val longs: Vector[Long] = (1L to 1000000L).toVector

def jLongs: java.util.stream.LongStream = java.util.stream.LongStream
    .iterate(1L, (i: Long) => i <= 1000000L, (i: Long) => i + 1L)

@State(Scope.Benchmark)
@BenchmarkMode(Array(Mode.AverageTime))
@OutputTimeUnit(TimeUnit.MILLISECONDS)
class BoxingScala {
  val longs: Vector[Long] = (1L to 1000000L).toVector
  def jLongs: java.util.stream.LongStream = java.util.stream.LongStream
    .iterate(1L, (i: Long) => i <= 1000000L, (i: Long) => i + 1L)
  @Benchmark def a: Long = longs.sum
  @Benchmark def b: java.lang.Long = jLongs.sum()
}

// access flags 0x1
public a()J
@Lorg/openjdk/jmh/annotations/Benchmark;()
 L0
  LINENUMBER <benchmark a> L0
  ALOAD 0
  INVOKEVIRTUAL gurghet/BoxingScala.longs ()Lscala/collection/immutable/Vector;
  GETSTATIC scala/math/Numeric$LongIsIntegral$.MODULE$ : Lscala/math/Numeric$LongIsIntegral$;
  INVOKEVIRTUAL scala/collection/immutable/Vector.sum (Lscala/math/Numeric;)Ljava/lang/Object;
  INVOKESTATIC scala/runtime/BoxesRunTime.unboxToLong (Ljava/lang/Object;)J
  LRETURN
 L1
  LOCALVARIABLE this Lgurghet/BoxingScala; L0 L1 0
  MAXSTACK = 2
  MAXLOCALS = 1
// access flags 0x1
public b()Ljava/lang/Long;
@Lorg/openjdk/jmh/annotations/Benchmark;()
 L0
  LINENUMBER <benchmark b> L0
  GETSTATIC scala/Predef$.MODULE$ : Lscala/Predef$;
  ALOAD 0
  INVOKEVIRTUAL gurghet/BoxingScala.jLongs ()Ljava/util/stream/LongStream;
  INVOKEINTERFACE java/util/stream/LongStream.sum ()J (itf)
  INVOKEVIRTUAL scala/Predef$.long2Long (J)Ljava/lang/Long;
  ARETURN
 L1
  LOCALVARIABLE this Lgurghet/BoxingScala; L0 L1 0
  MAXSTACK = 3
  MAXLOCALS = 1

L1
  LINENUMBER <init> L1
  ALOAD 0
  NEW scala/runtime/RichLong
  DUP
  GETSTATIC scala/Predef$.MODULE$ : Lscala/Predef$;
  LCONST_1
  INVOKEVIRTUAL scala/Predef$.longWrapper (J)J
  INVOKESPECIAL scala/runtime/RichLong.<init> (J)V
  LDC 1000000
  INVOKESTATIC scala/runtime/BoxesRunTime.boxToLong (J)Ljava/lang/Long;
  INVOKEVIRTUAL scala/runtime/RichLong.to (Ljava/lang/Object;)Lscala/collection/immutable/NumericRange$Inclusive;
  INVOKEVIRTUAL scala/collection/immutable/NumericRange$Inclusive.toVector ()Lscala/collection/immutable/Vector;
  PUTFIELD gurghet/BoxingScala.longs : Lscala/collection/immutable/Vector;

<!-- language-all: scala -->
查看字节码是徒劳的。区别在于`Stream`的本质。`LongStream`根据需要产生元素。它不是数据结构；它是控制结构——潜在的循环，用于某些其他数据源。你的Java代码可以简化为
    var sum: Long = 0
    for(i <- 1L to 1000000L) sum += i;
`Vector`是一个实际的数据结构，实际上必须存储100000个`long`，使得你的Scala版本本质上是
    val oops = new Array[java.lang.Long](1000000) // 装箱！
    for(i <- 0 until 1000000) oops(i) = i + 1
    var sum: Long = 0
    for(i <- 0 until 1000000) sum += oops(i)
这两者之间绝对没有等价关系。还要注意，`1L to 1000000L`是一个`NumericRange[Long]`，它已经是一个Scala集合，`(1 to 1000000L).sum`的速度比这两者都要快，因为它使用简单的算术公式来计算结果。最接近`LongStream`的东西实际上是`SeqView[Long]`，你可以通过`(1L to 1000000).view`获得它。如果在其上调用`sum`，我认为集合库并不足够智能，无法将其简化为对`NumericRange`的`sum`调用，而是会像在Java版本中那样迭代它，从而进行更近似的比较。不过它不会被专门化为`Long`，所以仍然会有装箱的惩罚。