IO

Newtype encoding for an IO datatype that has a cats.Applicative capable of doing parallel processing in ap and map2, needed for implementing cats.Parallel.

For converting back and forth you can use either the Parallel[IO] instance or the methods cats.effect.kernel.Par.ParallelF.apply for wrapping any IO value and cats.effect.kernel.Par.ParallelF.value for unwrapping it.

The encoding is based on the "newtypes" project by Alexander Konovalov, chosen because it's devoid of boxing issues and a good choice until opaque types will land in Scala.

The names of the product elements

The name of the type

Suspends a synchronous side effect in IO. Use IO.apply if your side effect is not thread-blocking; otherwise you should use IO.blocking (uncancelable) or IO.interruptible (cancelable).

Alias for IO.delay.

Suspends an asynchronous side effect in IO.

The given function will be invoked during evaluation of the IO to "schedule" the asynchronous callback, where the callback of type Either[Throwable, A] => Unit is the parameter passed to that function. Only the ''first'' invocation of the callback will be effective! All subsequent invocations will be silently dropped.

The process of registering the callback itself is suspended in IO (the outer IO of IO[Option[IO[Unit]]]).

The effect returns Option[IO[Unit]] which is an optional finalizer to be run in the event that the fiber running async(k) is canceled.

For example, here is a simplified version of IO.fromCompletableFuture:

def fromCompletableFuture[A](fut: IO[CompletableFuture[A]]): IO[A] = {
 fut.flatMap { cf =>
   IO.async { cb =>
     IO {
       //Invoke the callback with the result of the completable future
       val stage = cf.handle[Unit] {
         case (a, null) => cb(Right(a))
         case (_, e) => cb(Left(e))
       }

       //Cancel the completable future if the fiber is canceled
       Some(IO(stage.cancel(false)).void)
     }
   }
 }
}

Suspends an asynchronous side effect in IO.

The given function will be invoked during evaluation of the IO to "schedule" the asynchronous callback, where the callback is the parameter passed to that function. Only the ''first'' invocation of the callback will be effective! All subsequent invocations will be silently dropped.

As a quick example, you can use this function to perform a parallel computation given an ExecutorService:

def fork[A](body: => A, exc: ExecutorService): IO[A] =
 IO async_ { cb =>
   exc.execute(new Runnable {
     def run() =
       try cb(Right(body)) catch { case NonFatal(t) => cb(Left(t)) }
   })
 }

The fork function will do exactly what it sounds like: take a thunk and an ExecutorService and run that thunk on the thread pool. Or rather, it will produce an IO which will do those things when run; it does not schedule the thunk until the resulting IO is run! Note that there is no thread blocking in this implementation; the resulting IO encapsulates the callback in a pure and monadic fashion without using threads.

This function can be thought of as a safer, lexically-constrained version of Promise, where IO is like a safer, lazy version of Future.

Introduces a fairness boundary that yields control back to the scheduler of the runtime system. This allows the carrier thread to resume execution of another waiting fiber.

This function is primarily useful when performing long-running computation that is outside of the monadic context. For example:

 fa.map(data => expensiveWork(data))

In the above, we're assuming that expensiveWork is a function which is entirely compute-bound but very long-running. A good rule of thumb is to consider a function "expensive" when its runtime is around three or more orders of magnitude higher than the overhead of the map function itself (which runs in around 5 nanoseconds on modern hardware). Thus, any expensiveWork function which requires around 10 microseconds or longer to execute should be considered "long-running".

The danger is that these types of long-running actions outside of the monadic context can result in degraded fairness properties. The solution is to add an explicit cede both before and after the expensive operation:

 (fa <* IO.cede).map(data => expensiveWork(data)).guarantee(IO.cede)

Note that extremely long-running expensiveWork functions can still cause fairness issues, even when used with cede. This problem is somewhat fundamental to the nature of scheduling such computation on carrier threads. Whenever possible, it is best to break apart any such functions into multiple pieces invoked independently (e.g. via chained map calls) whenever the execution time exceeds five or six orders of magnitude beyond the overhead of map itself (around 1 millisecond on most hardware).

This operation is not ''required'' in most applications, particularly those which are primarily I/O bound, as IO itself will automatically introduce fairness boundaries without requiring user input. These automatic boundaries are controlled by the cats.effect.unsafe.IORuntimeConfig.autoYieldThreshold configuration parameter, which in turn may be adjusted by overriding IOApp.runtimeConfig.

This is a low-level API which is meant for implementors, please use background, start, async, or Deferred instead, depending on the use case

Suspends a synchronous side effect which produces an IO in IO.

This is useful for trampolining (i.e. when the side effect is conceptually the allocation of a stack frame). Any exceptions thrown by the side effect will be caught and sequenced into the IO.

Suspends a synchronous side effect in IO. Use IO.delay if your side effect is not thread-blocking; otherwise you should use IO.blocking (uncancelable) or IO.interruptible (cancelable).

Any exceptions thrown by the effect will be caught and sequenced into the IO.

Lifts an Eval into IO.

This function will preserve the evaluation semantics of any actions that are lifted into the pure IO. Eager Eval instances will be converted into thunk-less IO (i.e. eager IO), while lazy eval and memoized will be executed as such.

Lifts an Either[Throwable, A] into the IO[A] context, raising the throwable if it exists.

Constructs an IO which evaluates the given Future and produces the result (or failure).

Because Future eagerly evaluates, as well as because it memoizes, this function takes its parameter as an IO, which could be lazily evaluated. If this laziness is appropriately threaded back to the definition site of the Future, it ensures that the computation is fully managed by IO and thus referentially transparent.

Example:

 // Lazy evaluation, equivalent with by-name params
 IO.fromFuture(IO(f))

 // Eager evaluation, for pure futures
 IO.fromFuture(IO.pure(f))

Roughly speaking, the following identities hold:

IO.fromFuture(IO(f)).unsafeToFuture() === f // true-ish (except for memoization)
IO.fromFuture(IO(ioa.unsafeToFuture())) === ioa // true

Lifts an Option[A] into the IO[A] context, raising the throwable if the option is empty.

Lifts an Try[A] into the IO[A] context, raising the throwable if it exists.

A non-terminating IO, alias for async(_ => ()).

An IO that contains an empty Option.

Like Parallel.parReplicateA, but limits the degree of parallelism.

Like Parallel.parSequence, but limits the degree of parallelism.

Like Parallel.parTraverse, but limits the degree of parallelism.

Prints a value to the standard output using the implicit cats.Show instance.

Prints a value to the standard output followed by a new line using the implicit cats.Show instance.

Lifts a pure value into IO.

This should ''only'' be used if the value in question has "already" been computed! In other words, something like IO.pure(readLine) is most definitely not the right thing to do! However, IO.pure(42) is correct and will be more efficient (when evaluated) than IO(42), due to avoiding the allocation of extra thunks.

Run two IO tasks concurrently, and return the first to finish, either in success or error. The loser of the race is canceled.

The two tasks are executed in parallel, the winner being the first that signals a result.

As an example see IO.timeout and IO.timeoutTo

Also see racePair for a version that does not cancel the loser automatically on successful results.

Run two IO tasks concurrently, and returns a pair containing both the winner's successful value and the loser represented as a still-unfinished task.

On usage the user has the option of canceling the losing task, this being equivalent with plain race:

 val ioA: IO[A] = ???
 val ioB: IO[B] = ???

 IO.racePair(ioA, ioB).flatMap {
   case Left((a, fiberB)) =>
     fiberB.cancel.as(a)
   case Right((fiberA, b)) =>
     fiberA.cancel.as(b)
 }

See race for a simpler version that cancels the loser immediately.

Constructs an IO which sequences the specified exception.

If this IO is run using unsafeRunSync or unsafeRunTimed, the exception will be thrown. This exception can be "caught" (or rather, materialized into value-space) using the attempt method.

Returns raiseError when cond is false, otherwise IO.unit

Returns raiseError when the cond is true, otherwise IO.unit

Creates an asynchronous task that on evaluation sleeps for the specified duration, emitting a notification on completion.

This is the pure, non-blocking equivalent to:

• Thread.sleep (JVM)
• ScheduledExecutorService.schedule (JVM)
• setTimeout (JavaScript)

You can combine it with flatMap to create delayed tasks:

 val timeout = IO.sleep(10.seconds).flatMap { _ =>
   IO.raiseError(new TimeoutException)
 }

The created task is cancelable and so it can be used safely in race conditions without resource leakage.

An IO that contains some Option of the given value.

Alias for IO.pure(()).

Returns the given argument if cond is false, otherwise IO.Unit

Returns the given argument if cond is true, otherwise IO.Unit