FunDA（9）－ Stream Source：reactive data streams

    上篇我们讨论了静态数据源（Static Source, snapshot）。这种方式只能在预知数据规模有限的情况下使用，对于超大型的数据库表也可以说是不安全的资源使用方式。Slick3.x已经增加了支持Reactive-Streams功能，可以通过Reactive-Streams API来实现有限内存空间内的无限规模数据读取，这正符合了FunDA的设计理念：高效、便捷、安全的后台数据处理工具库。我们在前面几篇讨论里介绍了Iteratee模式，play-iteratees支持Reactive-Streams并且提供与Slick3.x的接口API，我们就在这篇讨论里介绍如何把Slick-Reactive-Streams转换成fs2-Streams。根据Slick官方文档：Slick可以通过db.stream函数用Reactive-Stream方式来读取后台数据，具体的配置如下：

  val disableAutocommit = SimpleDBIO(_.connection.setAutoCommit(false))  val action = queryAction.withStatementParameters(fetchSize = 512)  val publisher = db.stream(disableAutocommit andThen action)

首先，我们需要取消自动提交（disableAutocommit）。fetchSize是缓存数据页长度（每批次读取数据字数），然后用db.stream来构成一个Reactive-Streams标准的数据源publisher。Slick官方网页只提供了下面这个使用publisher的例子：

  val fut = publisher.foreach(s => println(s))  Await.ready(fut,Duration.Inf)

除了数据枚举外就没什么用处，也无法提供更细节点的示范。FunDA的具体解决方案是把publisher转换成play-iteratee的Enumerator。play-iteratee支持Reactive-Streams，所以这个Enumerator应该具备协调后台数据和内存缓冲之间关系（back-pressure）的功能。play-iteratee是如下构建Enumerator的；

import play.api.libs.iteratee._val enumerator = streams.IterateeStreams.publisherToEnumerator(publisher)

enumerator从后台数据库表中产生的数据源通过Iteratee把数据元素enqueue推送给一个fs2的queue：

    private def pushData[R](q: async.mutable.Queue[Task,Option[R]]): Iteratee[R,Unit] = Cont {      case Input.EOF => {        q.enqueue1(None).unsafeRun        Done((), Input.Empty)      }      case Input.Empty => pushData(q)      case Input.El(e) => {        q.enqueue1(Some(e)).unsafeRun        pushData(q)      }    }

然后fs2进行dequeue后生成fs2的Stream：

      Stream.eval(async.boundedQueue[Task,Option[SOURCE]](queSize)).flatMap { q =>        Task { Iteratee.flatten(enumerator |>> pushData(q)).run }.unsafeRunAsyncFuture()        pipe.unNoneTerminate(q.dequeue)      }

整个构建Stream的过程在FunDA的fdasources包是这样定义的：

package com.bayakala.funda.fdasourcesimport fs2._import play.api.libs.iteratee._import com.bayakala.funda.fdapipes._import slick.driver.JdbcProfileobject FDADataStream {  class FDAStreamLoader[SOURCE, TARGET](slickProfile: JdbcProfile, convert: SOURCE => TARGET) {    import slickProfile.api._    def fda_typedStream(action: DBIOAction[Iterable[SOURCE],Streaming[SOURCE],Effect.Read])(slickDB: Database)(fetchSize: Int, queSize: Int): FDAPipeLine[TARGET] = {      val disableAutocommit = SimpleDBIO(_.connection.setAutoCommit(false))      val action_ = action.withStatementParameters(fetchSize = fetchSize)      val publisher = slickDB.stream(disableAutocommit andThen action)      val enumerator = streams.IterateeStreams.publisherToEnumerator(publisher)      Stream.eval(async.boundedQueue[Task,Option[SOURCE]](queSize)).flatMap { q =>        Task { Iteratee.flatten(enumerator |>> pushData(q)).run }.unsafeRunAsyncFuture()        pipe.unNoneTerminate(q.dequeue).map {row => convert(row)}      }    }    def fda_plainStream(action: DBIOAction[Iterable[SOURCE],Streaming[SOURCE],Effect.Read])(slickDB: Database)(fetchSize: Int, queSize: Int): FDAPipeLine[SOURCE] = {      val disableAutocommit = SimpleDBIO(_.connection.setAutoCommit(false))      val action_ = action.withStatementParameters(fetchSize = fetchSize)      val publisher = slickDB.stream(disableAutocommit andThen action)      val enumerator = streams.IterateeStreams.publisherToEnumerator(publisher)      Stream.eval(async.boundedQueue[Task,Option[SOURCE]](queSize)).flatMap { q =>        Task { Iteratee.flatten(enumerator |>> pushData(q)).run }.unsafeRunAsyncFuture()        pipe.unNoneTerminate(q.dequeue)      }    }    private def pushData[R](q: async.mutable.Queue[Task,Option[R]]): Iteratee[R,Unit] = Cont {      case Input.EOF => {        q.enqueue1(None).unsafeRun        Done((), Input.Empty)      }      case Input.Empty => pushData(q)      case Input.El(e) => {        q.enqueue1(Some(e)).unsafeRun        pushData(q)      }    }  }  object FDAStreamLoader {    def apply[SOURCE, TARGET](slickProfile: JdbcProfile, converter: SOURCE => TARGET): FDAStreamLoader[SOURCE, TARGET] =      new FDAStreamLoader[SOURCE, TARGET](slickProfile, converter)  }}

FDADataStream对象内主要实现了fda_typedStream和fda_plainStream。fda_typedStream提供了SOURCE=>TARGET的转换。从Enumerator转换到Stream整个过程和原理我们在FunDA(7)里已经详细介绍过了。下面我们看看FunDA-Example中fda_typedStream的具体应用例子：

package com.bayakala.funda.fdasources.examplesimport slick.driver.H2Driver.api._import com.bayakala.funda.fdasources.FDADataStream._import com.bayakala.funda.samples._import com.bayakala.funda.fdarows._import com.bayakala.funda.fdapipes._import FDANodes._import FDAValves._object Example2 extends App {   val albums = SlickModels.albums   val companies = SlickModels.companies//数据源query   val albumsInfo = for {     (a,c) <- albums join companies on (_.company === _.id)   } yield (a.title,a.artist,a.year,c.name)//query结果强类型（用户提供）  case class Album(title: String, artist: String, year: Int, publisher: String) extends FDAROW//转换函数（用户提供）  def toTypedRow(row: (String, String, Option[Int], String)): Album =    Album(row._1, row._2, row._3.getOrElse(2000), row._4)  val db = Database.forConfig("h2db")  val streamLoader = FDAStreamLoader(slick.driver.H2Driver, toTypedRow _)  val albumStream = streamLoader.fda_typedStream(albumsInfo.result)(db)(512,128)//定义一个用户作业函数：列印数据内容  def printAlbums: FDATask[FDAROW] = row => {    row match {      case album: Album =>        println("____________________")        println(s"品名：${album.title}")        println(s"演唱：${album.artist}")        println(s"年份：${album.year}")        println(s"发行：${album.publisher}")        fda_next(album)      case _ => fda_skip    }  }  albumStream.through(fda_execUserTask(printAlbums)).run.unsafeRun}

运算结果：

品名：Keyboard Cat's Greatest Hits演唱：Keyboard Cat年份：1999发行：Sony Music Inc____________________品名：Spice演唱：Spice Girls年份：1999发行：Columbia Records____________________品名：Whenever You Need Somebody演唱：Rick Astley年份：1999发行：Sony Music Inc____________________品名：The Triumph of Steel演唱：Manowar年份：1999发行：The K-Pops Singers____________________品名：Believe演唱：Justin Bieber年份：1999发行：Columbia RecordsProcess finished with exit code 0