A nice way to define field validations in Haskell.
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validations.cabal

README.markdown

validations

table of contents

what is "validations"?

existing solutions, and their problems

    smart constructors

    digestive-functors formlet style

ideas behind the structure of "validations"

hello world

testing

digestive-functors integration

What is "validations"?

validations is a Haskell library that provides a flexible, composable way to define validations of a domain model.

existing solutions, and their problems

There is a number of ways to do domain model validation in Haskell, but each current method has drawbacks. Let's imagine a simple user model:

data User = User
  { _firstName    :: Text
  , _lastName     :: Text
  , _emailAddress :: Text
  } deriving Show

We want to check that the first name is not empty and starts with the letter A, that the last name is not empty, that the email address is not empty, and that the email address is confirmed by a value that isn't stored in User. We also want all checkers to conform to the type

a -> Either e b

or

(Monad m) => a -> m (Either a b)

.

So, our checkers could look something like

notEmpty :: (Monoid a, Eq a) => a -> Either Text a
notEmpty x =
  if (x == mempty)
     then Left "is empty"
     else Right x

,

startsWith :: Text -> Text -> Either Text Text
startsWith predicate input = 
  if predicate `isPrefixOf` input
     then Right input
     else Left $ "does not start with " <> predicate

, and

confirms :: (Eq a) => a -> a -> Either Text a
confirms a b = case (a == b) of
  True -> Right b
  False -> Left "fields do not match."

.

smart constructors

The simplest way to do this is with a smart constructor:

user :: Text -> Text -> Text -> Text -> Either Text User
user firstName lastName emailAddress emailAddressConfirm = do
  firstName'    <- notEmpty firstName >>= startsWith "A"
  lastName'     <- notEmpty lastName
  emailAddress' <- notEmpty emailAddress
  confirmed     <- emailAddressConfirm `confirms` emailAddress'
  return $ User {_firstName = firstName', _lastName = lastName', _emailAddress = confirmed }

This will enforce all of our invariants, but there's a problem. If any of our validations fail, then we only get the results of the failure of first validation. If firstName and lastName are both empty, we'd like to know that the validation logic failed for both. Also, if we use the pattern of exposing only the smart constructor (user), and keeping the data constructor (User) hidden, then a User record can only be used in contexts where all the invariants must always be held, which can be inflexible.

digestive-functors formlet style

digestive-functors solves the multiple validations problem. Our formlet could look like:

userForm :: (Monad m) => Form Text m User
userForm = User
  <$>  "firstName"    .: validate ((notEmpty >=> startsWith "A") >>> eitherToResult) (text Nothing)
  <*>  "lastName "    .: validate  (notEmpty >>> eitherToResult)                     (text Nothing)
  <*>  "emailAddress" .: validate  (notEmpty >>> eitherToResult)                     (text Nothing)

But, how do we handle the email confirmations? Since formlets are applicatives and not monadic, one way we could do this is by threading state through the monad and using validateM, but that's mistake prone, and puts unnecessary constraints on our monad. We could intercept the View record from digestive-functors once the form has been rendered, but then we're splitting our validation logic into two different places.

ideas behind the structure of "validations"

validations is based around 4 different data types. First, a Checker is function with type

a -> Either e b

Checkers tend to be non domain model specific, reusable pieces of code. For example,

nonEmpty :: (Monoid a, Eq a) => Checker Text a a
nonEmpty x =
  if (x == mempty)
     then Left "is empty"
     else Right x

. Notice that a Checker can transform its input as well, which consumer is free to ignore. This is useful for turning string typed user input into structured data.

A Monadic Checker is the same as a checker, but with type

(Monad m) => a -> m (Either e b)

.

The next data type is a Validator. It's a function with type

a -> monad (Either (errorKey, errorValue) b)

It's very similar to a Monadic Checker, but it also uses an "errorKey" type. This allows us to map a validator failure back to some given input (e.g. a form input field). If you look at the type signature for a Validator, you'll notice that it's very similar to

a -> m b

, so it's a Kleisli category, but the Either inside doesn't allow us to use Haskell stuff for composing Kleisi categories (e.g. (>=>)). However, we do now how to unwrap and rewrap Either, so validations provides an instance of Category to allow for validator composition. To create validators, we typically combine either a checker with a field name using

attach :: (Monad m) => Checker ev a b -> ek -> Validator ek ev m a b

or

attachM :: (Monad m) => MonadicChecker ev m a b -> ek -> Validator ek ev m a b

for monadic checkers. Both attach and attachM are included with validations, but you're free to wrap any conforming function as the Validator data constructor is public.

The final important data type is a Validation. The type of a validations is:

state -> monad (newState, errors)

where state is a type like a user record. newState is typically the same type as state, but a transformation is allowed. Validations can be constructed with

validation :: (Monad m) => Lens b s -> a -> Validator ek ev m a b -> Validation [(ek,ev)] m s s

Validations also form a category and can be composed, similar to Validators.

hello world

Let's see the Validators and Validations in action. First, let's define a Account record:

data Account = Account
  { _name          :: Text
  , _accountNumber :: Text
  } deriving Show

Next, we want to define lenses for accessing and mutating the fields. In this example, we are using the internal lens functionality of validations, but we'd typically use something like lens in our application.

name :: Lens Text Account
name = lens _name (\s a -> s {_name = a})
accountNumber :: Lens Text Account
accountNumber = lens _accountNumber (\s a -> s {_accountNumber = a})

We also want to use digestive-functors to define our form to bring data in.

nameField :: Text
nameField          = "name"
confirmNameField :: Text
confirmNameField   = "confirmName"
accountNumberField :: Text
accountNumberField = "accountNumber"
accountForm :: (Monad m) => Form Text m (Text, Text, Text)
accountForm = (,,)
  <$> nameField           .: (text Nothing)
  <*> confirmNameField    .: (text Nothing)
  <*> accountNumberField  .: (text Nothing)

We don't use field names directly in our formlet because we need to use them in our validation as well. Also, notice that we are outputting a 3-tuple instead of a Account record. This is because there isn't a one to one correspondence between our input fields and Account record fields (the name confirm field will be discarded). So, our validation looks like

lengthIs :: Int -> Checker Text Text Text
lengthIs predicate txt = case (compareLength txt predicate) of
  EQ -> Right txt
  _  -> Left "account number not correct length"
accountValidation :: (Monad m) => (Text, Text, Text) -> Validation [(Text, Text)] m Account Account
accountValidation (f1, f2, f3) = 
  validation name f1 (
    notEmpty        `attach` nameField
    >>>
    (f2 `confirms`) `attach` confirmNameField
  )
  >>>
  validation accountNumber f3 (
    notEmpty    `attach` accountNumberField
    >>>
    (lengthIs 10) `attach` accountNumberField
  ) 

What's going on here? First, since both Validations and Validators are Categories, we can use the (>>>) operator from Control.Arrow. If you're unfamiliar with this operator, for normal functions,

a >>> b ≡ b . a

so you can think of it as a composition operator that reads left to right.

Next, let's take a look at our first validation. It takes the "f1" parameter passed into "accountValidation" and feeds it into "notEmpty". If "notEmpty" returns a "Left", then the validation will make no state changes, and instead adds

("name" , "is empty")

to its "errors" value. On the other hand, if "f1" is not empty, it will be passed onto the confirms function, where similar validation will happen. If confirms also succeeds, the outputted value will passed to the "name" lens, and the outputted state will be mutated with a new name value.

testing

Testing a validation is simple. For example,

_ = runIdentity $ (runValidation  (accountValidation ("hi", "hi", "1234567890")) Account { _name = "", _accountNumber = "" } :: Identity (Account, [(Text, Text)]))

yields

(Account {_name = "hi", _accountNumber = "1234567890"},[])

.

_ = runIdentity $ (runValidation  (accountValidation ("hi", "bye", "12345678900")) Account { _name = "", _accountNumber = "" } :: Identity (Account, [(Text, Text)]))

yields

(Account {_name = "", _accountNumber = ""},[("confirmName","fields do not match."),("accountNumber","account number not correct length")])

.

digestive-functors integration

Integration with digestive-functors is also pretty simple. Instead of

posted :: (Monad m) => m (View Text, Maybe (Text,Text,Text))
posted = postForm "f" accountForm $ testEnv [("f.name", "hello"), ("f.confirmName", "hello"), ("f.phoneNumber", "1(333)333-3333x3")]

add validateView in as well, like

validatedPosted :: (Monad m) => m (View Text, Maybe Account)
validatedPosted =  posted >>= validateView accountValidation Account { _name = "", _accountNumber = "" }

You can also use validateView' if your domain record has a Monoid instance.