This page describes experimental support for exception checking in Scala 3. It is enabled by the language import
The reason for publishing this extension now is to get feedback on its usability. We are working on more advanced type systems that build on the general ideas put forward in the extension. Those type systems have application areas beyond checked exceptions. Exception checking is a useful starting point since exceptions are familiar to all Scala programmers and their current treatment leaves room for improvement.
Exceptions are an ideal mechanism for error handling in many situations. They serve the intended purpose of propagating error conditions with a minimum of boilerplate. They cause zero overhead for the "happy path", which means they are very efficient as long as errors arise infrequently. Exceptions are also debug friendly, since they produce stack traces that can be inspected at the handler site. So one never has to guess where an erroneous condition originated.
However, exceptions in current Scala and many other languages are not reflected in the type system. This means that an essential part of the contract of a function - i.e. what exceptions can it produce? - is not statically checked. Most people acknowledge that this is a problem, but that so far the alternative of checked exceptions was just too painful to be considered. A good example are Java checked exceptions, which do the right thing in principle, but are widely regarded as a mistake since they are so difficult to deal with. So far, none of the successor languages that are modeled after Java or that build on the JVM has copied this feature. See for example Anders Hejlsberg's statement on why C# does not have checked exceptions.
The main problem with Java's checked exception model is its inflexibility, which is due to lack of polymorphism. Consider for instance the
map function which is declared on
List[A] like this:
def map[B](f: A => B): List[B]
In the Java model, function
f is not allowed to throw a checked exception. So the following call would be invalid:
xs.map(x => if x < limit then x * x else throw LimitExceeded())
The only way around this would be to wrap the checked exception
LimitExceeded in an unchecked
RuntimeException that is caught at the callsite and unwrapped again. Something like this:
try xs.map(x => if x < limit then x * x else throw Wrapper(LimitExceeded())) catch case Wrapper(ex) => throw ex
Ugh! No wonder checked exceptions in Java are not very popular.
So the dilemma is that exceptions are easy to use only as long as we forgo static type checking. This has caused many people working with Scala to abandon exceptions altogether and to use an error monad like
Either instead. This can work in many situations but is not without its downsides either. It makes code a lot more complicated and harder to refactor. It means one is quickly confronted with the problem how to work with several monads. In general, dealing with one monad at a time in Scala is straightforward but dealing with several monads together is much less pleasant since monads don't compose. A great number of techniques have been proposed, implemented, and promoted to deal with this, from monad transformers, to free monads, to tagless final. But none of these techniques is universally liked; each introduces a complicated DSL that's hard to understand for non-experts, introduces runtime overheads, and makes debugging difficult. In the end, quite a few developers prefer to work instead with a single "super-monad" like ZIO that has error propagation built in alongside other aspects. This one-size fits all approach can work very nicely, even though (or is it because?) it represents an all-encompassing framework.
However, a programming language is not a framework; it has to cater also for those applications that do not fit the framework's use cases. So there's still a strong motivation for getting exception checking right.
map work so poorly with Java's checked exception model? It's because
map's signature limits function arguments to not throw checked exceptions. We could try to come up with a more polymorphic formulation of
map. For instance, it could look like this:
def map[B, E](f: A => B throws E): List[B] throws E
This assumes a type
A throws E to indicate computations of type
A that can throw an exception of type
E. But in practice the overhead of the additional type parameters makes this approach unappealing as well. Note in particular that we'd have to parameterize every method that takes a function argument that way, so the added overhead of declaring all these exception types looks just like a sort of ceremony we would like to avoid.
But there is a way to avoid the ceremony. Instead of concentrating on possible effects such as "this code might throw an exception", concentrate on capabilities such as "this code needs the capability to throw an exception". From a standpoint of expressiveness this is quite similar. But capabilities can be expressed as parameters whereas traditionally effects are expressed as some addition to result values. It turns out that this can make a big difference!
In the effects as capabilities model, an effect is expressed as an (implicit) parameter of a certain type. For exceptions we would expect parameters of type
E stands for the exception that can be thrown. Here is the definition of
erased class CanThrow[-E <: Exception]
This shows another experimental Scala feature: erased definitions. Roughly speaking, values of an erased class do not generate runtime code; they are erased before code generation. This means that all
CanThrow capabilities are compile-time only artifacts; they do not have a runtime footprint.
Now, if the compiler sees a
throw Exc() construct where
Exc is a checked exception, it will check that there is a capability of type
CanThrow[Exc] that can be summoned as a given. It's a compile-time error if that's not the case.
How can the capability be produced? There are several possibilities:
Most often, the capability is produced by having a using clause
(using CanThrow[Exc]) in some enclosing scope. This roughly corresponds to a
throws clause in Java. The analogy is even stronger since alongside
CanThrow there is also the following type alias defined in the
infix type $throws[R, +E <: Exception] = CanThrow[E] ?=> R
R $throws E is a context function type that takes an implicit
CanThrow[E] parameter and that returns a value of type
R. What's more, the compiler will translate an infix types with
throws as the operator to
$throws applications according to the rules
A throws E --> A $throws E A throws E₁ | ... | Eᵢ --> A $throws E₁ ... $throws Eᵢ
Therefore, a method written like this:
def m(x: T)(using CanThrow[E]): U
can alternatively be expressed like this:
def m(x: T): U throws E
CanThrow capabilities can be combined in a single throws clause. For instance, the method
def m2(x: T)(using CanThrow[E1], CanThrow[E2]): U
can alternatively be expressed like this:
def m(x: T): U throws E1 | E2
throws combo essentially propagates the
CanThrow requirement outwards. But where are these capabilities created in the first place? That's in the
try expression. Given a
try like this:
try body catch case ex1: Ex1 => handler1 ... case exN: ExN => handlerN
the compiler generates capabilities for
CanThrow[ExN] that are in scope as givens in
body. It does this by augmenting the
try roughly as follows:
try erased given CanThrow[Ex1] = ??? ... erased given CanThrow[ExN] = ??? body catch ...
Note that the right-hand side of all givens is
??? (undefined). This is OK since these givens are erased; they will not be executed at runtime.
That's it. Let's see it in action in an example. First, add an import
to enable exception checking. Now, define an exception
LimitExceeded and a function
f like this:
val limit = 10e9 class LimitExceeded extends Exception def f(x: Double): Double = if x < limit then x * x else throw LimitExceeded()
You'll get this error message:
9 | if x < limit then x * x else throw LimitExceeded() | ^^^^^^^^^^^^^^^^^^^^^ |The capability to throw exception LimitExceeded is missing. |The capability can be provided by one of the following: | - A using clause `(using CanThrow[LimitExceeded])` | - A `throws` clause in a result type such as `X throws LimitExceeded` | - an enclosing `try` that catches LimitExceeded | |The following import might fix the problem: | | import unsafeExceptions.canThrowAny
As the error message implies, you have to declare that
f needs the capability to throw a
LimitExceeded exception. The most concise way to do so is to add a
def f(x: Double): Double throws LimitExceeded = if x < limit then x * x else throw LimitExceeded()
Now put a call to
f in a
try that catches
@main def test(xs: Double*) = try println(xs.map(f).sum) catch case ex: LimitExceeded => println("too large")
Run the program with some inputs:
> scala test 1 2 3 14.0 > scala test 0.0 > scala test 1 2 3 100000000000 too large
Everything typechecks and works as expected. But wait - we have called
map without any ceremony! How did that work? Here's how the compiler expands the
// compiler-generated code @main def test(xs: Double*) = try erased given ctl: CanThrow[LimitExceeded] = ??? println(xs.map(x => f(x)(using ctl)).sum) catch case ex: LimitExceeded => println("too large")
CanThrow[LimitExceeded] capability is passed in a synthesized
using clause to
f requires it. Then the resulting closure is passed to
map. The signature of
map does not have to account for effects. It takes a closure as always, but that closure may refer to capabilities in its free variables. This means that
map is already effect polymorphic even though we did not change its signature at all. So the takeaway is that the effects as capabilities model naturally provides for effect polymorphism whereas this is something that other approaches struggle with.
Another advantage is that the model allows a gradual migration from current unchecked exceptions to safer exceptions. Imagine for a moment that
experimental.saferExceptions is turned on everywhere. There would be lots of code that breaks since functions have not yet been properly annotated with
throws. But it's easy to create an escape hatch that lets us ignore the breakages for a while: simply add the import
This will provide the
CanThrow capability for any exception, and thereby allow all throws and all other calls, no matter what the current state of
throws declarations is. Here's the definition of
package scala object unsafeExceptions: given canThrowAny: CanThrow[Exception] = ???
Of course, defining a global capability like this amounts to cheating. But the cheating is useful for gradual typing. The import could be used to migrate existing code, or to enable more fluid explorations of code without regard for complete exception safety. At the end of these migrations or explorations the import should be removed.
To summarize, the extension for safer exception checking consists of the following elements:
- It adds to the standard library the class
scala.CanThrow, the type
scala.$throws, and the
scala.unsafeExceptionsobject, as they were described above.
- It adds some desugaring rules ro rewrite
throwstypes to cascaded
- It augments the type checking of
throwby demanding a
CanThrowcapability or the thrown exception.
- It augments the type checking of
CanThrowcapabilities for every caught exception.
That's all. It's quite remarkable that one can do exception checking in this way without any special additions to the type system. We just need regular givens and context functions. Any runtime overhead is eliminated using
Our capability model allows to declare and check the thrown exceptions of first-order code. But as it stands, it does not give us enough mechanism to enforce the absence of capabilities for arguments to higher-order functions. Consider a variant
map that should enforce that its argument does not throw exceptions or have any other effects (maybe because wants to reorder computations transparently). Right now we cannot enforce that since the function argument to
pureMap can capture arbitrary capabilities in its free variables without them showing up in its type. One possible way to address this would be to introduce a pure function type (maybe written
A -> B). Pure functions are not allowed to close over capabilities. Then
pureMap could be written like this:
def pureMap(f: A -> B): List[B]
Another area where the lack of purity requirements shows up is when capabilities escape from bounded scopes. Consider the following function
def escaped(xs: Double*): () => Int = try () => xs.map(f).sum catch case ex: LimitExceeded => -1
With the system presented here, this function typechecks, with expansion
// compiler-generated code def escaped(xs: Double*): () => Int = try given ctl: CanThrow[LimitExceeded] = ??? () => xs.map(x => f(x)(using ctl)).sum catch case ex: LimitExceeded => -1
But if you try to call
escaped like this
val g = escaped(1, 2, 1000000000) g()
the result will be a
LimitExceeded exception thrown at the second line where
g is called. What's missing is that
try should enforce that the capabilities it generates do not escape as free variables in the result of its body. It makes sense to describe such scoped effects as ephemeral capabilities - they have lifetimes that cannot be extended to delayed code in a lambda.
We are working on a new class of type system that supports ephemeral capabilities by tracking the free variables of values. Once that research matures, it will hopefully be possible to augment the language so that we can enforce the missing properties.
And it would have many other applications besides: Exceptions are a special case of algebraic effects, which has been a very active research area over the last 20 years and is finding its way into programming languages (e.g. Koka, Eff, Multicore OCaml, Unison). In fact, algebraic effects have been characterized as being equivalent to exceptions with an additional resume operation. The techniques developed here for exceptions can probably be generalized to other classes of algebraic effects.
But even without these additional mechanisms, exception checking is already useful as it is. It gives a clear path forward to make code that uses exceptions safer, better documented, and easier to refactor. The only loophole arises for scoped capabilities - here we have to verify manually that these capabilities do not escape. Specifically, a
try always has to be placed in the same computation stage as the throws that it enables.
Put another way: If the status quo is 0% static checking since 100% is too painful, then an alternative that gives you 95% static checking with great ergonomics looks like a win. And we might still get to 100% in the future.