This library aims to add some useful functionality to Scala's Futures without introducing a full effect system.
Scala's built-in Futures are a pretty good abstraction for concurrent and asynchronous programming, but they have some quirks (e.g., lack of referential transparency). Effect systems and IO Monads like those provided by cats-effect, ZIO, monix, akka, etc. have many useful features for concurrent and asynchronous programming, but they can be difficult to introduce in an established codebase.
If you're starting a green-field project then you should totally learn and use a real effect system. If you just need to limit the parallelism of some Futures or implement a simple Retry, you might give futil a try.
⚠ I consider futil feature-complete, but the interfaces could still change.
// Typical async stuff.
import scala.concurrent._
import duration._
import scala.util._
// Futil imports.
import futil._
// Most methods require an implicit ExecutionContext.
import ExecutionContext.Implicits.global
// Some methods require an implicit ScheduledExecutorService.
import Futil.Implicits.scheduler
// Let's pretend this is calling some external web service that does something useful.
def callService(i: Int): Future[Int] = Future(i + 1)Scala Futures execute eagerly. This means when we define a val foo: Future[Int] = ..., it starts running now.
To account for this, some methods in Futil use a thunk.
Thunk is just a fancy word for a function that takes Unit and returns something.
For example, a thunk for a Future[Int]:
def future(): Future[Int] = Future(42)
val aThunk: () => Future[Int] = () => future()Futil has a helper method for defining a thunk:
val alsoAThunk: () => Future[Int] = Futil.thunk(future())A thunk of a Future is useful in two cases:
- When we need to delay the execution of a Future.
- When we need a way to re-run the Future on-demand.
Thunks are not fool-proof. For instance, if we define the Future as a val, and then wrap it in a thunk,
it will still execute eagerly and silently defeat the purpose of the whole exercise.
Note that nanosecond precision is technically supported, but the overhead of scheduling, executing, etc. usually negates that level of precision.
Time the execution of a Future.
// Times the service call, returning the value and the execution duration.
val timed: Future[(Int, Duration)] = Futil.timed(callService(42))Limit the time a Future spends executing.
// Returns a failed Future if the service call exceeds the given duration.
val deadline: Future[Int] = Futil.deadline(1.seconds)(callService(42))Delay the execution of a Future.
// Waits the given duration before executing the Future.
val delayed: Future[Int] = Futil.delay(1.seconds)(callService(42))Sleep asynchronously.
// Sleeps the given duration before continuing.
val slept: Future[Unit] = Futil.sleep(1.seconds)Run a Future for every item in a Seq, limiting the number of Futures running in parallel at any given time. This is a form of self rate-limiting, particularly useful when dealing with flaky or rate-limited external services.
val numInParallel = 16
val inputs: Seq[Int] = 0 to 9999
def f(i: Int): Future[Double] = callService(i).map(_ * 3.14)
// This has the same type signature as Future.traverse.
val outputs: Future[Seq[Double]] = Futil.traverseParN(numInParallel)(inputs)(f)Run a Future for every item in a Seq, exactly one at a time.
val outputsSerial: Future[Seq[Double]] = Futil.traverseSerial(inputs)(f)Retry a fixed number of times.
// Retry on failure 3 times.
Futil.retry(RetryPolicy.Repeat(3))(() => callService(42))Retry a fixed number of times, or stop early based on the result of the previous call.
// Early stop if the last call returned a throwable containing the word "please".
def earlyStop(t: Try[Int]): Future[Boolean] = t match {
case Failure(t) => Future.successful(t.getMessage.contains("please"))
case _ => Future.successful(false)
}
Futil.retry(RetryPolicy.Repeat(3), earlyStop)(() => callService(42))Retry with a fixed delay between calls.
// Retry 3 times, waiting 3 seconds between each call.
Futil.retry(RetryPolicy.FixedBackoff(3, 3.seconds))(() => callService(42))
// Early stop if asked nicely.
Futil.retry(RetryPolicy.FixedBackoff(3, 3.seconds), earlyStop)(() => callService(42))Retry with exponential delay between calls.
// Retry 3 times, first delay is 2s, then 4s, then 8s.
Futil.retry(RetryPolicy.ExponentialBackoff(3, 2.seconds))(() => callService(42))
// Early stop if asked nicely.
Futil.retry(RetryPolicy.ExponentialBackoff(3, 2.seconds), earlyStop)(() => callService(42))A semaphore lets us acquire and release a fixed number of permits in order to limit access to some resource. An asynchronous semaphore lets us acquire and release asynchronously.
Acquire and release permits:
val sem = Futil.semaphore(2)
for {
_ <- sem.acquire()
_ <- callService(42)
_ <- sem.release()
} yield ()Be careful: if the method fails, the release method must still be called:
for {
_ <- sem.acquire()
_ <- Future.failed(new Exception("uh oh!"))
_ <- sem.release() // This won't be called!
} yield ()Use the withPermit method to ensure the permit is released:
for {
_ <- sem.withPermit(() => callService(42))
_ <- sem.withPermit(() => Future.failed(new Exception("uh oh!"))) // Will still release the permit.
} yield ()Here's a real use-case: we have a singleton client to some service, and want to ensure the client makes at most 10 parallel calls to the service at any given time.
class SomeServiceClient(parallelism: Int) {
private val sem = Futil.semaphore(parallelism)
def getFooById(id: Int): Future[String] =
sem.withPermit(() => callService(id).map(i => s"Foo: $i"))
def getBarById(id: Int): Future[String] =
sem.withPermit(() => callService(id).map(i => s"Bar: $i"))
}
// The service can only handle 10 parallel calls.
val client = new SomeServiceClient(10)
// Get all the foos without making the service fall over.
val foos = Future.sequence((0 to 999).map(client.getFooById(_)))