在上一篇讨论里我们介绍了Source，它的类型款式是这样的：Process[F[_],O]。Source是通过await函数来产生数据流。await函数款式如下：
def await[F[_], A, O](req: F[A])(rcv: A => Process[F, O]): Process[F, O]
await函数的作用是：运算F从外界数据源获取数据A，如：从数据库读取记录、从网络读取网页或读取键盘鼠标输入等。获取数据A后输入函数rcv来生成Process[F,O]类型。这是一种产生数据的数据源Source模式。有了数据源Source后我们可能需要对Source提供的数据O进行加工处理，这就是transducer的功能了。我们先看看Transducer的类型款式：
type Process1[-I,+O] = Process[Env[I,Any]#Is, O]
从类型参数来看transducer不会产生任何的副作用，它的作用应该是把接收到的数据元素I加工处理后转换成O类型数据输出。transducer不会主动产生任何数据而是被动接收输入数据I，所以Process1类型的await函数被称为await1，款式如下：
/** The `Process1` which awaits a single input, emits it, then halts normally. */
def await1[I]: Process1[I, I] =
receive1(emit)
def receive1[I, O](rcv: I => Process1[I, O]): Process1[I, O] =
await(Get[I])(rcv)
首先可以看出await1就是await函数的特别版本：产生数据的F[A]被替换成了帮助compiler推导类型I的Get[I]，也就是说await1不会主动产生数据，它的rcv是个lambda：需要提供给它一个I，它才会返回Process1[I,O]。我们来看看await1的用例：
1 import Process._
2 def multiplyBy(n: Int): Process1[Int,Int] =
3 await1[Int].flatMap { i => emit(i * n) }.repeat
4 //> multiplyBy: (n: Int)scalaz.stream.Process1[Int,Int]
5 def addPosfix: Process1[Int,String] =
6 receive1[Int,String]{ i => emit(i.toString + "!") }.repeat
7 //> addPosfix: => scalaz.stream.Process1[Int,String]
可以看出无论await1或者receive1都在被动等待一个元素i来继续进行数据转换功能。我们可以用pipe把Process1连接到一个Source上，然后对Source产生的元素进行转换处理：
1 (range(11,16).toSource pipe multiplyBy(5) |> addPosfix).runLog.run
2 //> res0: Vector[String] = Vector(55!, 60!, 65!, 70!, 75!)
我们也可以把一个普通函数lift成Process1：
1 import process1._
2 (range(11,16).toSource |> lift {(i: Int) => i * 5} |> addPosfix).runLog.run
3 //> res1: Vector[String] = Vector(55!, 60!, 65!, 70!, 75!)
上面的|>是pipe符号。实际上我们可以直接对Source输出元素进行转换来达到同样目的：
1 range(11,16).toSource.flatMap { i =>
2 emit(i * 5) }.flatMap { i =>
3 emit(i.toString + "!") }.runLog.run //> res1: Vector[String] = Vector(55!, 60!, 65!, 70!, 75!)
虽然用更直接的方法获得相同结果，但值得注意的是现在这个Source已经是一个特殊的版本了，附加在它上面的这些元素转换特性是无法分割的了。实际上pipe就是Process组合函数，我们用它来把Source和Transducer、Transducer与Transducer对接起来。这样我们就可以保证Source和Transducer都是功能单一的函数组件了。
只要连接上一个数据源，我们就可以对它发出的元素进行转换处理。这些transduce功能函数都在process1对象里：
1 import process1._
2 (range(1,6).toSource pipe take(2))
3 .runLog.run //> res2: Vector[Int] = Vector(1, 2)
4 (range(1,10).toSource |> filter {_ % 2 == 0 }
5 |> collect {
6 case 4 => "the number four"
7 case 5 => "the number five"
8 case 6 => "the number six"
9 case 100 => "the number one hundred"
10 }
11 ).runLog.run //> res3: Vector[String] = Vector(the number four, the number six)
基本上所有对scala标准库List使用的函数都可以对Process1施用：
1 (range(1,6).toSource
2 |> fold(Nil:List[Int]){ (b,a) => a :: b }
3 ).runLog.run //> res5: Vector[List[Int]] = Vector(List(5, 4, 3, 2, 1))
4 (range(1,6).toSource
5 |> foldMap { List(_) }
6 ).runLog.run //> res6: Vector[List[Int]] = Vector(List(1, 2, 3, 4, 5))
7 (range(1,6).toSource
8 |> foldMap { identity }
9 ).runLog.run //> res7: Vector[Int] = Vector(15)
10 (range(1,6).toSource
11 |> sum
12 ).runLog.run //> res8: Vector[Int] = Vector(15)
13 (range(1,6).toSource
14 |> scan(0){(a,b) => a + b}
15 ).runLog.run //> res9: Vector[Int] = Vector(0, 1, 3, 6, 10, 15)
我们也可以把一串现成的元素插入一个Process1：
1 (range(1,6).toSource
2 |> feed(6 to 10)(lift(identity))
3 ).runLog.run //> res10: Vector[Int] = Vector(6, 7, 8, 9, 10, 1, 2, 3, 4, 5)
4 (range(1,6).toSource
5 |> feed(6 to 10)(lift(identity))
6 |> foldMap {identity}
7 ).runLog.run //> res11: Vector[Int] = Vector(55)
从上面的示范可以得出：Process1只是被动接受从上游发过来的元素，我们必须把它和上游接驳后才能发生作用，pipe就是这样一个连接器。同样原理：我们也可以用tee来连接两个数据源，然后把两个源头数据合并形成一个按左右顺序的数据流。tee的类型定义如下：
/**
* A stream transducer that can read from one of two inputs,
* the 'left' (of type `I`) or the 'right' (of type `I2`).
* `Process1[I,O] <: Tee[I,I2,O]`.
*/
type Tee[-I,-I2,+O] = Process[Env[I,I2]#T, O]
我们看到tee的类型款式很像Process1，只不过有I1,i2两个输入。如果Process1的驱动函数是await1即receive1，那么tee的就是receiveL和receiveR了：
/**
* Awaits to receive input from Left side,
* than if that request terminates with `End` or is terminated abnormally
* runs the supplied `continue` or `cleanup`.
* Otherwise `rcv` is run to produce next state.
*
* If you don't need `continue` or `cleanup` use rather `awaitL.flatMap`
*/
def receiveL[I, I2, O](rcv: I => Tee[I, I2, O]): Tee[I, I2, O] =
await[Env[I, I2]#T, I, O](L)(rcv)
/**
* Awaits to receive input from Right side,
* than if that request terminates with `End` or is terminated abnormally
* runs the supplied continue.
* Otherwise `rcv` is run to produce next state.
*
* If you don't need `continue` or `cleanup` use rather `awaitR.flatMap`
*/
def receiveR[I, I2, O](rcv: I2 => Tee[I, I2, O]): Tee[I, I2, O] =
await[Env[I, I2]#T, I2, O](R)(rcv)
与await1同样，receiveL和receiveR都是await的特别版。其中L,R和上面await1的Get[I]都在Env类里：
case class Env[-I, -I2]() {
sealed trait Y[-X] {
def tag: Int
def fold[R](l: => R, r: => R, both: => R): R
}
sealed trait T[-X] extends Y[X]
sealed trait Is[-X] extends T[X]
case object Left extends Is[I] {
def tag = 0
def fold[R](l: => R, r: => R, both: => R): R = l
}
case object Right extends T[I2] {
def tag = 1
def fold[R](l: => R, r: => R, both: => R): R = r
}
case object Both extends Y[ReceiveY[I, I2]] {
def tag = 2
def fold[R](l: => R, r: => R, both: => R): R = both
}
}
private val Left_ = Env[Any, Any]().Left
private val Right_ = Env[Any, Any]().Right
private val Both_ = Env[Any, Any]().Both
def Get[I]: Env[I, Any]#Is[I] = Left_
def L[I]: Env[I, Any]#Is[I] = Left_
def R[I2]: Env[Any, I2]#T[I2] = Right_
def Both[I, I2]: Env[I, I2]#Y[ReceiveY[I, I2]] = Both_
L[I1],R[I2],Get[I]都没什么实际作用，它们是为了compiler类型推导而设。tee的顺序特性是指我们可以用receiveL,receiveR来指定从那边输入元素。可以想象tee的主要作用应该是合并两个数据源发出的元素。tee的数据合并操作方式基本上是按下面这个tee函数款式进行的：
/**
* Use a `Tee` to interleave or combine the outputs of `this` and
* `p2`. This can be used for zipping, interleaving, and so forth.
* Nothing requires that the `Tee` read elements from each
* `Process` in lockstep. It could read fifty elements from one
* side, then two elements from the other, then combine or
* interleave these values in some way, etc.
*
* If at any point the `Tee` awaits on a side that has halted,
* we gracefully kill off the other side, then halt.
*
* If at any point `t` terminates with cause `c`, both sides are killed, and
* the resulting `Process` terminates with `c`.
*/
final def tee[F2[x] >: F[x], O2, O3](p2: Process[F2, O2])(t: Tee[O, O2, O3]): Process[F2, O3]
用伪代码表示就是：leftProcess.tee(rightProcess)(teeFunction): newProcess
以下是几个常用的tee功能函数：
/** Alternate emitting elements from `this` and `p2`, starting with `this`. */
def interleave[F2[x] >: F[x], O2 >: O](p2: Process[F2, O2]): Process[F2, O2] =
this.tee(p2)(scalaz.stream.tee.interleave[O2])
/** Call `tee` with the `zipWith` `Tee[O,O2,O3]` defined in `tee.scala`. */
def zipWith[F2[x] >: F[x], O2, O3](p2: Process[F2, O2])(f: (O, O2) => O3): Process[F2, O3] =
this.tee(p2)(scalaz.stream.tee.zipWith(f))
/** Call `tee` with the `zip` `Tee[O,O2,O3]` defined in `tee.scala`. */
def zip[F2[x] >: F[x], O2](p2: Process[F2, O2]): Process[F2, (O, O2)] =
this.tee(p2)(scalaz.stream.tee.zip)
/**
* When `condition` is `true`, lets through any values in `this` process, otherwise blocks
* until `condition` becomes true again. Note that the `condition` is checked before
* each and every read from `this`, so `condition` should return very quickly or be
* continuous to avoid holding up the output `Process`. Use `condition.forwardFill` to
* convert an infrequent discrete `Process` to a continuous one for use with this
* function.
*/
def when[F2[x] >: F[x], O2 >: O](condition: Process[F2, Boolean]): Process[F2, O2] =
condition.tee(this)(scalaz.stream.tee.when)
/**
* Halts this `Process` as soon as `condition` becomes `true`. Note that `condition`
* is checked before each and every read from `this`, so `condition` should return
* very quickly or be continuous to avoid holding up the output `Process`. Use
* `condition.forwardFill` to convert an infrequent discrete `Process` to a
* continuous one for use with this function.
*/
def until[F2[x] >: F[x], O2 >: O](condition: Process[F2, Boolean]): Process[F2, O2] =
condition.tee(this)(scalaz.stream.tee.until)
下面是它们的具体实现方法：
/** A `Tee` which ignores all input from left. */
def passR[I2]: Tee[Any, I2, I2] = awaitR[I2].repeat
/** A `Tee` which ignores all input from the right. */
def passL[I]: Tee[I, Any, I] = awaitL[I].repeat
/** Echoes the right branch until the left branch becomes `true`, then halts. */
def until[I]: Tee[Boolean, I, I] =
awaitL[Boolean].flatMap(kill => if (kill) halt else awaitR[I] ++ until)
/** Echoes the right branch when the left branch is `true`. */
def when[I]: Tee[Boolean, I, I] =
awaitL[Boolean].flatMap(ok => if (ok) awaitR[I] ++ when else when)
/** Defined as `zipWith((_,_))` */
def zip[I, I2]: Tee[I, I2, (I, I2)] = zipWith((_, _))
/** Defined as `zipWith((arg,f) => f(arg)` */
def zipApply[I,I2]: Tee[I, I => I2, I2] = zipWith((arg,f) => f(arg))
/** A version of `zip` that pads the shorter stream with values. */
def zipAll[I, I2](padI: I, padI2: I2): Tee[I, I2, (I, I2)] =
zipWithAll(padI, padI2)((_, _))
我们用以下例子来示范这些函数的使用方法：
1 import tee._
2 val source = range(1,6).toSource //> source : scalaz.stream.Process[scalaz.concurrent.Task,Int] = Append(Halt(End),Vector(<function1>))
3 val seq = emitAll(Seq("a","b","c")) //> seq : scalaz.stream.Process0[String] = Emit(List(a, b, c))
4 val signalw = Process(true,true,false,true) //> signalw : scalaz.stream.Process0[Boolean] = Emit(WrappedArray(true, true, false, true))
5 val signalu = Process(false,true,false,true) //> signalu : scalaz.stream.Process0[Boolean] = Emit(WrappedArray(false, true,false, true))
6
7 source.tee(seq)(interleave).runLog.run //> res12: Vector[Any] = Vector(1, a, 2, b, 3, c)
8 (source interleave seq).runLog.run //> res13: Vector[Any] = Vector(1, a, 2, b, 3, c)
9 signalu.tee(source)(until).runLog.run //> res14: Vector[Int] = Vector(1)
10 signalw.tee(source)(when).runLog.run //> res15: Vector[Int] = Vector(1, 2, 3)
11 source.tee(seq)(passL).runLog.run //> res16: Vector[Int] = Vector(1, 2, 3, 4, 5)
12 source.tee(seq)(passR).runLog.run //> res17: Vector[String] = Vector(a, b, c)
13 (source zip seq).runLog.run //> res18: Vector[(Int, String)] = Vector((1,a), (2,b), (3,c))
14 (seq zip source).runLog.run //> res19: Vector[(String, Int)] = Vector((a,1), (b,2), (c,3))
15 (source.zipWith(seq){(a,b) => a.toString + b}).runLog.run
16 //> res20: Vector[String] = Vector(1a, 2b, 3c)
与Process1同样，我们也可以对tee注入一串元素，这次我们用feedL和feedR：
/** Feed a sequence of inputs to the left side of a `Tee`. */
def feedL[I, I2, O](i: Seq[I])(p: Tee[I, I2, O]): Tee[I, I2, O] = {...}
/** Feed a sequence of inputs to the right side of a `Tee`. */
def feedR[I, I2, O](i: Seq[I2])(p: Tee[I, I2, O]): Tee[I, I2, O] = {...}
用例：（好像只能用feedL。不过已经足够了。我们的目的是把一串现成的元素插入形成的流，无论从左或右都无所谓）
1 val ltee = tee.feedL(Seq(1,2,3))(id[Int]) //> ltee : scalaz.stream.Tee[Int,Any,Int] = Append(Emit(Vector(1, 2)),Vector(<function1>))
2 halt.tee[Task,Int,Int](halt)(ltee).runLog.run //> res21: Vector[Int] = Vector(1, 2, 3)
3 source.tee[Task,Int,Int](halt)(ltee).runLog.run //> res22: Vector[Int] = Vector(1, 2, 3, 1, 2, 3, 4, 5)
还有一种多源头元素合并方式是wye。wye与tee相似：都是连接到左右两个数据源头。与tee不同的是通过wye合并的数据流是不确定顺序的。wye从源头接收元素的方式不按照左右顺序而是随机的。特别是当左右两个源头产生数据的速度不同时wye采取先到先收的策略，因而增加了接收顺序的不确定性。与tee相同：wye的操作基本上是在wye函数的定义上：
/**
* Like `tee`, but we allow the `Wye` to read non-deterministically
* from both sides at once.
*
* If `y` is in the state of awaiting `Both`, this implementation
* will continue feeding `y` from either left or right side,
* until either it halts or _both_ sides halt.
*
* If `y` is in the state of awaiting `L`, and the left
* input has halted, we halt. Likewise for the right side.
*
* For as long as `y` permits it, this implementation will _always_
* feed it any leading `Emit` elements from either side before issuing
* new `F` requests. More sophisticated chunking and fairness
* policies do not belong here, but should be built into the `Wye`
* and/or its inputs.
*
* The strategy passed in must be stack-safe, otherwise this implementation
* will throw SOE. Preferably use one of the `Strategys.Executor(es)` based strategies
*/
final def wye[O2, O3](p2: Process[Task, O2])(y: Wye[O, O2, O3])(implicit S: Strategy): Process[Task, O3] =
scalaz.stream.wye[O, O2, O3](self, p2)(y)(S)
wye有几个重要的数据合并操作函数：
/**
* After each input, dynamically determine whether to read from the left, right, or both,
* for the subsequent input, using the provided functions `f` and `g`. The returned
* `Wye` begins by reading from the left side and is left-biased--if a read of both branches
* returns a `These(x,y)`, it uses the signal generated by `f` for its next step.
*/
def dynamic[I,I2](f: I => wye.Request, g: I2 => wye.Request): Wye[I,I2,ReceiveY[I,I2]] = {
import scalaz.stream.wye.Request._
def go(signal: wye.Request): Wye[I,I2,ReceiveY[I,I2]] = signal match {
case L => receiveL { i => emit(ReceiveL(i)) ++ go(f(i)) }
case R => receiveR { i2 => emit(ReceiveR(i2)) ++ go(g(i2)) }
case Both => receiveBoth {
case [email protected](i) => emit(t) ++ go(f(i))
case [email protected](i2) => emit(t) ++ go(g(i2))
case HaltOne(rsn) => Halt(rsn)
}
}
go(L)
}
/**
* Non-deterministic interleave of both inputs. Emits values whenever either
* of the inputs is available.
*
* Will terminate once both sides terminate.
*/
def merge[I]: Wye[I,I,I] =
receiveBoth {
case ReceiveL(i) => emit(i) ++ merge
case ReceiveR(i) => emit(i) ++ merge
case HaltL(End) => awaitR.repeat
case HaltR(End) => awaitL.repeat
case HaltOne(rsn) => Halt(rsn)
}
/**
* Nondeterminstic interleave of both inputs. Emits values whenever either
* of the inputs is available.
*/
def either[I,I2]: Wye[I,I2,I // I2] =
receiveBoth {
case ReceiveL(i) => emit(left(i)) ++ either
case ReceiveR(i) => emit(right(i)) ++ either
case HaltL(End) => awaitR[I2].map(right).repeat
case HaltR(End) => awaitL[I].map(left).repeat
case [email protected](rsn) => Halt(rsn)
}
我们用一些例子来示范它们的用法：
1 import wye._
2 source.wye(seq)(either).runLog.run //> res23: Vector[scalaz.//[Int,String]] = Vector(-//(1), //-(a), //-(b), //-(c), -//(2), -//(3), -//(4), -//(5))
3 (source either seq).runLog.run //> res24: Vector[scalaz.//[Int,String]] = Vector(-//(1), //-(a), //-(b), //-(c), -//(2), -//(3), -//(4), -//(5))
4 source.wye(seq)(merge).runLog.run //> res25: Vector[Any] = Vector(1, a, b, c, 2, 3, 4, 5)
5 (source merge seq).runLog.run //> res26: Vector[Any] = Vector(1, a, b, c, 2, 3, 4, 5)
实际上我们也可以实现某些程度的接收顺序。我们可以用dynamic函数来要求wye从左或右提供数据元素：
1 val w = dynamic((r:Int) => Request.R, (l:String) => Request.L)
2 //> w : scalaz.stream.Wye[Int,String,scalaz.stream.ReceiveY[Int,String]] = Await(Left,<function1>,<function1>)
3 source.wye(seq)(w).runLog.run //> res27: Vector[scalaz.stream.ReceiveY[Int,String]] = Vector(ReceiveL(1), ReceiveR(a), ReceiveL(2), ReceiveR(b), ReceiveL(3), ReceiveR(c), ReceiveL(4))
4 val fw = dynamic((r: Int) => if (r % 3 == 0) {
5 Request.R } else {Request.L}, (l:String) => Request.L)
6 //> fw : scalaz.stream.Wye[Int,String,scalaz.stream.ReceiveY[Int,String]] = Await(Left,<function1>,<function1>)
7 source.wye(seq)(fw).runLog.run //> res28: Vector[scalaz.stream.ReceiveY[Int,String]] = Vector(ReceiveL(1), ReceiveL(2), ReceiveL(3), ReceiveR(a), ReceiveL(4), ReceiveL(5))
与tee同样：我们可以用feedL来把一串现成的元素插入合并流里：
1 val lwye = wye.feedL(Seq(1,2,3))(id[Int]) //> lwye : scalaz.stream.Wye[Int,Any,Int] = Append(Emit(Vector(1, 2)),Vector(<
2 //| function1>))
3 halt.wye(halt)(lwye).runLog.run //> res29: Vector[Int] = Vector(1, 2, 3)
4 source.wye(halt)(lwye).runLog.run //> res30: Vector[Int] = Vector(1, 2, 3, 1, 2, 3, 4, 5)
原创文章，作者：ItWorker，如若转载，请注明出处：https://blog.ytso.com/12900.html