Once you’ve grokked traversable’s you’ll wonder how you ever lived without them. Trying to gain intuition about them by staring at the type signature never brought me much joy. So in this post we’ll take a different approach and invent them ourselves by solving a real problem. This will help us get to that “aha” moment where we finally understand how they work and when to use them.

# The scenario

Imagine we’re working for an e-commerce site where we sell one-time offers, such that when all the stock is sold we never have anymore. When a user places an order we must check the stock levels. If there is availability we temporarily reserve the amount they requested before letting them proceed to the checkout.

Our specific task is to write a
`createCheckout`

function that will take a
`Basket`

and try to reserve the items in it. If they can be successfully reserved it will create a
`Checkout`

which includes the total price of the items along with other metadata we might need to take the payment.

Our domain model looks something like this.

```
type BasketItem =
{ ItemId: ItemId
Quantity: float }
type Basket =
{ Id: BasketId;
Items: BasketItem list }
type ReservedBasketItem =
{ ItemId: ItemId
Price: float }
type Checkout =
{ Id: CheckoutId
BasketId: BasketId
Price: float }
```

The
`createCheckout`

function will return
`Checkout option`

. It will return
`Some`

if all of the items are available and
`None`

if any of them aren’t. *A better implementation would return
Result
and detail the specific errors, but we’ll use
option
to keep the example simple.*

```
let createCheckout (basket: Basket): Checkout option
```

Fortunately for us, someone else has already written a function which can reserve a
`BasketItem`

if it is in stock, which looks like this.

```
let reserveBasketItem (item: BasketItem): ReservedBasketItem option
```

Again, this will return
`None`

if there are not enough items in stock.

# Our first implementation

So it seems that all we need to do is write a function that calls
`reserveBasketItem`

for each item in the basket. If they all succeed then it calculates the total price in order to create the
`Checkout`

. Let’s try it.

```
let createCheckout basket =
let reservedItems =
basket.Items |> List.map reserveBasketItem
let totalPrice =
reservedItems
|> List.sumBy (fun item -> item.Price)
{ Id = CheckoutId "some-checkout-id"
BasketId = basket.Id
Price = totalPrice }
```

Here we’re just mapping over the items in the basket to reserve each one and then summing their individual prices to get the total basket price. Seems straight forward, except that’s not going to compile, because it’s not quite right.

The problem is that
`reservedItems`

has the type
`list<option<ReservedBasketItem>>`

but we need it to be
`option<list<ReservedBasketItem>>`

, where it is
`None`

if any one of the reservations fail. That way we’d only be able to calculate the total price and create the
`Checkout`

if all of the items are available. Let’s imagine we’ve written such a function called
`reserveItems`

that does return this type instead and updated
`createCheckout`

to use it.

```
let reserveItems (items: BasketItem list): option<list<ReservedBasketItem>>
let createCheckout basket =
let reservedItems = basket.Items |> reserveItems
reservedItems
|> Option.map
(fun items ->
{ Id = CheckoutId "some-checkout-id"
BasketId = basket.Id
Price = items |> List.sumBy (fun x -> x.Price) })
```

That’s better! Now if the items are all reserved and
`reservedItems`

returns
`Some`

then we can access the list of
`ReservedBasketItem`

and use them to create the
`Checkout`

. If any of the items can’t be reserved then
`reservedItems`

returns
`None`

and the
`Option.map`

just short circuits meaning
`createCheckout`

will also return
`None`

, as we wanted.

So we’ve reduced the task to implementing
`reserveItems`

. We’ve already seen that we can’t just call
`List.map reserveBasketItem`

because that gives us a
`list<option<ReservedBasketItem>>`

and so the
`list`

and the
`option`

are the wrong way around. We need a way to invert them.

# An invertor 🙃

Let’s invent a function called
`invert`

that converts
`list<option<ReservedBasketItem>>`

into
`option<list<ReservedBasketItem>>`

. If we can do that then we can implement
`reserveItems`

like this.

```
let invert (reservedItems: list<option<ReservedBasketItem>>) : option<list<ReservedBasketItem>>
let reserveItems (items: BasketItem list) : option<list<ReservedBasketItem>> =
items
|> List.map reserveBasketItem
|> invert
```

In order to implement
`invert`

let’s start off by pattern matching on the list.

```
let invert (reservedItems: list<option<ReservedBasketItem>>) : option<list<ReservedBasketItem>> =
match reservedItems with
| head :: tail -> // do something when the list isn't empty
| [] -> // do something when the list is empty
```

So we’ve got two cases to deal with, when the list has at least one item and when the list is empty. Let’s start with the base case because it’s trivial. If the list is empty then it doesn’t contain any failures, so we should just return
`Some []`

.

In the non empty case then we’ve got to do something with
`head`

which is a
`ReservedBaskedItem option`

and
`tail`

which is a
`list<option<ReservedBasketItem>>`

. Well we know that our goal is to turn
`list<option<ReservedBasketItem>>`

into
`option<list<ReservedBaskedItem>>`

, so we can just recursively call
`invert`

on the
`tail`

to do this.

```
let rec invert (reservedItems: list<option<ReservedBasketItem>>) : option<list<ReservedBasketItem>> =
match reservedItems with
| head :: tail ->
let invertedTail = invert tail
// Need to recombine the head and the inverted tail
| [] -> Some []
```

Now we just need a way to combine a
`ReservedBasketItem option`

with a
`option<list<ReservedBasketItem>>`

. If neither of these were wrapped in an
`option`

then we would just “cons” them using the
`::`

operator, so let’s write a
`consOptions`

function which does this but for
`option`

arguments.

```
let consOptions (head: option 'a) (tail: option<list<'a>>): option<list<'a>> =
match head, tail with
| Some h, Some t -> Some (h :: t)
| _ -> None
```

Nothing too complicated going on here. Simply check if both the
`head`

and
`tail`

are
`Some`

and if so cons them with
`::`

operator and wrap that in a
`Some`

. Otherwise if either one is
`None`

then return
`None`

.

Putting it all together we can finally implement
`invert`

like this.

```
let rec invert (reservedItems: list<option<'a>>) : option<list<'a>> =
match reservedItems with
| head :: tail -> consOptions head (invert tail)
| [] -> Some []
```

We’ve also been able to make it completely generic on the type inside the list as it doesn’t depend on
`ReservedBasketItem`

in any way.

# An Applicative clean up 🧽

If you’re familiar with applicatives, perhaps because you’ve followed this series and read Grokking Applicatives then you might have spotted that
`consOptions`

looks sort of like a specialised version of
`apply`

. What
`consOptions`

is trying to do is take some values that are wrapped in
`options`

and apply them to a function, in this case cons.

Let’s make use of
`apply`

and clean up
`invert`

.

```
let rec invert list =
// An alias for :: so we can pass it as a function below
let cons head tail = head :: tail
match list with
| head :: tail -> Some cons |> apply head |> apply (invert tail)
| [] -> Some []
```

In fact, a proper
`Applicative`

instance should also have a
`pure`

function. All
`pure`

does is create a default value for the
`Applicative`

. In the case of
`option`

`pure`

is just
`Some`

. Let’s use
`pure`

to replace the
`Some`

uses.

```
let rec invert list =
let cons head tail = head :: tail
match list with
| head :: tail -> pure cons |> apply head |> apply (invert tail)
| [] -> pure []
```

This might not seem like much of a change, but what we’ve done is eliminate all direct dependencies on
`option`

. In theory this could work with any applicative, such as
`Result`

or
`Validation`

and what it would do is go from
`list<Applicative<_>>`

to
`Applicative<list<_>>`

. In practice however F# doesn’t *quite* allow such an abstraction and so we have to create a version of
`invert`

for each applicative type we want to use it with.

*You can technically get around this with statically resolved type parameters. I would recommend checking out FSharpPlus if you want this abstraction rather than rolling it yourself though.*

# You just discovered

`sequence`

👏

`invert`

is usually called
`sequence`

and it’s one of the functions that a
`Traversable`

type gives us. As we can see
`sequence`

takes a collection of wrapped values like an
`option`

and turns it into wrapped collection instead. You can think of
`sequence`

as flipping the two types over.

`sequence`

works for all sorts of other type combinations too. For example you can take a
`list<Result<'a>>`

and flip it into a
`Result<list<'a>>`

. You can even use it with different collection types and some that don’t even seem like typical collections, for instance you could go from
`Result<option<'a>, 'e>`

to
`option<Result<'a, 'e>>`

.

# Test yourself on

`sequence`

🧑🏫
See if you can implement
`sequence`

for
`list<Result<_>>`

to
`Result<list<_>>`

.

```
module Result =
let apply a f =
match f, a with
| Ok g, Ok x -> g x |> Ok
| Error e, Ok _ -> e |> Error
| Ok _, Error e -> e |> Error
| Error e1, Error _ -> e1 |> Error
let pure = Ok
let rec sequence list =
let cons head tail = head :: tail
match list with
| head :: tail -> Result.pure cons |> Result.apply head |> Result.apply (sequence tail)
| [] -> Result.pure []
```

That’s right, it’s exactly the same as for the
`list<option<_>>`

providing we use the applicative
`Result.apply`

and
`Result.pure`

functions for
`Result`

. I’ve included their definitions too in a
`Result`

module above.

# There’s still more land to discover 🏞

Let’s go back to our original program and see how it looks with our new
`sequence`

discovery.

```
let createCheckout basket =
let reservedItems =
basket.Items
|> List.map reserveBasketItem
|> sequence
reservedItems
|> Option.map
(fun items ->
{ Id = CheckoutId "some-checkout-id"
BasketId = basket.Id
Price = items |> Seq.sumBy (fun x -> x.Price) })
```

It’s pretty good, but we have to make two passes over the
`basket.Items`

when creating
`reservedItems`

. In the first pass we try and reserve each item and then in the second pass we combine all of those reservations together to determine whether the whole operation succeed or not. It would be nice if we could do that in one go.

Let’s see if we can do it all within
`sequence`

. That means that we’ll need to pass the
`reserveBasketItem`

function to
`sequence`

and we’ll end up with the following signature.

```
let sequence (f: 'a -> 'b option) (list: 'a list): option<list<'b>>
```

So we start with a list and we want to apply the function
`f`

to each element of it. Although, rather than just mapping over the list and returning
`list<option<'b>>`

we want to accumulate all of the
`option`

values into a single
`option<list<'b>>`

where it is
`None`

if for any element
`f`

produces a
`None`

.

```
let rec sequence f list =
let cons head tail = head :: tail
match list with
| head :: tail -> Some cons |> apply (f head) |> apply (sequence tail f)
| [] -> Some []
```

This is basically the same as before, except now we just apply
`f`

to
`head`

and pass it into the recursive call in order to also transform the
`tail`

elements. All we’ve done is combine the operation that generates the
`option`

values with the act of combining them together into a single
`option`

of the list.

# You just discovered

`traverse`

🙌
It turns out we typically call the function
`traverse`

when we combine both the sequencing and the mapping at the same time. So a
`Traversable`

actually has two functions associated with it called
`sequence`

and
`traverse`

. In fact,
`sequence`

is just a special case of
`traverse`

where we supply the identity function,
`id`

, for
`f`

. So we could actually write it like this.

```
let sequence = traverse id
```

With
`traverse`

in place we can finally finish off our task and write
`checkoutBasket`

nicely like this.

```
let createCheckout basket =
basket.Items
|> traverse reserveBasketItem
|> Option.map
(fun items ->
{ Id = CheckoutId "some-checkout-id"
BasketId = basket.Id
Price = items |> Seq.sumBy (fun x -> x.Price) })
```

# Test yourself on

`traverse`

🧑🏫
See if you can implement
`traverse`

when the input is
`option<'a>`

and the function is
`'a -> Result<'b, 'c>`

, so that it returns a
`Result<option<'b>, 'c>`

.

```
module Result =
let apply a f =
match f, a with
| Ok g, Ok x -> g x |> Ok
| Error e, Ok _ -> e |> Error
| Ok _, Error e -> e |> Error
| Error e1, Error _ -> e1 |> Error
let pure = Ok
let traverse f opt =
match opt with
| Some x -> Result.pure Some |> Result.apply (f x)
| None -> Result.pure None
```

Here I’ve included the definitions of
`apply`

and
`pure`

for
`Result`

and then implemented
`traverse`

using those. Hopefully this makes it clearer which parts of the traverse operation relate to the outer
`option`

type and which ones relate to the inner
`Result`

type.

One concrete use case for this transformation might be if we’re trying to write a parser. The parser function might say parse
`string`

into
`Result<int, ParseError>`

but we have to hand a
`string option`

. Of course we could pattern match on the
`option`

ourselves and then only run the parser in the
`Some`

case, but we could also write
`myOptionalValue |> traverse parseInt`

.

Another interesting case is when we’re dealing with a regular function, say
`string`

which just converts the argument to a string. See if you can figure out what traverse should look like in this case. Specifically, if we want to write
`[1; 2; 3] |> traverse string`

and have it output
`["1"; "2"; "3"]`

.

```
module Identity =
let apply a f = f a
let pure f = f
let rec traverse list f =
let cons head tail = head :: tail
match list with
| head :: tail -> Identity.pure cons |> Identity.apply (f head) |> Identity.apply (traverse tail f)
| [] -> Identity.pure []
```

I’ve written this in the same style as the others by extracting an
`Identity`

functor/applicative.
`Identity`

is actually the degenerate case for an applicative because all
`apply`

does is call the function with the argument and all
`pure`

does is return the function unaltered. So there is no wrapping going on like with the other applicatives. This is interesting though because
`traverse`

now has the type
`list<'a> -> ('a -> 'b) -> list<'b>`

, which you might recognise from Grokking Functors as
`map`

. So
`map`

is actually a special case of
`traverse`

when the inner type is just the
`Identity`

applicative.

# Spotting

`Traversable`

in the wild 🐾
Whenever you’ve got some collection of values wrapped in something like
`option`

or
`Result`

and what you actually need is an
`option<list<'a>>`

or
`Result<list<'a>, 'e>`

etc then
`sequence`

is probably what you need to use. Similarly, if you have to run a computation over a collection that produces wrapped values then you can use
`traverse`

and combine the mapping and flipping into one operation.

# Warning, two types of error handling ahead ⚠️

When we’re sequencing a
`list<option<_>>`

we only need to know that at least one of the elements is
`None`

in order to return
`None`

. However, when working with something like
`list<Result<'a, 'e>>`

then we might actually care about gathering up all of the errors. As we pointed out in Grokking Applicative Validation there can be applicative instances that either short circuit on the first error or accumulate all errors. The same applies here with
`Traversable`

. Let’s quickly run some experiments in the F# REPL with FSharpPlus to see how it handles things.

```
> [Ok 1; Error "first error"; Error "second error"] |> sequence;;
val it : Result<int list, string> = Error "first error"
[Success 1; Failure ["first error"]; Failure ["second error"]] |> sequence;;
val it : Validation<string list, int list> =
Failure ["first error"; "second error"]
```

In the first case, when using
`Result`

we see that it just returns the first error it encounters, while with
`Validation`

it actually accumulates all the errors for us.

# What did we learn 🧑🎓

`Traversable`

is more powerful version of
`map`

that is particularly useful when we have a computation that either needs to be run (or has already been run) over a list of values and we want to treat it as a failure if any single one fails. We can also grok it by realising that it flips the two outer types over. We use
`traverse`

when we still need to run the computation and
`sequence`

when we’ve been given the list of computation results instead.