地图合并功能的改进 [英] Improvements for a Map merge function

问题描述

我正在编写一个函数来将两个 Map 合并在一起.这是我目前所拥有的:

I'm writing a function to merge two Map's together. This is what I have so far:

def merge[K, V1, V2, V3](left: Map[K, V1], right: Map[K, V2])
    (fn: (Option[V1], Option[V2]) => V3): Map[K, V3] = {
  val r = (left.keySet ++ right.keySet) map {
    key =>
      (key -> fn(left.get(key), right.get(key)))
  }
  r.toMap
}

函数本身有效.您可以这样使用该函数:

The function itself works. You use the function as so:

val m1 = Map(1 -> "one", 3 -> "three", 5 -> "five")
val m2 = Map(1 -> "I", 5 -> "V", 10 -> "X")
merge(m1, m2) { (_, _) } 
// returns: 
// Map(1 -> (Some(one),Some(I)), 
//     3 -> (Some(three),None), 
//     5 -> (Some(five),Some(V)), 
//     10 -> (None,Some(X)))

我有两个问题:

1. 我担心 .get 和 .toMap 调用的性能计算复杂性.有人可以改进实施吗?
2. 我想要默认函数来生成一对值 ({ (_, _) }).我不太明白正确执行此操作的语法.

1. I'm worried about the performance computational complexity of the .get and .toMap calls. Can anyone improve the implementation?
2. I'd like the default function to make a pair of the values ({ (_, _) }). I can't quite get the syntax to do so properly.

虽然我最初说的是性能，但我的意思是计算复杂度.我的猜测是这个函数的执行时间为 O(n•ln(n)). 看起来我的函数的执行时间大致为 O(n).可以在 O(ln(n)) 中完成吗?

While I originally said performance, I meant computational complexity. My guess is that this function performs in O(n•ln(n)) time. Looks like my function performs roughly in O(n). Can it be done in O(ln(n))?

推荐答案

对于默认函数字面量使用:

For the default function literal use:

(fn: (Option[V1], Option[V2]) => V3 = 
  (x: Option[V1], y: Option[V2]) => Tuple2(x,y))

你必须像这样使用合并:merge(m1,m2)()

You'll have to use merge like this: merge(m1,m2)()

我想说在对实际数据进行一些测量之前不要担心性能.

I would say don't be worried about performance until you perform some measurements on actual data.

关于性能，通过提供视图而不是构建地图，您可以以查找为代价快速构建" - 假设我们正在处理不可变的地图.因此，根据实际数据和用例，您可以为某些操作获得更好的性能，但这需要权衡.

about performance, by providing a view instead of constructing a map you can get quick "construction" at the expense of lookup - assuming we are dealing with immutable maps. So depending on actual data and use case, you can get better performance for certain operations, but it has a trade-off.

class MergedView[K, V1, V2, V3](
    left: Map[K, V1], right: Map[K, V2]
  )(fn: (Option[V1], Option[V2]) => V3 = (x: Option[V1], y: Option[V2]) => Tuple2(x,y)
  ) extends collection.DefaultMap[K, V3] {
  def get(key: K): Option[V3] = (left.get(key), right.get(key)) match {
    case (None, None) => None
    case t => Some(fn(t._1, t._2))
  }
  lazy val tuples = (left.keys ++ right.keys).map(key => key -> get(key).get)
  def iterator: Iterator[(K, V3)] = tuples.iterator
} 

val r1 = new MergedView(m1, m2)() // use parens here for second param list.

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