Because we have an APPLY we are constrained to only having access to a type A.t('a) , the A.map function, and the A.apply function, but we can do a lot with just these. Note that you can see this in action in Relude_Extensions_Apply.

We're first defining functions called applyFirst and applySecond . These are interesting functions that take two effectful values, and runs them both, but then only produces the result of the first or the second, respectively. The key here is that we're actually running both effects, and not just discarding the undesired side immediately - both effects must succeed in order for us to get the result. If either or both effects fail, we get an unsuccessful result, which can mean different things depending on which APPLY instance we're using. E.g for option , the “unsuccessful value” is None , and for Result the unsucessful value is Error(e) , etc. These two functions are more commonly seen in their operator forms: <* and *> they kind of look like half of the apply operator <*> , and point at the argument that we want to keep. See ReludeParse for a real-world non-trivial use case for these operators. You can also think of these operators as similar to tuple2 where we first run and tuple the results, then just take one side or the other.

Next we're defining a bunch of mapN functions - here we only go up to map2 and map3 , but in your own library, you could go as high as you wanted. Note that if you go above 5 or so arguments, you might be better off just using the more flexible map/ap/ap pattern with <$> and <*> operators. If you were curious, the reason we need all these numbered map and other functions, rather than operating on lists or arrays of effectful values, it's because lists and arrays are homogenous - they can only carry values of the same type, but we want to operate on values of different types. In a later blog post, we'll see a new abstraction called TRAVERSABLE which can help us deal with lists/arrays/options/etc. that containing effectful values.

We then define an alternative version of mapN called mapTupleN - this function simply takes a tuple of the effectful values, and then just destructures the tuple and runs them through mapN . Using a tuple can sometimes be a more convenient way of collecting and chaining a series of operations, e.g.

( Some ( 42 ) , Some ( " hi " ) , Some ( bool ) ) | > Option.mapTuple3 ( ( i , s , b ) = > .. . do something here .. . ) ;

Finally we create a separate module functor for defining infix operators. Having a separate module for infix operators can be handy for when you want to do a local open and just get access to the operators for a few small operations. Note that using the OCaml/ReasonML include mechanism, you can choose to include the infix operators into any organizational module you want.

The pattern we use in Relude to incorporate these extensions is something like this:

module Option = { type t ( ' a ) = option ( ' a ) ; let map = .. . ; let apply = .. . ; let pure = .. . ; module Functor : FUNCTOR = { type nonrec t ( ' a ) = t ( ' a ) ; // alias let map = map ; // alias } ; include FunctorExtensions ( Functor ) ; module Apply : APPLY = { .. . } ; include ApplyExtensions ( Apply ) ; module Applicative : APPLICATIVE = { .. . } ; include ApplicativeExtensions ( Applicative ) ; // .. . other typeclasses and extension module functors module Infix = { include FunctorInfix ( Functor ) ; include ApplyInfix ( Functor ) ; // .. . other infix module functors } ; } ;

This way, all the top-level functions are exposed at the top level of the module for convenience, then we have the typeclasses also at the top level with their corresponding functions and types defined as just aliases, and we immediately construct and include each of the typeclass extension modules. This include puts the extension functions like map3 , tuple3 , etc. right at the top-level of the module, so you can do things like Option.map3(...) . Finally, we create a separate Infix module, where we include all of the Infix extension modules. This puts all the operators in one common scope, so you can do things like this:

let x = Option.Infix. ( f < $> Some ( 42 ) < * > Some ( " hi " ) < * > Some ( true ) ) ;

For types with more than one type parameter (like Result.t('a, 'e) ), we unfortunately have to implement all of our typeclass instances and extensions inside a module functor (like Result.WithError ), so we lose a little of the convenience of the extensions. In order to get access to map3 , etc. for Result , you have to do something like this:

module ResultE = Result. WithError ( { type t = myErrorType } ) ; ResultE.map3 ( .. . ) ;

Unfortunately, you can't inline module functor stuff with function invocations, so you can't do this, which is a big bummer:

// Can't do this : ( let x = Result. WithError ( { type t = string } ) . map3 ( .. . ) ;

Also, as we saw in the functor article, you can't pass modules that deal with higher-kinded types via first-class modules, so we're kind of stuck with the extra boilerplate of instantiating our module in one line, and using it where we need it.

That was a lot of discussion, but in case you missed it, we just gave ourselves an implementation of mapN , tupleN , mapTupleN , <*> , <* , *> for any module that has an APPLY instance! I think that's pretty awesome! Your option , Result , Js.Promise , Future , IO , and every other APPLY gets a handy, consistent and powerful set of functions for free, just because we took the time to implement map and apply and setup some typeclass instances. Plus, we now have a layer of centralized abstraction and extension where we can add more of these types of functions, and we just get them all for free anywhere we're using ApplyExtensions ! If you think this is cool, you should take a look at languages like Haskell or PureScript and see how they do all this with much less ceremony, but it's still pretty cool that we can achieve something very similar in ReasonML!

Applicative validation

We now have a bunch of useful tools for dealing with APPLY and APPLICATIVE instances and their “effectful values,” so let's see what we can do with them.

First of all, if you've used Promise.all in JavaScript (or the Js.Promise.allN equivalents in ReasonML), you've already used an applicative-style API. We have our own versions of these in our extensions like mapN , tupleN , mapTupleN , etc. These are all just helper functions that let us process a bunch of effectful values in parallel and combine the results in different ways, which is quite handy all by itself.

If you have a situation where you have a bunch effectful values to deal with (say N+1 values), and your mapN functions only “go up to N ,” you can deal with an arbitrary number of arguments using the map/ap/ap pattern using <$> and <*> , which we'll see below.

Let's now look at one more very useful use case for applicatives: applicative validation, parsing, and decoding. But first, let's introduce a few additional concepts to help us out:

SEMIGROUP typeclass

Now that we've seen FUNCTOR , APPLY , and APPLICATIVE , we know that typeclasses are sort of just sets of functions that must conform to some laws, so I think we can start to introduce more of these things without fear. Just look at the types of the functions and the laws - you don't necessarily need to understand why it exists or what it's for right away, just see it for what it is: functions and laws.

The SEMIGROUP typeclass is simply about the ability to combine values of the same type:

module type SEMIGROUP = { type t ; let append : ( t , t ) = > t ; } ;

The append function often has an operator version too - in Haskell, it's <> , but we'll ignore that for now (other than for noting the law). The name append is commonly used, but you could also think of this operation as combine .

The laws for this typeclass is that the append function must be associative, i.e.

// Associativity ( < > is the operator for append ) ( a < > b ) < > c = = a < > ( b < > c )

If you remember from algebra class, it's the same law of associativity that we learned for addition and multiplication. Basically, it requires that if you have 3 values, you can combine the first two first, then combine the third and that should be the same as combining the second two, then combining that with the first. Note that this is not commutativity - the items have to be combined in the order above, we're not saying that you can switch the order of the arguments, we're just saying exactly what's described above. There is another typeclass that requires commutativity, but SEMIGROUP doesn't - only associativity.

// Commutativity - this is NOT a law required by SEMIGROUP a < > b = = b < > a

Let's see a few quick examples for int addition, int multiplication, bool AND and OR, string , and list('a) .

Integers actually have multiple semigroups. One semigroup is integer addition, where the append operation is just + . Another different semigroup is multiplication where the append function is * . You might hear these notions described as something like “integers form a semigroup under addition.”

module Integer = { module Addition = { module Semigroup : SEMIGROUP = { type t = int ; let append = ( a , b ) = > a + b ; } ; } ; module Multiplication = { module Semigroup : SEMIGROUP = { type t = int ; let append = ( a , b ) = > a * b ; } ; } ; } ;

We define these as distinct instances, because they are distinct semigroups. Note that integer subtraction and division are not SEMIGROUPS because those operations are not associative (they don't conform to the semigroup law, which requires that the append operation be associative). These operations might be usable for other typeclasses, but not SEMIGROUP .

( 1 - 2 ) - 3 != 1 - ( 2 - 3 ) ( 1 / 2 ) / 3 != 1 / ( 2 / 3 )

Boolean has a few semigroups too: the AND and OR operators. These would be implemented similarly to how I split up Addition and Multiplication for Integer , and for booleans, you'll see words like Conjunctive for AND and Disjunctive for OR .

Strings have a SEMIGROUP too, just string concatenation:

let append = ( str1 , str2 ) = > str1 + + str2 ;

Lists and arrays also form a semigroup, but since these are type constructors of the form t('a) , we need a variation of SEMIGROUP that works with types of that form. We'll call it SEMIGROUP_ANY - that's what it's called in bs-abstract.

module type SEMIGROUP_ANY = { type t ( ' a ) ; let append : ( t ( ' a ) , t ( ' a ) ) = > t ( ' a ) ; } ;

The semigroup for list and array is just concatenation, just like string . Here's a quick implementation for list('a)

module List = { type t ( ' a ) = list ( ' a ) ; let concat = ( list1 , list2 ) = > Belt.List.concat ( list1 , list2 ) ; // I hope I got the order right for Belt.List... module SemigroupAny : SEMIGROUP_ANY = { type nonrec t ( ' a ) = t ( ' a ) ; let append = concat ; } ; } ;

I hope that's clear enough for now, and let's move on.

NonEmptyList data type

Let's now introduce a useful type for error handling - a NonEmptyList . Sometimes when you're dealing with collections of values, it can be really helpful to guarantee that there's at least one value. Imagine a validation function in JavaScript, where maybe it gives you back an array of errors - what happens if it returns you an empty array? That seems odd and undesirable, so let's not allow ambiguous things like that by using better types. Just for expedience, I'm going to go ahead and implement this now, including a SemigroupAny instance - below is how we typically implement new data types in Relude:

module NonEmptyList = { type t ( ' a ) = | Nel ( ' a , list ( ' a ) ) ; let map = .. . ; let apply = .. . ; let pure = a = > Nel ( a , [] ) ; let concat = ( Nel ( h1 , t1 ) , Nel ( h2 , t2 ) ) = > { Nel ( h1 , Belt.List.concat ( t1 , [ h2 , .. . t2 ] ) ) ; } ; module SemigroupAny : SEMIGROUP_ANY = { type nonrec t ( ' a ) = t ( ' a ) ; let append = concat ; } ; include SemigroupAnyExtensions ( SemigroupAny ) ; module Functor : FUNCTOR with type t ( ' a ) = t ( ' a ) = .. . include FunctorExtensions ( Functor ) ; module Apply : APPLY with type t ( ' a ) = t ( ' a ) = .. . include ApplyExtensions ( Apply ) ; module Applicative : APPLY with type t ( ' a ) = t ( ' a ) = .. . include ApplicativeExtensions ( Applicative ) ; } ;

The NonEmptyList is basically just a data structure with a constructor Nel that has one guaranteed value 'a , and a “tail” list('a) , which can be empty. We can implement map , apply , pure , concat and many other useful things for this type, and all the corresponding typeclass instances.

There's nothing magic about any of this - it's simply a type that is exactly what it says it is: a non-empty list. We're going to use this below for “collecting errors” in our applicative validation, so that we can be sure that if an applicative operation fails, we'll get at least one error value out.

Validation data type

If you recall above when we implemented apply for Result.t('a, 'e) , we ran into a conundrum when faced with the “both sides failed” case:

let apply = ( ff : Result.t ( ' a = > ' b , ' e ) , fa : Result.t ( ' a , ' e ) ) = > switch ( ff , fa ) { | ( Ok ( aToB ) , Ok ( a ) ) = > Ok ( aToB ( a ) ) | ( OK ( aToB ) , Error ( e2 ) ) = > Error ( e2 ) | ( Error ( e1 ) , Ok ( a ) ) = > Error ( e1 ) | ( Error ( e1 ) , Error ( e2 ) ) = > Error ( e1 ) // !! ! }

In this Error/Error case, we have no way to combine the errors, and no real way to decide which one to return, so we'll just arbitrarily return the left side's error.

Let's introduce a new data type called Validation.t('a, 'e) to deal with this case specifically. We're going to add a little extra spice to our apply implementation to deal with these two errors. The type itself is going to look almost exactly the same as Result.t('a, 'e) , but it will have different semantics for the applicative behavior.

module Validation = { type t ( ' a , ' e ) = | VOk ( ' a ) | VError ( ' e ) ; let map = ( aToB , fa ) = > switch ( fa ) { | VOk ( a ) = > VOk ( aToB ( a ) ) | VError ( e ) = > VError ( e ) } ; let applyWithCombineErrors = ( combineErrors : ( ' e , ' e ) = > ' e , ff : t ( ' a = > ' b , ' e ) , fa : t ( ' a , ' e ) ) : t ( ' b , ' e ) = > { switch ( ff , fa ) { | ( VOk ( aToB ) , VOk ( a ) ) = > VOk ( aToB ( a ) ) | ( VError ( e1 ) , VOk ( a ) ) = > VError ( e1 ) | ( VOk ( aToB ) , VError ( e2 ) ) = > VError ( e2 ) | ( VError ( e1 ) , VError ( e2 ) ) = > VError ( combineErrors ( e1 , e2 ) ) // 🎉 } ; } ; let pure = a = > VOk ( a ) ; } ;

To implement our typeclass instances like Functor , etc. we'll use the WithError module functor trick to “lock down” our error type. However, when we get to APPLY , where is this combineErrors function going to come from? We don't know what the error type is (well, we'll know once we lock it down using WithError ), and moreover, we don't know how to combine these errors. Oftentimes, when you need to do something, and you don't know how to do it, you can just make someone pass it to you. In this case, we need a mechanism for combining values of the same type, and for this, we have the SEMIGROUP abstraction! Let's see how this works:

module Validation = { type t ( ' a , ' e ) = | VOk ( ' a ) | VError ( ' e ) ; let map = ( aToB , fa ) = > switch ( fa ) { | VOk ( a ) = > VOk ( aToB ( a ) ) | VError ( e ) = > VError ( e ) } ; let applyWithCombineErrors = ( combineErrors : ( ' e , ' e ) = > ' e , ff : t ( ' a = > ' b , ' e ) , fa : t ( ' a , ' e ) ) : t ( ' b , ' e ) = > { switch ( ff , fa ) { | ( VOk ( aToB ) , VOk ( a ) ) = > VOk ( aToB ( a ) ) | ( VError ( e1 ) , VOk ( a ) ) = > VError ( e1 ) | ( VOk ( aToB ) , VError ( e2 ) ) = > VError ( e2 ) | ( VError ( e1 ) , VError ( e2 ) ) = > VError ( combineErrors ( e1 , e2 ) ) // 🎉 } ; } ; let pure = a = > VOk ( a ) ; module WithErrors = ( Errors : SEMIGROUP_ANY , Error : TYPE ) = > { module Functor : FUNCTOR with type t ( ' a ) = t ( ' a , Errors.t ( Error.t ) ) = { type nonrec t ( ' a ) = t ( ' a , Errors.t ( Error.t ) ) ; let map = map ; } ; include FunctorExtensions ( Functor ) ; module Apply : APPLY with type t ( ' a ) = t ( ' a , Errors.t ( Error.t ) ) = { include Functor ; let apply = ( ff , fa ) = > applyWithCombineErrors ( Errors.append , ff , fa ) ; } ; include ApplyExtensions ( Apply ) ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a , Errors.t ( Error.t ) ) = { include Apply ; let pure = pure ; } ; include ApplicativeExtensions ( Applicative ) ; module Infix = { include FunctorInfix ( Functor ) ; include ApplyInfix ( Apply ) ; } ; } ; } ;

There are multiple ways of doing this, but I'm just going to require a SEMIGROUP_ANY instance to collect errors, and a TYPE instance to lock down my error type. You could also probably use a plain SEMIGROUP , but that requires some different techniques for dealing with the error type.

Now in order to use the applicative stuff with this type, I just need to give it a SEMIGROUP_ANY and an error TYPE , like so:

module ValidationNel = Validation. WithErrors ( NonEmptyList. SemigroupAny , { type t = string } ) ; ValidationNel.tuple3 ( VOk ( 42 ) , VOk ( " hi " ) , VOk ( true ) ) // VOk ( ( 42 , " hi " , true ) ) ValidationNel.tuple3 ( VOk ( 42 ) , VError ( " oops " ) , VError ( " darn " ) ) // VError ( Nel ( " oops " , [ " darn " ] ) )

With this type and the SEMIGROUP_ANY semantics, we have the ability to collect the error for each individual failure in the parallel operation, rather than just exploding at the first error we encounter! To understand how this works, just think about how apply is implemented for Validation , especially in the case of both sides failing. We can use a NonEmptyList to collect our errors, because there's never a scenario where this can fail and we don't have an error value to deal with. We could also use a plain old list('a) , but then you lose the guarantee at the callsite of there being at least one error - the caller has to deal with the empty list case, when we know that will never actually happen. This is all about making impossible states impossible.

Applicative validation with error collection

We now have a type Validation.t('a, 'e) that looks a lot like a Result.t('a, 'e) , but has slightly different semantics in its APPLICATIVE instance. The Result.t('a, 'e) apply function “fails fast” in that it just propagates the first error it encounters all the way to the final result, whereas the Validation has “error collecting” semantics where it will collect and combine each error it encounters using a SEMIGROUP or in our case a SEMIGROUP_ANY instance, which is just a semigroup for types of the form t('a) .

Note that when I say “Result fails fast” in its applicative, I don't mean it instantly explodes or early-returns out of the function. I just mean that if some individual operation fails, the apply function will just propagate that error along, and ignore any further errors, until it reaches the final result - just think about how apply is implemented for Result .

We can use applicatives for a wide variety of validation, decoding, parsing, and data normalization tasks. Some real world use cases for applicative validation might include CSV parsing, JSON decoding, web form validation, string parsing, database record parsing, parallel async data fetching, and countless more. Let's see a few examples, but for all of these remember that you can use any type that has an applicative instance to do the same thing but with different error handling semantics, and in fact, you can abstract the APPLICATIVE instance away using a module functor to make a validation module that can just work with any applicative right off the bat. When I say “desired semantics” I just mean that you can choose the type of error handling that fits your needs - if you just want to know whether the validation succeeded or failed, you could use option('a) , if you wanted to know whether it failed, and what the first error was, you might use Result.t('a, 'e) (which doesn't know how to combine the errors in apply ), or if you wanted to capture all the errors, you might use a Validation.t('a, 'e) . If your validation requires async computations, you might need to use something like Promise.t('a) or Future.t(Result.t('a, 'e)) , or even Future.t(Validation.t('a, 'e)) .

Anyway, let's see a quick example. Let's say we have a model like the following:

module User = { type t = { name : string , email : string , age : int , isAdmin : bool } ; let make = ( name : string , email : string , age : int , isAdmin : bool ) = > { name , email , age , isAdmin } ;

We have a type User.t which has information about users, and a pure make function which simply acts as a non-validating constructor for the type User.t . Let's say we're getting a JSON value representing a user, and we want to decode it into a value of type User.t , so that we can be confident that we know what data we're working with.

Let's build a tiny JSON decoder library based on Validation.t('a, 'e) , and then see how we can implement a basic JSON decoder using Validation, and the JSON decoder type we saw above. Here is the full example, and I'll explain more below.

module type TYPE = { type t ; } ; module type SEMIGROUP_ANY = { type t ( ' a ) ; let append : ( t ( ' a ) , t ( ' a ) ) = > t ( ' a ) ; } ; module type FUNCTOR = { type t ( ' a ) ; let map : ( ' a = > ' b , t ( ' a ) ) = > t ( ' b ) ; } ; module FunctorExtensions = ( F : FUNCTOR ) = > { let voidLeft = ( b : ' b , fa : F.t ( ' a ) ) = > F.map ( _ = > b , fa ) ; let voidRight = ( fa : F.t ( ' a ) , b : ' b ) = > F.map ( _ = > b , fa ) ; // etc . } ; module FunctorInfix = ( F : FUNCTOR ) = > { module FE = FunctorExtensions ( F ) ; let ( < $> ) = F.map ; let ( < $ ) = FE.voidLeft ; let ( $> ) = FE.voidRight ; // etc . } ; module type APPLY = { include FUNCTOR ; let apply : ( t ( ' a = > ' b ) , t ( ' a ) ) = > t ( ' b ) ; } ; module ApplyExtensions = ( A : APPLY ) = > { let map2 = ( f , fa , fb ) = > A.apply ( A.map ( f , fa ) , fb ) ; let map3 = ( f , fa , fb , fc ) = > A.apply ( A.apply ( A.map ( f , fa ) , fb ) , fc ) ; let map4 = ( f , fa , fb , fc , fd ) = > A.apply ( A.apply ( A.apply ( A.map ( f , fa ) , fb ) , fc ) , fd ) ; let map5 = ( f , fa , fb , fc , fd , fe ) = > A.apply ( A.apply ( A.apply ( A.apply ( A.map ( f , fa ) , fb ) , fc ) , fd ) , fe ) ; // etc . } ; module ApplyInfix = ( A : APPLY ) = > { let ( < * > ) = A.apply ; // etc . } ; module type APPLICATIVE = { include APPLY ; let pure : ' a = > t ( ' a ) ; } ; module NonEmptyList = { type t ( ' a ) = | Nel ( ' a , list ( ' a ) ) ; let pure = a = > Nel ( a , [] ) ; let concat = ( Nel ( h1 , t1 ) , Nel ( h2 , t2 ) ) = > { Nel ( h1 , List.concat ( [ t1 , [ h2 ] , t2 ] ) ) ; } ; let toList = ( Nel ( h , t ) ) = > [ h , .. . t ] ; let toArray = nonEmptyList = > nonEmptyList | > toList | > Belt.List.toArray ; module SemigroupAny : SEMIGROUP_ANY with type t ( ' a ) = t ( ' a ) = { type nonrec t ( ' a ) = t ( ' a ) ; let append = concat ; } ; } ; module Validation = { type t ( ' a , ' e ) = | VOk ( ' a ) | VError ( ' e ) ; let ok = a = > VOk ( a ) ; let error = e = > VError ( e ) ; let errorNel = e = > VError ( NonEmptyList.pure ( e ) ) ; let map = ( f , fa ) = > switch ( fa ) { | VOk ( a ) = > VOk ( f ( a ) ) | VError ( e ) = > VError ( e ) } ; let applyWithAppendErrors = ( appendErrors : ( ' e , ' e ) = > ' e , ff : t ( ' a = > ' b , ' e ) , fa : t ( ' a , ' e ) ) = > switch ( ff , fa ) { | ( VOk ( f ) , VOk ( a ) ) = > VOk ( f ( a ) ) | ( VError ( e1 ) , VOk ( _ ) ) = > VError ( e1 ) | ( VOk ( _ ) , VError ( e2 ) ) = > VError ( e2 ) | ( VError ( e1 ) , VError ( e2 ) ) = > VError ( appendErrors ( e1 , e2 ) ) } ; let pure = ok ; module WithErrors = ( S : SEMIGROUP_ANY , E : TYPE ) = > { type nonrec t ( ' a ) = t ( ' a , S.t ( E.t ) ) ; module Functor : FUNCTOR with type t ( ' a ) = t ( ' a ) = { type nonrec t ( ' a ) = t ( ' a ) ; let map = map ; } ; let map = map ; include FunctorExtensions ( Functor ) ; module Apply : APPLY with type t ( ' a ) = t ( ' a ) = { include Functor ; let apply = ( ff , fa ) = > applyWithAppendErrors ( S.append , ff , fa ) ; } ; let apply = Apply.apply ; include ApplyExtensions ( Apply ) ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a ) = { include Apply ; let pure = pure ; } ; let pure = pure ; module Infix = { include FunctorInfix ( Functor ) ; include ApplyInfix ( Apply ) ; } ; } ; } ; module Decode = { module Error = { type t = | ExpectedString ( Js.Json.t ) | ExpectedBool ( Js.Json.t ) | ExpectedInt ( Js.Json.t ) | ExpectedObject ( Js.Json.t ) | ExpectedObjectWithKey ( Js.Json.t , string ) ; let show = e = > switch ( e ) { | ExpectedString ( json ) = > " Expected string, got: " + + Js.Json.stringify ( json ) | ExpectedBool ( json ) = > " Expected bool, got: " + + Js.Json.stringify ( json ) | ExpectedInt ( json ) = > " Expected int, got: " + + Js.Json.stringify ( json ) | ExpectedObject ( json ) = > " Expected object, got: " + + Js.Json.stringify ( json ) | ExpectedObjectWithKey ( json , key ) = > " Expected object with key " + + key + + " , got: " + + Js.Json.stringify ( json ) } ; } ; // For this example , we're going to use ` Validation.t ( ' a , NonEmptyList.t ( Error.t ) ) ` // as our applicative " effect " type for the decoder . This will give us error - collecting // semantics in the decode operation . module ValidationE = Validation. WithErrors ( NonEmptyList. SemigroupAny , Error ) ; // Our decoder type is a function Js.Json.t = > A.t ( ' a ) // Think of ` A ` being option , Result , Validation , Promise , etc . but here we're just // specialized to using ValidationE type t ( ' a ) = | Decode ( Js.Json.t = > ValidationE.t ( ' a ) ) ; // define map / Functor let map = ( f , Decode ( jsonToFA ) ) = > Decode ( json = > jsonToFA ( json ) | > ValidationE.map ( f ) ) ; module Functor : FUNCTOR with type t ( ' a ) = t ( ' a ) = { type nonrec t ( ' a ) = t ( ' a ) ; let map = map ; } ; include FunctorExtensions ( Functor ) ; // define apply / Apply let apply = ( Decode ( jsonToFAToB ) , Decode ( jsonToFA ) ) = > Decode ( json = > { let fAToB = jsonToFAToB ( json ) ; let fA = jsonToFA ( json ) ; ValidationE.apply ( fAToB , fA ) ; } , ) ; module Apply : APPLY with type t ( ' a ) = t ( ' a ) = { include Functor ; let apply = apply ; } ; include ApplyExtensions ( Apply ) ; // define pure / Applicative let pure = a = > Decode ( _ = > ValidationE.pure ( a ) ) ; module Applicative : APPLICATIVE with type t ( ' a ) = t ( ' a ) = { include Apply ; let pure = pure ; } ; // Add an infix operator module module Infix = { include FunctorInfix ( Functor ) ; include ApplyInfix ( Apply ) ; } ; // Now define some specific types of decoders let string : t ( string ) = Decode ( json = > switch ( Js.Json.classify ( json ) ) { | JSONString ( str ) = > ValidationE.pure ( str ) | _ = > Validation.errorNel ( Error. ExpectedString ( json ) ) } , ) ; let bool : t ( bool ) = Decode ( json = > switch ( Js.Json.classify ( json ) ) { | JSONTrue = > VOk ( true ) | JSONFalse = > VOk ( false ) | _ = > Validation.errorNel ( Error. ExpectedBool ( json ) ) } , ) ; let int : t ( int ) = Decode ( json = > switch ( Js.Json.classify ( json ) ) { | JSONNumber ( f ) = > VOk ( Js.Math.floor ( f ) ) | _ = > Validation.errorNel ( Error. ExpectedInt ( json ) ) } , ) ; let field = ( key : string , decode : t ( ' a ) ) : t ( ' a ) = > Decode ( json = > switch ( Js.Json.classify ( json ) ) { | JSONObject ( dict ) = > switch ( Js.Dict.get ( dict , key ) ) { | Some ( value ) = > let Decode ( decodeA ) = decode ; decodeA ( value ) ; | None = > Validation.errorNel ( Error. ExpectedObjectWithKey ( json , key ) ) } | _ = > Validation.errorNel ( Error. ExpectedObject ( json ) ) } , ) ; let run = ( json : Js.Json.t , Decode ( f ) ) = > f ( json ) ; } ; module User = { type t = { name : string , email : string , age : int , isAdmin : bool , } ; let make = ( name , email , age , isAdmin ) = > { name , email , age , isAdmin } ; let decode = Decode.Infix. ( make < $> Decode.field ( " name " , Decode.string ) < * > Decode.field ( " email " , Decode.string ) < * > Decode.field ( " age " , Decode.int ) < * > Decode.field ( " isAdmin " , Decode.bool ) ) ; // Note : could also use map4 rather than < $> /< * > } ; let json1 = Js.Json.object_ ( Js.Dict.fromList ( [ ( " name " , Js.Json.string ( " Andy " ) ) , ( " email " , Js.Json.string ( " test@example.com " ) ) , ( " age " , Js.Json.number ( 55 . 0 ) ) , ( " isAdmin " , Js.Json.boolean ( true ) ) , ] ) , ) ; let validatedUser1 = User.decode | > Decode.run ( json1 ) ; switch ( validatedUser1 ) { | VOk ( user ) = > Js.log2 ( " Success! " , user ) | VError ( errors ) = > Js.log2 ( " Failure! " , errors | > NonEmptyList.toArray ) } ; // Success ! [ ' Andy' , ' test @ example . com' , 55 , true ] let json2 = Js.Json.object_ ( Js.Dict.fromList ( [ ( " name " , Js.Json.string ( " Andy " ) ) , ( " email " , Js.Json.number ( 42 . 0 ) ) , ( " age " , Js.Json.number ( 55 . 0 ) ) , ( " isAdmin " , Js.Json.string ( " supposed to be a boolean " ) ) , ] ) , ) ; let validatedUser2 = User.decode | > Decode.run ( json2 ) ; switch ( validatedUser2 ) { | VOk ( user ) = > Js.log2 ( " Success! " , user ) | VError ( errors ) = > Js.log2 ( " Failure! " , errors | > NonEmptyList.toArray | > Array.map ( Decode.Error.show ) , ) } ; // Failure ! [ ' Expected string , got : 42' , // ' Expected bool , got : " supposed to be a boolean " ' ]

So that was a long code sample, but most of it was just setting up all the core pieces that would normally come from a library like Relude - I'm just showing it here to have a complete and somewhat realistic example for demonstrating all the moving parts.

At the top of the file, we have all the core typeclass definitions TYPE SEMIGROUP_ANY FUNCTOR etc.

We also have a couple Extensions and Infix modules for a few of these, which we'll use below.

and modules for a few of these, which we'll use below. Next we define out NonEmptyList type, which we use in the Validation to collect the errors in the apply function

type, which we use in the to collect the errors in the function Then we have the Validation module, with the core type, functions, then the typeclass instances wrapped inthe WithErrors module functor, where we require a SEMIGROUP_ANY instance to collect our errors, and a TYPE instance to lock down the error type.

module, with the core type, functions, then the typeclass instances wrapped inthe module functor, where we require a instance to collect our errors, and a instance to lock down the error type. Lastly for the “library” pieces, we define a Decoder module, which is essentially a function Js.Json.t => Validation.t('a, NonEmptyList.t(Error.t)) , and all the supporting functions and typeclass instances.

As for the actual demonstration, I have a User module which has:

a basic record type with 4 fields

a pure, non-validating make function which acts as a constructor

function which acts as a constructor a decoder, which uses the applicative map/ap/ap pattern to map the pure make function over a series of decoders for the individual fields. Note that instead of <$> / <*> we could use the Decode.map4 function which we got “for free” from our Decode.Apply and the include ApplyExtensions(Apply)

pattern to map the pure function over a series of decoders for the individual fields.

The Decoder itself has an applicative instance, which is backed by the applicative for the Validation module, which has error collecting semantics. If you want to try an exercise, see if you can make the decoder a module functor like module WithApplicative => (A: APPLICATIVE, ???) => and replace all usages of Validation / ValidationE with just A references. Note: you'll need some way to deal with the errors, like the places where I have Validation.errorNel(...) .

Anyway, that was a long example, with possibly insufficient explanation, but I hope that by providing a complete, working example, anyone who is interested can dig in more.

If you've used Elm, you might have recognized this type of pattern from the elm-decode-pipeline library. That library is more built-out, but it is fundamentally built around the idea of applicatives and parallel decoding of values.

You can also see this same pattern in ReasonML in the bs-decode library. This library is also more robust than our simple example here, but the applicatives are there at the core!

The applicative laws

As I mentioned in the functor article, the typeclass laws are quite important, but given that this article is already quite long, I'm just going to link to the laws on a couple different sites - I encourage you to check these out on your own:

Conclusion

I hope you enjoyed my article on applicative functors - let me know if you find any problems with the code samples, or if you have any questions or suggestions.