Introducing error reporting in optics

A frequently requested feature is the ability to report why an optic failed. It is particularly crucial when you build a sophisticated optic. Say you have a large configuration document, and you want to focus on kafka.topics.order-events.partitions . There may not be a partitions key, or if it exists, it may have an unexpected format, e.g. it is a String instead of an Int. In Monocle and other optics libraries, we currently cannot provide any details about the failure. In this blog post, I will discuss my experiments with a new optics encoding that supports detailed error reporting. In particular, I will present a step-by-step refactoring of one specific type of optic such as you can see the intermediate attempts as well as the final solution.

This article is written using Dotty 0.21.0-RC1 . All code is available in the following GitHub repository.

Overview of Optional

An Optional (aka Affine Traversal) is an optics used to access a section ( To ) of a larger object ( From ). As the name implies, the area targeted by an Optional may be missing. Here is the interface of an Optional.

1 2 3 4 trait Optional [ From , To ] { def getOption ( from : From ) : Option [ To ] def replace ( to : To , from : From ) : From }

Unsurprisingly, we can use an Optional to access an optional field in a class.

1 2 3 4 5 6 7 8 9 10 case class User ( name : String , age : Int , email : Option [ String ] ) val email : Optional [ User , String ] = . . . val john = User ( "John Doe" , 23 , Some ( "john@foo.com" ) ) email . getOption ( john ) // res0: Option[String] = Some(john@foo.com) email . replace ( "john@doe.com" , john ) // res1: User = User(John Doe, 23, Some(john@doe.com))

We can also build an Optional to target any value in a Map .

1 2 3 4 5 6 7 8 9 10 def index [ K , V ] ( key : K ) : Optional [ Map [ K , V ] , V ] = . . . val users : Map [ String , Int ] = Map ( "john" -> 23 , "marie" -> 34 ) index ( "john" ) . getOption ( users ) // res2: Option[Int] = Some(23) index ( "bob" ) . getOption ( users ) // res3: Option[Int] = None index ( "john" ) . replace ( 20 , users ) // res4: Map[String, Int] = Map(john -> 20, marie -> 34)

The interfaces follow three rules which ensure that if an Optional successfully accesses a value, then it can only modify this particular section of the larger object and nothing else. These rules are generally checked using property based testing:

if getOption(from) == Some(x) , then replace(x, from) == from . if getOption(from) == Some(x) , then getOption(replace(y, from)) == Some(y) . if getOption(from) == None , then replace(x, from) == from .

Perhaps surprisingly, an Optional does not let you insert data. You can only change values that already exist, as this example shows.

1 2 index ( "bob" ) . replace ( 45 , users ) // res5: Map[String, Int] = Map(john -> 23, marie -> 34)

Optional offers a rich API with more than a dozen useful combinators, but most importantly, Optional composes with other Optional such as you can easily access nested data structure.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 trait Optional [ From , To ] { self => . . . def >>> [ Next ] ( other : Optional [ To , Next ] ) : Optional [ From , Next ] = . . . } val users : Map [ String , User ] = Map ( "john" -> User ( "John Doe" , 23 , Some ( "john@foo.com" ) ) , "marie" -> User ( "Marie Acme" , 34 , None ) ) ( index ( "john" ) >>> email ) . getOption ( users ) // res10: Option[String] = Some(john@foo.com) ( index ( "marie" ) >>> email ) . getOption ( users ) // res11: Option[String] = None ( index ( "bob" ) >>> email ) . getOption ( users ) // res12: Option[String] = None ( index ( "john" ) >>> email ) . replace ( "john@doe.com" , users ) // res13: Map[String, User] = Map( // "john" -> User(John Doe , 23, Some(john@doe.com)), // "marie" -> User(Marie Acme, 34, None) // )

Here is the core of the problem. Both index("marie") >>> email and index("bob") >>> email fail with users . The former because Marie doesn't have an email and the latter because Bob is not a valid user. Sadly, we have no way to distinguish these two cases.

The issue is that Optional returns an Option which does not provide any details when it fails. If we want to report a precise error message, we need to use something like Either . The question is then, what should be the type of the error message? Let's start with something simple like String and see how far we can go.

Optional with String error

We only need to change the signature of getOption to return an Either[String, From] . Let's also use that occasion to rename this method getOrError . We should probably rename Optional too, but I don't have a better idea at the moment.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 trait Optional [ From , To ] { def getOrError ( from : From ) : Either [ String , To ] def replace ( to : To , from : From ) : From def >>> [ Next ] ( other : Optional [ To , Next ] ) : Optional [ From , Next ] = . . . } val email = new Optional [ User , String ] { def getOrError ( from : User ) : Either [ String , String ] = from . email . toRight ( "email is missing" ) def replace ( to : String , from : User ) : User = if ( from . email . isDefined ) from . copy ( email = Some ( to ) ) else from } ( index ( "john" ) >>> email ) . getOrError ( users ) // res14: Either[String, String] = Right(john@foo.com) ( index ( "marie" ) >>> email ) . getOrError ( users ) // res15: Either[String, String] = Left(email is missing) ( index ( "bob" ) >>> email ) . getOrError ( users ) // res16: Either[String, String] = Left(Key bob is missing)

Yeah! That wasn't too difficult. We could stop here but having the error type hardcoded to String is a bit unsatisfying. What if someone wanted to build a DSL to manipulate JSON or YAML ala JsonPath from Circe. In that case, we may want to return a path from the root element as well as an error message.

1 2 3 ( index ( "users" ) >>> index ( "john" ) >>> index ( "age" ) ) . getOrError ( users ) // res17: Either[(Path, String), String] = // Left((users.john, "missing field age"))

Optional with custom error

If we want the error type to be fully customisable by the users, it needs to be a type parameter of Optional , e.g. Optional[CustomError, User, Email] .

1 2 3 4 5 6 trait Optional [ Error , From , To ] { def getOrError ( from : From ) : Either [ Error , To ] def replace ( to : To , from : From ) : From def >>> [ Next ] ( other : Optional [ Error , To , Next ] ) : Optional [ Error , From , Next ] = . . . }

Now, let's define a simple configuration language with three types of values: Int , String and Object .

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 enum Config { case IntConfig ( value : Int ) case StringConfig ( value : String ) case ObjectConfig ( value : Map [ String , Config ] ) } val config : Config = ObjectConfig ( Map ( "http-server" -> ObjectConfig ( Map ( "hostname" -> StringConfig ( "localhost" ) , "port" -> IntConfig ( 8080 ) ) ) , "db" -> ObjectConfig ( Map ( "connection" -> IntConfig ( 4 ) , "url" -> StringConfig ( "jdbc:postgresql://localhost:5432/db" ) ) ) ) )

When we access a Config , we can experience two kinds of failure. Either the data is missing, or it is an unexpected format, e.g. we want an Int , but it is a String . Let's also use an enumeration to encode errors.

1 2 3 4 enum ConfigFailure { case MissingKey ( key : String ) case InvalidFormat ( expectedFormat : String , actual : Config ) }

Now, we can define a few Optionals that parse a generic Config into each data type: Int , String , or Object . These Optionals can only fail because the config type is incorrect, so let's use a specific InvalidFormat error.

1 2 3 4 5 6 7 8 9 10 val int : Optional [ InvalidFormat , Config , Int ] = . . . val str : Optional [ InvalidFormat , Config , String ] = . . . val obj : Optional [ InvalidFormat , Config , Map [ String , Config ] ] = . . . int . getOrError ( IntConfig ( 12 ) ) // res18: Either[InvalidFormat, Int] = Right(12) int . getOrError ( StringConfig ( "hello" ) ) // res19: Either[InvalidFormat, Int] = // Left(InvalidFormat(Int,StringConfig(hello)))

If you are familiar with optics hierarchy, you may have noticed that int , str and obj could be a Prism (a more specific optics). However, Optional works fine too, so let's keep things simple. Next step, we can adapt index to return a MissingKey error.

1 2 3 4 def index ( key : String ) : Optional [ MissingKey , Map [ String , Config ] , Config ] = . . . index ( "db" ) . getOrError ( Map . empty ) // res20: Either[MissingKey, Int] = Left(MissingKey(db))

Finally, let's define an optics called property such as we can have a friendly syntax to access nested Config objects.

1 2 3 property ( "http-server" ) >>> property ( "hostname" ) >>> str property ( "db" ) >>> property ( "connection" ) >>> int

Property checks if a Config is an Object , and then it focuses into a key of the Map . The error type of property should be a ConfigFailure because it can fail for either reasons.

1 2 3 4 5 6 7 8 def property ( key : String ) : Optional [ ConfigFailure , Config , Config ] = obj >>> index ( key ) [ ERROR ] Type Mismatch Error : obj >>> index ( key ) ^^^^^^^^^^^^ Found : Optional [ MissingKey , Map [ String , Nothing ] , Nothing ] Required : Optional [ InvalidFormat , Map [ String , Config ] , Next ]

Regrettably, the compiler rejects composing obj and index together because they have different failure types. MissingKey and InvalidFormat are both ConfigFailure , but >>> is too strict. It requires both sides of >>> to have exactly the same error. There are two solutions to this problem:

We update the definition of obj and index to use ConfigFailure instead of a more specialised error. We use variance on the error type of Optional , and let the compiler automatically adapt the error type when required.

Historically, in the functional programming side of Scala, variance had a lousy reputation. However, libraries like fs2 or ZIO recently demonstrated that variance offers a great user experience in terms of type inference. The implementation is slightly more complicated, but it is completely acceptable if end-users enjoy a better experience.

Optional with covariant error

Variance is quite tricky to grasp. Fortunately, the compiler is here to help us. If we ever use the wrong variance annotation, the compiler will let us know and usually give us some suggestions. In our case, we can deduce Optional is covariant ( + ) in Error because the two core methods of Optional : getOrError and replace , only mention Error in their output.

1 2 3 4 5 6 7 trait Optional [ + Error , From , To ] { def getOrError ( from : From ) : Either [ Error , To ] def replace ( to : To , from : From ) : From def >>> [ NewError >: Error , Next ] ( other : Optional [ NewError , To , Next ] ) : Optional [ NewError , From , Next ] = . . . }

>>> signature is a bit scary, let's go through an example.

1 2 3 4 5 val obj : Optional [ InvalidFormat , Config , Map [ String , Config ] ] = . . . def index ( key : String ) : Optional [ MissingKey , Map [ String , Config ] , Config ] = . . . obj >>> index ( "foo" )

obj has an error type of InvalidFormat and >>> has the constraint NewError >: Error . It means the error type of index must be a super type of InvalidFormat . We defined index with a MissingKey error which is not a super type of InvalidFormat . However, since Optional is covariant in Error , the Scala/Dotty compiler can automatically upcast index error to satisfy the constraint (see types in green).

ConfigFailure , AnyRef , or Any are all valid options. The compiler has some heuristics to determine which type should be inferred. In this case, it chose the lower bound ConfigFailure , see this presentation from Guillaume Martres for more details about type inference.

Now, let's go back to our main use case.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 def property ( key : String ) : Optional [ ConfigFailure , Config , Config ] = obj >>> index ( key ) val config : Config = ObjectConfig ( Map ( "http-server" -> ObjectConfig ( Map ( "hostname" -> StringConfig ( "localhost" ) , "port" -> IntConfig ( 8080 ) ) ) , "db" -> ObjectConfig ( Map ( "connection" -> IntConfig ( 4 ) , "url" -> StringConfig ( "jdbc:postgresql://localhost:5432/db" ) ) ) ) ) ( property ( "http-server" ) >>> property ( "port" ) >>> int ) . getOrError ( config ) // res21: Either[ConfigFailure, Int] = Right(8080) ( property ( "db" ) >>> property ( "connection" ) >>> str ) . getOrError ( config ) // res22: Either[ConfigFailure, Int] = // Left(InvalidFormat(String,IntConfig(4))) ( property ( "kafka" ) >>> property ( "port" ) >>> int ) . getOrError ( config ) // res23: Either[ConfigFailure, Int] = Left(MissingKey(kafka))

Great, >>> lifted automatically the error of obj and index to ConfigFailure which is precisely what we wanted. Can we do better? Are they still some corner cases?

Imagine we have a third party library like refined, offering an Optional to check if an Int is a PortNumber .

1 2 3 4 5 6 7 val portNumber : Optional [ String , Int , PortNumber ] = . . . portNumber . getOrError ( 8080 ) // res24: Either[String, PortNumber] = Right(PortNumber(8080)) portNumber . getOrError ( - 1 ) // res25: Either[String, PortNumber] = Left("-1 is not a port number")

We may want to use this validation with our configuration DSL. However, portNumber uses a generic String error message and not our ConfigFailure enumeration. So, the type inference algorithm will use the first common parent of ConfigFailure and String which is a rather useless Anyref .

1 2 property ( "http-server" ) >>> property ( "port" ) >>> int >>> portNumber // res26: Optional[AnyRef, Config, PortNumber]

One could argue we should transform the String error message from the third-party library error type to our domain model using something like mapError .

1 2 3 4 5 trait Optional [ + Error , From , To ] { . . . def mapError [ NewError ] ( f : Error => NewError ) : Optional [ NewError , From , Next ] = . . . }

However, it would be better if by default combining two unrelated errors offered a more precise type than Any or AnyRef . In Scala 2, we are out of luck, but Dotty has some helpful features.

Composing error with union types

Union types are a new feature of Dotty. They allow defining the most precise upper bound between two or more types. In our previous example, String | ConfigFailure would be the best possible type to return when we compose int >>> portNumber .

We only need to change the signature of >>> ; the implementation would stay the same. In my opinion, union types simplify the signature of >>> since we don't need the constraint NewError >: Error anymore.

1 2 3 4 5 6 7 8 9 10 trait Optional [ + Error , From , To ] { . . . def >>> [ NewError , Next ] ( other : Optional [ NewError , To , Next ] ) : Optional [ Error | NewError , From , Next ] = . . . } ( property ( "http-server" ) >>> property ( "port" ) >>> int >>> portNumber ) . getOrError ( config ) // res27: Either[String | ConfigFailure, PortNumber] = // Right(PortNumber(8080))

Perfect. Type inference works, and we have the most precise error type. Since we are building an Optional , we can as quickly update an arbitrary Config (assuming we have some macro to convert a 9000 literal to PortNumber ).

1 2 3 4 5 6 7 8 9 10 11 12 ( property ( "http-server" ) >>> property ( "port" ) >>> int >>> portNumber ) . replace ( 9000 , config ) // res24: Config = ObjectConfig(Map( // "http-server" -> ObjectConfig(Map( // "hostname" -> StringConfig("localhost"), // "port" -> IntConfig(9000) // )), // "db" -> ObjectConfig(Map( // "connection" -> IntConfig(4), // "url" -> StringConfig("jdbc:postgresql://localhost:5432/db") // )) //))

Conclusion and future work

I am super excited about this new encoding. It is to my knowledge a novel approach leveraging Scala specific features: variance and union types. There were several attempts to add error reporting to Haskell Lens, but the main issue seems to be related to type inference (see discussion on coindexing).

In my next blog post, I will explore the impact of error reporting on the rest of the optics hierarchy, e.g. can we return errors with other optics like Prism and Traversal ? How can we add error reporting without breaking existing code? What does it mean for Optional to have Nothing as error type? Surprisingly, we will see that variance and inheritance combine exceptionally well and offer a compelling optics encoding.

Stay tuned. In the meantime, you can follow me on twitter or discuss this article on reddit.