eval

package eval

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Type Members

abstract class Callback[-A] extends Listener[A] with (Either[Throwable, A]) ⇒ Unit
Represents a callback that should be called asynchronously with the result of a computation.
Represents a callback that should be called asynchronously with the result of a computation. Used by Task to signal the completion of asynchronous computations on runAsync.
The onSuccess method should be called only once, with the successful result, whereas onError should be called if the result is an error.
Obviously Callback describes unsafe side-effects, a fact that is highlighted by the usage of Unit as the return type. Obviously callbacks are unsafe to use in pure code, but are necessary for describing asynchronous processes, like in Task.cancelable.
sealed abstract class Coeval[+A] extends () ⇒ A with Serializable
Coeval represents lazy computations that can execute synchronously.
Coeval represents lazy computations that can execute synchronously.
Word definition and origin:
- Having the same age or date of origin; a contemporary; synchronous.
- From the Latin "coævus": com- ‎("equal") in combination with aevum ‎(aevum, "age").
- The constructor of Coeval is the dual of an expression that evaluates to an A.
There are three evaluation strategies:
- now or raiseError: for describing strict values, evaluated immediately
- evalOnce: expressions evaluated a single time
- eval: expressions evaluated every time the value is needed
The Once and Always are both lazy strategies while Now and Error are eager. Once and Always are distinguished from each other only by memoization: once evaluated Once will save the value to be returned immediately if it is needed again. Always will run its computation every time.
Both Now and Error are represented by the Eager trait, a sub-type of Coeval that can be used as a replacement for Scala's own Try type.
Coeval supports stack-safe lazy computation via the .map and .flatMap methods, which use an internal trampoline to avoid stack overflows. Computations done within .map and .flatMap are always lazy, even when applied to a Coeval.Eager instance (e.g. Coeval.Now, Coeval.Error).
Evaluation Strategies
The "now" and "raiseError" builders are building Coeval instances out of strict values:
```
val fa = Coeval.now(1)
fa.value() // => 1

val fe = Coeval.raiseError(new RuntimeException("dummy"))
fe.failed // => has RuntimeException
```
The "always" strategy is equivalent with a plain function:
```
// For didactic purposes, don't use shared vars at home :-)
var i = 0
val coeval = Coeval.eval { i += 1; i }

coeval.value() // => 1
coeval.value() // => 2
coeval.value() // => 3
```
The "once" strategy is equivalent with Scala's lazy val (along with thread-safe idempotency guarantees):
```
var j = 0
val coevalOnce = Coeval.evalOnce { j += 1; j }

coevalOnce.value() // => 1
coevalOnce.value() // => 1
coevalOnce.value() // => 1
```
Versus Task
The other option of suspending side-effects is Task. As a quick comparison:
- Coeval's execution is always immediate / synchronous, whereas Task can describe asynchronous computations
- Coeval is not cancelable, obviously, since execution is immediate and there's nothing to cancel
Versus cats.Eval
The Coeval data type is very similar with cats.Eval. As a quick comparison:
- cats.Eval is only for controlling laziness, but it doesn't handle side effects, hence cats.Eval is a Comonad
- Monix's Coeval can handle side effects as well and thus it implements MonadError[Coeval, Throwable] and cats.effect.Sync, providing error-handling utilities
If you just want to delay the evaluation of a pure expression use cats.Eval, but if you need to suspend side effects or you need error handling capabilities, then use Coeval.
trait CoevalLike[F[_]] extends AnyRef
A lawless type class that provides conversions to Coeval.
A lawless type class that provides conversions to Coeval.
Sample:
```
// Conversion from cats.Eval
import cats.Eval

val source0 = Eval.always(1 + 1)
val task0 = CoevalLike[Eval].toCoeval(source0)

// Conversion from SyncIO
import cats.effect.SyncIO

val source1 = SyncIO(1 + 1)
val task1 = CoevalLike[SyncIO].toCoeval(source1)
```
This is an alternative to usage of cats.effect.Effect where the internals are specialized to Coeval anyway, like for example the implementation of monix.reactive.Observable.

trait Fiber[A] extends cats.effect.Fiber[Task, A]

Fiber represents the (pure) result of a Task 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 Task.start:

val task = Task.evalAsync(println("Hello!"))

val forked: Task[Fiber[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
}

abstract class MVar[A] extends AnyRef
A mutable location, that is either empty or contains a value of type A.
A mutable location, that is either empty or contains a value of type A.
It has 2 fundamental atomic operations:
- put which fills the var if empty, or blocks (asynchronously) until the var is empty again
- take which empties the var if full, returning the contained value, or blocks (asynchronously) otherwise until there is a value to pull
The MVar is appropriate for building synchronization primitives and performing simple inter-thread communications. If it helps, it's similar with a BlockingQueue(capacity = 1), except that it doesn't block any threads, all waiting being done asynchronously by means of Task.
Given its asynchronous, non-blocking nature, it can be used on top of Javascript as well.
Inspired by Control.Concurrent.MVar from Haskell and by scalaz.concurrent.MVar.
sealed abstract class Task[+A] extends TaskBinCompat[A] with Serializable
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 Task from a by-name parameters (thunks), we can use Task.eval or Task.apply:
```
val hello = Task.eval("Hello ")
val world = Task.evalAsync("World!")
```
Nothing gets executed yet, as Task is lazy, nothing executes until you trigger .runAsync on it.
To combine Task 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 Task reference will trigger a side effect on evaluation, but not yet. To make the above print its message:
```
import monix.execution.CancelableFuture

val f: CancelableFuture[Unit] = sayHello.runAsync
//=> 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
The fact that Task is lazy whereas Future is not has real consequences. For example with Task you can do this:
```
def retryOnFailure[A](times: Int, source: Task[A]): Task[A] =
  source.onErrorRecoverWith { 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)
    }
  }
```
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 can also do memoization of course:
```
task.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:
```
task.memoizeOnSuccess
```
This keeps repeating the computation for as long as the result is a failure and caches it only on success. Yes we can!
Parallelism
Because of laziness, invoking Task.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 Task.gather 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]] = ???

// Split our list in chunks of 30 items per chunk,
// this being the maximum parallelism allowed
val chunks = list.sliding(30, 30)

// Specify that each batch should process stuff in parallel
val batchedTasks = chunks.map(chunk => Task.gather(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 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 Task.cancelable.
```
import scala.concurrent.duration._

val delayedHello = Task.cancelable { (scheduler, callback) =>
  val task = scheduler.scheduleOnce(1.second) {
    println("Delayed Hello!")
    // Signaling successful completion
    callback(Success(()))
  }

  Cancelable { () => {
    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 f: CancelableFuture[Unit] = delayedHello.runAsync

// If we change our mind before the timespan has passed:
f.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._

val ta = Task(1 + 1).delayExecution(4.seconds)

val tb = Task.raiseError(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 Task.eval {
  println("A cancellation attempt was made!")
}
```
Controlling cancellation can be achieved with cancelable and uncancelable.
The former activates auto-cancelable flatMap chains, whereas the later ensures that a task becomes uncancelable such that it gets executed as an atomic unit (either all or nothing).
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.
trait TaskApp extends AnyRef
Safe App type that executes a Task.
Safe App type that executes a Task. Shutdown occurs after the Task 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 Task raises an error, the stack trace is printed to standard error and sys.exit(1) is called.
When a shutdown is requested via a signal, the Task 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 monix.eval._
import cats.implicits._

object MyApp extends TaskApp {
  def run(args: List[String]): Task[ExitCode] =
    args.headOption match {
      case Some(name) =>
        Task(println(s"Hello, ${name}.")).as(ExitCode.Success)
      case None =>
        Task(System.err.println("Usage: MyApp name")).as(ExitCode(2))
    }
}
```
N.B. this is homologous with cats.effect.IOApp, but meant for usage with Task.
Works on top of JavaScript as well ;-)
final class TaskCircuitBreaker extends AnyRef
The TaskCircuitBreaker is used to provide stability and prevent cascading failures in distributed systems.
The TaskCircuitBreaker is used to provide stability and prevent cascading failures in distributed systems.
Purpose
As an example, we have a web application interacting with a remote third party web service. Let's say the third party has oversold their capacity and their database melts down under load. Assume that the database fails in such a way that it takes a very long time to hand back an error to the third party web service. This in turn makes calls fail after a long period of time. Back to our web application, the users have noticed that their form submissions take much longer seeming to hang. Well the users do what they know to do which is use the refresh button, adding more requests to their already running requests. This eventually causes the failure of the web application due to resource exhaustion. This will affect all users, even those who are not using functionality dependent on this third party web service.
Introducing circuit breakers on the web service call would cause the requests to begin to fail-fast, letting the user know that something is wrong and that they need not refresh their request. This also confines the failure behavior to only those users that are using functionality dependent on the third party, other users are no longer affected as there is no resource exhaustion. Circuit breakers can also allow savvy developers to mark portions of the site that use the functionality unavailable, or perhaps show some cached content as appropriate while the breaker is open.
How It Works
The circuit breaker models a concurrent state machine that can be in any of these 3 states:
1. Closed: During normal operations or when the TaskCircuitBreaker starts
  - Exceptions increment the failures counter
  - Successes reset the failure count to zero
  - When the failures counter reaches the maxFailures count, the breaker is tripped into Open state
2. Open: The circuit breaker rejects all tasks with an ExecutionRejectedException
  - all tasks fail fast with ExecutionRejectedException
  - after the configured resetTimeout, the circuit breaker enters a HalfOpen state, allowing one task to go through for testing the connection
3. HalfOpen: The circuit breaker has already allowed a task to go through, as a reset attempt, in order to test the connection
  - The first task when Open has expired is allowed through without failing fast, just before the circuit breaker is evolved into the HalfOpen state
  - All tasks attempted in HalfOpen fail-fast with an exception just as in Open state
  - If that task attempt succeeds, the breaker is reset back to the Closed state, with the resetTimeout and the failures count also reset to initial values
  - If the first call fails, the breaker is tripped again into the Open state (the resetTimeout is multiplied by the exponential backoff factor)
Usage
```
import monix.eval._
import scala.concurrent.duration._

val circuitBreaker = TaskCircuitBreaker(
  maxFailures = 5,
  resetTimeout = 10.seconds
)

//...
val problematic = Task.evalAsync {
  val nr = util.Random.nextInt()
  if (nr % 2 == 0) nr else
    throw new RuntimeException("dummy")
}

val task = circuitBreaker.protect(problematic)
```
When attempting to close the circuit breaker and resume normal operations, we can also apply an exponential backoff for repeated failed attempts, like so:
```
val circuitBreaker = TaskCircuitBreaker(
  maxFailures = 5,
  resetTimeout = 10.seconds,
  exponentialBackoffFactor = 2,
  maxResetTimeout = 10.minutes
)
```
In this sample we attempt to reconnect after 10 seconds, then after 20, 40 and so on, a delay that keeps increasing up to a configurable maximum of 10 minutes.
Credits
This Monix data type was inspired by the availability of Akka's Circuit Breaker.
trait TaskLift[F[_]] extends AnyRef
A lawless type class that specifies conversions from Task to similar data types (i.e.
A lawless type class that specifies conversions from Task to similar data types (i.e. pure, asynchronous, preferably cancelable).

Annotations
@implicitNotFound( ... )

trait TaskLike[F[_]] extends AnyRef

A lawless type class that provides conversions to Task.

Sample:

// Conversion from cats.Eval
import cats.Eval

val source0 = Eval.always(1 + 1)
val task0 = TaskLike[Eval].toTask(source)

// Conversion from Future
import scala.concurrent.Future

val source1 = Future(1 + 1)
val task1 = TaskLike[Future].toTask(source)

// Conversion from IO
import cats.effect.IO

val source2 = IO(1 + 1)
val task2 = TaskLike[IO].toTask(source2)

This is an alternative to usage of cats.effect.Effect where the internals are specialized to Task anyway, like for example the implementation of monix.reactive.Observable.

Annotations: @implicitNotFound( ... )

final class TaskLocal[A] extends AnyRef

A TaskLocal is like a ThreadLocal that is pure and with a flexible scope, being processed in the context of the Task data type.

This data type wraps monix.execution.misc.Local.

Just like a ThreadLocal, usage of a TaskLocal 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 Task.Options.localContextPropagation set to true.

One way to achieve this is with Task.executeWithOptions, a single call is sufficient just before runAsync:

task.executeWithOptions(_.enableLocalContextPropagation)
  // triggers the actual execution
  .runAsync

Another possibility is to use .runAsyncOpt instead of runAsync and specify the set of options implicitly:

implicit val opts = Task.defaultOptions.enableLocalContextPropagation

// Options passed implicitly
val f = task.runAsyncOpt

Full example:

import monix.eval.{Task, TaskLocal}

val local = TaskLocal(0)

val task: Task[Unit] =
  for {
    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 = Task.defaultOptions.enableLocalContextPropagation

// Triggering actual execution
val f = task.runAsyncOpt

final class TaskSemaphore extends Serializable
The TaskSemaphore is an asynchronous semaphore implementation that limits the parallelism on task execution.
The TaskSemaphore is an asynchronous semaphore implementation that limits the parallelism on task execution.
The following example instantiates a semaphore with a maximum parallelism of 10:
```
val semaphore = TaskSemaphore(maxParallelism = 10)

def makeRequest(r: HttpRequest): Task[HttpResponse] = ???

// For such a task no more than 10 requests
// are allowed to be executed in parallel.
val task = semaphore.greenLight(makeRequest(???))
```

Value Members

object Callback extends Serializable
object Coeval extends CoevalInstancesLevel0 with Serializable
Coeval builders.
object CoevalLike
object Fiber
object MVar
Builders for MVar
object Task extends TaskInstancesLevel1 with Serializable
Builders for Task.
object TaskCircuitBreaker
object TaskLift extends TaskLiftImplicits0
object TaskLike extends TaskLikeImplicits0
object TaskLocal
Builders for TaskLocal
object TaskSemaphore extends Serializable

Packages

eval

package eval

Type Members

Evaluation Strategies

Versus Task

Versus cats.Eval

Getting Started

Laziness

Parallelism

Cancellation

Note on the ExecutionModel

Purpose

How It Works

Usage

Credits

Value Members

Ungrouped

Packages

eval 

package eval

Type Members

Evaluation Strategies

Versus Task

Versus cats.Eval

Getting Started

Laziness

Parallelism

Cancellation

Note on the ExecutionModel

Purpose

How It Works

Usage

Credits

Value Members

Ungrouped

eval