如何合理地估算线程池大小？

本文转自：http://ifeve.com/how-to-calculate-threadpool-size/

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：

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：

• 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：

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<Runnable> 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<Runnable> 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));