fs2是scalaz-stream的最新版本，沿用了scalaz-stream被动式（pull model）数据流原理但采用了全新的实现方法。fs2比较scalaz-stream而言具备了：更精简的基础组件（combinator）、更安全的类型、资源使用（type safe, resource safety）、更高的运算效率。由于fs2基本沿用了scalaz-stream的原理，所以我们会在下面的讨论里着重介绍fs2的使用。根据fs2的官方文件，fs2具备了以下新的特点：
1、完全不含任何外部依赖（third-party dependency）
2、流元素增加了节组（chunk）类型和相关的操作方法
3、fs2不再只局限于Task一种副作用运算方式（effect）。用户可以提供自己的effect类型
4、更精简的流转换组件（stream transformation primitives）
5、增加了更多并行运算组件（concurrent primitives）
6、通过bracket函数增强了资源使用安全，特别是异线程资源占用的事后处理过程。用onFinalize取代了onComplete
7、stream状态转换采用了全新的实现方式，使用了新的数据结构：Pull
8、Stream取代了Process。fs2中再没有Process1、Tee、Wye、Channel这些类型别名，取而代之的是：
type Pipe[F,A,B] = Stream[F,A] => Stream[F,B]type Pipe2[F,A,B,C] = (Stream[F,A], Stream[F,B]) => Stream[F,C]-
Pipe替代了Channel和Process1 -
Pipe2替代了Tee和Wye
下面我们来看看fs2的一些基本操作：
Stream() //> res0: fs2.Stream[Nothing,Nothing] = Segment(Emit(Chunk()))
Stream(,,) //> res1: fs2.Stream[Nothing,Int] = Segment(Emit(Chunk(1, 2, 3)))
Stream.emit() //> res2: fs2.Stream[Nothing,Int] = Segment(Emit(Chunk(4)))
Stream.emits(Seq(,,)) //> res3: fs2.Stream[Nothing,Int] = Segment(Emit(Chunk(1, 2, 3)))
Stream的类型款式是：Stream[F[_],A]。从上面的例子我们看到所有的F[_]都是Nothing，我们称这样的流为纯数据流（pure stream）。再值得注意的是每个流构建都形成了一个Chunk，代表一节元素。fs2增加了Chunk类型来提高数据元素处理效率。这是fs2的一项新功能。
我们可以用toList或者toVector来运算纯数据流中的元素值：
Stream.emits(Seq(,,)).toList //> res3: List[Int] = List(1, 2, 3)
Stream.emits(Seq(,,)).toVector //> res4: Vector[Int] = Vector(1, 2, 3)
纯数据流具备了许多与List相似的操作函数：
(Stream(,,) ++ Stream(,)).toList //> res5: List[Int] = List(1, 2, 3, 4, 5)
Stream(,,).map { _ + }.toList //> res6: List[Int] = List(2, 3, 4)
Stream(,,).filter { _ % == }.toList //> res7: List[Int] = List(2)
Stream(,,).fold()(_ + _).toList //> res8: List[Int] = List(6)
Stream(None,Some(),Some(),None).collect {
case None =>
case Some(i) => i
}.toList //> res9: List[Int] = List(0, 1, 3, 0)
Stream.range(,).intersperse().toList //> res10: List[Int] = List(1, 42, 2, 42, 3, 42, 4)
Stream(,,).flatMap {x => Stream(x,x)}.toList //> res11: List[Int] = List(1, 1, 2, 2, 3, 3)
Stream(,,).repeat.take().toList //> res12: List[Int] = List(1, 2, 3, 1, 2)
以上都是一些基本的List操作函数示范。
我们知道，纯数据流就是scalaz-stream里的Process1，即transducer，是负责对流进行状态转换的。在fs2里transducer就是Pipe（也是channel），我们一般用through来连接transducer。上面示范中的take,filter等都是transducer，我们可以在object pipe里找到这些函数：
object pipe {
...
/** Drop `n` elements of the input, then echo the rest. */
def drop[F[_],I](n: Long): Stream[F,I] => Stream[F,I] =
_ pull (h => Pull.drop(n)(h) flatMap Pull.echo)
...
/** Emits `true` as soon as a matching element is received, else `false` if no input matches */
def exists[F[_], I](p: I => Boolean): Stream[F, I] => Stream[F, Boolean] =
_ pull { h => Pull.forall[F,I](!p(_))(h) flatMap { i => Pull.output1(!i) }}
/** Emit only inputs which match the supplied predicate. */
def filter[F[_], I](f: I => Boolean): Stream[F,I] => Stream[F,I] =
mapChunks(_ filter f)
/** Emits the first input (if any) which matches the supplied predicate, to the output of the returned `Pull` */
def find[F[_],I](f: I => Boolean): Stream[F,I] => Stream[F,I] =
_ pull { h => Pull.find(f)(h).flatMap { case o #: h => Pull.output1(o) }}
/**
* Folds all inputs using an initial value `z` and supplied binary operator,
* and emits a single element stream.
*/
def fold[F[_],I,O](z: O)(f: (O, I) => O): Stream[F,I] => Stream[F,O] =
_ pull { h => Pull.fold(z)(f)(h).flatMap(Pull.output1) }
...
/** Emits all elements of the input except the first one. */
def tail[F[_],I]: Stream[F,I] => Stream[F,I] =
drop()
/** Emit the first `n` elements of the input `Handle` and return the new `Handle`. */
def take[F[_],I](n: Long): Stream[F,I] => Stream[F,I] =
_ pull Pull.take(n)
...
我们可以用through来连接这些transducer：
Stream(,,).repeat
.throughPure(pipe.take())
.throughPure(pipe.filter(_ % == ))
.toList //> res13: List[Int] = List(2, 2, 2)
以上的throughPure等于是through + pure。Pure是没有任何作用的F[_]，是专门为帮助compiler进行类型推导的类型。其实我们可以用pure先把纯数据流升格后再用through：
Stream(,,).repeat.pure
.through(pipe.take())
.through(pipe.filter(_ % == ))
.toList //> res14: List[Int] = List(2, 2, 2)
这时compiler不再出错误信息了。在fs2 pipe对象里的函数通过方法注入或者类型继承变成了Stream的自身函数，所以我们也可以直接在Stream类型上使用这些transducer：
Stream(,,).repeat.take().filter(_ % == ).toList
//> res15: List[Int] = List(2, 2, 2)
我们在前面提到过fs2使用了全新的方法和数据类型来实现transducer。transducer的类型是Pipe，即：
type Pipe[F[_],-I,+O] = Stream[F,I] => Stream[F,O]
我们看到Pipe就是一个Function1的类型别名，一个lambda：提供一个Stream[F,I]，返回Stream[F,O]。那么在fs2里是如何读取一个Stream[F,I]里的元素呢？我们前面提到是通过一个新的数据结构Pull来实现的，先来看看fs2是如何实现Stream >> Pull >> Stream转换的：
val pll = Stream(,,).pure.open //> pll : fs2.Pull[fs2.Pure,Nothing,fs2.Stream.Handle[fs2.Pure,Int]] = fs2.Pull
de5031f
val strm = pll.close //> strm : fs2.Stream[fs2.Pure,Nothing] = evalScope(Scope(Bind(Eval(Snapshot),<
function1>))).flatMap(<function1>)
对一个Stream施用open后得到一个Pull类型。pll是个Pull数据结构，它的类型定义如下：
class Pull[+F[_],+O,+R](private[fs2] val get: Free[P[F,O]#f,Option[Either[Throwable,R]]])
在Pull的类型参数中F是一个运算，O代表输出元素类型，R代表Pull里的数据资源。我们可以从R读取元素。在上面的例子里pll的R值是个Handle类型。这个类型里应该提供了读取元素的方法：
implicit class HandleOps[+F[_],+A](h: Handle[F,A]) {
def push[A2>:A](c: Chunk[A2])(implicit A2: RealSupertype[A,A2]): Handle[F,A2] =
self.push(h: Handle[F,A2])(c)
def push1[A2>:A](a: A2)(implicit A2: RealSupertype[A,A2]): Handle[F,A2] =
self.push1(h: Handle[F,A2])(a)
def #:[H](hd: H): Step[H, Handle[F,A]] = Step(hd, h)
def await: Pull[F, Nothing, Step[Chunk[A], Handle[F,A]]] = self.await(h)
def await1: Pull[F, Nothing, Step[A, Handle[F,A]]] = self.await1(h)
def awaitNonempty: Pull[F, Nothing, Step[Chunk[A], Handle[F,A]]] = Pull.awaitNonempty(h)
def echo1: Pull[F,A,Handle[F,A]] = Pull.echo1(h)
def echoChunk: Pull[F,A,Handle[F,A]] = Pull.echoChunk(h)
def peek: Pull[F, Nothing, Step[Chunk[A], Handle[F,A]]] = self.peek(h)
def peek1: Pull[F, Nothing, Step[A, Handle[F,A]]] = self.peek1(h)
def awaitAsync[F2[_],A2>:A](implicit S: Sub1[F,F2], F2: Async[F2], A2: RealSupertype[A,A2]):
Pull[F2, Nothing, AsyncStep[F2,A2]] = self.awaitAsync(Sub1.substHandle(h))
def await1Async[F2[_],A2>:A](implicit S: Sub1[F,F2], F2: Async[F2], A2: RealSupertype[A,A2]):
Pull[F2, Nothing, AsyncStep1[F2,A2]] = self.await1Async(Sub1.substHandle(h))
def covary[F2[_]](implicit S: Sub1[F,F2]): Handle[F2,A] = Sub1.substHandle(h)
}
implicit class HandleInvariantEffectOps[F[_],+A](h: Handle[F,A]) {
def invAwait1Async[A2>:A](implicit F: Async[F], A2: RealSupertype[A,A2]):
Pull[F, Nothing, AsyncStep1[F,A2]] = self.await1Async(h)
def invAwaitAsync[A2>:A](implicit F: Async[F], A2: RealSupertype[A,A2]):
Pull[F, Nothing, AsyncStep[F,A2]] = self.awaitAsync(h)
def receive1[O,B](f: Step[A,Handle[F,A]] => Pull[F,O,B]): Pull[F,O,B] = h.await1.flatMap(f)
def receive[O,B](f: Step[Chunk[A],Handle[F,A]] => Pull[F,O,B]): Pull[F,O,B] = h.await.flatMap(f)
}
果然在Handle提供的函数里有await,receive等这些读取函数。我们试着来实现一个简单的transducer：一个filter函数：
import scala.language.higherKinds
def myFilter[F[_],A](f: A => Boolean): Pipe[F, A, A] = {
def go(h: Stream.Handle[F,A]): Pull[F,A,Unit] = {
// h.receive1 {case Step(a,h) => if(f(a)) Pull.output1(a) >> go(h) else go(h)}
h.await1.flatMap { case Step(a,h) => if(f(a)) Pull.output1(a) >> go(h) else go(h)}
}
// sin => sin.open.flatMap {h => go(h)}.close
sin => sin.pull(go _)
} //> myFilter: [F[_], A](f: A => Boolean)fs2.Pipe[F,A,A] Stream.range(,).pure.through(myFilter(_ % == )).toList
//> res17: List[Int] = List(0, 2, 4, 6, 8)
我们从Pull里用await1或者receive1把一个Step数据结构从Handle里扯（pull）出来然后再output到Pull结构里。把这个Pull close后得到我们需要的Stream。我们把例子使用的类型及函数款式陈列在下面：
type Pipe[F[_],-I,+O] = Stream[F,I] => Stream[F,O]
def await1[F[_],I]: Handle[F,I] => Pull[F,Nothing,Step[I,Handle[F,I]]] = {...}
def receive1[F[_],I,O,R](f: Step[I,Handle[F,I]] => Pull[F,O,R]): Handle[F,I] => Pull[F,O,R] =
_.await1.flatMap(f)
def pull[F[_],F2[_],A,B](s: Stream[F,A])(using: Handle[F,A] => Pull[F2,B,Any])(implicit S: Sub1[F,F2])
: Stream[F2,B] =
Pull.close { Sub1.substPull(open(s)) flatMap (h => Sub1.substPull(using(h))) }
再示范另一个Pipe的实现：take
def myTake[F[_],A](n: Int): Pipe[F,A,A] = {
def go(n: Int): Stream.Handle[F,A] => Pull[F,A,Unit] = h => {
if (n <= ) Pull.done
else h.receive1 { case a #: h => Pull.output1(a).flatMap{_ => go(n-)(h)}}
}
sin => sin.pull(go(n))
} //> myTake: [F[_], A](n: Int)fs2.Pipe[F,A,A]
Stream.range(,).pure.through(myTake()).toList //> res18: List[Int] = List(0, 1, 2)
我们曾经提过fs2功能提升的其中一项是增加了节组（Chunk）数据类型和相关的操作函数。Chunk是fs2内部使用的一种集合，这样fs2就可以一节一节（by chunks）来处理数据了。Chunk本身具备了完整的集合函数：
/**
* Chunk represents a strict, in-memory sequence of `A` values.
*/
trait Chunk[+A] { self =>
def size: Int
def uncons: Option[(A, Chunk[A])] =
if (size == ) None
else Some(apply() -> drop())
def apply(i: Int): A
def copyToArray[B >: A](xs: Array[B]): Unit
def drop(n: Int): Chunk[A]
def take(n: Int): Chunk[A]
def filter(f: A => Boolean): Chunk[A]
def foldLeft[B](z: B)(f: (B,A) => B): B
def foldRight[B](z: B)(f: (A,B) => B): B
def indexWhere(p: A => Boolean): Option[Int] = {
val index = iterator.indexWhere(p)
if (index < ) None else Some(index)
}
def isEmpty = size ==
def toArray[B >: A: ClassTag]: Array[B] = {
val arr = new Array[B](size)
copyToArray(arr)
arr
}
def toList = foldRight(Nil: List[A])(_ :: _)
def toVector = foldLeft(Vector.empty[A])(_ :+ _)
def collect[B](pf: PartialFunction[A,B]): Chunk[B] = {
val buf = new collection.mutable.ArrayBuffer[B](size)
iterator.collect(pf).copyToBuffer(buf)
Chunk.indexedSeq(buf)
}
def map[B](f: A => B): Chunk[B] = {
val buf = new collection.mutable.ArrayBuffer[B](size)
iterator.map(f).copyToBuffer(buf)
Chunk.indexedSeq(buf)
}
def mapAccumulate[S,B](s0: S)(f: (S,A) => (S,B)): (S,Chunk[B]) = {
val buf = new collection.mutable.ArrayBuffer[B](size)
var s = s0
for { c <- iterator } {
val (newS, newC) = f(s, c)
buf += newC
s = newS
}
(s, Chunk.indexedSeq(buf))
}
def scanLeft[B](z: B)(f: (B, A) => B): Chunk[B] = {
val buf = new collection.mutable.ArrayBuffer[B](size + )
iterator.scanLeft(z)(f).copyToBuffer(buf)
Chunk.indexedSeq(buf)
}
def iterator: Iterator[A] = new Iterator[A] {
var i =
def hasNext = i < self.size
def next = { val result = apply(i); i += ; result }
}
...
fs2的大部分转换函数都考虑了对Chunk数据的处理机制。我们先看看fs2是如何表现Chunk数据的：
(Stream(,) ++ Stream(,,) ++ Stream(,)).chunks.toList
//> res16: List[fs2.Chunk[Int]] = List(Chunk(1, 2), Chunk(3, 4, 5), Chunk(6, 7))
fs2是按照Stream的构建批次来分节的。我们来示范一下如何使用Pull的Chunk机制：
def myTakeC[F[_],A](n: Int): Pipe[F,A,A] = {
def go(n: Int): Stream.Handle[F,A] => Pull[F,A,Unit] = h => {
if ( n <= ) Pull.done
else Pull.awaitLimit(n)(h).flatMap {case Step(chunk,h) =>
if (chunk.size <= n) Pull.output(chunk) >> go(n-chunk.size)(h)
else Pull.output(chunk.take(n)) }
}
sin => sin.pull(go(n))
} //> myTakeC: [F[_], A](n: Int)fs2.Pipe[F,A,A]
val s1 = (Stream(,) ++ Stream(,,) ++ Stream(,))
//> s1 : fs2.Stream[Nothing,Int] = append(append(Segment(Emit(Chunk(1, 2))), S
egment(Emit(Chunk(()))).flatMap(<function1>)), Segment(Emit(Chunk(()))).fla
tMap(<function1>))
s1.pure.through(myTake()).chunks.toList //> res20: List[fs2.Chunk[Int]] = List(Chunk(1), Chunk(2), Chunk(3), Chunk(4))
s1.pure.through(myTakeC()).chunks.toList //> res21: List[fs2.Chunk[Int]] = List(Chunk(1, 2), Chunk(3, 4))
myTake和myTakeC产生了不同的结果。
fs2的特长应该是多线程编程了。在Stream的类型款式中：Stream[F[_],A]，F[_]是一种可能产生副作用的运算方式，当F[_]等于Nothing时，Stream[Nothing,A]是一种纯数据流，而Stream[F[_],A]就是一种运算流了。我们可以在对运算流进行状态转换的过程中进行运算来实现F的副作用如：数据库读写、IO操作等。fs2不再绑定Task一种运算方式了。任何有Catchable实例的Monad都可以成为Stream的运算方式。但是，作为一种以多线程编程为主导的工具库，没有什么运算方式会比Task更合适了。
我们可以把一个纯数据流升格成运算流：
val s2 = Stream.emits(Seq(,,)).covary[Task] //> s2 : fs2.Stream[fs2.Task,Int] = Segment(Emit(Chunk(1, 2, 3)))
我们先运算这个运算流，结果为一个Task，然后再运算Task来获取运算值：
val s2 = Stream.emits(Seq(,,)).covary[Task] //> s2 : fs2.Stream[fs2.Task,Int] = Segment(Emit(Chunk(1, 2, 3)))
val t2 = s2.runLog //> t2 : fs2.Task[Vector[Int]] = Task
t2.unsafeRun //> res22: Vector[Int] = Vector(1, 2, 3)
现在使用myTake和myFilter就不需要pure升格了：
s3.through(myFilter(_ % == )).through(myTake()).runLog.unsafeRun
//> res23: Vector[Int] = Vector(2, 2, 2)
下面的例子里展示了fs2的运算流从源头（Source）到传换（Transducer）一直到终点（Sink）的使用示范：
def stdOut: Sink[Task,String] =
_.evalMap { x => Task.delay{ println(s"milli: $x")}}
//> stdOut: => fs2.Sink[fs2.Task,String]
Stream.repeatEval(Task.delay{System.currentTimeMillis})
.map(_.toString)
.through(myTake())
.to(stdOut)
.run.unsafeRun //> milli: 1472001934708
//| milli: 1472001934714
//| milli: 1472001934714
在上面的例子里我们使用了through,to等连接函数。由于数据最终发送到终点stdOut，我们无须用runLog来记录运算结果。