参考:http://m.blog.csdn.net/article/details?id=51287803

 

Java SE5的java.util.concurrent包中的执行器(Executor)将为你管理Thread对象,从而简化了并发编程。Executor在客户端和执行任务之间提供了一个间接层,Executor代替客户端执行任务。Executor允许你管理异步任务的执行,而无须显式地管理线程的生命周期。Executor在Java SE5/6中时启动任务的优选方法。Executor引入了一些功能类来管理和使用线程Thread,其中包括线程池,Executor,Executors,ExecutorService,CompletionService,Future,Callable等

 


CompletionService实例

创建线程池

Executors类,提供了一系列工厂方法用于创先线程池,返回的线程池都实现了ExecutorService接口。

 

public static ExecutorService newFixedThreadPool(int nThreads)

创建固定数目线程的线程池。

public static ExecutorService newCachedThreadPool()

创建一个可缓存的线程池,调用execute 将重用以前构造的线程(如果线程可用)。如果现有线程没有可用的,则创建一个新线程并添加到池中。终止并从缓存中移除那些已有 60 秒钟未被使用的线程。

public static ExecutorService newSingleThreadExecutor()

创建一个单线程化的Executor。

public static ScheduledExecutorService newScheduledThreadPool(int corePoolSize)

创建一个支持定时及周期性的任务执行的线程池,多数情况下可用来替代Timer类。

 

见类图,接口Executor只有一个方法execute,接口ExecutorService扩展了Executor并添加了一些生命周期管理的方法,如shutdown、submit等。一个Executor的生命周期有三种状态,运行 ,关闭 ,终止。

 

Callable,Future用于返回结果

Future<V>代表一个异步执行的操作,通过get()方法可以获得操作的结果,如果异步操作还没有完成,则,get()会使当前线程阻塞。FutureTask<V>实现了Future<V>和Runable<V>。Callable代表一个有返回值得操作。

实例:用ExecutorService  实现对一个大数组并行求和

 

package executor;

import java.util.*;
import java.util.concurrent.*;

/*
 * 并行计算求和.
 * 本例中,把一个整数数组的求和分解到每个线程中,每个线程求该数值的部分和,
 * 然后主程序把各个和再次求和就能得到最后的数字。从这个架构上跟mapreduce有点神似。
 * 
 */

public class ExecutorServiceParalelSumdemo {
	
	private int coreCpuNum;   
    private ExecutorService  executor;   
    
    /* 
     * save the result of each thread's sum calculation
     * 
     */
    private List<FutureTask<Long>> tasks = new ArrayList<FutureTask<Long>>();  
    
    public ExecutorServiceParalelSumdemo(){   
        coreCpuNum = Runtime.getRuntime().availableProcessors();   
        System.out.println("this host has "+coreCpuNum+ " CPU(s)");
        
        //for before Java 8.0
        //executor = Executors.newFixedThreadPool(coreCpuNum);   
        
        //this CPU parallelism API is Java8 or later ONLY 
        executor = Executors.newWorkStealingPool(coreCpuNum);   
    } 
    
    /*
     * thread main body
     */
    class CalculatorTask implements Callable<Long>{   
        int nums[];   
        int start;   
        int end;   
        public CalculatorTask(final int nums[],int start,int end){   
            this.nums = nums;   
            this.start = start;   
            this.end = end;   
        }
        
        @Override  
        public Long call() throws Exception {   
            long sum =0;   
            for(int i=start;i<end;i++){   
                sum += nums[i];   
            }   
          
            return sum;   
        }   
    }  
    
    private long getFinalSum(){   
        long sum = 0;   
        System.out.println(tasks.size() + " future tasks in pool");
        for(int i=0;i<tasks.size();i++){   
            try {   
            	/*
            	 * If this future's thread not return its result,
            	 * get() will block here. So perf issue introduced.
            	 * we can use CompletionService to solve this potential issue.
            	*/ 
                sum += tasks.get(i).get();   
            } catch (InterruptedException e) {   
                e.printStackTrace();   
            } catch (ExecutionException e) {   
                e.printStackTrace();   
            }   
        }   
        return sum;   
    }
    
    public long ParallelSum(int[] nums){   
        int start,end,increment;   
        
        // 根据CPU核心个数拆分任务,创建每个thread和对应的 FutureTask,并提交到ExecutorService中。    
        for(int i=0;i<coreCpuNum;i++) { 
            increment = (nums.length/coreCpuNum)+1;   
            start = i*increment;   
            end = start+increment;   
            if(end > nums.length){   
                end = nums.length;    
            }   
     
            //create thread tasks
            CalculatorTask calculator = new CalculatorTask(nums, start, end);  
            //create each future result per thread task
            FutureTask<Long> task = new FutureTask<Long>(calculator);   
            tasks.add(task);  
            
            if(!executor.isShutdown()){
            	//execute() can't return result
                executor.submit(task);   
            }   
        }  
        
        return getFinalSum();   
    }   
    
    public void close(){   
        executor.shutdown();   
    }   
}

 

CompletionService

在上述例子中,getResult()方法的实现过程中,迭代了FutureTask的数组,如果任务还没有完成则当前线程会阻塞,如果我们希望任意任务完成后就把其结果加到result中,而不用依次等待每个任务完成,可以使用CompletionService。

它与ExecutorService最主要的区别在于submit的task不一定是按照加入时的顺序完成的。CompletionService对ExecutorService进行了包装,内部维护一个保存Future对象的BlockingQueue。只有当这个Future对象状态是结束的时候,才会加入到这个Queue中,take()方法其实就是Producer-Consumer中的Consumer。它会从Queue中取出Future对象,如果Queue是空的,就会阻塞在那里,直到有完成的Future对象加入到Queue中。所以,先完成的必定先被取出。这样就减少了不必要的等待时间。

 

CompletionService版本的求和例子

 

package executor;

import java.util.*;
import java.util.concurrent.*;

public class CompletionServiceDemo {
	
	/*
	 * 并行计算求和.
	 * 本例中,把一个整数数组的求和分解到每个线程中,每个线程求该数值的部分和,
	 * 然后主程序把各个和再次求和就能得到最后的数字。从这个架构上跟mapreduce有点神似。
	 * 
	 */
	
		private int coreCpuNum;   
	    private ExecutorService  executor;
	    /*
	     * CompletionService与ExecutorService最主要的区别在于
	     *前者submit的task不一定是按照加入时的顺序完成的。CompletionService对ExecutorService进行了包装,
	     *内部维护一个保存Future对象的BlockingQueue。
	     *只有当这个Future对象状态是结束的时候,才会加入到这个Queue中,take()方法其实就是Producer-Consumer中的Consumer。
	     *它会从Queue中取出Future对象,如果Queue是空的,就会阻塞在那里,直到有完成的Future对象加入到Queue中。
	     *所以,先完成的必定先被取出。这样就减少了不必要的等待时间。
	     * 
	     */
	    /* 
	     * CompletionService has a internal bloking queue to save the result of each 
	     * thread's sum calculation. so List<FutureTask<Long>> tasks appears unnecessary now
	     * 
	     */
	    private CompletionService<Long> mcs;
	    /* 
	     * save the result of each thread's sum calculation
	     * 
	     */
	   
	    public CompletionServiceDemo(){   
	        coreCpuNum = Runtime.getRuntime().availableProcessors();   
	        
	        System.out.println("this host has "+coreCpuNum+ " CPU(s)");
	        
	        //for before Java 8.0
	        //executor = Executors.newFixedThreadPool(coreCpuNum);   
	        
	        //this CPU parallelism API is Java8 or later ONLY 
	        executor = Executors.newWorkStealingPool(coreCpuNum);   
	        mcs=new ExecutorCompletionService<>(executor);  
	    } 
	    
	    /*
	     * thread main body
	     */
	    class CalculatorTask implements Callable<Long>{   
	        int nums[];   
	        int start;   
	        int end;   
	        public CalculatorTask(final int nums[],int start,int end){   
	            this.nums = nums;   
	            this.start = start;   
	            this.end = end;   
	        }
	        
	        @Override  
	        public Long call() throws Exception {   
	            long sum =0;   
	            for(int i=start;i<end;i++){   
	                sum += nums[i];   
	            }   
	           
	            return sum;   
	        }   
	    }  
	    
	    private long getFinalSum(){   
	    	long sum = 0;	    	
	        for(int i=0;i<coreCpuNum;i++){   
	            try {   
	            /*
	             * get one complete result from CompletionServer internal 
	             * blocking queue
	             */
	            sum += mcs.take().get();   
	            } catch (InterruptedException e) {   
	                e.printStackTrace();   
	            } catch (ExecutionException e) {   
	                e.printStackTrace();   
	            }   
	        }   
	        return sum;    
	    }
	    
	    public long ParallelSum(int[] nums){   
	        int start,end,increment;   
	        
	        // 根据CPU核心个数拆分任务,创建每个thread和对应的 FutureTask,并提交到ExecutorService中。    
	        for(int i=0;i<coreCpuNum;i++) { 
	            increment = (nums.length/coreCpuNum)+1;   
	            start = i*increment;   
	            end = start+increment;   
	            if(end > nums.length){   
	                end = nums.length;    
	            }   
	            //create thread tasks
	            CalculatorTask mthread = new CalculatorTask(nums, start, end);        	            
	            if(!executor.isShutdown()){
	            	mcs.submit(mthread);   
	            }   
	        } 
	        
	        return getFinalSum();   
	    }   
	    
	    public void close(){   
	        executor.shutdown();   
	    }   
}

 

 

测试main方法:
package executor;

public class MainTest {
	public static void main(String[] args) {		
		System.out.println("ExcutorServer thread pool demo show");
		int[] nums={12890,345678,2345,5678,865,234,3434,454,4656,67678,678,1234,6789};
		ExecutorServiceParalelSumdemo mysum=new ExecutorServiceParalelSumdemo();
		
		System.out.println("result per ExecutorServiceParalelSumdemo = "
		                  +mysum.ParallelSum(nums));
		
		System.out.println("CompletionServiceDemo thread pool demo show");
		CompletionServiceDemo mcom=new CompletionServiceDemo();
		System.out.println("result per CompletionServiceDemo = "
		                 +mcom.ParallelSum(nums));
	}
}

输出:
 
ExcutorServer thread pool demo show
this host has 4 CPU(s)
4 future tasks in pool
result per ExecutorServiceParalelSumdemo = 452613
CompletionServiceDemo thread pool demo show
this host has 4 CPU(s)
result per CompletionServiceDemo = 452613

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