Scala 3: What Is “Direct Style”?

Dean Wampler
Scala 3
Published in
7 min readJun 4, 2023


Scala uses monads extensively for many operations with non-trivial effects, such as those involving asynchronous computation, including I/O. The idea of direct style is to allow writing code with non-trivial effects more simply without the boilerplate of monads.

Vista Tower, Chicago, © 2023, Dean Wampler

I recommend viewing this Martin Odersky talk about the on-going work on direct style. The following discussion is based on it.


Consider the example of Futures. We currently use them like this:

val sum =
val f1 = Future(
val f2 = Future(
x <- f1
y <- f2
yield x + y

The for comprehension invokes map and flatMap to wait on the futures and extract the values.

A new direct style implementation of futures would be used liked this:

  val sum = Future:
val f1 = Future(
val f2 = Future(
f1.value + f2.value

In both cases, the two futures are executing in parallel, which might take a while. The sum of the returned values is returned in a new future. The value method returns the result of a future once it is available or it throws an exception if the future returns a Failure. However, an implementation based on the boundary and break mechanism discussed below, this exception would be caught by the Future library, used to cancel the other running Future, if one is still running, and then return a Failure future for sum.

So, direct style simplifies code, making it is easier to write and understand, plus it enables cleaner separation of concerns, such as handling timeouts and failures in futures, and it cleanly supports composability, which monads don’t provide unless you use cumbersome machinary such as monad transformers.

Implementing Direct Style in Scala

In Martin’s talk, he discusses four aspects of building support for direct style in Scala:

  1. boundary and break — now available in Scala 3.3.0.
  2. Error handling — enabled, but not yet used in the library, which is still the Scala 2.13 library.
  3. Suspensions — work in progress.
  4. Concurrent library design built on the above — work in progress.

Let’s discuss these topics.

boundary and break

This mechanism is defined with a new addition to the API, scala.util.boundary$. It provides a cleaner alternative to non-local returns.

  • boundary defines a context for a computation.
  • break returns a value from within the enclosing boundary.

Here is an example from Martin’s talk, which is also discussed in the API. I have added this example and some tests in the Programming Scala code examples here and here.

import scala.util.boundary, boundary.break

def firstIndex[T](xs: List[T], elem: T): Int =
for (x, i) <- xs.zipWithIndex do
if x == elem then break(i)

This is one way to return the index for an element found in a collection or -1 if not found. The boundary defines the scope where a non-local return may be invoked. If the desired element elem is found, then we call break to return the index. This breaks out of the for comprehension, too, since there is no point in continuing. If elem isn’t found, -1 is returned through the normal return path.

Here is a slightly simplified implementation. (The full 3.3.0 source code is here):

package scala.util

object boundary:
final class Label[-T]

inline def apply[T](inline body: Label[T] ?=> T): T = ...

def break[T](value: T)(using label: Label[T]): Nothing =
throw Break(label, value)

final class Break[T] (val label: Label[T], val value: T) extends RuntimeException(...)

end boundary

In the firstIndex example above, the boundary: line is short for boundary.apply: with the indented code below it passed as the body.

Well actually, the block passed to boundary.apply is a context function that is called within boundary.apply to return the block of code shown in the example. Note the final class Label[T] declaration in boundary. Users don’t define Label instances themselves. Instead, this is done inside the implementation (not shown) of boundary.apply to provide the capability of doing a non-local return. Using a Label in this way prevents the user from trying to call break without an enclosing boundary.

Rephrasing all that, we don’t want users to call break without an enclosing boundary. That’s why break requires an in-scope given instance of Label, which the implementation of boundary.apply creates before it calls the code block you provide. If your code block calls break, a given Label will be in-scope.

You don’t have to do anything to create the context function passed to boundary.apply. It is synthesized from your block of code automatically when boundary.apply is called.

Look at firstIndex again. If we do find an element that is equal to elem, then we call break to return the index i from the boundary. If we don’t find the element, then a “normal” return is used to return -1. We never reach the -1 expression if break is called.

The boundary and break mechanism is a better alternative to scala.util.control.NonLocalReturns and scala.util.control.Breaks which are deprecated as of Scala 3.3.0. The new mechanism is easier for developers to use and it adds the following additional benefits:

  • The implementation uses a new scala.util.boundary$.Break class that derives from RuntimeException. Therefore, non-localbreaks are logically implemented as non-fatal exceptions and the implementation is optimized to suppress unnecessary stack trace generation. Stack traces are unnecessary because we are handling these exceptions, not barfing them on the user!
  • Better performance is provided when a break occurs to the enclosing scope inside the same method (i.e., the same stack frame), where it can be rewritten to a jump call.

Error Handling

Next Martin discussed new ways of handling errors that leverage boundary and break, and are partly inspired by the way Rust handles errors.

He used a simpler example of trying a computation that will hopefully return a result wrapped in a Some, but if it can’t succeed, then it will return None. Hence, an “optional” result, if you will. (This implementation is also now in the book’s code examples.)

import scala.util.boundary, boundary.break, boundary.Label

object optional:
inline def apply[T](inline body: Label[None.type] ?=> T): Option[T] =

extension [T](r: Option[T])
inline def ? (using label: Label[None.type]): T = r match
case Some(x) => x
case None => break(None)

We lose all information about why it failed. A proper error handling feature should retain this information.

optional.apply defines a boundary by calling boundary.apply. The reason the Label is typed with None.type, is because if and when we call break, it is because we are returning None to the boundary. We don’t break if we successfully computed something to return in a Some , which will be returned normally.

Inspired by a similar-looking construct in Rust, if you have an Option instance, calling the new extension method ? on it will either return the enclosed object or call break to return None out of the enclosing boundary. So, while the ? method looks more generally useful for deconstructing an Option, it really requires us to decide what to do when it is a None. Here, we are inside a boundary and we use a break. Let’s look at an example to understand how it used.

Suppose you have a sequence (rows) of sequences (columns). You want to return a new sequence with just the first column, i.e., a sequence with the first element in each row. Because a row might be empty, we must wrap the returned sequence in an Option. If any row is empty, we’ll return None for the whole thing:

def firstColumn[T](xss: Seq[Seq[T]]): Option[Seq[T]] =

The concision is very nice, but it requires some unpacking to understand what’s happening. optional defines the boundary, which will hopefully see a Some[Seq[T]] returned or, worst case, a None.

We map over the input sequence and for each nested sequence, we get the headOption, but then immediately extract the element using the new extension method ?. The map iteration will continue as long as those calls to ? return an instance of T, meaning the nested sequences are not empty. However, the first time an empty sequence is found, the implementation of ? will call break, immediately terminating the map iteration and returning a None out of the optional block.

Here’s what to expect:

 val xssSome = List(List(0), List(1,0), List(2,1,0), List(3,2,1,0))
val xssNone = List(List(0), Nil, List(2,1,0), List(3,2,1,0))
assert(firstColumn(xssSome) == Some(List(0,1,2,3)))
assert(firstColumn(xssNone) == None)

To really appreciate this example, try writing the same method without the boundary and break mechanism. It’s certainly doable, but more tedious!

Suspensions and a New Concurrency Library

Back to the futures :) , boundary and break can be used for adding new concurrency abstractions to Scala following a direct style, like the Futures example above, as well continuations, which Martin is calling suspensions. I won’t all discuss the details he covered here, but I will mention a few topics.

The implementation of new concurrency features is not trivial for Scala, because:

  1. Scala now runs on three platforms: JVM, JavaScript, and native.
  2. Even on the JVM, using the new lightweight fibers coming in Project Loom would only be available to users on the most recent JVMs (19 and later).

Besides writing custom implementations for each of these scenarios, other possible implementations might use source or bytecode rewriting.

Work has started in the lampepfl/async repo, a “strawman” for ideas, both for conceptual abstractions for concurrency (like a new Future type), as well as implementations. The plan is for this repo to evolve into a new concurrency library for Scala.

Final Thoughts

I’ve always liked Scala for the principled and deeply-considered approaches Martin and his team have taken to fundamental language and feature design. All programming language communities have ad hoc and “good enough” implementations for effects, like concurrency. Scala has very thoughtful systems for these purposes. However, we need to continue making it easier and more robust for people to write such code, which is commonplace and can’t be reserved for the few elite programmers.

For these reasons, Scala’s emerging direct style is a welcome trend.

For a concise summary of the more “mainstream” notable changes in Scala 3, see my Scala 3 Highlights page.

See Programming Scala, Third Edition for a comprehensive introduction to Scala 3, including details on how to migrate from Scala 2.



Dean Wampler
Scala 3

The person who is wrong on the Internet. ML/AI and FP enthusiast. Lurks at the AI Alliance and IBM Research. Speaker, author, pretend photographer.