Asynchronous streams in direct style with and without macros

Transcript

1. Asynchronous streams in
   direct style with and
   without macros
   Philipp Haller
   KTH Royal Institute of Technology
   
   Stockholm, Sweden
   

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2. About myself
   • Associate Professor at KTH Royal Institute of Technology in Stockholm, Sweden
   
   • Previous positions at Typesafe Inc., Stanford University, and EPFL
   
   • PhD 2010 EPFL
   
   • Research interests in programming language design and implementation, type
   systems, concurrency, and distributed programming
   

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3. A simple task
   • Given an asynchronous method that returns the day of the year as a string wrapped in a future:

   def dayOfYear(): Future[String]
   
   • However, sometimes, for unknown reasons, dayOfYear completes the returned future with "",
   or null, or the string "nope".
   
   • Task: create a future which is completed either
   
   • with "It's !" where is the textual representation matching the month
   returned by dayOfYear, or
   
   • with "Not a date, mate!" when that's really the best we can do.
   "04/11" for April 11, or
   "06/13" for June 13
   

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4. A solution using for-
   comprehensions
   def solution() = {
   val date = """(\d+)/(\d+)""".r
   for {
   dayString response case date(month, day) =>
   for (name yield s"It's $name!"
   case _ =>
   Future.successful("Not a date, mate!")
   }
   } yield response
   }
   Can we improve
   readability?
   

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5. Enter scala-async
   • Purpose:

   Simplify asynchronous programming primarily based on futures.
   
   • Created by Jason Zaugg (Lightbend) and yours truly, first released in 2013
   

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6. A solution using scala-async
   def solution() = async {
   val date = """(\d+)/(\d+)""".r
   await(dayOfYear()) match {
   case date(month, day) =>
   s"It's ${await(nameOfMonth(month.toInt))}!"
   case _ =>
   "Not a date, mate!"
   }
   }
   No need to name all
   intermediate results
   No need for explicit
   Future.successful()
   Fewer closures
   allocated:
   async {} = 1 closure
   

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7. Let’s make our previous task
   slightly more complex!
   • Instead of converting a single string "04/11" (or "06/13" etc.), "", null, or "nope",
   we should consume a stream of multiple such strings
   
   • Output should be a publisher which produces a stream of results
   

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8. Intermezzo: Reactive Streams
   • a.k.a. java.util.concurrent.Flow (since JDK 9)
   
   • Interfaces: j.u.c.Flow.{Publisher, Subscriber, Subscription, Processor}
   
   • Some key prior work:
   
   • Observer design pattern (Gamma et al. 1994)1
   
   • Reactive Extensions (Erik Meijer 2012)2
   
   • Key innovation of Reactive Streams: backpressure control
   2Erik Meijer, Your mouse is a database, Commun. ACM 55(5) (2012) 66–73.
   1Gamma, Erich, Richard Helm, Ralph Johnson, and John Vlissides, Design Patterns:

   Elements of Reusable Object Oriented Software, Addison-Wesley Professional, 1994
   Composition of
   observable streams using
   higher-order functions
   

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9. Publisher
   • A Flow.Publisher produces events of type T which are received by subscribers
   if they have expressed interest.
   public static interface Flow.Publisher {
   void subscribe(Flow.Subscriber super T> subscriber);
   }
   

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10. Subscriber
    • A Flow.Subscriber s consumes events of type T from publishers to which s has
    subscribed and from which s has requested to receive (a certain number of) events.
    public static interface Flow.Subscriber {
    void onSubscribe(Flow.Subscription subscription);
    void onNext(T item);
    void onError(Throwable throwable);
    void onComplete();
    }
    

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11. Subscription
    • A Flow.Subscription enables subscribers to control the flow of events received
    from publishers.
    public static interface Flow.Subscription {
    void request(long n);
    void cancel();
    }
    

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12. Reactive Extensions
    • Libraries building on basic interfaces for publishers, subscribers etc.
    
    • Key: higher-order functions for composing publishers
    
    • Implementations for many languages available
    

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13. TextChanges(input)
    .Select(word => Completions(word))
    .Switch()
    .Subscribe(ObserveChanges(output));
    Example from
    (Erik Meijer 2012)
    Image source: Erik Meijer, Your mouse is a database, Commun. ACM 55(5) (2012) 66–73.

    https://queue.acm.org/detail.cfm?id=2169076
    

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14. Challenges
    • Often need diagrams to understand semantics (cf. “marble diagrams” used to
    document Reactive Extensions)
    
    • Creation of combinators challenging
    
    • Stateful combinators in particular
    
    • Inversion of control
    

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15. Back to our task
    • Instead of converting a single string "04/11" (or "06/13" etc.), "", null, or "nope",
    we should consume a stream of multiple such strings
    
    • Output should be a publisher which produces a stream of results
    
    • Problem:
    • await only takes futures as arguments, but we need to await stream events!
    
    • async creates a future, but we need to create a stream publisher which yields
    multiple events instead of producing just one result
    We can’t use
    async/await! :-(
    

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16. scala-async + Flow = scala-async-flow
    Extension of async/await model which:
    
    • Provides a variant of async { … } which creates a stream publisher:

    rasync { … }
    
    • Generalizes await from futures to stream events: next, done, error
    
    • Provides additional methods for yielding events:

    yieldNext, yieldError, yieldDone
    

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17. Generalizing await
    • Previously: await the completion of futures

    def await[T](future: Future[T]): T
    
    • Now: await events produced by different asynchronous objects (Future[T],
    Flow.Publisher[T], etc.)
    trait Async[T] {
    def onComplete[S](handler: Try[T] => S) given (executor: ExecutionContext)
    def getCompleted: Try[T]
    }
    

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18. Solution using scala-async-flow
    val stream = rasync[String] {
    var dateOpt = await(dateStream)
    while (dateOpt.nonEmpty) {
    dateOpt.get match {
    case date(month, day) =>
    yieldNext(s"It's ${await(nameOfMonth(month.toInt))}!")
    case _ =>
    yieldNext("Not a date, mate!")
    }
    dateOpt = await(dateStream)
    }
    yieldDone()
    }
    Type of stream:
    Flow.Publisher[String]
    Here, we are
    awaiting a future!
    

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19. Implementing scala-async-flow
    Some alternatives:
    
    1. Extension of scala-async macro for Scala 2.12 and 2.13
    
    2. Direct compiler support
    
    3. Build on top of continuations or fibers
    

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20. Library Component
    def rasync[T](body: given Flow[T] => T): Flow.Publisher[T] = {
    delegate for Flow[T] = new Flow[T]
    ...
    }
    def await[T, S](a: Async[T]) given (flow: Flow[S],
    executor: ExecutionContext): T
    def yieldNext[T](event: T) given (flow: Flow[T]) = {
    flow.yieldNext(event)
    }
    Contextual
    function type
    

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21. Library Component
    def rasync[T](body: given Flow[T] => T): Flow.Publisher[T] = {
    delegate for Flow[T] = new Flow[T]
    ...
    }
    def await[T, S](a: Async[T]) given (flow: Flow[S],
    executor: ExecutionContext): T
    def yieldNext[T](event: T) given (flow: Flow[T]) = {
    flow.yieldNext(event)
    }
    Contextual
    function type
    

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22. Library Component
    def rasync[T](body: given Flow[T] => T): Flow.Publisher[T] = {
    delegate for Flow[T] = new Flow[T]
    ...
    }
    def await[T, S](a: Async[T]) given (flow: Flow[S],
    executor: ExecutionContext): T
    def yieldNext[T](event: T) given (flow: Flow[T]) = {
    flow.yieldNext(event)
    }
    Contextual
    function type
    

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23. Macro-based implementation
    • Example program:
    def fwd(s: Flow.Publisher[Int]) = rasync {
    var x: Option[Int] = None
    x = await(s)
    x.get
    }
    

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24. class StateMachine(flow: Flow[Int]) {
    var state = 0
    val result = Promise[Int]()
    var x: Option[Int] = _
    def apply(tr: Try[Int]): Unit = state match {
    case 0 =>
    x = None
    val sub = flow.pubToSub(s)
    val completed = sub.getCompleted
    if (completed == null) {
    state = 2
    sub.onComplete(evt => apply(evt))
    } else if (completed.isFailure) {
    result.complete(completed)
    } else {
    x = completed.get
    state = 1
    apply(null)
    }
    case 1 =>
    result.complete(x.get)
    case 2 =>
    if (tr.isFailure) {
    result.complete(tr)
    } else {
    x = tr.get
    state = 1
    apply(null)
    }
    }}
    Generated state
    machine
    

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25. Alternative: what if we
    could extend the JVM?
    • OpenJDK Project Loom
    
    • Goal:

    JVM features for supporting lightweight, high-throughput concurrency constructs
    
    • Key features:
    
    • Delimited continuations
    
    • Fibers (“user-mode threads”)
    • Project under active development
    
    • Sponsored by OpenJDK HotSpot Group
    

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26. Continuations in Project Loom
    • A continuation turns a task into a suspendable computation
    
    • Suspend and resume at yield points
    
    • Transfer values into and out of using higher-level abstractions
    val cont = new Continuation(SCOPE, new Runnable {
    def run(): Unit = {
    
    }
    })
    

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27. var continue = false
    val cont =
    new Continuation(SCOPE, new Runnable {
    def run(): Unit = {
    println("hello from continuation")
    while (!continue) {
    println("suspending")
    Continuation.`yield`(SCOPE)
    println("resuming")
    }
    println("all the way to the end")
    }
    })
    cont.run()
    println("isDone: " + cont.isDone())
    continue = true
    cont.run()
    println("isDone: " + cont.isDone())
    hello from continuation
    suspending
    isDone: false
    resuming
    all the way to the end
    isDone: true
    Output:
    Example
    

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28. def rasync[T](body: given Flow[T] => T): Flow.Publisher[T] = {
    delegate flow for Flow[T] = new Flow[T]
    ...
    val cont = new Continuation(SCOPE, new Runnable {
    def run(): Unit = {
    try {
    val v = body
    flow.emitNext(v)
    flow.emitComplete()
    } catch {
    case NonFatal(error) =>
    flow.emitError(error)
    }
    }
    })
    ...
    flow
    }
    

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29. def await[T, S](a: Async[T]) given (flow: Flow[S],
    executor: ExecutionContext): T = {
    val res = a.getCompleted
    if (res eq null) {
    a.onComplete(x => flow.resume(x))
    flow.suspend()
    a.getCompleted.get
    } else {
    res.get
    }
    }
    

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30. Theoretical foundations
    Paper:

    P. Haller and H. Miller: A reduction semantics for direct-style asynchronous observables.

    Journal of Logical and Algebraic Methods in Programming 105 (2019) 75–111

    https://doi.org/10.1016/j.jlamp.2019.03.002
    
    • Formalization of programming model
    
    • Type soundness proof
    
    • Proof of protocol conformance
    
    • Describes macro-based implementation
    

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31. Open Issues
    • “Blue and red functions” problem
    
    • Calling a suspendible method (given Flow[T]) requires the caller to be
    suspendible, too
    
    • Need two variants for each higher-order function: regular and suspendible
    
    • Loom continuations don’t suffer from that problem; can suspend as long as there
    is an active continuation
    
    • Relaxing need for Flow[T] capabilities may be feasible
    

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32. Resources
    • Prototype implementation:

    https://github.com/phaller/scala-async-flow

    Branch “cont” makes use of Project Loom continuations and Scala 3
    
    • Papers and more:

    http://www.csc.kth.se/~phaller/
    
    • Twitter: @philippkhaller
    Thank you!
    

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