如何合理地估算Java线程池大小？

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）：

1. 如果是CPU密集型应用，则线程池大小设置为N+1
2. 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：

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：

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：

1. 尽量提高短板操作的并行化比率，比如多线程下载技术
2. 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：

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：

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：

package pool_size_calculate;    import java.math.BigDecimal;  import java.math.RoundingMode;  import java.util.Timer;  import java.util.TimerTask;  import java.util.concurrent.BlockingQueue;    /**   * A class that calculates the optimal thread pool boundaries. It takes the   * desired target utilization and the desired work queue memory consumption as   * input and retuns thread count and work queue capacity.   *   * @author Niklas Schlimm   *   */  public abstract class PoolSizeCalculator {     /**    * The sample queue size to calculate the size of a single {@link Runnable}    * element.    */   private final int SAMPLE_QUEUE_SIZE = 1000;     /**    * Accuracy of test run. It must finish within 20ms of the testTime    * otherwise we retry the test. This could be configurable.    */   private final int EPSYLON = 20;     /**    * Control variable for the CPU time investigation.    */   private volatile boolean expired;     /**    * Time (millis) of the test run in the CPU time calculation.    */   private final long testtime = 3000;     /**    * Calculates the boundaries of a thread pool for a given {@link Runnable}.    *    * @param targetUtilization    *            the desired utilization of the CPUs (0 <= targetUtilization <=   *            1)   * @param targetQueueSizeBytes   *            the desired maximum work queue size of the thread pool (bytes)   */  protected void calculateBoundaries(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) {      throw new IllegalStateException("Test not accurate");     }     expired = false;     start = System.currentTimeMillis();     Timer timer = new Timer();     timer.schedule(new TimerTask() {      public void run() {       expired = true;      }     }, testtime);     while (!expired) {      task.run();     }     start = System.currentTimeMillis() - start;     timer.cancel();    } while (Math.abs(start - testtime) > EPSYLON);    collectGarbage(3);   }     private void collectGarbage(int times) {    for (int i = 0; i < times; i++) {     System.gc();     try {      Thread.sleep(10);     } catch (InterruptedException e) {      Thread.currentThread().interrupt();      break;     }    }   }     /**    * Calculates the memory usage of a single element in a work queue. Based on    * Heinz Kabbutz' ideas    * (http://www.javaspecialists.eu/archive/Issue029.html).    *    * @return memory usage of a single {@link Runnable} element in the thread    *         pools work queue    */   public long calculateMemoryUsage() {    BlockingQueue queue = createWorkQueue();    for (int i = 0; i < SAMPLE_QUEUE_SIZE; i++) {     queue.add(creatTask());    }    long mem0 = Runtime.getRuntime().totalMemory()      - Runtime.getRuntime().freeMemory();    long mem1 = Runtime.getRuntime().totalMemory()      - Runtime.getRuntime().freeMemory();    queue = null;    collectGarbage(15);    mem0 = Runtime.getRuntime().totalMemory()      - Runtime.getRuntime().freeMemory();    queue = createWorkQueue();    for (int i = 0; i < SAMPLE_QUEUE_SIZE; i++) {     queue.add(creatTask());    }    collectGarbage(15);    mem1 = Runtime.getRuntime().totalMemory()      - Runtime.getRuntime().freeMemory();    return (mem1 - mem0) / SAMPLE_QUEUE_SIZE;   }     /**    * Create your runnable task here.    *    * @return an instance of your runnable task under investigation    */   protected abstract Runnable creatTask();     /**    * Return an instance of the queue used in the thread pool.    *    * @return queue instance    */   protected abstract BlockingQueue createWorkQueue();     /**    * Calculate current cpu time. Various frameworks may be used here,    * depending on the operating system in use. (e.g.    * http://www.hyperic.com/products/sigar). The more accurate the CPU time    * measurement, the more accurate the results for thread count boundaries.    *    * @return current cpu time of current thread    */   protected abstract long getCurrentThreadCPUTime();    }

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：

package pool_size_calculate;    import java.io.BufferedReader;  import java.io.IOException;  import java.io.InputStreamReader;  import java.lang.management.ManagementFactory;  import java.math.BigDecimal;  import java.net.HttpURLConnection;  import java.net.URL;  import java.util.concurrent.BlockingQueue;  import java.util.concurrent.LinkedBlockingQueue;    public class SimplePoolSizeCaculatorImpl extends PoolSizeCalculator {     @Override   protected Runnable creatTask() {    return new AsyncIOTask();   }     @Override   protected BlockingQueue createWorkQueue() {    return new LinkedBlockingQueue(1000);   }     @Override   protected long getCurrentThreadCPUTime() {    return ManagementFactory.getThreadMXBean().getCurrentThreadCpuTime();   }     public static void main(String[] args) {    PoolSizeCalculator poolSizeCalculator = new SimplePoolSizeCaculatorImpl();    poolSizeCalculator.calculateBoundaries(new BigDecimal(1.0), new BigDecimal(100000));   }    }    /**   * 自定义的异步IO任务   * @author Will   *   */  class AsyncIOTask implements Runnable {     @Override   public void run() {    HttpURLConnection connection = null;    BufferedReader reader = null;    try {     String getURL = "http://baidu.com";     URL getUrl = new URL(getURL);       connection = (HttpURLConnection) getUrl.openConnection();     connection.connect();     reader = new BufferedReader(new InputStreamReader(       connection.getInputStream()));       String line;     while ((line = reader.readLine()) != null) {      // empty loop     }    }      catch (IOException e) {      } finally {     if(reader != null) {      try {       reader.close();      }      catch(Exception e) {        }     }     connection.disconnect();    }     }    }

得到的输出如下：

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

推荐的任务队列大小为2500，线程数为256，有点出乎意料之外。我可以如下构造一个线程池：

ThreadPoolExecutor pool =   new ThreadPoolExecutor(256, 256, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue(2500));