fs2的多线程编程模式不但提供了无阻碍I/O（java nio）能力，更为并行运算提供了良好的编程工具。在进入并行运算讨论前我们先示范一下fs2 pipe2对象里的一些Stream合并功能。我们先设计两个帮助函数（helper）来跟踪运算及模拟运算环境：
1 def log[A](prompt: String): Pipe[Task,A,A] = _.evalMap {a =>
2 Task.delay { println(prompt + a); a}} //> log: [A](prompt: String)fs2.Pipe[fs2.Task,A,A]
3
4 Stream(1,2,3).through(log(">")).run.unsafeRun //> >1
5 //| >2
6 //| >3
log是个运算跟踪函数。
1 implicit val strategy = Strategy.fromFixedDaemonPool(4)
2 //> strategy : fs2.Strategy = Strategy
3 implicit val scheduler = Scheduler.fromFixedDaemonPool(2)
4 //> scheduler : fs2.Scheduler = Scheduler(java.util.concurrent.ScheduledThreadPoolExecutor@16022d9d[Running, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 0])
5 def randomDelay[A](max: FiniteDuration): Pipe[Task, A, A] = _.evalMap { a => {
6 val delay: Task[Int] = Task.delay {
7 scala.util.Random.nextInt(max.toMillis.toInt)
8 }
9 delay.flatMap { d => Task.now(a).schedule(d.millis) }
10 }
11 } //> randomDelay: [A](max: scala.concurrent.duration.FiniteDuration)fs2.Pipe[fs2.Task,A,A]
12 Stream(1,2,3).through(randomDelay(1.second)).through(log("delayed>")).run.unsafeRun
13 //> delayed>1
14 //| delayed>2
15 //| delayed>3
randomDelay是一个模拟任意延迟运算环境的函数。我们也可以在连接randomDelay前后进行跟踪：
1 Stream(1,2,3).through(log("befor delay>"))
2 .through(randomDelay(1.second))
3 .through(log("after delay>")).run.unsafeRun
4 //> befor delay>1
5 //| after delay>1
6 //| befor delay>2
7 //| after delay>2
8 //| befor delay>3
9 //| after delay>3
值得注意的是randomDelay并不会阻碍（block）当前运算。
下面我们来看看pipe2对象里的合并函数interleave：
1 val sa = Stream(1,2,3).through(randomDelay(1.second)).through(log("A>"))
2 //> sa : fs2.Stream[fs2.Task,Int] = Segment(Emit(Chunk(1, 2, 3))).flatMap(<function1>).flatMap(<function1>)
3 val sb = Stream(1,2,3).through(randomDelay(1.second)).through(log("B>"))
4 //> sb : fs2.Stream[fs2.Task,Int] = Segment(Emit(Chunk(1, 2, 3))).flatMap(<function1>).flatMap(<function1>)
5 (sa interleave sb).through(log("AB")).run.unsafeRun
6 //> A>1
7 //| B>1
8 //| AB>1
9 //| AB>1
10 //| A>2
11 //| B>2
12 //| AB>2
13 //| AB>2
14 //| A>3
15 //| B>3
16 //| AB>3
17 //| AB>3
我们看到合并后的数据发送必须等待sa,sb完成了元素发送之后。这是一种固定顺序的合并操作。merge是一种不定顺序的合并方式，我们看看它的使用示范：
1 (sa merge sb).through(log("AB>")).run.unsafeRun //> B>1
2 //| AB>1
3 //| B>2
4 //| AB>2
5 //| B>3
6 //| AB>3
7 //| A>1
8 //| AB>1
9 //| A>2
10 //| AB>2
11 //| A>3
12 //| AB>3
我们看到merge不会同时等待sa,sb完成后再发送结果，只要其中一个完成发送就开始发送结果了。换言之merge合并基本上是跟着跑的快的那个，所以结果顺序是不规则不可确定的（nondeterministic）。那么从运算时间上来讲：interleave合并所花费时间就是确定的sa+sb，而merge则选sa,sb之间最快的时间。当然总体运算所需时间是相当的，但在merge时我们可以对发出的元素进行并行运算，能大大缩短运算时间。用merge其中一个问题是我们无法确定当前的元素是从那里发出的，我们可以用either来解决这个问题：
1 (sa either sb).through(log("AB>")).run.unsafeRun //> A>1
2 //| AB>Left(1)
3 //| B>1
4 //| AB>Right(1)
5 //| A>2
6 //| AB>Left(2)
7 //| B>2
8 //| AB>Right(2)
9 //| B>3
10 //| AB>Right(3)
11 //| A>3
12 //| AB>Left(3)
我们通过left,right分辨数据源头。如果再增多一个Stream源头，我们还是可以用merge来合并三个Stream：
1 val sc = Stream.range(1,10).through(randomDelay(1.second)).through(log("C>"))
2 //> sc : fs2.Stream[fs2.Task,Int] = Segment(Emit(Chunk(()))).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>)
3 ((sa merge sb) merge sc).through(log("ABC>")).run.unsafeRun
4 //> B>1
5 //| ABC>1
6 //| C>1
7 //| ABC>1
8 //| A>1
9 //| ABC>1
10 //| B>2
11 //| ABC>2
12 //| A>2
13 //| ABC>2
14 //| B>3
15 //| ABC>3
16 //| C>2
17 //| ABC>2
18 //| A>3
19 //| ABC>3
20 //| C>3
21 //| ABC>3
22 //| C>4
23 //| ABC>4
24 //| C>5
25 //| ABC>5
26 //| C>6
27 //| ABC>6
28 //| C>7
29 //| ABC>7
30 //| C>8
31 //| ABC>8
32 //| C>9
33 //| ABC>9
如果我们无法确定数据源头数量的话，那么我们可以用以下的类型款式来表示：
Stream[Task,Stream[Task,A]]
这个类型代表的是Stream of Streams。在外部的Stream里包含了不确定数量的Streams。用具体的例子可以解释：外部的Stream代表客端数据连接（connection），内部的Stream代表每个客端读取的数据。把上面的三个Stream用这种类型来表示的话：
1 val streams:Stream[Task,Stream[Task,Int]] = Stream(sa,sb,sc)
2 //> streams : fs2.Stream[fs2.Task,fs2.Stream[fs2.Task,Int]] = Segment(Emit(Chunk(Segment(Emit(Chunk(1, 2, 3))).flatMap(<function1>).flatMap(<function1>),Segment(Emit(Chunk(1, 2, 3))).flatMap(<function1>).flatMap(<function1>), S
3 egment(Emit(Chunk(()))).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>))))
现在我们不但需要对内部Stream进行运算还需要把结果打平成Stream[Task,A]。在fs2.concurrent包里就有这样一个组件（combinator）：
def join[F[_],O](maxOpen: Int)(outer: Stream[F,Stream[F,O]])(implicit F: Async[F]): Stream[F,O] = {...}
输入参数outer和运算结果类型都对得上。maxOpen代表最多并行运算数。我们可以用join运算上面合并sa,sb,sc的例子：
1 val ms = concurrent.join(3)(streams) //> ms : fs2.Stream[fs2.Task,Int] = attemptEval(Task).flatMap(<function1>).flatMap(<function1>)
2 ms.through(log("ABC>")).run.unsafeRun //> C>1
3 //| ABC>1
4 //| A>1
5 //| ABC>1
6 //| C>2
7 //| ABC>2
8 //| B>1
9 //| ABC>1
10 //| C>3
11 //| ABC>3
12 //| A>2
13 //| ABC>2
14 //| B>2
15 //| ABC>2
16 //| C>4
17 //| ABC>4
18 //| A>3
19 //| ABC>3
20 //| B>3
21 //| ABC>3
22 //| C>5
23 //| ABC>5
24 //| C>6
25 //| ABC>6
26 //| C>7
27 //| ABC>7
28 //| C>8
29 //| ABC>8
30 //| C>9
31 //| ABC>9
结果就是我们预料的。上面提到过maxOpen是最大并行运算数。我们用另一个例子来观察：
1 val rangedStreams = Stream.range(0,5).map {id =>
2 Stream.range(1,5).through(randomDelay(1.second)).through(log((('A'+id).toChar).toString +">")) }
3 //> rangedStreams : fs2.Stream[Nothing,fs2.Stream[fs2.Task,Int]] = Segment(Emit(Chunk(()))).flatMap(<function1>).mapChunks(<function1>)
4 concurrent.join(3)(rangedStreams).run.unsafeRun //> B>1
5 //| A>1
6 //| C>1
7 //| B>2
8 //| C>2
9 //| A>2
10 //| B>3
11 //| C>3
12 //| C>4
13 //| D>1
14 //| A>3
15 //| A>4
16 //| B>4
17 //| E>1
18 //| E>2
19 //| E>3
20 //| D>2
21 //| D>3
22 //| E>4
23 //| D>4
可以看到一共只有三个运算过程同时存在，如：ABC, ED...
当我们的程序需要与外界程序交互时，可能会以下面的几种形式进行：
1、产生副作用的运算是同步运行的。这种情况最容易处理，因为直接可以获取结果
2、产生副作用的运算是异步的：通过调用一次callback函数来提供运算结果
3、产生副作用的运算是异步的，但结果必须通过多次调用callback函数来分批提供
下面我们就一种一种情况来分析：
1、同步运算最容易处理：我们只需要把运算包嵌在Stream.eval里就行了：
1 def destroyUniverse: Unit = println("BOOOOM!!!") //> destroyUniverse: => Unit
2 val s = Stream.eval_(Task.delay(destroyUniverse)) ++ Stream("...move on")
3 //> s : fs2.Stream[fs2.Task,String] = append(attemptEval(Task).flatMap(<function1>).flatMap(<function1>), Segment(Emit(Chunk(()))).flatMap(<function1>))
4 s.runLog.unsafeRun //> BOOOOM!!!
5 //| res8: Vector[String] = Vector(...move on)
2、第二种情况：fs2里的Async trait有个async是用来登记callback函数的：
trait Async[F[_]] extends Effect[F] { self =>
/**
Create an `F[A]` from an asynchronous computation, which takes the form
of a function with which we can register a callback. This can be used
to translate from a callback-based API to a straightforward monadic
version.
*/
def async[A](register: (Either[Throwable,A] => Unit) => F[Unit]): F[A] =
bind(ref[A]) { ref =>
bind(register { e => runSet(ref)(e) }) { _ => get(ref) }}
...
我们用一个实际的例子来做示范，假设我们有一个callback函数readBytes：
1 trait Connection {
2 def readBytes(onSuccess: Array[Byte] => Unit, onFailure: Throwable => Unit): Unit
这个Connection就是一个交互界面（interface）。假设它是这样实现实例化的：
1 val conn = new Connection {
2 def readBytes(onSuccess: Array[Byte] => Unit, onFailure: Throwable => Unit): Unit = {
3 Thread.sleep(1000)
4 onSuccess(Array(1,2,3,4,5))
5 }
6 } //> conn : demo.ws.fs2Concurrent.connection = demo.ws.fs2Concurrent$$anonfun$main$1$$anon$1@4c40b76e
我们可以用async登记（register）这个callback函数，把它变成纯代码可组合的（monadic）组件Task[Array[Byte]]：
1 val bytes = T.async[Array[Byte]] { (cb: Either[Throwable,Array[Byte]] => Unit) => {
2 Task.delay { conn.readBytes (
3 ready => cb(Right(ready)),
4 fail => cb(Left(fail))
5 ) }
6 }} //> bytes : fs2.Task[Array[Byte]] = Task
这样我们才能用Stream.eval来运算bytes：
1 Stream.eval(bytes).map(_.toList).runLog.unsafeRun //> res9: Vector[List[Byte]] = Vector(List(1, 2, 3, 4, 5))
这种只调用一次callback函数的情况也比较容易处理：当我们来不及处理数据时停止读取就是了。如果需要多次调用callback，比如外部程序也是一个Stream API：一旦数据准备好就调用一次callback进行传送。这种情况下可能出现我们的程序来不及处理收到的数据的状况。我们可以用fs2.async包提供的queue来解决这个问题：
1 import fs2.async
2 import fs2.util.Async
3
4 type Row = List[String]
5 // defined type alias Row
6
7 trait CSVHandle {
8 def withRows(cb: Either[Throwable,Row] => Unit): Unit
9 }
10 // defined trait CSVHandle
11
12 def rows[F[_]](h: CSVHandle)(implicit F: Async[F]): Stream[F,Row] =
13 for {
14 q <- Stream.eval(async.unboundedQueue[F,Either[Throwable,Row]])
15 _ <- Stream.suspend { h.withRows { e => F.unsafeRunAsync(q.enqueue1(e))(_ => ()) }; Stream.emit(()) }
16 row <- q.dequeue through pipe.rethrow
17 } yield row
18 // rows: [F[_]](h: CSVHandle)(implicit F: fs2.util.Async[F])fs2.Stream[F,Row]
enqueue1和dequeue在Queue trait里是这样定义的：
/**
* Asynchronous queue interface. Operations are all nonblocking in their
* implementations, but may be 'semantically' blocking. For instance,
* a queue may have a bound on its size, in which case enqueuing may
* block until there is an offsetting dequeue.
*/
trait Queue[F[_],A] {
/**
* Enqueues one element in this `Queue`.
* If the queue is `full` this waits until queue is empty.
*
* This completes after `a` has been successfully enqueued to this `Queue`
*/
def enqueue1(a: A): F[Unit]
/** Repeatedly call `dequeue1` forever. */
def dequeue: Stream[F, A] = Stream.repeatEval(dequeue1)
/** Dequeue one `A` from this queue. Completes once one is ready. */
def dequeue1: F[A]
...
我们用enqueue1把一次callback调用存入queue。dequeue的运算结果是Stream[F,Row]，所以我们用dequeue运算存在queue里的任务取出数据。
fs2提供了signal,queue,semaphore等数据类型。下面是一些使用示范：async.signal
1 Stream.eval(async.signalOf[Task,Int](0)).flatMap {s =>
2 val monitor: Stream[Task,Nothing] =
3 s.discrete.through(log("s updated>")).drain
4 val data: Stream[Task,Int] =
5 Stream.range(10,16).through(randomDelay(1.second))
6 val writer: Stream[Task,Unit] =
7 data.evalMap {d => s.set(d)}
8 monitor merge writer
9 }.run.unsafeRun //> s updated>0
10 //| s updated>10
11 //| s updated>11
12 //| s updated>12
13 //| s updated>13
14 //| s updated>14
15 //| s updated>15
async.queue使用示范:
1 Stream.eval(async.boundedQueue[Task,Int](5)).flatMap {q =>
2 val monitor: Stream[Task,Nothing] =
3 q.dequeue.through(log("dequeued>")).drain
4 val data: Stream[Task,Int] =
5 Stream.range(10,16).through(randomDelay(1.second))
6 val writer: Stream[Task,Unit] =
7 data.to(q.enqueue)
8 monitor mergeHaltBoth writer
9
10 }.run.unsafeRun //> dequeued>10
11 //| dequeued>11
12 //| dequeued>12
13 //| dequeued>13
14 //| dequeued>14
15 //| dequeued>15
fs2还在time包里提供了一些定时自动产生数据的函数和类型。我们用一些代码来示范它们的用法：
1 time.awakeEvery[Task](1.second)
2 .through(log("time:"))
3 .take(5).run.unsafeRun //> time:1002983266 nanoseconds
4 //| time:2005972864 nanoseconds
5 //| time:3004831159 nanoseconds
6 //| time:4002104307 nanoseconds
7 //| time:5005091850 nanoseconds
awakeEvery产生的是一个无穷数据流，所以我们用take(5)来取前5个元素。我们也可以让它运算5秒钟：
1 val tick = time.awakeEvery[Task](1.second).through(log("time:"))
2 //> tick : fs2.Stream[fs2.Task,scala.concurrent.duration.FiniteDuration] = Segment(Emit(Chunk(()))).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>)
3 tick.run.unsafeRunFor(5.seconds) //> time:1005685270 nanoseconds
4 //| time:2004331473 nanoseconds
5 //| time:3005046945 nanoseconds
6 //| time:4002795227 nanoseconds
7 //| time:5002807816 nanoseconds
8 //| java.util.concurrent.TimeoutException
如果我们希望避免TimeoutException，可以用Task.schedule:
1 val tick = time.awakeEvery[Task](1.second).through(log("time:"))
2 //> tick : fs2.Stream[fs2.Task,scala.concurrent.duration.FiniteDuration] = Seg
3 ment(Emit(Chunk(()))).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>)
4 tick.interruptWhen(Stream.eval(Task.schedule(true,5.seconds)))
5 .run.unsafeRun //> time:1004963839 nanoseconds
6 //| time:2005325025 nanoseconds
7 //| time:3005238921 nanoseconds
8 //| time:4004240985 nanoseconds
9 //| time:5001334732 nanoseconds
10 //| time:6003586673 nanoseconds
11 //| time:7004728267 nanoseconds
12 //| time:8004333608 nanoseconds
13 //| time:9003907670 nanoseconds
14 //| time:10002624970 nanoseconds
最直接的方法是用fs2的tim.sleep：
1 (time.sleep[Task](5.seconds) ++ Stream.emit(true)).runLog.unsafeRun
2 //> res14: Vector[Boolean] = Vector(true)
3 tick.interruptWhen(time.sleep[Task](5.seconds) ++ Stream.emit(true))
4 .run.unsafeRun //> time:1002078506 nanoseconds
5 //| time:2005144318 nanoseconds
6 //| time:3004049135 nanoseconds
7 //| time:4002963861 nanoseconds
8 //| time:5000088103 nanoseconds