Programming Flix

Effect Polymorphism

In Flix, we can express that a function is pure (i.e. has no side-effects):

def inc(x: Int32): Int32 \ { } = x + 1
                        // ^^^ empty effect set

The inc function is pure because its effect set is empty. When a function is pure, we know that the function must return the same value when given the same arguments. Moreover, the function cannot have any side-effect on the outside world.

We do not have to write the empty effect set. We can simply write:

def inc(x: Int32): Int32 = x + 1

In Flix, we can express that a function has a single effect:

def incAndPrint(x: Int32): Int32 \ {IO} = 
    let result = x + 1;         // ^^^^ singleton effect set
    println(result);
    result

Here the incAndPrint function has the primitive IO effect.

We can also express that a function has multiple effects:

def copyFile(src: File, dst: File): Unit \ {FsRead, FsWrite, IO} = ...
                                         // ^^^^^^^^^^^^^^^^^^^^ multiple effects

Here the copyFile function has three primitive effects: FsRead, FsWrite, and IO.

In Flix, we can express a function that has a heap effect:

def nth(i: Int32, a: Array[t, r]): Option[a] \ {r} = ....
                                            // ^^^ heap effect

Here the nth function has a heap effect in the region r.

We can also write functions that mix different effects:

def strange(a: Array[t, r]): Unit \ {r, Clock, Net, IO} 
                                 // ^^^^^^^^^^^^^^^^^^^ a mixture of effects

This function has a heap effect r and three primitive effects: Clock, Net, and IO.

Higher-Order Functions

When we write higher-order functions, we must think carefully about their effect behavior.

For example, we can write a higher-order function Set.exists:

def exists(f: a -> Bool \ { }, s: Set[a]): Bool = ...
                          ^^^

Here the exists function enforces the predicate function f to be pure. Why would we do this? For at least two reasons: (a) it allows us to hide the iteration order used in the set, and (b) it allows us to perform the counting in parallel.

Nevertheless, requiring a function to be pure unless necessary is considered a bad programming style. Instead, we should write effect polymorphic functions. An effect polymorphic function is a higher-order function whose effects depend on the effects of its function arguments.

For example, we can write an effect polymorphic map function:

def map(f: a -> b \ ef, l: List[a]): List[b] \ ef = ...
                    ^^ // effect variable      ^^ effect variable

The type and effect signature of map states: If map is given a function f with effects ef then calling map has the effects ef. That is, if f is pure (i.e. has no effects) then the call to map will be pure. If f has the IO effect then the call to map will have the IO effect:

List.map(x -> x + 1, l)               // has the { } effect (i.e., is pure)
List.map(x -> {println(x); x + 1}, l) // has the { IO } effect

A higher-order function that takes multiple function arguments may combine their effects.

For example, the Flix Standard Library definition of forward function composition >>takes two functions f and g, and composes them:

def >>(f: a -> b \ ef1, g: b -> c \ ef2): a -> c \ (ef1 + ef2) = x -> g(f(x))

The type and effect signature of >> states: If map is given two functions f with effects ef1 and g with effects ef2 then it returns a new function which has the union of effects ef1 + ef2.

In Flix, the language of effects is based on set formulas:

• The complement of ef is written ~ef.
• The union of ef1 and ef2 is written ef1 + ef2.
• The intersection of ef1 and ef2 is written ef1 & ef2.
• The difference of ef1 and ef2 is written ef1 - ef2.

By far the most common operation is to compute the union of effects.

Its important to understand that there can be several ways to write the same effect set. For example, ef1 + ef2 is equivalent to ef2 + ef1, as one would expect.

Effect Exclusion

A novel feature of Flix is its support for effect exclusion. In simple terms, effect exclusion allows us to write higher-order functions that disallow specific effects while allowing all other effects.

For example, we can write an event listener registration function:

def onClick(listener: KeyEvent -> Unit \ (ef - Block), ...): ... 

Here the onClick function takes an event listener that may have any effect, except the Block effect. Hence listener can perform any action, except for an action that would block the UI thread.

As another example, we can write an exception handler function:

def recoverWith(f: Unit -> a \ Throw, h: ErrMsg -> a \ (ef - Throw)): a = ... 

Here the recoverWith function takes two function arguments: the function f that may throw an exception and a handler h which can handle the error. Notably, the effect system enforces that h cannot itself throw an exception.

Sub-Effecting

Note: This feature is not yet enabled by default.

Flix supports sub-effecting which allows an expression or a function to widen its effect set.

For example, if we write:

if (???) { x -> x + 1 } else { x -> {println(x); x + 1}}

The first branch should have type Int32 -> Int32 \ { } (i.e. it is pure) whereas the second branch has type Int32 -> Int32 \ { IO }. Without sub-effecting these two types are incompatible because { } != { IO }. However, because of sub-effecting, Flix gives the first branch the type Int32 -> Int32 \ ef for some fresh effect variable ef. This allows type inference to widen the effect of the first branch to IO. Hence the compiler is able to type check the whole expression.

As another example:

def handle(f: Unit -> a \ (ef + Throw)): a = ...

Here the handle function expects a function argument f with the Throw effect. However, due to sub-effecting, we can still call the handle function with a pure function, i.e.:

def handle(x -> Throw.throw(x))    // OK, has the `Throw` effect.
def handle(x -> x)                 // OK, because of sub-effecting.
def handle(x -> println(x))        // Not OK, handle does not permit `IO`.

Flix also allows sub-effect in instance declarations.

For example, we can define the trait:

trait Foo[t] {
    def f(x: t): Bool \ { IO }
}

where f has the IO effect. We can implement it:

instance Foo[Int32] {
    def f(x: Int32): Bool = x == 0 // Pure function
}

The declared effect of f is IO, but here the implementation of f is pure (i.e., it has the empty effect set { }). The program still type checks because { } can be widened to IO.

Flix, however, does not allow sub-effecting for top-level functions.

For example, if we declare the function:

def foo(): Bool \ IO = true

The Flix compiler emits the error message:

❌ -- Type Error ------------------------------

>> Expected type: 'IO' but found type: 'Pure'.

1 | def foo(): Bool \ IO = true
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
    expression has unexpected type.

In summary, Flix allows effect widening in two cases: for (a) lambda expressions and (b) instance definitions. We say that Flix supports abstraction site sub-effecting and instance definition sub-effecting.