如何合理地估算线程池大小？(转)-阿里云开发者社区

如何合理地估算线程池大小？

这个问题虽然看起来很小，却并不那么容易回答。大家如果有更好的方法欢迎赐教，先来一个天真的估算方法：假设要求一个系统的TPS（Transaction Per Second或者Task Per Second）至少为20，然后假设每个Transaction由一个线程完成，继续假设平均每个线程处理一个Transaction的时间为4s。那么问题转化为：

如何设计线程池大小，使得可以在1s内处理完20个Transaction？

计算过程很简单，每个线程的处理能力为0.25TPS，那么要达到20TPS，显然需要20/0.25=80个线程。

很显然这个估算方法很天真，因为它没有考虑到CPU数目。一般服务器的CPU核数为16或者32，如果有80个线程，那么肯定会带来太多不必要的线程上下文切换开销。

再来第二种简单的但不知是否可行的方法（N为CPU总核数）：

如果是CPU密集型应用，则线程池大小设置为N+1
如果是IO密集型应用，则线程池大小设置为2N+1

如果一台服务器上只部署这一个应用并且只有这一个线程池，那么这种估算或许合理，具体还需自行测试验证。

接下来在这个文档：服务器性能IO优化中发现一个估算公式：

 
      1 
      最佳线程数目 = （（线程等待时间+线程CPU时间）/线程CPU时间 ）* CPU数目

比如平均每个线程CPU运行时间为0.5s，而线程等待时间（非CPU运行时间，比如IO）为1.5s，CPU核心数为8，那么根据上面这个公式估算得到：((0.5+1.5)/0.5)*8=32。这个公式进一步转化为：

 
      1 
      最佳线程数目 = （线程等待时间与线程CPU时间之比 + 1）* CPU数目

可以得出一个结论：

线程等待时间所占比例越高，需要越多线程。线程CPU时间所占比例越高，需要越少线程。

上一种估算方法也和这个结论相合。

一个系统最快的部分是CPU，所以决定一个系统吞吐量上限的是CPU。增强CPU处理能力，可以提高系统吞吐量上限。但根据短板效应，真实的系统吞吐量并不能单纯根据CPU来计算。那要提高系统吞吐量，就需要从“系统短板”（比如网络延迟、IO）着手：

尽量提高短板操作的并行化比率，比如多线程下载技术
增强短板能力，比如用NIO替代IO

第一条可以联系到Amdahl定律，这条定律定义了串行系统并行化后的加速比计算公式：

 
      1 
      加速比=优化前系统耗时 / 优化后系统耗时

加速比越大，表明系统并行化的优化效果越好。Addahl定律还给出了系统并行度、CPU数目和加速比的关系，加速比为Speedup，系统串行化比率（指串行执行代码所占比率）为F，CPU数目为N：

 
      1 
       Speedup <= 1 / (F + (1-F)/N)

当N足够大时，串行化比率F越小，加速比Speedup越大。

写到这里，我突然冒出一个问题。

是否使用线程池就一定比使用单线程高效呢？

答案是否定的，比如Redis就是单线程的，但它却非常高效，基本操作都能达到十万量级/s。从线程这个角度来看，部分原因在于：

多线程带来线程上下文切换开销，单线程就没有这种开销
锁

当然“Redis很快”更本质的原因在于：Redis基本都是内存操作，这种情况下单线程可以很高效地利用CPU。而多线程适用场景一般是：存在相当比例的IO和网络操作。

所以即使有上面的简单估算方法，也许看似合理，但实际上也未必合理，都需要结合系统真实情况（比如是IO密集型或者是CPU密集型或者是纯内存操作）和硬件环境（CPU、内存、硬盘读写速度、网络状况等）来不断尝试达到一个符合实际的合理估算值。

最后来一个“Dark Magic”估算方法（因为我暂时还没有搞懂它的原理），使用下面的类：

 
      001 
       package pool_size_calculate;  
     
      002 
        
      003 
       import java.math.BigDecimal;  
     
      004 
       import java.math.RoundingMode;  
     
      005 
       import java.util.Timer;  
     
      006 
       import java.util.TimerTask;  
     
      007 
       import java.util.concurrent.BlockingQueue;  
     
      008 
        
      009 
      /** 
     
      010 
        * A class that calculates the optimal thread pool boundaries. It takes the  
     
      011 
        * desired target utilization and the desired work queue memory consumption as  
     
      012 
        * input and retuns thread count and work queue capacity.  
     
      013 
        *  
     
      014 
        * @author Niklas Schlimm  
     
      015 
        *  
     
      016 
        */  
     
      017 
       public abstract class PoolSizeCalculator {  
     
      018 
        
      019 
           /**  
     
      020 
            * The sample queue size to calculate the size of a single {@link Runnable}  
     
      021 
            * element.  
     
      022 
            */  
     
      023 
           private final int SAMPLE_QUEUE_SIZE = 1000;  
     
      024 
        
      025 
           /**  
     
      026 
            * Accuracy of test run. It must finish within 20ms of the testTime  
     
      027 
            * otherwise we retry the test. This could be configurable.  
     
      028 
            */  
     
      029 
           private final int EPSYLON = 20;  
     
      030 
        
      031 
           /**  
     
      032 
            * Control variable for the CPU time investigation.  
     
      033 
            */  
     
      034 
           private volatile boolean expired;  
     
      035 
        
      036 
           /**  
     
      037 
            * Time (millis) of the test run in the CPU time calculation.  
     
      038 
            */  
     
      039 
           private final long testtime = 3000;  
     
      040 
        
      041 
           /**  
     
      042 
            * Calculates the boundaries of a thread pool for a given {@link Runnable}.  
     
      043 
            *  
     
      044 
            * @param targetUtilization  
     
      045 
            *            the desired utilization of the CPUs (0 <= targetUtilization <=   *            1)     * @param targetQueueSizeBytes   *            the desired maximum work queue size of the thread pool (bytes)     */     protected voidcalculateBoundaries(BigDecimal targetUtilization,            BigDecimal targetQueueSizeBytes) {      calculateOptimalCapacity(targetQueueSizeBytes);         Runnable task = creatTask();        start(task);        start(task); // warm up phase       long cputime = getCurrentThreadCPUTime();       start(task); // test intervall      cputime = getCurrentThreadCPUTime() - cputime;      long waittime = (testtime * 1000000) - cputime;         calculateOptimalThreadCount(cputime, waittime, targetUtilization);  }   private void calculateOptimalCapacity(BigDecimal targetQueueSizeBytes) {        long mem = calculateMemoryUsage();      BigDecimal queueCapacity = targetQueueSizeBytes.divide(new BigDecimal(              mem), RoundingMode.HALF_UP);        System.out.println("Target queue memory usage (bytes): "                + targetQueueSizeBytes);        System.out.println("createTask() produced "                 + creatTask().getClass().getName() + " which took " + mem               + " bytes in a queue");         System.out.println("Formula: " + targetQueueSizeBytes + " / " + mem);       System.out.println("* Recommended queue capacity (bytes): "                 + queueCapacity);   }   /**      * Brian Goetz' optimal thread count formula, see 'Java Concurrency in   * Practice' (chapter 8.2)   *       * @param cpu    *            cpu time consumed by considered task   * @param wait   *            wait time of considered task   * @param targetUtilization      *            target utilization of the system   */     private void calculateOptimalThreadCount(long cpu, long wait,           BigDecimal targetUtilization) {         BigDecimal waitTime = new BigDecimal(wait);         BigDecimal computeTime = new BigDecimal(cpu);       BigDecimal numberOfCPU = new BigDecimal(Runtime.getRuntime()                .availableProcessors());        BigDecimal optimalthreadcount = numberOfCPU.multiply(targetUtilization)                 .multiply(                      new BigDecimal(1).add(waitTime.divide(computeTime,                              RoundingMode.HALF_UP)));        System.out.println("Number of CPU: " + numberOfCPU);        System.out.println("Target utilization: " + targetUtilization);         System.out.println("Elapsed time (nanos): " + (testtime * 1000000));        System.out.println("Compute time (nanos): " + cpu);         System.out.println("Wait time (nanos): " + wait);       System.out.println("Formula: " + numberOfCPU + " * "                + targetUtilization + " * (1 + " + waitTime + " / "                 + computeTime + ")");       System.out.println("* Optimal thread count: " + optimalthreadcount);    }   /**      * Runs the {@link Runnable} over a period defined in {@link #testtime}.     * Based on Heinz Kabbutz' ideas     * (http://www.javaspecialists.eu/archive/Issue124.html).    *       * @param task   *            the runnable under investigation   */     public void start(Runnable task) {      long start = 0;         int runs = 0;       do {            if (++runs > 5) {  
     
      046 
                       throw new IllegalStateException("Test not accurate");  
     
      047 
                   }  
     
      048 
                   expired = false;  
     
      049 
                   start = System.currentTimeMillis();  
     
      050 
                   Timer timer = new Timer();  
     
      051 
                   timer.schedule(new TimerTask() {  
     
      052 
                       public void run() {  
     
      053 
                           expired = true;  
     
      054 
                       }  
     
      055 
                   }, testtime);  
     
      056 
                   while (!expired) {  
     
      057 
                       task.run();  
     
      058 
                   }  
     
      059 
                   start = System.currentTimeMillis() - start;  
     
      060 
                   timer.cancel();  
     
      061 
               } while (Math.abs(start - testtime) > EPSYLON);  
     
      062 
               collectGarbage(3);  
     
      063 
           }  
     
      064 
        
      065 
           private void collectGarbage(int times) {  
     
      066 
               for (int i = 0; i < times; i++) {  
     
      067 
                   System.gc();  
     
      068 
                   try {  
     
      069 
                       Thread.sleep(10);  
     
      070 
                   } catch (InterruptedException e) {  
     
      071 
                       Thread.currentThread().interrupt();  
     
      072 
                       break;  
     
      073 
                   }  
     
      074 
               }  
     
      075 
           }  
     
      076 
        
      077 
           /**  
     
      078 
            * Calculates the memory usage of a single element in a work queue. Based on  
     
      079 
            * Heinz Kabbutz' ideas  
     
      080 
            * (http://www.javaspecialists.eu/archive/Issue029.html).  
     
      081 
            *  
     
      082 
            * @return memory usage of a single {@link Runnable} element in the thread  
     
      083 
            *         pools work queue  
     
      084 
            */  
     
      085 
           public long calculateMemoryUsage() {  
     
      086 
               BlockingQueue queue = createWorkQueue();  
     
      087 
               for (int i = 0; i < SAMPLE_QUEUE_SIZE; i++) {  
     
      088 
                   queue.add(creatTask());  
     
      089 
               }  
     
      090 
               long mem0 = Runtime.getRuntime().totalMemory()  
     
      091 
                       - Runtime.getRuntime().freeMemory();  
     
      092 
               long mem1 = Runtime.getRuntime().totalMemory()  
     
      093 
                       - Runtime.getRuntime().freeMemory();  
     
      094 
               queue = null;  
     
      095 
               collectGarbage(15);  
     
      096 
               mem0 = Runtime.getRuntime().totalMemory()  
     
      097 
                       - Runtime.getRuntime().freeMemory();  
     
      098 
               queue = createWorkQueue();  
     
      099 
               for (int i = 0; i < SAMPLE_QUEUE_SIZE; i++) {  
     
      100 
                   queue.add(creatTask());  
     
      101 
               }  
     
      102 
               collectGarbage(15);  
     
      103 
               mem1 = Runtime.getRuntime().totalMemory()  
     
      104 
                       - Runtime.getRuntime().freeMemory();  
     
      105 
               return (mem1 - mem0) / SAMPLE_QUEUE_SIZE;  
     
      106 
           }  
     
      107 
        
      108 
           /**  
     
      109 
            * Create your runnable task here.  
     
      110 
            *  
     
      111 
            * @return an instance of your runnable task under investigation  
     
      112 
            */  
     
      113 
           protected abstract Runnable creatTask();  
     
      114 
        
      115 
           /**  
     
      116 
            * Return an instance of the queue used in the thread pool.  
     
      117 
            *  
     
      118 
            * @return queue instance  
     
      119 
            */  
     
      120 
           protected abstract BlockingQueue createWorkQueue();  
     
      121 
        
      122 
           /**  
     
      123 
            * Calculate current cpu time. Various frameworks may be used here,  
     
      124 
            * depending on the operating system in use. (e.g.  
     
      125 
            * http://www.hyperic.com/products/sigar). The more accurate the CPU time  
     
      126 
            * measurement, the more accurate the results for thread count boundaries.  
     
      127 
            *  
     
      128 
            * @return current cpu time of current thread  
     
      129 
            */  
     
      130 
           protected abstract long getCurrentThreadCPUTime();  
     
      131 
        
      132 
      }

然后自己继承这个抽象类并实现它的三个抽象方法，比如下面是我写的一个示例（任务是请求网络数据），其中我指定期望CPU利用率为1.0（即100%），任务队列总大小不超过100,000字节：

 
      01 
       package pool_size_calculate;  
     
      02 
        
      03 
       import java.io.BufferedReader;  
     
      04 
       import java.io.IOException;  
     
      05 
       import java.io.InputStreamReader;  
     
      06 
       import java.lang.management.ManagementFactory;  
     
      07 
       import java.math.BigDecimal;  
     
      08 
       import java.net.HttpURLConnection;  
     
      09 
       import java.net.URL;  
     
      10 
       import java.util.concurrent.BlockingQueue;  
     
      11 
       import java.util.concurrent.LinkedBlockingQueue;  
     
      12 
        
      13 
       public class SimplePoolSizeCaculatorImpl extends PoolSizeCalculator {  
     
      14 
        
      15 
           @Override  
     
      16 
           protected Runnable creatTask() {  
     
      17 
               return new AsyncIOTask();  
     
      18 
           }  
     
      19 
        
      20 
           @Override  
     
      21 
           protected BlockingQueue createWorkQueue() {  
     
      22 
               return new LinkedBlockingQueue(1000);  
     
      23 
           }  
     
      24 
        
      25 
           @Override  
     
      26 
           protected long getCurrentThreadCPUTime() {  
     
      27 
               return ManagementFactory.getThreadMXBean().getCurrentThreadCpuTime();  
     
      28 
           }  
     
      29 
        
      30 
           public static void main(String[] args) {  
     
      31 
               PoolSizeCalculator poolSizeCalculator = new SimplePoolSizeCaculatorImpl();  
     
      32 
               poolSizeCalculator.calculateBoundaries(new BigDecimal(1.0), newBigDecimal(100000));  
     
      33 
           }  
     
      34 
        
      35 
      } 
     
      36 
        
      37 
      /** 
     
      38 
        * 自定义的异步IO任务  
     
      39 
        * @author Will  
     
      40 
        *  
     
      41 
        */  
     
      42 
       class AsyncIOTask implements Runnable {  
     
      43 
        
      44 
           @Override  
     
      45 
           public void run() {  
     
      46 
               HttpURLConnection connection = null;  
     
      47 
               BufferedReader reader = null;  
     
      48 
               try {  
     
      49 
                   String getURL = "http://baidu.com";  
     
      50 
                   URL getUrl = new URL(getURL);  
     
      51 
        
      52 
                   connection = (HttpURLConnection) getUrl.openConnection();  
     
      53 
                   connection.connect();  
     
      54 
                   reader = new BufferedReader(new InputStreamReader(  
     
      55 
                           connection.getInputStream()));  
     
      56 
        
      57 
                   String line;  
     
      58 
                   while ((line = reader.readLine()) != null) {  
     
      59 
                       // empty loop  
     
      60 
                   }  
     
      61 
               }  
     
      62 
        
      63 
               catch (IOException e) {  
     
      64 
        
      65 
               } finally {  
     
      66 
                   if(reader != null) {  
     
      67 
                       try {  
     
      68 
                           reader.close();  
     
      69 
                       }  
     
      70 
                       catch(Exception e) {  
     
      71 
        
      72 
                       }  
     
      73 
                   }  
     
      74 
                   connection.disconnect();  
     
      75 
               }  
     
      76 
        
      77 
           }  
     
      78 
        
      79 
      }

得到的输出如下：

 
      01 
      Target queue memory usage (bytes): 100000 
     
      02 
      createTask() produced pool_size_calculate.AsyncIOTask which took 40 bytes in a queue 
     
      03 
      Formula: 100000 / 40 
     
      04 
      * Recommended queue capacity (bytes): 2500 
     
      05 
      Number of CPU: 4 
     
      06 
      Target utilization: 1 
     
      07 
      Elapsed time (nanos): 3000000000 
     
      08 
      Compute time (nanos): 47181000 
     
      09 
      Wait time (nanos): 2952819000 
     
      10 
      Formula: 4 * 1 * (1 + 2952819000 / 47181000) 
     
      11 
      * Optimal thread count: 256