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：
trait Process[+F[_],+O] case object Cause case class Emit[O](out: O) extends Process[Nothing, O] case class Halt(cause: Cause) extends Process[Nothing,Nothing] case class Await[+F[_],E,+O]( req: F[E], rcv: E => Process[F,O], preempt: E => Process[F,Nothing] = Halt) extends Process[F,O] case class Append[+F[_],+O]( head: Process[F,O], 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：
import Process._ emit(0) //> res0: scalaz.stream.Process0[Int] = Emit(Vector(0)) emitAll(Seq(1,2,3)) //> res1: scalaz.stream.Process0[Int] = Emit(List(1, 2, 3)) Process(1,2,3) //> res2: scalaz.stream.Process0[Int] = Emit(WrappedArray(1, 2, 3)) 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？
val p: Process[Task,Int] = emitAll(Seq(1,2,3)) //> p : scalaz.stream.Process[scalaz.concurrent.Task,Int] = Emit(List(1, 2, 3)) emitAll(Seq(1,2,3)).toSource //> res4: scalaz.stream.Process[scalaz.concurrent.Task,Int] = Emit(List(1, 2, 3))
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：
await(Task.delay{3})(emit)
//> res5: scalaz.stream.Process[scalaz.concurrent.Task,Int] = Await([email protected],<function1>,<function1>)
eval(Task.delay{3})
//> 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？
def emitSeq[A](xa: Seq[A]):Process[Task,A] =
xa match {
case h :: t => await(Task.delay {h})(emit) ++ emitSeq(t)
case Nil => halt
} //> emitSeq: [A](xa: Seq[A])scalaz.stream.Process[scalaz.concurrent.Task,A]
val es1 = emitSeq(Seq(1,2,3)) //> es1 : scalaz.stream.Process[scalaz.concurrent.Task,Int] = Append(Await([email protected],<function1>,<function1>),Vector(<function1>))
val es2 = emitSeq(Seq("a","b","c")) //> es2 : scalaz.stream.Process[scalaz.concurrent.Task,String] = Append(Await(
[email protected],<function1>,<function1>),Vector(<function1>))
es1.runLog.run //> res7: Vector[Int] = Vector(1, 2, 3)
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：
def getData(dbName: String): Task[String] = Task.async { cb =>
import scala.concurrent._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.util.{Success,Failure}
Future { s"got data from $dbName" }.onComplete {
case Success(a) => cb(a.right)
case Failure(e) => cb(e.left)
}
} //> getData: (dbName: String)scalaz.concurrent.Task[String]
val procGetData = eval(getData("MySQL")) //> procGetData : scalaz.stream.Process[scalaz.concurrent.Task,String] = Await([email protected],<function1>,<function1>)
procGetData.runLog.run //> res9: Vector[String] = Vector(got data from MySQL)
我们也可以把java的callback转变成Task：
import com.ning.http.client._
val asyncHttpClient = new AsyncHttpClient() //> asyncHttpClient : com.ning.http.client.AsyncHttpClient = [email protected]
def get(s: String): Task[Response] = Task.async[Response] { callback =>
asyncHttpClient.prepareGet(s).execute(
new AsyncCompletionHandler[Unit] {
def onCompleted(r: Response): Unit = callback(r.right)
def onError(e: Throwable): Unit = callback(e.left)
}
)
} //> get: (s: String)scalaz.concurrent.Task[com.ning.http.client.Response]
val prcGet = Process.eval(get("http://sina.com"))
//> prcGet : scalaz.stream.Process[scalaz.concurrent.Task,com.ning.http.client.Response] = Await([email protected],<function1>,<function1>)
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)：
def read(callback: (Throwable \/ Array[Byte]) => Unit): Unit = ???
//> read: (callback: scalaz.\/[Throwable,Array[Byte]] => Unit)Unit
val t: Task[Array[Byte]] = Task.async(read) //> t : scalaz.concurrent.Task[Array[Byte]] = [email protected]
val t2: Task[Array[Byte]] = for {
bytes <- t
moarBytes <- t
} yield (bytes ++ moarBytes) //> t2 : scalaz.concurrent.Task[Array[Byte]] = [email protected]
val prct2 = Process.eval(t2) //> prct2 : scalaz.stream.Process[scalaz.concurrent.Task,Array[Byte]] = Await([email protected],<function1>,<function1>)
def asyncRead(succ: Array[Byte] => Unit, fail: Throwable => Unit): Unit = ???
//> asyncRead: (succ: Array[Byte] => Unit, fail: Throwable => Unit)Unit
val t3: Task[Array[Byte]] = Task.async { callback =>
asyncRead(b => callback(b.right), err => callback(err.left))
} //> t3 : scalaz.concurrent.Task[Array[Byte]] = [email protected]
val t4: Task[Array[Byte]] = t3.flatMap(b => Task(b))
//> t4 : scalaz.concurrent.Task[Array[Byte]] = [email protected]
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]：
import scala.concurrent.duration._
implicit val scheduler = java.util.concurrent.Executors.newScheduledThreadPool(3)
//> scheduler : java.util.concurrent.ScheduledExecutorService = [email protected][Running, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 0]
val fizz = time.awakeEvery(3.seconds).map(_ => "fizz")
//> fizz : scalaz.stream.Process[scalaz.concurrent.Task,String] = Await([email protected],<function1>,<function1>)
val fizz3 = fizz.take(3) //> fizz3 : scalaz.stream.Process[scalaz.concurrent.Task,String] = Append(Halt(End),Vector(<function1>))
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：
type BigStringResult = String
val qJobResult = async.unboundedQueue[BigStringResult]
//> qJobResult : scalaz.stream.async.mutable.Queue[demo.ws.blogStream.BigStringResult] = [email protected]
def longGet(jobnum: Int): BigStringResult = {
Thread.sleep(2000)
s"Some large data sets from job#${jobnum}"
} //> longGet: (jobnum: Int)demo.ws.blogStream.BigStringResult
// multi-tasking
val start = System.currentTimeMillis() //> start : Long = 1468556250797
Task.fork(qJobResult.enqueueOne(longGet(1))).unsafePerformAsync{case _ => ()}
Task.fork(qJobResult.enqueueOne(longGet(2))).unsafePerformAsync{case _ => ()}
Task.fork(qJobResult.enqueueOne(longGet(3))).unsafePerformAsync{case _ => ()}
val timemill = System.currentTimeMillis() - start
//> timemill : Long = 17
Thread.sleep(3000)
qJobResult.close.run
val bigData = {
//multi-tasking
val j1 = qJobResult.dequeue
val j2 = qJobResult.dequeue
val j3 = qJobResult.dequeue
for {
r1 <- j1
r2 <- j2
r3 <- j3
} yield r1 + ","+ r2 + "," + r3
} //> bigData : scalaz.stream.Process[[x]scalaz.concurrent.Task[x],String] = Await([email protected],<function1>,<function1>)
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：
import scala.concurrent._
import scala.concurrent.duration._
import scalaz.stream.async.mutable._
import scala.concurrent.ExecutionContext.Implicits.global
val sharedData: Topic[BigStringResult] = async.topic()
//> sharedData : scalaz.stream.async.mutable.Topic[demo.ws.blogStream.BigStringResult] = [email protected]
val subscriber = sharedData.subscribe.runLog //> subscriber : scalaz.concurrent.Task[Vector[demo.ws.blogStream.BigStringResult]] = [email protected]
val otherThread = future {
subscriber.run // Added this here - now subscriber is really attached to the topic
} //> otherThread : scala.concurrent.Future[Vector[demo.ws.blogStream.BigStringResult]] = List()
// Need to give subscriber some time to start up.
// I doubt you'd do this in actual code.
// topics seem more useful for hooking up things like
// sensors that produce a continual stream of data,
// and where individual values can be dropped on
// floor.
Thread.sleep(100)
sharedData.publishOne(longGet(1)).run // don't just call publishOne; need to run the resulting task
sharedData.close.run // Don't just call close; need to run the resulting task
// Need to wait for the output
val result = Await.result(otherThread, Duration.Inf)
//> 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。