Scala 嵌套方法的成本_Scala_Methods

Scala 嵌套方法的成本

scala methods

Scala 嵌套方法的成本,scala,methods,Scala,Methods,在Scala中，可以在其他方法中定义方法。这将其使用范围限制在定义块内部。我使用它们来提高使用几个高阶函数的代码的可读性。与匿名函数文字不同，这允许我在传递它们之前为它们指定有意义的名称例如： class AggregatedPerson extends HashSet[PersonRecord] { def mostFrequentName: String = { type NameCount = (String, Int) def moreFirst(a: NameCo

在Scala中，可以在其他方法中定义方法。这将其使用范围限制在定义块内部。我使用它们来提高使用几个高阶函数的代码的可读性。与匿名函数文字不同，这允许我在传递它们之前为它们指定有意义的名称

例如：

class AggregatedPerson extends HashSet[PersonRecord] {
  def mostFrequentName: String = {
    type NameCount = (String, Int)
    def moreFirst(a: NameCount, b: NameCount) = a._2 > b._2
    def countOccurrences(nameGroup: (String, List[PersonRecord])) =
      (nameGroup._1, nameGroup._2.size) 

    iterator.toList.groupBy(_.fullName).
      map(countOccurrences).iterator.toList.
      sortWith(moreFirst).head._1
  }
}

我应该注意的嵌套方法定义是否会导致运行时开销

闭包的答案是否不同？

在编译过程中，嵌套函数是

moveFirst

，

countoccurrences

被移出到与

mostFrequentName

相同的级别。它们得到编译器合成的名称：

moveFirst$1

和

countoccurrences$1

此外，当您在没有参数列表的情况下引用其中一个方法时，它将被提升到函数中。所以

map（countoccurrences）

与编写

map（（a:（String，List[PersonRecord]）=>countoccurrences（a））是一样的。这个匿名函数被编译成一个单独的类AggregatedPerson$$anonfun$mostFrequentName$2
，它只不过是转发到countoccurrencess$

作为旁注，将方法提升为函数的过程称为Eta扩展。如果在预期函数类型的上下文中忽略参数列表（如示例中），或者使用
代替整个参数列表，或者代替每个参数（val f1=countoccurrences；val f2=countoccurrences（
）
如果代码直接在闭包中，那么堆栈中的方法调用将减少一个，生成的合成方法也将减少一个。在大多数情况下，这对性能的影响可能为零
<>我发现它是构造代码的一个非常有用的工具，并且考虑到你的例子非常地道的Scala。
另一个有用的工具是使用小块初始化val：
val a = {
   val temp1, temp2 = ...
   f(temp1, temp2)
}

您可以使用scalac-print
查看Scala代码是如何转换为JVM所需的格式的。以下是程序的输出：
[[syntax trees at end of cleanup]]// Scala source: nested-method.scala
package <empty> {

  class AggregatedPerson extends scala.collection.mutable.HashSet with ScalaObject {
    def mostFrequentName(): java.lang.String = AggregatedPerson.this.iterator().toList().groupBy({
      (new AggregatedPerson$$anonfun$mostFrequentName$1(AggregatedPerson.this): Function1)
    }).map({
      {
        (new AggregatedPerson$$anonfun$mostFrequentName$2(AggregatedPerson.this): Function1)
      }
    }, collection.this.Map.canBuildFrom()).$asInstanceOf[scala.collection.MapLike]().iterator().toList().sortWith({
      {
        (new AggregatedPerson$$anonfun$mostFrequentName$3(AggregatedPerson.this): Function2)
      }
    }).$asInstanceOf[scala.collection.IterableLike]().head().$asInstanceOf[Tuple2]()._1().$asInstanceOf[java.lang.String]();
    final def moreFirst$1(a: Tuple2, b: Tuple2): Boolean = scala.Int.unbox(a._2()).>(scala.Int.unbox(b._2()));
    final def countOccurrences$1(nameGroup: Tuple2): Tuple2 = new Tuple2(nameGroup._1(), scala.Int.box(nameGroup._2().$asInstanceOf[scala.collection.SeqLike]().size()));
    def this(): AggregatedPerson = {
      AggregatedPerson.super.this();
      ()
    }
  };

  @SerialVersionUID(0) @serializable final <synthetic> class AggregatedPerson$$anonfun$mostFrequentName$1 extends scala.runtime.AbstractFunction1 {
    final def apply(x$1: PersonRecord): java.lang.String = x$1.fullName();
    final <bridge> def apply(v1: java.lang.Object): java.lang.Object = AggregatedPerson$$anonfun$mostFrequentName$1.this.apply(v1.$asInstanceOf[PersonRecord]());
    def this($outer: AggregatedPerson): AggregatedPerson$$anonfun$mostFrequentName$1 = {
      AggregatedPerson$$anonfun$mostFrequentName$1.super.this();
      ()
    }
  };

  @SerialVersionUID(0) @serializable final <synthetic> class AggregatedPerson$$anonfun$mostFrequentName$2 extends scala.runtime.AbstractFunction1 {
    final def apply(nameGroup: Tuple2): Tuple2 = AggregatedPerson$$anonfun$mostFrequentName$2.this.$outer.countOccurrences$1(nameGroup);
    <synthetic> <paramaccessor> private[this] val $outer: AggregatedPerson = _;
    final <bridge> def apply(v1: java.lang.Object): java.lang.Object = AggregatedPerson$$anonfun$mostFrequentName$2.this.apply(v1.$asInstanceOf[Tuple2]());
    def this($outer: AggregatedPerson): AggregatedPerson$$anonfun$mostFrequentName$2 = {
      if ($outer.eq(null))
        throw new java.lang.NullPointerException()
      else
        AggregatedPerson$$anonfun$mostFrequentName$2.this.$outer = $outer;
      AggregatedPerson$$anonfun$mostFrequentName$2.super.this();
      ()
    }
  };
  @SerialVersionUID(0) @serializable final <synthetic> class AggregatedPerson$$anonfun$mostFrequentName$3 extends scala.runtime.AbstractFunction2 {
    final def apply(a: Tuple2, b: Tuple2): Boolean = AggregatedPerson$$anonfun$mostFrequentName$3.this.$outer.moreFirst$1(a, b);
    <synthetic> <paramaccessor> private[this] val $outer: AggregatedPerson = _;
    final <bridge> def apply(v1: java.lang.Object, v2: java.lang.Object): java.lang.Object = scala.Boolean.box(AggregatedPerson$$anonfun$mostFrequentName$3.this.apply(v1.$asInstanceOf[Tuple2](), v2.$asInstanceOf[Tuple2]()));
    def this($outer: AggregatedPerson): AggregatedPerson$$anonfun$mostFrequentName$3 = {
      if ($outer.eq(null))
        throw new java.lang.NullPointerException()
      else
        AggregatedPerson$$anonfun$mostFrequentName$3.this.$outer = $outer;
      AggregatedPerson$$anonfun$mostFrequentName$3.super.this();
      ()
    }
  }
}

[[清理结束时的语法树]]//Scala源代码：nested-method.Scala
包装{
类AggregatedPerson使用ScalaObject扩展scala.collection.mutable.HashSet{
def mostFrequentName（）：java.lang.String=AggregatedPerson.this.iterator（）.toList（）.groupBy({
（新的AggregatedPerson$$anonfun$mostFrequentName$1（AggregatedPerson.this）：函数1）
}).地图({
{
（新的AggregatedPerson$$anonfun$mostFrequentName$2（AggregatedPerson.this）：函数1）
}
}，collection.this.Map.canBuildFrom（））.$asInstanceOf[scala.collection.MapLike]（）.iterator（）.toList（）.sortWith({
{
（新的AggregatedPerson$$anonfun$mostFrequentName$3（AggregatedPerson.this）：函数2）
}
}).$asInstanceOf[scala.collection.IterableLike]（）.head（）.$asInstanceOf[Tuple2]（）。\u 1（）.$asInstanceOf[java.lang.String]（）；
final def moreFirst$1（a:Tuple2，b:Tuple2）：Boolean=scala.Int.unbox（a._2（））。>（scala.Int.unbox（b._2（））；
final def countoccurrents$1（nameGroup:Tuple2）：Tuple2=新的Tuple2（nameGroup.\u 1（），scala.Int.box（nameGroup.\u 2（）.$asInstanceOf[scala.collection.SeqLike]（）.size（））；
def this（）：AggregatedPerson={
AggregatedPerson.super.this（）；
()
}
};
@SerialVersionUID（0）@serializable final class AggregatedPerson$$anonfun$mostFrequentName$1扩展了scala.runtime.AbstractFunction1{
最终def apply（x$1:PersonRecord）：java.lang.String=x$1.fullName（）；
final def apply（v1:java.lang.Object）：java.lang.Object=AggregatedPerson$$anonfun$mostFrequentName$1.this.apply（v1.$asInstanceOf[PersonRecord]（））；
定义此（$outer:AggregatedPerson）：AggregatedPerson$$anonfun$mostFrequentName$1={
AggregatedPerson$$anonfun$mostFrequentName$1.super.this（）；
()
}
};
@SerialVersionUID（0）@serializable final class AggregatedPerson$$anonfun$mostFrequentName$2扩展了scala.runtime.AbstractFunction1{
最终定义应用（名称组：Tuple2）：Tuple2=AggregatedPerson$$anonfun$mostFrequentName$2.this.$outer.CountOccents$1（名称组）；
private[this]val$outer:AggregatedPerson=\uux；
final def apply（v1:java.lang.Object）：java.lang.Object=AggregatedPerson$$anonfun$mostFrequentName$2.this.apply（v1.asInstanceOf[Tuple2]（））；
定义此（$outer:AggregatedPerson）：AggregatedPerson$$anonfun$mostFrequentName$2={
如果（$outer.eq（null））
抛出新的java.lang.NullPointerException（）
其他的
AggregatedPerson$$anonfun$mostFrequentName$2。此。$outer=$outer；
AggregatedPerson$$anonfun$mostFrequentName$2.super.this（）；
()
}
};
@SerialVersionUID（0）@serializable final class AggregatedPerson$$anonfun$mostFrequentName$3扩展了scala.runtime.AbstractFunction2{
final def apply（a:Tuple2，b:Tuple2）：Boolean=AggregatedPerson$$anonfun$mostFrequentName$3.this.$outer.moreFirst$1（a，b）；
private[this]val$outer:AggregatedPerson=\uux；
final def apply（v1:java.lang.Object，v2:java.lang.Object）：java.lang.Object=scala.Boolean.box（AggregatedPerson$$anonfun$mostFrequentName$3.this.apply（v1.$asInstanceOf[Tuple2]（），v2.$asInstanceOf[Tuple2]（））；
定义此（$outer:AggregatedPerson）：AggregatedPerson$$anonfun$mostFrequentName$3={
如果（$outer.eq（null））
抛出新的java.lang.NullPointerException（）
其他的
AggregatedPerson$$anonfun$mostFrequentName$3。此。$outer=$outer；
AggregatedPerson$$anonfun$mostFrequentName$3.super.this（）；
()
}
}
}
运行时成本很低。您可以在此处观察到这一点（抱歉代码太长）：
因此，底线是：在简单的情况下，嵌套函数确实不会对您造成任何伤害——JVM会发现调用可以内联（因此raw
和internal给出相同的时间）。如果您采取更具功能性的方法，函数调用不能完全忽略，但是tim
object NestBench {
  def countRaw() = {
    var sum = 0
    var i = 0
    while (i<1000) {
      sum += i
      i += 1
      var j = 0
      while (j<1000) {
        sum += j
        j += 1
        var k = 0
        while (k<1000) {
          sum += k
          k += 1
          sum += 1
        }
      }
    }
    sum
  }
  def countClosure() = {
    var sum = 0
    var i = 0
    def sumI {
      sum += i
      i += 1
      var j = 0
      def sumJ {
        sum += j
        j += 1
        var k = 0
        def sumK {
          def sumL { sum += 1 }
          sum += k
          k += 1
          sumL
        }
        while (k<1000) sumK
      }
      while (j<1000) sumJ
    }
    while (i<1000) sumI
    sum
  }
  def countInner() = {
    var sum = 0
    def whileI = {
      def whileJ = {
        def whileK = {
          def whileL() = 1
          var ksum = 0
          var k = 0
          while (k<1000) { ksum += k; k += 1; ksum += whileL }
          ksum
        }
        var jsum = 0
        var j = 0
        while (j<1000) {
          jsum += j; j += 1
          jsum += whileK
        }
        jsum
      }
      var isum = 0
      var i = 0
      while (i<1000) {
        isum += i; i += 1
        isum += whileJ
      }
      isum
    }
    whileI
  }
  def countFunc() = {
    def summer(f: => Int)() = {
      var sum = 0
      var i = 0
      while (i<1000) {
        sum += i; i += 1
        sum += f
      }
      sum
    }
    summer( summer( summer(1) ) )()
  }
  def nsPerIteration(f:() => Int): (Int,Double) = {
    val t0 = System.nanoTime
    val result = f()
    val t1 = System.nanoTime
    (result , (t1-t0)*1e-9)
  }
  def main(args: Array[String]) {
    for (i <- 1 to 5) {
      val fns = List(countRaw _, countClosure _, countInner _, countFunc _)
      val labels = List("raw","closure","inner","func")
      val results = (fns zip labels) foreach (fl => {
        val x = nsPerIteration( fl._1 )
        printf("Method %8s produced %d; time/it = %.3f ns\n",fl._2,x._1,x._2)
      })
    }
  }
}

scala> NestBench.main(Array[String]())
Method      raw produced -1511174132; time/it = 0.422 ns
Method  closure produced -1511174132; time/it = 2.376 ns
Method    inner produced -1511174132; time/it = 0.402 ns
Method     func produced -1511174132; time/it = 0.836 ns
Method      raw produced -1511174132; time/it = 0.418 ns
Method  closure produced -1511174132; time/it = 2.410 ns
Method    inner produced -1511174132; time/it = 0.399 ns
Method     func produced -1511174132; time/it = 0.813 ns
Method      raw produced -1511174132; time/it = 0.411 ns
Method  closure produced -1511174132; time/it = 2.372 ns
Method    inner produced -1511174132; time/it = 0.399 ns
Method     func produced -1511174132; time/it = 0.813 ns
Method      raw produced -1511174132; time/it = 0.411 ns
Method  closure produced -1511174132; time/it = 2.370 ns
Method    inner produced -1511174132; time/it = 0.399 ns
Method     func produced -1511174132; time/it = 0.815 ns
Method      raw produced -1511174132; time/it = 0.412 ns
Method  closure produced -1511174132; time/it = 2.357 ns
Method    inner produced -1511174132; time/it = 0.400 ns
Method     func produced -1511174132; time/it = 0.817 ns

import com.google.caliper.SimpleBenchmark

class Benchmark extends SimpleBenchmark {
    def timeRaw(reps: Int) = {
        var i = 0
        var result = 0L
        while (i < reps) {
            result += 0xc37e ^ (i * 0xd5f3)
            i = i + 1
        }
        result
    }

    def normal(i: Int): Long = 0xc37e ^ (i * 0xd5f3)
    def timeNormal(reps: Int) = {
        var i = 0
        var result = 0L
        while (i < reps) {
            result += normal(i)
            i = i + 1
        }
        result
    }

    def timeInner(reps: Int) = {
        def inner(i: Int): Long = 0xc37e ^ (i * 0xd5f3)

        var i = 0
        var result = 0L
        while (i < reps) {
            result += inner(i)
            i = i + 1
        }
        result
    }

    def timeClosure(reps: Int) = {
        var i = 0
        var result = 0L
        val closure = () => result += 0xc37e ^ (i * 0xd5f3)
        while (i < reps) {
            closure()
            i = i + 1
        }
        result
    }

    def normal(i: Int, j: Int, k: Int, l: Int): Long = i ^ j ^ k ^ l 
    def timeUnboxed(reps: Int) = {
        var i = 0
        var result = 0L
        while (i < reps) {
            result += normal(i,i,i,i)
            i = i + 1
        }
        result
    }

    val closure = (i: Int, j: Int, k: Int, l: Int) => (i ^ j ^ k ^ l).toLong 
    def timeBoxed(reps: Int) = {
        var i = 0
        var result = 0L
        while (i < reps) {
            closure(i,i,i,i)
            i = i + 1
        }
        result
    }
}

benchmark     ns linear runtime
   Normal  0.576 =
      Raw  0.576 =
    Inner  0.576 =
  Closure  0.532 =
  Unboxed  0.893 =
    Boxed 15.210 ==============================

 benchmark     ns linear runtime
       Raw  0.574 =
    Normal  0.576 =
     Inner  0.575 =
   Closure  0.645 =
   Unboxed  0.889 =
     Boxed 15.107 ==============================

java version "1.7.0_51"
Java(TM) SE Runtime Environment (build 1.7.0_51-b13)
Java HotSpot(TM) 64-Bit Server VM (build 24.51-b03, mixed mode)

Scala compiler version 2.10.3 -- Copyright 2002-2013, LAMP/EPFL