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  • Scalaz(59)- scalaz-stream: fs2-程序并行运算,fs2 running effects in parallel

        scalaz-stream-fs2是一种函数式的数据流编程工具。fs2的类型款式是:Stream[F[_],O],F[_]代表一种运算模式,O代表Stream数据元素的类型。实际上F就是一种延迟运算机制:F中间包含的类型如F[A]的A是一个可能会产生副作用不纯代码(impure code)的运算结果类型,我们必须用F对A运算的延迟机制才能实现编程过程中的函数组合(compositionality),这是函数式编程的标准做法。如果为一个Stream装备了F[A],就代表这个Stream会在处理数据元素O的过程中对O施用运算A,如果这个运算A会与外界交互(interact with outside world)如:文件、数据库、网络等的读写操作,那么这个Stream有数据元素I/O功能的需求。我们可以通过fs2 Stream的状态机器特性(state machine)及F[A]与外界交互功能来编写完整的数据处理(data processing)程序。如果能够在数据库程序编程中善用fs2的多线程运算模式来实现对数据库存取的并行运算,将会大大提高数据处理的效率。我们将在本篇着重讨论fs2在实现I/O程序中的有关方式方法。

    首先,我们需要以整体Stream为程序运算框架,把与外界交互的运算A串联起来,然后通过Stream的节点来代表程序状态。我们首先需要某种方式把F[A]与Stream[F,A]关联起来,也就是我们所说的把一个F[A]升格成Stream[F,A]。fs2提供了Stream.eval函数,我们看看它的类型款式:

    def eval[F[_], A](fa: F[A]): Stream[F, A] = attemptEval(fa) flatMap { _ fold(fail, emit) }

    很明显,提供一个F[A],eval返回Stream[F,A]。这个返回结果Stream[F,A]的元素A是通过运算F[A]获取的:在一个数据库程序应用场景里这个A可能是个数据库连接(connection),那么F[A]就是一个连接数据库的操作函数,返回的A是个连接connection。这次我们来模拟一个对数据库表进行新纪录存储的场景。一般来说我们会按以下几个固定步骤进行:

    1、连接数据库,获取connection连接

    2、产生新数据(在其它场景里可能是读取数据然后更新)。这可能是一个循环的操作

    3、将数据写入数据库

    这三个步骤可以用Stream的三种状态来表示:一个源头(source)、传转(pipe transducer)、终点(sink)。

    我们先示范如何构建源头:这是一种占用资源的操作,会产生副作用,所以我们必须用延迟运算方式来编程:

    1 //用Map模拟数据库表
    2 import scala.collection.mutable.Map
    3 type DataStore = Map[Long, String]
    4 val dataStore: DataStore = Map()       //> dataStore  : fs2Eval.DataStore = Map()
    5 case class Connection(id: String, store: DataStore)
    6 def src(producer: String): Stream[Task,Connection] =
    7   Stream.eval(Task.delay { Connection(producer,dataStore)})
    8//> src: (producer: String)fs2.Stream[fs2.Task,fs2Eval.Connection]

    这个示范用了一个mutable map类型来模拟会产生副作用的数据库表。我们把具体产生数据的源头用Connection.id传下去便于在并行运算示范里进行跟踪。在这个环节里我们模拟了连接数据库dataStore操作。

    产生数据是在内存里进行的,不会使用到connection,但我们依然需要把这个connection传递到下个环节:

    1 case class Row(conn: Connection, key: Long, value: String)
    2 val recId = new java.util.concurrent.atomic.AtomicLong(1)
    3//> recId  : java.util.concurrent.atomic.AtomicLong = 1
    4 def createData(conn: Connection): Row =
    5    Row(conn, recId.incrementAndGet, s"Producer $conn.id: at ${System.currentTimeMillis}")
    6//> createData: (conn: fs2Eval.Connection)fs2Eval.Row
    7 val trans: Pipe[Task,Connection,Row] = _.map {conn => createData(conn)}
    8//> trans  : fs2.Pipe[fs2.Task,fs2Eval.Connection,fs2Eval.Row] = <function1>

    trans是个Pipe。我们可以用through把它连接到src。

    向数据库读写都会产生副作用。下一个环节我们模拟把trans传递过来的Row写入数据库。这里我们需要用延迟运算机制:

    1 def log: Pipe[Task, Row, Row] = _.evalMap { r =>
    2  Task.delay {println(s"saving row pid:${r.conn.id}, rid:${r.key}"); r}}
    3 def saveRow(row: Row) = row.conn.store += (row.key -> row.value)
    4 
    5 val snk: Sink[Task,Row] = _.evalMap { r =>
    6   Task.delay { saveRow(r); () } }

    增加了个跟踪函数log。从上面的代码可以看出:实际上Sink就是Pipe,只不过返回了()。

    我们试试把这几个步骤连接起来运算一下:

    1 val sprg = src("001").through(trans).repeat.take(3).through(log).to(snk)
    2//> sprg  : fs2.Stream[fs2.Task,Unit] = evalScope(Scope(Bind(Eval(Snapshot),<function1>))).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>)
    3 sprg.run.unsafeRun                                //> saving row pid:001, rid:2
    4                                                   //| saving row pid:001, rid:3
    5                                                   //| saving row pid:001, rid:4
    6 println(dataStore)       //> Map(2 -> Connection(001,Map()).id: at 1472605736214, 4 -> Connection(001,Map(2 -> Connection(001,Map()).id: at 1472605736214, 3 -> Connection(001,Map(2 -> Connection(001,Map()).id: at 1472605736214)).id: at 1472605736245)).id : at 1472605736248, 3 -> Connection(001,Map(2 -> Connection(001,Map()).id:  at 1472605736214)).id: at 1472605736245)

    我们看到mutable map dataStore内容有变化了。

    如果我们把以上的例子用并行运算方式来实现的话,应该如何调整?为方便观察结果,我们先在几个环节增加一些时间延迟:

    1 implicit val strategy = Strategy.fromFixedDaemonPool(4)
    2 implicit val scheduler = Scheduler.fromFixedDaemonPool(2)
    3 def src(producer: String): Stream[Task,Connection] =
    4   Stream.eval(Task.delay { Connection(producer,dataStore)}
    5   .schedule(3.seconds))
    6 
    7 val trans: Pipe[Task,Connection,Row] = _.evalMap {conn => 
    8  Task.delay{createData(conn)}.schedule(1.second)}

    下面我们把一些类型调整成Stream[Task,Stream[Row]],然后把concurrent.join函数掺进去:

     1 val srcs = concurrent.join(3)(Stream(src("001"),src("002"),src("003"),src("004")))
     2     //> srcs  : fs2.Stream[fs2.Task,fs2Eval.Connection] = attemptEval(Task).flatMap
     3 <function1>).flatMap(<function1>)
     4 val recs: Pipe[Task,Connection,Row] = src => {
     5     concurrent.join(4)(src.map { conn =>
     6       Stream.repeatEval(Task {createData(conn)}.schedule(1.second)) })
     7   }        //> recs  : fs2.Pipe[fs2.Task,fs2Eval.Connection,fs2Eval.Row] = <function1>
     8   
     9 def saveRows(row: Row) = { row.conn.store += (row.key -> row.value); row}
    10//> saveRows: (row: fs2Eval.Row)fs2Eval.Row
    11 val snks: Pipe[Task,Row,Row] = rs => {
    12     concurrent.join(4)(rs.map { r =>
    13       Stream.eval(Task {saveRows(r)}.schedule(1.second)) })
    14   }                                      //> snks  : fs2.Pipe[fs2.Task,fs2Eval.Row,fs2Eval.Row] = <function1>

    我们试着把它们连接起来进行运算:

     1 val par = srcs.through(recs).take(10).through(log("before")).through(chnn).through(log("after"))
     2//> par  : fs2.Stream[fs2.Task,fs2Eval.Row] = attemptEval(Task).flatMap(<function1>).flatMap(<function1>).flatMap(<function1>)
     3 par.run.unsafeRun                                 //> before saving pid:001, rid:3
     4                                                   //| before saving pid:003, rid:2
     5                                                   //| before saving pid:002, rid:4
     6                                                   //| before saving pid:001, rid:5
     7                                                   //| after saving pid:001, rid:3
     8                                                   //| after saving pid:003, rid:2
     9                                                   //| before saving pid:003, rid:6
    10                                                   //| after saving pid:002, rid:4
    11                                                   //| before saving pid:002, rid:7
    12                                                   //| after saving pid:001, rid:5
    13                                                   //| before saving pid:001, rid:8
    14                                                   //| before saving pid:003, rid:9
    15                                                   //| after saving pid:003, rid:6
    16                                                   //| after saving pid:002, rid:7
    17                                                   //| before saving pid:002, rid:10
    18                                                   //| before saving pid:004, rid:11
    19                                                   //| after saving pid:001, rid:8
    20                                                   //| after saving pid:003, rid:9
    21                                                   //| after saving pid:002, rid:10
    22                                                   //| after saving pid:004, rid:11

    从跟踪函数显示可以看出before,after是交叉发生的,这就代表已经实现了并行运算。

    下面是本篇示范源代码:

     1 import fs2._
     2 import scala.concurrent.duration._
     3 object fs2Eval {
     4 
     5 //用Map模拟数据库表
     6 import scala.collection.mutable.Map
     7 type DataStore = Map[Long, String]
     8 val dataStore: DataStore = Map()
     9 case class Connection(id: String, store: DataStore)
    10 implicit val strategy = Strategy.fromFixedDaemonPool(4)
    11 implicit val scheduler = Scheduler.fromFixedDaemonPool(2)
    12 def src(producer: String): Stream[Task,Connection] =
    13   Stream.eval(Task.delay { Connection(producer,dataStore)}
    14   .schedule(3.seconds))
    15 case class Row(conn: Connection, key: Long, value: String)
    16 val recId = new java.util.concurrent.atomic.AtomicLong(1)
    17 def createData(conn: Connection): Row =
    18    Row(conn, recId.incrementAndGet, s"$conn.id: at ${System.currentTimeMillis}")
    19 val trans: Pipe[Task,Connection,Row] = _.evalMap {conn =>
    20  Task.delay{createData(conn)}.schedule(1.second)}
    21 
    22 def log(pfx: String): Pipe[Task, Row, Row] = _.evalMap { r =>
    23  Task.delay {println(s"$pfx saving pid:${r.conn.id}, rid:${r.key}"); r}}
    24 def saveRow(row: Row) = row.conn.store += (row.key -> row.value)
    25 
    26 val snk: Sink[Task,Row] = _.evalMap { r =>
    27   Task.delay { saveRow(r); () } }
    28 
    29 val sprg = src("001").through(trans).repeat.take(3).through(log("")).to(snk)
    30 //sprg.run.unsafeRun
    31 //println(dataStore)
    32 
    33 val srcs = concurrent.join(3)(Stream(src("001"),src("002"),src("003"),src("004")))
    34 val recs: Pipe[Task,Connection,Row] = src => {
    35     concurrent.join(4)(src.map { conn =>
    36       Stream.repeatEval(Task {createData(conn)}.schedule(1.second)) })
    37   }
    38   
    39 def saveRows(row: Row) = { row.conn.store += (row.key -> row.value); row}
    40 val chnn: Pipe[Task,Row,Row] = rs => {
    41     concurrent.join(4)(rs.map { r =>
    42       Stream.eval(Task {saveRows(r)}.schedule(1.second)) })
    43   }
    44 
    45 
    46 val par = srcs.through(recs).repeat.take(10).through(log("before")).through(chnn).through(log("after"))
    47 par.run.unsafeRun
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  • 原文地址:https://www.cnblogs.com/tiger-xc/p/5825241.html
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