Implicit Design Patterns in Scala

Apart from the old design patterns from the 1990s, the Scala programming language in 2016 has a whole new set of design patterns that apply to it. These are patterns that you see in Scala code written across different organizations, cultures and communities.

This blog post will describe some of these design patterns around the use of Scala implicits: specifically around the use of implicit parameters. Hopefully this should help document some of the more fundamental patterns around how people use implicit parameters "in the wild", and provide some insight into what can easily be a confusing language feature.

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy the Author's book Hands-on Scala Programming

"Implicits" - implicit parameters and implicit conversions - are a major feature of the Scala language. An implicit parameter is one that can be automatically inferred based on its type and the values in scope, without you needing to pass in the value of the argument explicitly, and an implicit conversion function converts one type to another automatically on-demand, without needing to call the function explicitly.

However, implicits themselves are a pretty low-level feature. You generally do not use implicits for the sake of using implicits, neither do you use implicits freely in all possible ways. Rather, you most often use implicits as a tool to help you implement one of a small number of patterns. This blog post documents some of those, specifically around the use of implicit parameters:

Since there isn't that much published literature about design patterns in Scala, all these are names I just made up off the top of my head, so hopefully the names will make sense. This is not going to be anywhere near an exhaustive list of the things you can do with implicits, but should hopefully provide a foundation that you can use when trying to use them yourself or understanding other people's code.

Implicit Contexts

The most basic use of implicits is the Implicit Context pattern: using them to pass in some "context" object into all your methods. This is something you could pass in manually, but is common enough that simply "not having to pass it everywhere" is itself a valuable goal.

For example, the standard library uses it to pass around an implicit ExecutionContext to every piece of code that needs to "run something asynchronously":

implicit val ec: ExecutionContext = scala.concurrent.ExecutionContext.Implicits.global def getEmployee(id: Int)(implicit e: ExecutionContext): Future[Employee] = ??? def getRole(employee :Employee)(implicit e: ExecutionContext): Future[Role] = ??? val bigEmployee: Future[EmployeeWithRole] = getEmployee(100).flatMap { e => getRole(e).map { r => EmployeeWithRole(e.id, e.name,r) } }

getEmployee and getRole both require an ExecutionContext to run their asynchronous fetches, and the .flatMap and .map methods also need an ExecutionContext to run their callbacks. Without implicits you would need to pass it into each of those functions manually:

val ec: ExecutionContext = scala.concurrent.ExecutionContext.Implicits.global val bigEmployee: Future[EmployeeWithRole] = getEmployee(100)(ec).flatMap { e => getRole(e)(ec).map { r => EmployeeWithRole(e.id, e.name,r) }(ec) }(ec)

While there are only 4 copies of ec in this short snippet, in a larger file or codebase you could easily have dozens, hundreds or thousands of redundant copies. By passing it around as an Implicit Context using implicit , it saves all that duplication and cleans up the code considerably.

Similarly, the Play framework also uses it to pass around the request object:

def index = Action { implicit request => Ok(views.html.index()) }

Akka uses it to pass around ActorContext s and ActorSystem s, and so on. In all these cases, the goal is to pass around some object that is ubiquitous enough that explicitly passing it into each and every function call is tedious and verbose.

The Implicit Context pattern is just one use of the implicit keyword in Scala, which is a broadly flexible tool with many uses. Implicit Contexts usually have some properties that make them distinct from many other uses of implicits:

The implicit parameter usually is not generic type, and does not have any type parameters

The same implicit is being passed to all sorts of different functions with different signatures

Different values of the implicit will be passed into the same function when called at different times, e.g. every Play Framework HTTP request gets a new Request value that gets passed around implicitly

The implicit value might even be mutable! This is certainly the case for Akka's ActorSystem s, which encapsulate a large pool of Actors and the ability to spawn new ones or send them messages.

Essentially, the Implicit Context pattern is the only use of implicit parameters which treat them as "a convenient way to pass extra arguments", which are not much different from any other arguments you might pass except being ubiquitous enough you pass them everywhere.

Type-class Implicits

The Type-class Implicit pattern is named after a language feature in Haskell which provides the same functionality. In short, it is using generic implicits which take a type parameter, e.g. Foo[T] , and resolving them based on that type parameter. That means you an implicit of the same Foo[T] type almost always resolves to the same, immutable value. This is in contrast to the Implicit Context pattern where the implicit Foo type typically has no type parameter but you provide a different (possibly mutable!) value each time.

For example, you may want to define a function that lets you serialize an object to JSON:

sealed trait Json object Json{ case class Str(s: String) extends Json case class Num(value: Double) extends Json // ... many more definitions } def convertToJson(x) // converts x to a Json object

However, what would the type signature of convertToJson be? It should probably return Json if that's what we want out of it:

def convertToJson(x): Json

But what about it's argument?

It could be Any , and we could pattern-match on it to figure out what kind of Json object we want to create:

def convertToJson(x: Any): Json = { x match{ case s: String => Json.Str(s) case d: Double => Json.Num(d) case i: Int => Json.Num(i.toDouble) // maybe more cases for float, short, etc. } }

This works if you pass in the right thing:

@ convertToJson("hello") res: Json = Str("hello") @ convertToJson(1234) res: Json = Num(1234.0)

But if you pass in the wrong thing, it blows up:

@ convertToJson(new java.io.File(".")) scala.MatchError: . (of class java.io.File) $sess.cmd2$.convertToJson(cmd2.sc:5) $sess.cmd6$.<init>(cmd6.sc:1) $sess.cmd6$.<clinit>(cmd6.sc:-1)

This works, but could be improved: what if we could make convertToJson(x) only compile if x is of type String , Double or Int ? We can't use the common supertype because that's just Any , which also includes things we don't want like java.io.File . And of course, using any of String , Double or Int directly prevents you from passing in the other two.

It turns out there is a solution to this problem, using the Type-class Implicit pattern:

trait Jsonable[T]{ def serialize(t: T): Json } object Jsonable{ implicit object StringJsonable extends Jsonable[String]{ def serialize(t: String) = Json.Str(t) } implicit object DoubleJsonable extends Jsonable[Double]{ def serialize(t: Double) = Json.Num(t) } implicit object IntJsonable extends Jsonable[Int]{ def serialize(t: Int) = Json.Num(t.toDouble) } }

Now, we can define our convertToJson as:

def convertToJson[T](x: T)(implicit converter: Jsonable[T]): Json = { converter.serialize(x) }

And it works for the cases where it should work:

@ convertToJson("hello") res: Json = Str("hello") @ convertToJson(123) res: Json = Num(123.0) @ convertToJson(123.56) res: Json = Num(123.56)

And fails for the cases it shouldn't work:

@ convertToJson(new java.io.File(".")) could not find implicit value for parameter converterJsonable[java.io.File] convertToJson(new java.io.File(".")) ^ Compilation Failed

This pattern is common enough that Scala provides a shorthand syntax for writing the convertToJson function:

def convertToJson[T: Jsonable](x: T): Json = { implicitly[Jsonable[T]].serialize(x) }

Thus, using Type-class implicits, we are able to make convertToJson take any one of an arbitrary set of types, with no common super-type between them, while still letting the compiler reject cases where you pass in an invalid type. While there is some amount of boilerplate setting this up (e.g. all the implicit Object FooJsonable... declarations above) the boilerplate only needs to be defined once per type (e.g. Jsonable ) for functions all over a codebase to make use of.

Method Overloading

Someone who has programmed in Java or a similar language may have used method overloading in the past to get this kind of functionality. e.g. defining convertToJson as:

def convertToJson(t: String) = Json.Str(t) def convertToJson(t: Double) = Json.Num(t) def convertToJson(t: Int) = Json.Num(t.toDouble)

This works, allowing multiple different types to be passed to convertToJson while disallowing invalid types at compile time, just as our Type-class Implicits version written above:

@ convertToJson("Hello") res5: Json.Str = Str("Hello") @ convertToJson(1.23) res6: Json.Num = Num(1.23) @ convertToJson(new java.io.File(",")) cmd7.sc:1: overloaded method value convertToJson with alternatives: (t: Int)$sess.cmd0.Json.Num <and> (t: Double)$sess.cmd0.Json.Num <and> (t: String)$sess.cmd0.Json.Str cannot be applied to (java.io.File) val res7 = convertToJson(new java.io.File(",")) ^ Compilation Failed

However, where this falls down is if you need to use convertToJson in another function. For example, maybe I want to write the following functions:

def convertToJsonAndPrint(x: T): Unit def convertMultipleItemsToJson(x: Array[T]): Seq[Json] def convertFutureToJson(x: Future[T]): Future[Json]

Using operator overloading, you have to duplicate each of these methods once for each type that can be converted to JSON. This results in a lot of duplication:

def convertToJson(t: String) = Json.Str(t) def convertToJson(t: Double) = Json.Num(t) def convertToJson(t: Int) = Json.Num(t.toDouble) def convertToJsonAndPrint(t: String) = println(convertToJson(t)) def convertToJsonAndPrint(t: Double) = println(convertToJson(t)) def convertToJsonAndPrint(t: Int) = println(convertToJson(t)) def convertMultipleItemsToJson(t: Array[String]) = t.map(convertToJson) def convertMultipleItemsToJson(t: Array[Double]) = t.map(convertToJson) def convertMultipleItemsToJson(t: Array[Int]) = t.map(convertToJson)

This works:

@ convertToJsonAndPrint(123) Num(123.0) @ convertMultipleItemsToJson(Array("Hello", "world")) res14: Array[Json.Str] = Array(Str("Hello"), Str("world"))

But at the cost of duplicating every operation once per-type. Here we only have three types and three operation, resulting in 9 methods in total, but in a larger program you may easily have 10 different types which are convertible to JSON, which are called by a hundred different methods. While you could still use method overloading, you'd need to write 1000 duplicate methods to make it work.

Using Type-class implicits, there's some boilerplate in defining the implicits as we did above, but once that's done each additional operation no longer needs N duplicate methods in order to work with any Jsonable type T :

def convertToJson[T: Jsonable](x: T): Json = { implicitly[Jsonable[T]].serialize(x) } def convertToJsonAndPrint[T: Jsonable](x: T) = println(convertToJson(x)) def convertMultipleItemsToJson[T: Jsonable](t: Array[T]) = t.map(convertToJson(_))

And this works just the same as the method-overloaded version above, just with less duplication:

@ convertToJsonAndPrint(123) Num(123.0) @ convertMultipleItemsToJson(Array("Hello", "world")) res22: Array[Json] = Array(Str("Hello"), Str("world"))

In general, while method overloading works, it is better to use Type-class implicits. It is a bit more verbose to set up the Jsonable trait at the start, but that it avoids having to duplicate methods throughout your codebase which use convertToJson . Instead, any method in your codebase just needs to take a [T: Jsonable] type parameter and it will automatically (and consistently!) be able to work with the same set of types that the original convertToJson method could, and work with other [T: Jsonable] methods (e.g. calling into convertToJson in their implementation) completely seamlessly.

Type-class Implicits are a broadly useful pattern, and are a very different pattern than the Implicit Contexts we describe above:

Implicit Contexts tend to have different values injected in each time, selected by the user of the library. Type-Class Implicits tend to always have the same value for each type, e.g. Jsonable[Int] or Jsonable[Seq[String]] , and often it is selected by the author of the library instead of the programmer using it. While it's possible you may find yourself wanting to serialize an Int into something other than a JSON number, it's not going to be something you do very often.

Implicit Contexts are usually full of data, and are often even mutable. Type-Class Implicits tend to have none of that: their contribution is usually a single pure function.

Note that although technically Type-class implicits and Implicit Contexts use the same "implicit parameter" language features, they are totally different patterns and should not be mixed. For example, you should not pass in mutable or stateful Type-class implicits. Although technically valid Scala, it will definitely confuse most future readers of your code.

Many libraries in the wild use Type-class Implicits:

Scalatags provides Type-Class Implicits for AttrValue[T] and StyleValue[T] , for every T that can be used as a HTML attribute or CSS style value

Spray-Json uses Type-Class Implicits almost identically as described here: to control which types are acceptable for JSON serialization

PPrint uses Type-Class Implicits to control how things get pretty-printed: there are defaults for most built in types and case classes, but you can define your own pretty-printing-style for your own types if you wish.

Derived Implicits

One neat thing about using Type-class Implicits is that you can perform "deep" checks. For example, what if apart from Str and Num , we also wanted to support JSON lists?

sealed trait Json object Json{ case class Str(s: String) extends Json case class Num(value: Double) extends Json case class List(items: Json*) extends Json // ... many more definitions }

Now, we want to be able to serialize scala.Seq into a Json.List , but with a caveat: only scala.Seq s which contain serializable things should be serializable! For example we want this to work:

convertToJson(Seq(1, 2, 3)) convertToJson(Seq("baz", "bar", "foo"))

But we want this to fail:

convertToJson(Seq(new java.io.File("."), new java.io.File("/")))

To do this, we can define an implicit Jsonable just like we did earlier, but with a catch: this new SeqJsonable itself takes a type T , for which there must be an implicit Jsonable available! This can be seen below in the implicit def SeqJsonable[T: Jsonable] :

trait Jsonable[T]{ def serialize(t: T): Json } object Jsonable{ implicit object StringJsonable extends Jsonable[String]{ def serialize(t: String) = Json.Str(t) } implicit object DoubleJsonable extends Jsonable[Double]{ def serialize(t: Double) = Json.Num(t) } implicit object IntJsonable extends Jsonable[Int]{ def serialize(t: Int) = Json.Num(t.toDouble) } implicit def SeqJsonable[T: Jsonable]: Jsonable[Seq[T]] = new Jsonable[Seq[T]]{ def serialize(t: Seq[T]) = { Json.List(t.map(implicitly[Jsonable[T]].serialize):_*) } } }

Now, we can convert any Seq into a Json , as long as it contains something that itself can be converted, such as an Int or String :

@ convertToJson(Seq(1, 2, 3)) res: Json = List(List(Num(1.0), Num(2.0), Num(3.0))) @ convertToJson(Seq("baz", "bar", "foo")) res: Json = List(List(Str("baz"), Str("bar"), Str("foo")))

But Seq s with non-convertable contents, like some java.io.File s, are rejected by the compiler:

@ convertToJson(Seq(new java.io.File("."), new java.io.File("/"))) cmd.sc:1: could not find implicit value for evidence parameter of type Jsonable[Seq[java.io.File]] val res = convertToJson(Seq(new java.io.File("."), new java.io.File("/"))) ^ Compilation Failed @

It even works for "deep" types, like if we pass in a Seq[Seq[Seq[Int]]] , it resolves it and serializes it correctly:

@ convertToJson(Seq(Seq(Seq(1, 2, 3)))) res22: Json = List(List(List(List(List(List(Num(1.0), Num(2.0), Num(3.0)))))))

Whereas if we pass in a Seq[Seq[Seq[java.io.File]]] , it fails to compile:

@ convertToJson(Seq(Seq(Seq(new java.io.File("."))))) cmd.sc:1: could not find implicit value for evidence parameter of type Jsonable[Seq[Seq[Seq[java.io.File]]]] val res = convertToJson(Seq(Seq(Seq(new java.io.File("."))))) ^ Compilation Failed @

What we have done is we have set up the implicits such that at compile time, it first look for something satisfying Jsonable[Seq[T]] , and then finding our definition of SeqJsonable , it then tries to look for an implicit definition for Jsonable[T] . The compiler does this recursively, and hence is able to, at compile time, decide that Seq(Seq(Seq(1, 2, 3))) is fine but Seq(Seq(Seq(new java.io.File(".")))) is unacceptable.

Apart from better controlling what types are acceptable when serializing to JSON and rejected bad types at compile-time, and working recursively, using Derived Implicits has another advantage over a naive match statement on an Any value: you can let a user define implicits for their own types, e.g. if I decide I want to let java.io.File s be serialized to JSON as a string containing their fully qualified path, I can do so:

@ implicit object FileJsonable extends Jsonable[java.io.File]{ def serialize(t: java.io.File) = Json.Str(t.getAbsolutePath) } defined object FileJsonable @ convertToJson(Seq(Seq(Seq(new java.io.File("."))))) res: Json = List(List(List(List(List(List(Str("/Users/lihaoyi/test/.")))))))

This opens up your protocol for users of your library to plug into: rather than allowing just a fixed set of types, they can "register" their own types by defining their own implicits for whatever they want. This is similar to registering handlers for each type in a global dictionary somewhere, but using Derived Implicits the compiler is always able to ensure you never use a type nobody has registered a handler for. With a global dictionary of types, mistakes result in runtime errors.

Type-driving Implicits

One neat feature of implicits is that they do not just depend on types to be inferred, but they themselves can also affect the types a compiler infers as part of an expression. For example, consider the way that you can automatically "widen" numbers by assigning a number of a smaller type to one of a larger type:

@ val x: Byte = 123 x: Byte = 123 @ val y: Short = x y: Short = 123 @ val z: Long = y z: Long = 123L @ val a: Float = 1.23f a: Float = 1.23F @ val b: Double = a b: Double = 1.2300000190734863

This generally does what you want, but sometimes misbehaves. For example:

@ val bigLong = Long.MaxValue - 1 bigLong: Long = 9223372036854775806L @ val bigLong: Long = Long.MaxValue - 1 bigLong: Long = 9223372036854775806L @ val bigFloat: Float = bigLong bigFloat: Float = 9.223372E18F @ val bigLong2: Long = bigFloat.toLong bigLong2: Long = 9223372036854775807L @ bigLong == bigLong2 res40: Boolean = false

Here, we see that bigLong is being automatically widened to bigFloat , but when converting it back to bigLong2 , we end up a different value. That's probably not what someone would expect from a simple "widening", which is meant to let you put a smaller value in a "wider" type but leave the value unchanged.

It turns out, you can define a function that does this widening manually:

@ { def widen[T, V](x: T)(implicit widener: Widener[T, V]): V = widener.widen(x) class Widener[T, V](val widen: T => V) object Widener{ implicit object FloatWiden extends Widener[Float, Double](_.toDouble) implicit object ByteWiden extends Widener[Byte, Short](_.toShort) implicit object ShortWiden extends Widener[Short, Int](_.toInt) implicit object IntWiden extends Widener[Int, Long](_.toLong) } } @ widen(1.23f: Float) res12: Double = 1.2300000190734863 @ val byte: Byte = 123 byte: Byte = 123 @ val smallValue: Byte = 123 smallValue: Byte = 123 @ val shortValue = widen(smallValue) shortValue: Short = 123 @ val intValue = widen(shortValue) intValue: Int = 123 @ val longValue = widen(intValue) longValue: Long = 123L

Here, you can see that every call to widen returns a different type; in fact, the returned types are entirely arbitrary, based on what implicit Widener objects we defined! In addition, trying to widen things that we didn't define Widener s for, such as Long or Double , fails to compile:

@ widen(longValue) cmd20.sc:1: could not find implicit value for parameter widener: $sess.cmd11.Widener[Long,V] val res20 = widen(longValue) ^ Compilation Failed @ widen(1.23: Double) cmd20.sc:1: could not find implicit value for parameter widener: $sess.cmd11.Widener[Double,V] val res20 = widen(1.23: Double) ^ Compilation Failed

Which is a nice property that ensures we don't have runtime errors from trying to widen the wrong type.

This isn't a totally complete implementation of widen : in particular, it can't widen things more than one step, e.g. from Byte to Long , and isn't applied implicitly like Scala's default number-widening behavior. Nevertheless, it demonstrates an important fact: that you can define implicits that don't just depend on the expected types, but also play an active role in deciding what type gets inferred by the compiler.

The same technique could be used to define a function that "generically" extends a Tuple into a larger Tuple:

@ { def extend[T, V, R](tuple: T, value: V)(implicit extender: Extender[T, V, R]): R = { extender.extend(tuple, value) } case class Extender[T, V, R](val extend: (T, V) => R) object Extender{ implicit def tuple2[T1, T2, V, R] = Extender[(T1, T2), V, (T1, T2, V)]{ case ((t1, t2), v) => (t1, t2, v) } implicit def tuple3[T1, T2, T3, V, R] = Extender[(T1, T2, T3), V, (T1, T2, T3, V)]{ case ((t1, t2, t3), v) => (t1, t2, t3, v) } implicit def tuple4[T1, T2, T3, T4, V, R] = Extender[(T1, T2, T3, T4), V, (T1, T2, T3, T4, V)]{ case ((t1, t2, t3, t4), v) => (t1, t2, t3, t4, v) } // ... and so on until tuple21 ... } }

Just like in the widen example earlier, the return type of extend depends on what implicit Extender s you defined. As a result, although e.g. Tuple2 and Tuple3 have no direct relation to each other in the class hierarchy, we can now use extend on a Tuple2 and the compiler automatically figures out the result should be a Tuple3 .

@ val t = (1, "lol") t: (Int, String) = (1, "lol") @ val bigger = extend(t, true) bigger: (Int, String, Boolean) = (1, "lol", true) @ val evenBigger = extend(bigger, List()) evenBigger: (Int, String, Boolean, List[Nothing]) = (1, "lol", true, List())

The takeaway from this section is that you can use the Type-driving Implicits pattern to control how a function's return type gets inferred, depending on what instances of an implicit parameter are in scope. This is a somewhat clever trick that you probably shouldn't use lightly: having the return type of a function depend on the argument types in completely arbitrary ways can easily be extremely confusing! Nevertheless, in the rare cases where you really want to do this (e.g. my FastParse library uses a Extender implicit similar to this one) this is how you do it.

That ends this quick overview of some of the more fundamental patterns around using implicit parameters in Scala. This is by no means exhaustive, as there are countless others not described here:

More advanced ones like the Aux Pattern from Shapeless. Shapeless in-general is full of clever things you can do with implicits, too many to even list here, let alone discuss.

Those using Implicit Conversions instead of Parameters, e.g. to implement implicit constructors (similar to those in C#) or extension methods.

Derived Implicits that work on case classes, versus just generic collections as those shown above. Shapeless provides these, and I perform a similar (though more primitive and far less principled) sort of derivation in my own uPickle and PPrint libraries.

Compiler/Macro-powered Implicit Contexts, such as implicit ClassTags or those in my SourceCode library, which let you on-demand access "additional" information from the compiler such as line numbers or file names that is not normally available to your program.

What are your own favorite implicit tricks and patterns that you use in your own code, or you've seen in someone else's? Let us know in the comments below!

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy the Author's book Hands-on Scala Programming