monix.bio
Type members
Classlikes
Safe App
type that executes a IO. Shutdown occurs after
the IO
completes, as follows:
Safe App
type that executes a IO. Shutdown occurs after
the IO
completes, as follows:
-
If completed with
ExitCode.Success
, the main method exits and shutdown is handled by the platform. -
If completed with any other
ExitCode
,sys.exit
is called with the specified code. -
If the
IO
raises an error, the stack trace is printed to standard error andsys.exit(1)
is called.
When a shutdown is requested via a signal, the IO
is canceled and
we wait for the IO
to release any resources. The process exits
with the numeric value of the signal plus 128.
import cats.effect._
import cats.implicits._
import monix.bio._
object MyApp extends BIOApp {
def run(args: List[String]): UIO[ExitCode] =
args.headOption match {
case Some(name) =>
UIO(println(s"Hello, \${name}.")).as(ExitCode.Success)
case None =>
UIO(System.err.println("Usage: MyApp name")).as(ExitCode(2))
}
}
N.B. this is homologous with cats.effect.IOApp, but meant for usage with IO.
Works on top of JavaScript as well ;-)
Callback type which supports two channels of errors.
Callback type which supports two channels of errors.
- Companion
- object
Represent a complete cause of the failed IO
exposing both typed and untyped error channel.
Represent a complete cause of the failed IO
exposing both typed and untyped error channel.
- Companion
- object
Fiber
represents the (pure) result of a IO being started concurrently
and that can be either joined or cancelled.
Fiber
represents the (pure) result of a IO being started concurrently
and that can be either joined or cancelled.
You can think of fibers as being lightweight threads, a fiber being a concurrency primitive for doing cooperative multi-tasking.
For example a Fiber
value is the result of evaluating IO.start:
val task = UIO.evalAsync(println("Hello!"))
val forked: UIO[Fiber[Nothing, Unit]] = task.start
Usage example:
val launchMissiles = Task(println("Missiles launched!"))
val runToBunker = Task(println("Run Lola run!"))
for {
fiber <- launchMissiles.start
_ <- runToBunker.onErrorHandleWith { error =>
// Retreat failed, cancel launch (maybe we should
// have retreated to our bunker before the launch?)
fiber.cancel.flatMap(_ => Task.raiseError(error))
}
aftermath <- fiber.join
} yield {
aftermath
}
- Companion
- object
Task
represents a specification for a possibly lazy or
asynchronous computation, which when executed will produce an A
as a result, along with possible side-effects.
Task
represents a specification for a possibly lazy or
asynchronous computation, which when executed will produce an A
as a result, along with possible side-effects.
Compared with Future
from Scala's standard library, Task
does
not represent a running computation or a value detached from time,
as Task
does not execute anything when working with its builders
or operators and it does not submit any work into any thread-pool,
the execution eventually taking place only after runAsync
is
called and not before that.
Note that Task
is conservative in how it spawns logical threads.
Transformations like map
and flatMap
for example will default
to being executed on the logical thread on which the asynchronous
computation was started. But one shouldn't make assumptions about
how things will end up executed, as ultimately it is the
implementation's job to decide on the best execution model. All
you are guaranteed is asynchronous execution after executing
runAsync
.
=Getting Started=
To build a IO
from a by-name parameters (thunks), we can use
IO.apply (
alias IO.eval),
monix.bio.IO.evalTotal if the thunk is guaranteed to not throw any exceptions, or
IO.evalAsync:
val hello = IO("Hello ")
val world = IO.evalAsync("World!")
Nothing gets executed yet, as IO
is lazy, nothing executes
until you trigger its evaluation via runAsync or
runToFuture.
To combine IO
values we can use .map and
.flatMap, which describe sequencing and this time
it's in a very real sense because of the laziness involved:
val sayHello = hello
.flatMap(h => world.map(w => h + w))
.map(println)
This IO
reference will trigger a side effect on evaluation, but
not yet. To make the above print its message:
import monix.execution.CancelableFuture
import monix.execution.Scheduler.Implicits.global
val f = sayHello.runToFuture
// => Hello World!
The returned type is a CancelableFuture which inherits from Scala's standard Future, a value that can be completed already or might be completed at some point in the future, once the running asynchronous process finishes. Such a future value can also be canceled, see below.
=Laziness, Purity and Referential Transparency=
The fact that Task
is lazy whereas Future
is not
has real consequences. For example with Task
you can do this:
import scala.concurrent.duration._
def retryOnFailure[A](times: Int, source: Task[A]): Task[A] =
source.onErrorHandleWith { err =>
// No more retries left? Re-throw error:
if (times <= 0) Task.raiseError(err) else {
// Recursive call, yes we can!
retryOnFailure(times - 1, source)
// Adding 500 ms delay for good measure
.delayExecution(500.millis)
}
}
Future
being a strict value-wannabe means that the actual value
gets "memoized" (means cached), however Task
is basically a function
that can be repeated for as many times as you want.
Task
is a pure data structure that can be used to describe
pure functions, the equivalent of Haskell's IO
.
==Memoization==
Task
can also do memoization, making it behave like a "lazy"
Scala Future
, meaning that nothing is started yet, its
side effects being evaluated on the first runAsync
and then
the result reused on subsequent evaluations:
Task(println("boo")).memoize
The difference between this and just calling runAsync()
is that
memoize()
still returns a Task
and the actual memoization
happens on the first runAsync()
(with idempotency guarantees of
course).
But here's something else that the Future
data type cannot do,
memoizeOnSuccess:
Task.eval {
if (scala.util.Random.nextDouble() > 0.33)
throw new RuntimeException("error!")
println("moo")
}.memoizeOnSuccess
This keeps repeating the computation for as long as the result is a failure and caches it only on success. Yes we can!
''WARNING:'' as awesome as memoize
can be, use with care
because memoization can break referential transparency!
==Parallelism==
Because of laziness, invoking
IO.sequence will not work like
it does for Future.sequence
, the given Task
values being
evaluated one after another, in ''sequence'', not in ''parallel''.
If you want parallelism, then you need to use
IO.parSequence and thus be explicit about it.
This is great because it gives you the possibility of fine tuning the execution. For example, say you want to execute things in parallel, but with a maximum limit of 30 tasks being executed in parallel. One way of doing that is to process your list in batches:
// Some array of tasks, you come up with something good :-)
val list: Seq[Task[Int]] = Seq.tabulate(100)(Task(_))
// Split our list in chunks of 30 items per chunk,
// this being the maximum parallelism allowed
val chunks = list.sliding(30, 30).toSeq
// Specify that each batch should process stuff in parallel
val batchedTasks = chunks.map(chunk => Task.parSequence(chunk))
// Sequence the batches
val allBatches = Task.sequence(batchedTasks)
// Flatten the result, within the context of Task
val all: Task[Seq[Int]] = allBatches.map(_.flatten)
Note that the built Task
reference is just a specification at
this point, or you can view it as a function, as nothing has
executed yet, you need to call runAsync
or runToFuture explicitly.
=Cancellation=
The logic described by an Task
task could be cancelable,
depending on how the Task
gets built.
CancelableFuture references
can also be canceled, in case the described computation can be
canceled. When describing Task
tasks with Task.eval
nothing
can be cancelled, since there's nothing about a plain function
that you can cancel, but we can build cancelable tasks with
IO.cancelable.
import scala.concurrent.duration._
import scala.util._
val delayedHello = Task.cancelable0[Unit] { (scheduler, callback) =>
val task = scheduler.scheduleOnce(1.second) {
println("Delayed Hello!")
// Signaling successful completion
callback(Success(()))
}
// Returning a cancel token that knows how to cancel the
// scheduled computation:
Task {
println("Cancelling!")
task.cancel()
}
}
The sample above prints a message with a delay, where the delay
itself is scheduled with the injected Scheduler
. The Scheduler
is in fact an implicit parameter to runAsync()
.
This action can be cancelled, because it specifies cancellation
logic. In case we have no cancelable logic to express, then it's
OK if we returned a
Cancelable.empty reference,
in which case the resulting Task
would not be cancelable.
But the Task
we just described is cancelable, for one at the
edge, due to runAsync
returning Cancelable
and CancelableFuture references:
// Triggering execution
val cf = delayedHello.runToFuture
// If we change our mind before the timespan has passed:
cf.cancel()
But also cancellation is described on Task
as a pure action,
which can be used for example in race conditions:
import scala.concurrent.duration._
import scala.concurrent.TimeoutException
val ta = Task(1 + 1).delayExecution(4.seconds)
val tb = Task.raiseError[Int](new TimeoutException)
.delayExecution(4.seconds)
Task.racePair(ta, tb).flatMap {
case Left((a, fiberB)) =>
fiberB.cancel.map(_ => a)
case Right((fiberA, b)) =>
fiberA.cancel.map(_ => b)
}
The returned type in racePair
is Fiber, which is a data
type that's meant to wrap tasks linked to an active process
and that can be canceled or joined.
Also, given a task, we can specify actions that need to be triggered in case of cancellation, see doOnCancel:
val task = Task.eval(println("Hello!")).executeAsync
task doOnCancel IO.evalTotal {
println("A cancellation attempt was made!")
}
Given a task, we can also create a new task from it that atomic (non cancelable), in the sense that either all of it executes or nothing at all, via uncancelable.
=Note on the ExecutionModel=
Task
is conservative in how it introduces async boundaries.
Transformations like map
and flatMap
for example will default
to being executed on the current call stack on which the
asynchronous computation was started. But one shouldn't make
assumptions about how things will end up executed, as ultimately
it is the implementation's job to decide on the best execution
model. All you are guaranteed (and can assume) is asynchronous
execution after executing runAsync
.
Currently the default
ExecutionModel specifies
batched execution by default and Task
in its evaluation respects
the injected ExecutionModel
. If you want a different behavior,
you need to execute the Task
reference with a different scheduler.
- Companion
- object
A lawless type class that specifies conversions from IO
to similar data types (i.e. pure, asynchronous, preferably
cancelable).
A lawless type class that specifies conversions from IO
to similar data types (i.e. pure, asynchronous, preferably
cancelable).
- Companion
- object
A lawless type class that provides conversions into a IO.
A lawless type class that provides conversions into a IO.
Sample:
// Conversion from cats.Eval
import cats.Eval
val source0 = Eval.always(1 + 1)
val task0 = IOLike[Eval].apply(source0)
// Conversion from Future
import scala.concurrent.Future
val source1 = Future.successful(1 + 1)
val task1 = IOLike[Future].apply(source1)
// Conversion from IO
import cats.effect.{IO => CIO}
val source2 = CIO(1 + 1)
val task2 = IOLike[CIO].apply(source2)
This is an alternative to the usage of cats.effect.Effect,
where the internals are specialized to IO
anyway, like for
example the implementation of monix.reactive.Observable
.
- Companion
- object
A IOLocal
is like a
ThreadLocal
that is pure and with a flexible scope, being processed in the
context of the IO data type.
A IOLocal
is like a
ThreadLocal
that is pure and with a flexible scope, being processed in the
context of the IO data type.
This data type wraps monix.execution.misc.Local.
Just like a ThreadLocal
, usage of a IOLocal
is safe,
the state of all current locals being transported over
async boundaries (aka when threads get forked) by the Task
run-loop implementation, but only when the Task
reference
gets executed with IO.Options.localContextPropagation
set to true
, or it uses a monix.execution.schedulers.TracingScheduler.
One way to achieve this is with IO.executeWithOptions,
a single call is sufficient just before runAsync
:
import monix.execution.Scheduler.Implicits.global
val t = Task(42)
t.executeWithOptions(_.enableLocalContextPropagation)
// triggers the actual execution
.runToFuture
Another possibility is to use IO.runToFutureOpt or
IO.runToFutureOpt instead of runAsync
and specify the set of
options implicitly:
{
implicit val options = IO.defaultOptions.enableLocalContextPropagation
// Options passed implicitly
val f = t.runToFutureOpt
}
Full example:
import monix.bio.{UIO, IOLocal}
val task: UIO[Unit] =
for {
local <- IOLocal(0)
value1 <- local.read // value1 == 0
_ <- local.write(100)
value2 <- local.read // value2 == 100
value3 <- local.bind(200)(local.read.map(_ * 2)) // value3 == 200 * 2
value4 <- local.read // value4 == 100
_ <- local.clear
value5 <- local.read // value5 == 0
} yield {
// Should print 0, 100, 400, 100, 0
println("value1: " + value1)
println("value2: " + value2)
println("value3: " + value3)
println("value4: " + value4)
println("value5: " + value5)
}
// For transporting locals over async boundaries defined by
// Task, any Scheduler will do, however for transporting locals
// over async boundaries managed by Future and others, you need
// a `TracingScheduler` here:
import monix.execution.Scheduler.Implicits.global
// Needs enabling the "localContextPropagation" option
// just before execution
implicit val opts = IO.defaultOptions.enableLocalContextPropagation
// Triggering actual execution
val result = task.runToFutureOpt
- Companion
- object
Types
Type alias that represents IO
which is expected to fail with any Throwable.
Similar to monix.eval.Task
and cats.effect.IO
.
Type alias that represents IO
which is expected to fail with any Throwable.
Similar to monix.eval.Task
and cats.effect.IO
.
WARNING: There are still two error channels (both Throwable
) so use with care.
If error is thrown from what was expected to be a pure function (map, flatMap, finalizers, etc.)
then it will terminate the Task, instead of a normal failure.