<U> U reduce(U identity,BiFunction<U, ? super T, U> accumulator,BinaryOperator<U> combiner)

在串行流(stream)中，第三个参数combiner不会起作用。

在并行流(parallelStream)中，流被fork join出多个线程进行执行，此时每个线程的执行流程就跟第二个方法reduce(identity, accumulator)一样，而第三个参数combiner函数，则是将每个线程的执行结果当成一个新的流，然后使用第一个方法reduce(accumulator)流程进行规约。
 

List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5);

// 示例1
System.out.println( numbers.stream().reduce(Integer::sum).get() );
System.out.println( numbers.stream().reduce(100, Integer::sum) );
System.out.println( numbers.stream().reduce(100, (x,y)-> x = x+y, (x,y)-> x = x+y) );
System.out.println( numbers.parallelStream().reduce(100, (x,y)-> x = x+y, (x,y)-> x = x+y) );
//15
//115
//115
//515

// 示例2
List<Integer> list = new ArrayList<>();
list.add(100);
System.out.println(
   numbers.stream().reduce(list,
   (x,y)-> {List<Integer> ll = new ArrayList<>(x);ll.add(y); return ll;},
   (x,y)-> {List<Integer> ll = new ArrayList<>(x);ll.addAll(y); return ll;})
);
System.out.println(
   numbers.parallelStream().reduce(list,
   (x,y)-> {List<Integer> ll = new ArrayList<>(x);ll.add(y); return ll;},
   (x,y)-> {List<Integer> ll = new ArrayList<>(x);ll.addAll(y); return ll;})
);
//[100, 1, 2, 3, 4, 5]
//[100, 1, 100, 2, 100, 3, 100, 4, 100, 5]