带Executor和不带Executor的多线程区别

我试图找出普通多线程和使用执行器的多线程（维护线程池）之间的性能差异。

以下是两者的代码示例。

没有执行器代码（使用多线程）：

import java.lang.management.ManagementFactory;
import java.lang.management.MemoryPoolMXBean;
import java.lang.management.MemoryUsage;
import java.lang.management.ThreadMXBean;
import java.util.List;

public class Demo1 {
public static void main(String arg[]) {
    Demo1 demo = new Demo1();
    Thread t5  = new Thread(new Runnable() {
       public void run() {
              int count=0;
              // Thread.State;
              // System.out.println("ClientMsgReceiver started-----");
              Demo1.ChildDemo  obj = new Demo1.ChildDemo();
              while(true) {

                // System.out.println("Threadcount is"+Thread);
                // System.out.println("count is"+(count++));
                Thread t=new Thread(obj);
                t.start();
                ThreadMXBean tb = ManagementFactory.getThreadMXBean();
                List<MemoryPoolMXBean> pools = ManagementFactory.getMemoryPoolMXBeans();
                for (MemoryPoolMXBean pool : pools) {
                   MemoryUsage peak = pool.getPeakUsage();
                   System.out.format("Peak %s memory used: %,d%n",
                             pool.getName(), peak.getUsed());
                   System.out.format("Peak %s memory reserved: %,d%n",
                             pool.getName(), peak.getCommitted());
                } 

                System.out.println("Current Thread Count"+ tb.getThreadCount());
                System.out.println("Peak Thread Count"+ tb.getPeakThreadCount());
                System.out.println("Current_Thread_Cpu_Time " 
                         + tb.getCurrentThreadCpuTime());
                System.out.println("Daemon Thread Count" +tb.getDaemonThreadCount());
       }
       // ChatLogin = new ChatLogin();
     }
  });
  t5.start();
}

static class ChildDemo implements Runnable {
   public void run() {
        try {
        //  System.out.println("Thread Started with custom Run method");
            Thread.sleep(100000);
        } catch (InterruptedException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        finally {
            System.out.println("A" +Thread.activeCount());
        }
    }
  }
}

使用执行器（多线程）：

import java.lang.management.ManagementFactory;
import java.lang.management.MemoryPoolMXBean;
import java.lang.management.MemoryUsage;
import java.lang.management.ThreadMXBean;
import java.util.List;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;

public class Executor_Demo {
public static void main(String arg[]) {
   BlockingQueue<Runnable> queue = new ArrayBlockingQueue<Runnable>(10);
   ThreadPoolExecutor executor   = new ThreadPoolExecutor(
          10, 100, 10, TimeUnit.MICROSECONDS, queue);
   Executor_Demo demo = new Executor_Demo();

   executor.execute(new Runnable() {
       public void run() {
          int count=0;
          // System.out.println("ClientMsgReceiver started-----");
          Executor_Demo.Demo demo2 = new Executor_Demo.Demo();
          BlockingQueue<Runnable> queue1 = new ArrayBlockingQueue<Runnable>(1000);
          ThreadPoolExecutor executor1   = new ThreadPoolExecutor(
                  1000, 10000, 10, TimeUnit.MICROSECONDS, queue1);

          while(true) {
             // System.out.println("Threadcount is"+Thread);
             // System.out.println("count is"+(count++));
             Runnable command= new Demo();
             // executor1.execute(command);
             executor1.submit(command);         
             // Thread t=new Thread(demo2);
             // t.start();
             ThreadMXBean tb = ManagementFactory.getThreadMXBean();
             /* try {
                  executor1.awaitTermination(100, TimeUnit.MICROSECONDS);
                } catch (InterruptedException e) {
                   // TODO Auto-generated catch block
                   e.printStackTrace();
                } */
              List<MemoryPoolMXBean> pools = ManagementFactory.getMemoryPoolMXBeans();
              for (MemoryPoolMXBean pool : pools) {
                 MemoryUsage peak = pool.getPeakUsage();
                 System.out.format("Peak %s memory used: %,d%n",
                          pool.getName(), peak.getUsed());
                 System.out.format("Peak %s memory reserved: %,d%n",
                          pool.getName(), peak.getCommitted());
          }
              System.out.println("daemon threads"+tb.getDaemonThreadCount());
              System.out.println("All threads"+tb.getAllThreadIds());
              System.out.println("current thread CPU time "
                       + tb.getCurrentThreadCpuTime());
              System.out.println("current thread user time "
                       + tb.getCurrentThreadUserTime());
              System.out.println("Total started thread count " 
                       + tb.getTotalStartedThreadCount());
              System.out.println("Current Thread Count"+ tb.getThreadCount());
              System.out.println("Peak Thread Count"+ tb.getPeakThreadCount());
              System.out.println("Current_Thread_Cpu_Time " 
                       + tb.getCurrentThreadCpuTime());
              System.out.println("Daemon Thread Count"
                       + tb.getDaemonThreadCount());
              // executor1.shutdown();  
            }
            //ChatLogin = new ChatLogin();
          }
     });
     executor.shutdown();
}

static class Demo implements Runnable {
    public void run() {
      try {
        // System.out.println("Thread Started with custom Run method");
        Thread.sleep(100000);
      } catch (InterruptedException e) {
          // TODO Auto-generated catch block
          e.printStackTrace();
      }
      finally {
         System.out.println("A" +Thread.activeCount());
      }
   }
  }
}

Sample output

当我运行这两个程序时，发现执行器比普通的多线程更昂贵。为什么会这样呢？

那么执行器到底有什么用呢？我们使用执行器来管理线程池。

我希望执行器能够提供比普通多线程更好的结果。

基本上我这样做是因为我需要使用带有多线程的套接字编程来处理数百万个客户端。

任何建议都会有帮助。

为了了解某些东西如何扩展，我会尽力将监控成本​​保持在最低限度，并且我会比较一个小数字和一个大数字。

public class Executor_Demo {
    public static void main(String... arg) throws ExecutionException, InterruptedException {
        int nThreads = 5100;
        ExecutorService executor = Executors.newFixedThreadPool(nThreads, new DaemonThreadFactory());

        List<Future<Results>> futures = new ArrayList<Future<Results>>();
        for (int i = 0; i < nThreads; i++) {
            futures.add(executor.submit(new BackgroundCallable()));
        }
        Results result = new Results();
        for (Future<Results> future : futures) {
            result.merge(future.get());
        }
        executor.shutdown();

        result.print(System.out);

    }

    static class Results {
        private long cpuTime;
        private long userTime;

        Results() {
            final ThreadMXBean tb = ManagementFactory.getThreadMXBean();
            cpuTime = tb.getCurrentThreadCpuTime();
            userTime = tb.getCurrentThreadUserTime();
        }


        public void merge(Results results) {
            cpuTime += results.cpuTime;
            userTime += results.userTime;
        }

        public void print(PrintStream out) {
            ThreadMXBean tb = ManagementFactory.getThreadMXBean();

            List<MemoryPoolMXBean> pools = ManagementFactory.getMemoryPoolMXBeans();
            for (int i = 0, poolsSize = pools.size(); i < poolsSize; i++) {
                MemoryPoolMXBean pool = pools.get(i);
                MemoryUsage peak = pool.getPeakUsage();
                out.format("Peak %s memory used:\t%,d%n", pool.getName(), peak.getUsed());
                out.format("Peak %s memory reserved:\t%,d%n", pool.getName(), peak.getCommitted());
            }

            out.println("Total thread CPU time\t" + cpuTime);
            out.println("Total thread user time\t" + userTime);
            out.println("Total started thread count\t" + tb.getTotalStartedThreadCount());
            out.println("Current Thread Count\t" + tb.getThreadCount());
            out.println("Peak Thread Count\t" + tb.getPeakThreadCount());
            out.println("Daemon Thread Count\t" + tb.getDaemonThreadCount());
        }
    }

    static class DaemonThreadFactory implements ThreadFactory {
        @Override
        public Thread newThread(Runnable r) {
            Thread t = new Thread(r);
            t.setDaemon(true);
            return t;
        }
    }

    static class BackgroundCallable implements Callable<Results> {
        @Override
        public Results call() throws Exception {
            Thread.sleep(100);
            return new Results();
        }
    }
}

当测试时-XX:MaxNewSize=64m（这限制了临时内存空间的大小会增加）

100 threads
Peak Code Cache memory used:    386,880
Peak Code Cache memory reserved:    2,555,904
Peak PS Eden Space memory used: 41,280,984
Peak PS Eden Space memory reserved: 50,331,648
Peak PS Survivor Space memory used: 0
Peak PS Survivor Space memory reserved: 8,388,608
Peak PS Old Gen memory used:    0
Peak PS Old Gen memory reserved:    192,675,840
Peak PS Perm Gen memory used:   3,719,616
Peak PS Perm Gen memory reserved:   21,757,952
Total thread CPU time   20000000
Total thread user time  20000000
Total started thread count  105
Current Thread Count    93
Peak Thread Count   105
Daemon Thread Count 92

5100 threads
Peak Code Cache memory used:    425,728
Peak Code Cache memory reserved:    2,555,904
Peak PS Eden Space memory used: 59,244,544
Peak PS Eden Space memory reserved: 59,244,544
Peak PS Survivor Space memory used: 2,949,152
Peak PS Survivor Space memory reserved: 8,388,608
Peak PS Old Gen memory used:    3,076,400
Peak PS Old Gen memory reserved:    192,675,840
Peak PS Perm Gen memory used:   3,787,096
Peak PS Perm Gen memory reserved:   21,757,952
Total thread CPU time   810000000
Total thread user time  150000000
Total started thread count  5105
Current Thread Count    5105
Peak Thread Count   5105
Daemon Thread Count 5104

主要的增加是旧一代使用量的增加约 3 MB 或每个线程约 6 KB。 CPU 使用时间为 956 毫秒，即每个线程大约 0.2 毫秒。

在第一个示例中，您创建一个线程，在第二个示例中您创建 1000 个线程。

您正在执行的输出似乎是大部分工作，并且第二种情况下的输出比第一种情况要多得多。

您需要确保您的测试和监控比您想要监控/测量的要轻得多。