Suppose we're writing a service that accepts JSON requests and returns some kind of response. If there's a problem with a request—it's not even valid JSON, it doesn't match the schema we expect, etc.—we want to return an error, and of course it'd be nice if these errors were actually useful to the caller.

Unfortunately "useful" in this context can mean lots of different things, and the differences will usually depend at least in part on how involved a human was in creating the request. In the case of validation errors—i.e. we successfully received some JSON, but it's not a shape we understand—then if there's no human in sight, we generally only need to say something like "hey, we're not even speaking the same language, you should probably go try somewhere else". A detailed breakdown of all the reasons we don't understand the request is unlikely to be useful, so we might as well fail as fast as possible and save resources.

If on the other hand a human was responsible for the content of the request, it's possible that the caller will be able to make use of detailed information about all the problems with that content. Suppose for example that the JSON comes from a web form or spreadsheet that for whatever reason needs to be at least partially validated on the server side. In this case we probably don't want to fail fast—we want to accumulate all of the errors and send them back together, so that the human can correct them in a single pass.

Error accumulation in Play JSON🔗

This means that in many cases it's useful for a JSON decoder to have two modes for error reporting: fail fast and error accumulating. In Play JSON (one of my favorite JSON libraries in Scala), for example, we can determine whether we want a fail-fast or accumulating decoder by using either monadic or applicative operations in our definition:

import play.api.libs.functional.syntax. _ import play.api.libs.json. _ case class Foo ( i : Int , s : String ) object Foo { val monadicReads : Reads [ Foo ] = for { i <- ( __ \ 'i ).read[ Int ] s <- ( __ \ 's ).read[ String ] } yield Foo(i, s) val applicativeReads : Reads [ Foo ] = ( ( __ \ 'i ).read[ Int ] and ( __ \ 's ).read[ String ] )(Foo.apply _ ) }

Which works like this:

val json = Json.parse(""" { "i": "abc", "s": 123 } """) val JsError( monadicErrors ) = json.validate(Foo.monadicReads) val JsError( applicativeErrors ) = json.validate(Foo.applicativeReads)

Now monadicErrors will be a sequence of length one (with the error indicating that the i field isn't a number), while applicativeErrors will contain both errors.

This is great, but there are two arguments against this approach, one practical and the other more theoretical. First of all, it means the creator of the decoder has to decide once and for all whether or not the decoder accumulates errors. The library provides an accumulating decoder for List[String] , for example, so in the following code we're going to end up with five errors:

val JsError( errors ) = Json.parse(" [1, 2, 3, 4, 5] ").validate[ List [ String ]]

There's no way to switch to fail-fast behavior if we don't need or want to pay the cost of validating all the elements and collecting the errors into a sequence.

On the more theoretical side, people who care about these things tend to get grouchy about monadic binding ( flatMap here) being inconsistent with applicative composition. Any type constructor with a monad instance also has an applicative functor instance, and the applicative operators can always be written in terms of map and flatMap . Purists argue that you shouldn't ever be able to see differences between a computation built up using applicative composition and an equivalent computation using map and flatMap . Our applicativeReads above is equivalent to monadicReads in this sense, so the fact that they fail differently is a problem (for these purists—I don't personally care all that much, as we'll see in a minute).

Error accumulation in Argonaut🔗

I love Argonaut, but it basically punts on this issue. Not only does it not provide any special support for defining error-accumulating decoders, it's not even really possible to accumulate errors manually. You could do something like this if you were desperate:

import argonaut. _ , Argonaut. _ import scalaz. _ , Scalaz. _ case class Foo ( i : Int , s : String ) object Foo { implicit val decoder : DecodeJson [ Foo ] = DecodeJson { c => val iResult = (c --\ " i " ).as[ Int ] val sResult = (c --\ " s " ).as[ String ] (iResult.toDisjunction, sResult.toDisjunction) match { case (\/-( i ), \/-( s )) => DecodeResult.ok(Foo(i, s)) case (-\/(( iMsg , history )), -\/(( sMsg , _ ))) => DecodeResult.fail( s " $iMsg , $sMsg " , history) case (-\/(( msg , history )), _ ) => DecodeResult.fail(msg, history) case ( _ , -\/(( msg , history ))) => DecodeResult.fail(msg, history) } } }

But this just mashes up the error messages and ignores all histories except the first (and it's also just extremely unpleasant to write).

Error accumulation in circe🔗

I wanted to do better than this in circe, and this morning I merged a first draft of a solution (thanks to a lot of feedback from the community on the core idea and help from Pere Villega on the implementation).

In circe now anyone implementing Decoder[A] can optionally provide an implementation of the following method (in addition to the required HCursor => Xor[DecodingFailure, A] ):

import cats.data.ValidationNel import io.circe. _ // ... private [circe] def decodeAccumulating ( c : HCursor ): ValidationNel [ DecodingFailure , A ]

This method is not part of the public API, but it supports conversion of any Decoder to a new AccumulatingDecoder type. To show how this works, we can write the following:

import cats.syntax.apply. _ import io.circe. _ , io.circe.jawn.decode case class Foo ( i : Int , s : String ) object Foo { val monadicDecoder : Decoder [ Foo ] = for { i <- Decoder[ Int ] s <- Decoder[ String ] } yield Foo(i, s) val applicativeDecoder : Decoder [ Foo ] = (Decoder[ Int ] |@| Decoder[ String ]).map(Foo.apply) }

This is very similar to the Play code above, but these decoders both fail fast:

scala> import cats.data.{ Validated, Xor } import cats.data.{Validated, Xor} scala> val Xor.Right( json ) = parse(""" { "i": "abc", "s": 123 } """) json: io.circe. Json = { " i " : " abc ", " s " : 123 } scala> Foo.monadicDecoder(json.hcursor) res0: io.circe. Decoder . Result [ Foo ] = Left(io.circe.DecodingFailure: Int ) scala> Foo.applicativeDecoder(json.hcursor) res1: io.circe. Decoder . Result [ Foo ] = Left(io.circe.DecodingFailure: String )

We can convert them into accumulating decoders, though:

val monadicAccDecoder = Foo.monadicDecoder.accumulating val applicativeAccDecoder = Foo.applicativeDecoder.accumulating

And then we can see the difference:

val Validated.Invalid( monadicErrors ) = monadicAccDecoder(json.hcursor) val Validated.Invalid( applicativeErrors ) = applicativeAccDecoder(json.hcursor)

Now monadicErrors contains only the first error and applicativeErrors contains both.

I'm not sure this will please all of the purists—monadic binding and applicative composition are still inconsistent, we just can't see that without converting the decoders to an entirely different type.

I care more about the fact that it means that the user gets to decide which behavior they want. If the user never calls accumulating , the behavior will be exactly the same as Argonaut's: decoding will fail immediately on the first error, and only the first error will be returned. If they want to accumulate errors, though, they can call accumulating to get an accumulating decoder, and will get as much accumulation as is possible.

The "as is possible" here is constrained by the use of monadic binding (or alternatively failure to implement decodeAccumulating appropriately) in any decoder you use. As we can see in monadicDecoder above, if you use flatMap (in this case via a for -comprehension), you lose error accumulation. The data dependency in monadicDecoder isn't real—we don't actually need the result of the "i" decoder to decode the "s" —but the compiler doesn't know that. Any flatMap at any point "forgets" all accumulation after it, and the accumulating decoder we get from accumulating will be limited to accumulation above the flatMap .

I don't foresee this being a problem in practice (although I could be wrong, of course). All of the decoders provided by circe (including generically derived ones) avoid monadic binding when they can, which means that something like the following just works:

import cats.data.{ Validated, Xor } import io.circe. _ , io.circe.generic.auto. _ , io.circe.jawn.parse case class Bar ( i : Int , s : List [ String ]) val Xor.Right( json ) = parse(""" { "i": "abc", "s": ["foo", "bar", 123, "baz", 456] } """) val Validated.Invalid( errors ) = Decoder[ Bar ].accumulating(json.hcursor)

We'll get all three errors, as expected.

This functionality is now available in the circe 0.3.0 snapshot, and while I'm sure it's not perfect, I think it's solid enough for anyone who's interested to try it out. Existing code should not be affected in any way (if it is, that's a bug). Any feedback on the design or implementation would of course be greatly appreciated.