Scalaz(47)- scalaz-stream: 深入了解-Source

来源:转载

   scalaz-stream库的主要设计目标是实现函数式的I/O编程(functional I/O)。这样用户就能使用功能单一的基础I/O函数组合成为功能完整的I/O程序。还有一个目标就是保证资源的安全使用(resource safety):使用scalaz-stream编写的I/O程序能确保资源的安全使用,特别是在完成一项I/O任务后自动释放所有占用的资源包括file handle、memory等等。我们在上一篇的讨论里笼统地解释了一下scalaz-stream核心类型Process的基本情况,不过大部分时间都用在了介绍Process1这个通道类型。在这篇讨论里我们会从实际应用的角度来介绍整个scalaz-stream链条的设计原理及应用目的。我们提到过Process具有Emit/Await/Halt三个状态,而Append是一个链接stream节点的重要类型。先看看这几个类型在scalaz-stream里的定义:

case class Emit[+O](seq: Seq[O]) extends HaltEmitOrAwait[Nothing, O] with EmitOrAwait[Nothing, O]

case class Await[+F[_], A, +O](

req: F[A]

, rcv: (EarlyCause \/ A) => Trampoline[Process[F, O]] @uncheckedVariance

, preempt : A => Trampoline[Process[F,Nothing]] @uncheckedVariance = (_:A) => Trampoline.delay(halt:Process[F,Nothing])

) extends HaltEmitOrAwait[F, O] with EmitOrAwait[F, O]

case class Halt(cause: Cause) extends HaltEmitOrAwait[Nothing, Nothing] with HaltOrStep[Nothing, Nothing]

case class Append[+F[_], +O](

head: HaltEmitOrAwait[F, O]

, stack: Vector[Cause => Trampoline[Process[F, O]]] @uncheckedVariance

) extends Process[F, O]

我们看到Process[F,O]被包嵌在Trampoline类型里,所以Process是通过Trampoline来实现函数结构化的,可以有效解决大量stream运算堆栈溢出问题(StackOverflowError)。撇开Trampoline等复杂的语法,以上类型可以简化成以下理论结构:

 1 rait Process[+F[_],+O]

2 case object Cause

3

4 case class Emit[O](out: O) extends Process[Nothing, O]

5

6 case class Halt(cause: Cause) extends Process[Nothing,Nothing]

7

8 case class Await[+F[_],E,+O](

9 req: F[E],

10 rcv: E => Process[F,O],

11 preempt: E => Process[F,Nothing] = Halt) extends Process[F,O]

12

13 case class Append[+F[_],+O](

14 head: Process[F,O],

15 stack: Vector[Cause => Process[F,O]]) extends Process[F,O]

我们来说明一下:

Process[F[_],O]:从它的类型可以推断出scalaz-stream可以在输出O类型元素的过程中进行可能含副作用的F类型运算。

Emit[O](out: O):发送一个O类型元素;不可能进行任何附加运算

Halt(cause: Cause):停止发送;cause是停止的原因:End-完成发送,Err-出错终止,Kill-强行终止

Await[+F[_],E,+O]:这个是运算流的核心Process状态。先进行F运算req,得出结果E后输入函数rcv转换到下一个Process状态,完成后执行preempt这个事后清理函数。这不就是个flatMap函数结构版嘛。值得注意的是E类型是个内部类型,由F运算产生后输入rcv后就不再引用了。我们可以在preepmt函数里进行资源释放。如果我们需要构建一个运算流,看来就只有使用这个Await类型了

Append[+F[_],+O]:Append是一个Process[F,O]链接类型。首先它不但担负了元素O的传送,更重要的是它还可以把上一节点的F运算传到下一个节点。这样才能在下面节点时运行对上一个节点的事后处置函数(finalizer)。Append可以把多个节点结成一个大节点:head是第一个节点,stack是一串函数,每个函数接受上一个节点完成状态后运算出下一个节点状态

一个完整的scalaz-stream由三个类型的节点组成Source(源点)/Transducer(传换点)/Sink(终点)。节点间通过Await或者Append来链接。我们再来看看Source/Transducer/Sink的类型款式:

上游:Source       >>> Process0[O]   >>> Process[F[_],O]

中游:Transduce    >>> Process1[I,O] 

下游:Sink/Channel >>> Process[F[_],O => F[Unit]], Channel >>> Process[F[_],I => F[O]]

我们可以用一个文件处理流程来描述完整scalaz-stream链条的作用:

Process[F[_],O],用F[O]方式读取文件中的O值,这时F是有副作用的 

>>> Process[I,O],I代表从文件中读取的原始数据,O代表经过筛选、处理产生的输出数据

>>> O => F[Unit]是一个不返回结果的函数,代表对输入的O类型数据进行F运算,如把O类型数据存写入一个文件

/>> I => F[O]是个返回结果的函数,对输入I进行F运算后返回O,如把一条记录写入数据库后返回写入状态

以上流程简单描述:从文件中读出数据->加工处理读出数据->写入另一个文件。虽然从描述上看起来很简单,但我们的目的是资源安全使用:无论在任何终止情况下:正常读写、中途强行停止、出错终止,scalaz-stream都会主动关闭开启的文件、停止使用的线程、释放占用的内存等其它资源。这样看来到不是那么简单了。我们先试着分析Source/Transducer/Sink这几种类型的作用:

1 import Process._

2 emit(0) //> res0: scalaz.stream.Process0[Int] = Emit(Vector(0))

3 emitAll(Seq(1,2,3)) //> res1: scalaz.stream.Process0[Int] = Emit(List(1, 2, 3))

4 Process(1,2,3) //> res2: scalaz.stream.Process0[Int] = Emit(WrappedArray(1, 2, 3))

5 Process() //> res3: scalaz.stream.Process0[Nothing] = Emit(List())

以上都是Process0的构建方式,也算是数据源。但它们只是代表了内存中的一串值,对我们来说没什么意义,因为我们希望从外设获取这些值,比如从文件或者数据库里读取数据,也就是说需要F运算效果。Process0[O] >>> Process[Nothing,O],而我们需要的是Process[F,O]。那么我们这样写如何呢?

1 val p: Process[Task,Int] = emitAll(Seq(1,2,3))

2 //> p : scalaz.stream.Process[scalaz.concurrent.Task,Int] = Emit(List(1, 2, 3))

3

4 emitAll(Seq(1,2,3)).toSource

5 //> res4: scalaz.stream.Process[scalaz.concurrent.Task,Int] = Emit(List(1, 2, 3))

6

类型倒是匹配了,但表达式Emit(...)里没有任何Task的影子,这个无法满足我们对Source的需要。看来只有以下这种方式了:

1 await(Task.delay{3})(emit)

2 //> res5: scalaz.stream.Process[scalaz.concurrent.Task,Int] = Await([email protected],<function1>,<function1>)

3 eval(Task.delay{3})

4 //> res6: scalaz.stream.Process[scalaz.concurrent.Task,Int] = Await([email protected],<function1>,<function1>)

现在不但类型匹配,而且表达式里还包含了Task运算。我们通过Task.delay可以进行文件读取等带有副作用的运算,这是因为Await将会运行req:F[E] >>> Task[Int]。这正是我们需要的Source。那我们能不能用这个Source来发出一串数据呢?

 1 def emitSeq[A](xa: Seq[A]):Process[Task,A] =

2 xa match {

3 case h :: t => await(Task.delay {h})(emit) ++ emitSeq(t)

4 case Nil => halt

5 } //> emitSeq: [A](xa: Seq[A])scalaz.stream.Process[scalaz.concurrent.Task,A]

6 val es1 = emitSeq(Seq(1,2,3)) //> es1 : scalaz.stream.Process[scalaz.concurrent.Task,Int] = Append(Await([email protected],<function1>,<function1>),Vector(<function1>))

7 val es2 = emitSeq(Seq("a","b","c")) //> es2 : scalaz.stream.Process[scalaz.concurrent.Task,String] = Append(Await(

8 [email protected],<function1>,<function1>),Vector(<function1>))

9 es1.runLog.run //> res7: Vector[Int] = Vector(1, 2, 3)

10 es2.runLog.run //> res8: Vector[String] = Vector(a, b, c)

以上示范中我们用await运算了Task,然后返回了Process[Task,?],一个可能带副作用运算的Source。实际上我们在很多情况下都需要从外部的源头用Task来获取一些数据,通常这些数据源都对数据获取进行了异步(asynchronous)运算处理,然后通过callback方式来提供这些数据。我们可以用Task.async函数来把这些callback函数转变成Task,下一步我们只需要用Process.eval或者await就可以把这个Task升格成Process[Task,?]。我们先看个简单的例子:假如我们用scala.concurrent.Future来进行异步数据读取,可以这样把Future转换成Process:

 1 def getData(dbName: String): Task[String] = Task.async { cb =>

2 import scala.concurrent._

3 import scala.concurrent.ExecutionContext.Implicits.global

4 import scala.util.{Success,Failure}

5 Future { s"got data from $dbName" }.onComplete {

6 case Success(a) => cb(a.right)

7 case Failure(e) => cb(e.left)

8 }

9 } //> getData: (dbName: String)scalaz.concurrent.Task[String]

10 val procGetData = eval(getData("MySQL")) //> procGetData : scalaz.stream.Process[scalaz.concurrent.Task,String] = Await([email protected],<function1>,<function1>)

11 procGetData.runLog.run //> res9: Vector[String] = Vector(got data from MySQL)

我们也可以把java的callback转变成Task:

 1 import com.ning.http.client._

2 val asyncHttpClient = new AsyncHttpClient() //> asyncHttpClient : com.ning.http.client.AsyncHttpClient = [email protected]

3 def get(s: String): Task[Response] = Task.async[Response] { callback =>

4 asyncHttpClient.prepareGet(s).execute(

5 new AsyncCompletionHandler[Unit] {

6 def onCompleted(r: Response): Unit = callback(r.right)

7

8 def onError(e: Throwable): Unit = callback(e.left)

9 }

10 )

11 } //> get: (s: String)scalaz.concurrent.Task[com.ning.http.client.Response]

12 val prcGet = Process.eval(get("http://sina.com"))

13 //> prcGet : scalaz.stream.Process[scalaz.concurrent.Task,com.ning.http.client.Response] = Await([email protected],<function1>,<function1>)

14 prcGet.run.run //> 12:25:27.852 [New I/O worker #1] DEBUG c.n.h.c.p.n.r.NettyConnectListener -Request using non cached Channel '[id: 0x23fa1307, /192.168.200.3:50569 =>sina.com/66.102.251.33:80]':

如果直接按照scalaz Task callback的类型款式 def async(callback:(Throwable \/ Unit) => Unit):

 1 def read(callback: (Throwable \/ Array[Byte]) => Unit): Unit = ???

2 //> read: (callback: scalaz.\/[Throwable,Array[Byte]] => Unit)Unit

3 val t: Task[Array[Byte]] = Task.async(read) //> t : scalaz.concurrent.Task[Array[Byte]] = [email protected]

4 val t2: Task[Array[Byte]] = for {

5 bytes <- t

6 moarBytes <- t

7 } yield (bytes ++ moarBytes) //> t2 : scalaz.concurrent.Task[Array[Byte]] = [email protected]

8 val prct2 = Process.eval(t2) //> prct2 : scalaz.stream.Process[scalaz.concurrent.Task,Array[Byte]] = Await([email protected],<function1>,<function1>)

9

10 def asyncRead(succ: Array[Byte] => Unit, fail: Throwable => Unit): Unit = ???

11 //> asyncRead: (succ: Array[Byte] => Unit, fail: Throwable => Unit)Unit

12 val t3: Task[Array[Byte]] = Task.async { callback =>

13 asyncRead(b => callback(b.right), err => callback(err.left))

14 } //> t3 : scalaz.concurrent.Task[Array[Byte]] = [email protected]

15 val t4: Task[Array[Byte]] = t3.flatMap(b => Task(b))

16 //> t4 : scalaz.concurrent.Task[Array[Byte]] = [email protected]

17 val prct4 = Process.eval(t4) //> prct4 : scalaz.stream.Process[scalaz.concurrent.Task,Array[Byte]] = Await([email protected],<function1>,<function1>)

我们也可以用timer来产生Process[Task,A]:

1 import scala.concurrent.duration._

2 implicit val scheduler = java.util.concurrent.Executors.newScheduledThreadPool(3)

3 //> scheduler : java.util.concurrent.ScheduledExecutorService = [email protected][Running, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 0]

4 val fizz = time.awakeEvery(3.seconds).map(_ => "fizz")

5 //> fizz : scalaz.stream.Process[scalaz.concurrent.Task,String] = Await([email protected],<function1>,<function1>)

6 val fizz3 = fizz.take(3) //> fizz3 : scalaz.stream.Process[scalaz.concurrent.Task,String] = Append(Halt(End),Vector(<function1>))

7 fizz3.runLog.run //> res9: Vector[String] = Vector(fizz, fizz, fizz)

Queue、Top和Signal都可以作为带副作用数据源的构建器。我们先看看Queue是如何产生数据源的:

 1 type BigStringResult = String

2 val qJobResult = async.unboundedQueue[BigStringResult]

3 //> qJobResult : scalaz.stream.async.mutable.Queue[demo.ws.blogStream.BigStringResult] = [email protected]

4 def longGet(jobnum: Int): BigStringResult = {

5 Thread.sleep(2000)

6 s"Some large data sets from job#${jobnum}"

7 } //> longGet: (jobnum: Int)demo.ws.blogStream.BigStringResult

8

9 // multi-tasking

10 val start = System.currentTimeMillis() //> start : Long = 1468556250797

11 Task.fork(qJobResult.enqueueOne(longGet(1))).unsafePerformAsync{case _ => ()}

12 Task.fork(qJobResult.enqueueOne(longGet(2))).unsafePerformAsync{case _ => ()}

13 Task.fork(qJobResult.enqueueOne(longGet(3))).unsafePerformAsync{case _ => ()}

14 val timemill = System.currentTimeMillis() - start

15 //> timemill : Long = 17

16 Thread.sleep(3000)

17 qJobResult.close.run

18 val bigData = {

19 //multi-tasking

20 val j1 = qJobResult.dequeue

21 val j2 = qJobResult.dequeue

22 val j3 = qJobResult.dequeue

23 for {

24 r1 <- j1

25 r2 <- j2

26 r3 <- j3

27 } yield r1 + ","+ r2 + "," + r3

28 } //> bigData : scalaz.stream.Process[[x]scalaz.concurrent.Task[x],String] = Await([email protected],<function1>,<function1>)

29

30 bigData.runLog.run //> res9: Vector[String] = Vector(Some large data sets from job#2,Some large data sets from job#3,Some large data sets from job#1)

再看看Topic示范:

 1 import scala.concurrent._

2 import scala.concurrent.duration._

3 import scalaz.stream.async.mutable._

4 import scala.concurrent.ExecutionContext.Implicits.global

5 val sharedData: Topic[BigStringResult] = async.topic()

6 //> sharedData : scalaz.stream.async.mutable.Topic[demo.ws.blogStream.BigStringResult] = [email protected]

7 val subscriber = sharedData.subscribe.runLog //> subscriber : scalaz.concurrent.Task[Vector[demo.ws.blogStream.BigStringResult]] = [email protected]

8 val otherThread = future {

9 subscriber.run // Added this here - now subscriber is really attached to the topic

10 } //> otherThread : scala.concurrent.Future[Vector[demo.ws.blogStream.BigStringResult]] = List()

11 // Need to give subscriber some time to start up.

12 // I doubt you'd do this in actual code.

13

14 // topics seem more useful for hooking up things like

15 // sensors that produce a continual stream of data,

16

17 // and where individual values can be dropped on

18 // floor.

19 Thread.sleep(100)

20

21 sharedData.publishOne(longGet(1)).run // don't just call publishOne; need to run the resulting task

22 sharedData.close.run // Don't just call close; need to run the resulting task

23

24 // Need to wait for the output

25 val result = Await.result(otherThread, Duration.Inf)

26 //> result : Vector[demo.ws.blogStream.BigStringResult] = Vector(Some large data sets from job#1)

以上对可能带有副作用的Source的各种产生方法提供了解释和示范。scalaz-stream的其他类型节点将在下面的讨论中深入介绍。

 

 

 

 

 

 

 

 

 

 

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