如何合理地估算线程池大小？

Posted 2021-01-18 tiancai

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如何合理地估算线程池大小？

感谢网友【蒋小强】投稿。

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 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) {

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() {

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                    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;

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import java.math.BigDecimal;

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import java.net.HttpURLConnection;

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import java.net.URL;

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import java.util.concurrent.BlockingQueue;

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import java.util.concurrent.LinkedBlockingQueue;

12
 
13
public class SimplePoolSizeCaculatorImpl extends PoolSizeCalculator {

14
 
15
    @Override

16
    protected Runnable creatTask() {

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        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), new BigDecimal(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

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* Recommended queue capacity (bytes): 2500

05
Number of CPU: 4

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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)

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* 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：

1
ThreadPoolExecutor pool =

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

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