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.fdasources import fs2._ import play.api.libs.iteratee._ import com.bayakala.funda.fdapipes._ import slick.driver.JdbcProfile object 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.examples import 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 Records Process finished with exit code 0