问题描述
我一直在阅读 reduce 并刚刚发现有一个 3 参数版本基本上可以像这样执行 map reduce:
I've been reading up on reduce and have just found out that there is a 3 argument version that can essentially perform a map reduce like this:
String[] strarr = {"abc", "defg", "vwxyz"};
System.out.println(Arrays.stream(strarr).reduce(0, (l, s) -> l + s.length(), (s1, s2) -> s1 + s2));
但是我看不出这比带有 reduce 的 mapToInt 有什么优势.
However I can't see the advantage of this over a mapToInt with a reduce.
System.out.println(Arrays.stream(strarr).mapToInt(s -> s.length()).reduce(0, (s1, s2) -> s1 + s2));
两者都产生 12 的正确答案，并且两者似乎都可以并行工作.
Both produce the correct answer of 12, and both appear to work fine in parallel.
一个比另一个好，如果是，为什么?
Is one better than the other, and if so, why?
推荐答案
一个比另一个好，如果是，为什么?
Is one better than the other, and if so, why?
使用第一个 reduce 方法会有一个隐蔽的装箱成本.
With the first reduce approach there’s an insidious boxing cost.
mapToInt.reduce(...) 方法避免了这种情况.
The mapToInt.reduce(...) approach avoids that.
因此，如果您对求和感兴趣，那么 average 等人只需使用原始流专业化，因为它们更有效.
So, the idea is if you're interested in summation, average et al just use the primitive stream specializations as they're more efficient.
顺便提一下代码:
Arrays.stream(strarr).mapToInt(s -> s.length()).reduce(0, (s1, s2) -> s1 + s2)
可以简化为:
Arrays.stream(strarr).mapToInt(s -> s.length()).sum();
这篇关于Java mapToInt vs Reduce with map的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！