JAVA8-Stream parallel 并行执行 Stream parallel 并行执行范例：求1~100000000的和，执行10次，看时间效率。代码package com.example.study.java8.collector; import java.util.function.Function; import java.util.stream.LongStream; import java.util.stream.Stream; /** * Stream parallel并行执行 * 实列：求10次，1~100000000的和，看时间效率。 */ public class ParallelProcessing { public static void main(String[] args) { //获取电脑CPU核数 System.out.println("当前电脑CPU核数= " + Runtime.getRuntime().availableProcessors()); System.out.println("The best process time(normalAdd)=> " + measureSumPerformance(ParallelProcessing::normalAdd, 100_000_000) + " MS"); System.out.println("The best process time(iterateStream1)=> " + measureSumPerformance(ParallelProcessing::iterateStream1, 100_000_000) + " MS"); System.out.println("The best process time(iterateStream2)=> " + measureSumPerformance(ParallelProcessing::iterateStream2, 100_000_000) + " MS"); System.out.println("The best process time(iterateStream3)=> " + measureSumPerformance(ParallelProcessing::iterateStream3, 100_000_000) + " MS"); System.out.println("The best process time(iterateStream4)=> " + measureSumPerformance(ParallelProcessing::iterateStream4, 100_000_000) + " MS"); System.out.println("The best process time(iterateStream5)=> " + measureSumPerformance(ParallelProcessing::iterateStream5, 100_000_000) + " MS"); } private static long measureSumPerformance(Function<Long, Long> adder, long limist) { long fastest = Long.MAX_VALUE; for (int i = 0; i < 10; i++) { Long startTimestamp = System.currentTimeMillis(); long result = adder.apply(limist); long duration = System.currentTimeMillis() - startTimestamp; // System.out.println("The result of sum=>"+result); if (duration < fastest) fastest = duration; } return fastest; } /** * 1、函数式编程实现：没有使用并行执行 * * @param limit * @return */ public static long iterateStream1(long limit) { return Stream.iterate(1L, i -> i + 1).limit(limit).reduce(0L, Long::sum); } /** * 2、函数式编程实现-进化：使用并行执行，要进行拆箱装箱 * * @param limit * @return */ public static long iterateStream2(long limit) { return Stream.iterate(1L, i -> i + 1).parallel().limit(limit).reduce(0L, Long::sum); } /** * 3、函数式编程实现-再次进化：只用并行执行，不进行拆箱装箱 * * @param limit * @return */ public static long iterateStream3(long limit) { return Stream.iterate(1L, i -> i + 1).mapToLong(Long::longValue).parallel().limit(limit).reduce(0L, Long::sum); } /** * 4、函数式编程实现-再次再次进化：只用并行执行 * * @param limit * @return */ public static long iterateStream4(long limit) { return LongStream.rangeClosed(1L, limit).parallel().sum(); } /** * 5、函数式编程实现-再次再次再次进化 * * @param limit * @return */ public static long iterateStream5(long limit) { return LongStream.rangeClosed(1L, limit).parallel().reduce(0L, Long::sum); } /** * 原始写法 * * @param limit * @return */ public static long normalAdd(long limit) { long result = 0L; for (long i = 1; i < limit; i++) { result++; } return result; } } 输出结果当前电脑CPU核数= 16 The best process time(normalAdd)=> 29 MS The best process time(iterateStream1)=> 794 MS The best process time(iterateStream2)=> 2718 MS The best process time(iterateStream3)=> 2132 MS The best process time(iterateStream4)=> 6 MS The best process time(iterateStream5)=> 24 MS4和5效率差不多输出结果：当前电脑CPU核数= 16 The best process time(normalAdd)=> 29 MS The best process time(iterateStream4)=> 4 MS The best process time(iterateStream5)=> 5 MS结果可以看到使用LongStream的parallel并发执行效率最高。使用注意点Source DecomposabilityArrayList Excellent( 极好的)LinkedList Poor(不好的)IntStream.range Excellent( 极好的)Stream.iterate Poor(不好的)HashSet Good(好的)TreeSet Good(好的)上面的例子就是使用的LongStream.rangeClosed（），就是IntStream.range效率Excellent( 极好的)。