Stream的并行计算

一、Stream并行计算体验，利用多核加快计算速度

stream的并发，多个cpu执行同一个任务，提高效率；

需求：从1+...+10000000，看下各种计算方法的运行时间是多少

代码例子如下：

 package com.cy.java8;

 import java.util.function.Function;
 import java.util.stream.LongStream;
 import java.util.stream.Stream;

 public class ParallelProcessing {

     public static void main(String[] args) {
         //查看计算机核心线程数
         //System.out.println(Runtime.getRuntime().availableProcessors());

         long fastest1 = measureSumPerformance(ParallelProcessing::normalAdd, 10000000);
         System.out.println("normalAdd the best processing time : " + fastest1 + " ms");

         long fastest2 = measureSumPerformance(ParallelProcessing::iterateStream, 10000000);
         System.out.println("iterateStream the best processing time : " + fastest2 + " ms");

         long fastest3 = measureSumPerformance(ParallelProcessing::parallelStream, 10000000);
         System.out.println("parallelStream the best processing time : " + fastest3 + " ms");

         long fastest4 = measureSumPerformance(ParallelProcessing::parallelStream2, 10000000);
         System.out.println("parallelStream2 the best processing time : " + fastest4 + " ms");

         long fastest5 = measureSumPerformance(ParallelProcessing::parallelStream3, 10000000);
         System.out.println("parallelStream3 the best processing time : " + fastest5 + " ms");

     }

     /**
      * 将下面的求和方法分别计算10次，取10次中运行最短的时间
      */
     private static long measureSumPerformance(Function<Long, Long> adder, long limit){
         long fastest = Long.MAX_VALUE;

         for(int i=0; i<10; i++){
             long startTime = System.currentTimeMillis();
             long result = adder.apply(limit);
             long spendTime = System.currentTimeMillis() - startTime;
             System.out.println("the sum result is " + result);
             if(spendTime < fastest){
                 fastest = spendTime;
             }
         }
         return fastest;
     }

     /**
      * 计算一串long类型的总和，普通的stream
      * @param limit
      * @return
      */
     private static long iterateStream(long limit){
         return Stream.iterate(1L, i->i+1).limit(limit).reduce(0L, Long::sum);
     }

     /**
      * 使用Stream.parallel
      * 比较慢，为什么？
      * Stream.iterate不适合并行计算
      */
     private static long parallelStream(long limit){
         return Stream.iterate(1L, i->i+1).parallel().limit(limit).reduce(0L, Long::sum);
     }

     /**
      * 将上面的Stream先自动拆箱为long，再并行
      * 虽然拆箱为LongStream，还是很慢，为什么？
      * Stream.iterate不适合并行计算
      */
     private static long parallelStream2(long limit){
         return Stream.iterate(1L, i -> i + 1)
                         .mapToLong(Long::longValue)
                         .parallel().limit(limit).reduce(0L, Long::sum);
     }

     /**
      * 使用LongStream.range
      * 很快，比normalAdd快了近一倍，为什么？
      * LongStream、IntStream等..它们的IntStream.range非常卓越的适合并行计算
      */
     private static long parallelStream3(long limit){
         return LongStream.rangeClosed(1, limit).parallel().reduce(0L, Long::sum);
     }

     /**
      * 以前的写法
      * @param limit
      * @return
      */
     private static long normalAdd(long limit){
         long result = 0L;
         for(long i=0L; i <= limit; i++){
             result += i;
         }
         return result;
     }
 }

console：

the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
normalAdd the best processing time : 3 ms
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
iterateStream the best processing time : 78 ms
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
parallelStream the best processing time : 128 ms
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
parallelStream2 the best processing time : 178 ms
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
the sum result is 50000005000000
parallelStream3 the best processing time : 0 ms

结论：不一定是所有的方法产生的Stream都适合于并行的方式去做的，一定要注意有些方法是对于并行是厌恶的，有些方法是喜欢并行的；

列举一些例子如下：

数据源 　　　　　　　　　　分解性能
Source 　　　　　　　　　　Decomposability
ArrayList 　　　　　　　　　 Excellent
LinkedList 　　　　　　　　  Poor
IntStream.range 　　　　　  Excellent
Stream.iterate 　　　　　　  Poor
HashSet 　　　　　　　　    Good
TreeSet 　　　　　　　　     Good

----