如何合理地估算线程池大小？（转载）

2018-06-18 00:55:51来源：未知阅读 ()

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：

  1 package threadpool;
  2 
  3 import java.math.BigDecimal;
  4 import java.math.RoundingMode;
  5 import java.util.Timer;
  6 import java.util.TimerTask;
  7 import java.util.concurrent.BlockingQueue;
  8 
  9 /**
 10  * A class that calculates the optimal thread pool boundaries. It takes the
 11  * desired target utilization and the desired work queue memory consumption as
 12  * input and retuns thread count and work queue capacity.
 13  *
 14  * @author Niklas Schlimm
 15  */
 16 public abstract class PoolSizeCalculator {
 17 
 18     /**
 19      * The sample queue size to calculate the size of a single {@link Runnable}
 20      * element.
 21      */
 22     private final int SAMPLE_QUEUE_SIZE = 1000;
 23 
 24     /**
 25      * Accuracy of test run. It must finish within 20ms of the testTime
 26      * otherwise we retry the test. This could be configurable.
 27      */
 28     private final int EPSYLON = 20;
 29 
 30     /**
 31      * Control variable for the CPU time investigation.
 32      */
 33     private volatile boolean expired;
 34 
 35     /**
 36      * Time (millis) of the test run in the CPU time calculation.
 37      */
 38     private final long testtime = 3000;
 39 
 40     /**
 41      * Calculates the boundaries of a thread pool for a given {@link Runnable}.
 42      *
 43      * @param targetUtilization the desired utilization of the CPUs (0 <= targetUtilization <=      *            1)      * @param targetQueueSizeBytes      *            the desired maximum work queue size of the thread pool (bytes)
 44      */
 45     protected void calculateBoundaries(BigDecimal targetUtilization, BigDecimal targetQueueSizeBytes) {
 46         calculateOptimalCapacity(targetQueueSizeBytes);
 47         Runnable task = creatTask();
 48         start(task);
 49         start(task); // warm up phase
 50         long cputime = getCurrentThreadCPUTime();
 51         start(task); // test intervall
 52         cputime = getCurrentThreadCPUTime() - cputime;
 53         long waittime = (testtime * 1000000) - cputime;
 54         calculateOptimalThreadCount(cputime, waittime, targetUtilization);
 55     }
 56 
 57     private void calculateOptimalCapacity(BigDecimal targetQueueSizeBytes) {
 58         long mem = calculateMemoryUsage();
 59         BigDecimal queueCapacity = targetQueueSizeBytes.divide(new BigDecimal(mem),
 60                 RoundingMode.HALF_UP);
 61         System.out.println("Target queue memory usage (bytes): "
 62                 + targetQueueSizeBytes);
 63         System.out.println("createTask() produced " + creatTask().getClass().getName() + " which took " + mem + " bytes in a queue");
 64         System.out.println("Formula: " + targetQueueSizeBytes + " / " + mem);
 65         System.out.println("* Recommended queue capacity (bytes): " + queueCapacity);
 66     }
 67 
 68     /**
 69      * Brian Goetz' optimal thread count formula, see 'Java Concurrency in
 70      * * Practice' (chapter 8.2)      *
 71      * * @param cpu
 72      * *            cpu time consumed by considered task
 73      * * @param wait
 74      * *            wait time of considered task
 75      * * @param targetUtilization
 76      * *            target utilization of the system
 77      */
 78     private void calculateOptimalThreadCount(long cpu, long wait,
 79                                              BigDecimal targetUtilization) {
 80         BigDecimal waitTime = new BigDecimal(wait);
 81         BigDecimal computeTime = new BigDecimal(cpu);
 82         BigDecimal numberOfCPU = new BigDecimal(Runtime.getRuntime()
 83                 .availableProcessors());
 84         BigDecimal optimalthreadcount = numberOfCPU.multiply(targetUtilization)
 85                 .multiply(new BigDecimal(1).add(waitTime.divide(computeTime,
 86                         RoundingMode.HALF_UP)));
 87         System.out.println("Number of CPU: " + numberOfCPU);
 88         System.out.println("Target utilization: " + targetUtilization);
 89         System.out.println("Elapsed time (nanos): " + (testtime * 1000000));
 90         System.out.println("Compute time (nanos): " + cpu);
 91         System.out.println("Wait time (nanos): " + wait);
 92         System.out.println("Formula: " + numberOfCPU + " * "
 93                 + targetUtilization + " * (1 + " + waitTime + " / "
 94                 + computeTime + ")");
 95         System.out.println("* Optimal thread count: " + optimalthreadcount);
 96     }
 97 
 98     /**
 99      * * Runs the {@link Runnable} over a period defined in {@link #testtime}.
100      * * Based on Heinz Kabbutz' ideas
101      * * (http://www.javaspecialists.eu/archive/Issue124.html).
102      * *
103      * * @param task
104      * *            the runnable under investigation
105      */
106     public void start(Runnable task) {
107         long start = 0;
108         int runs = 0;
109         do {
110             if (++runs > 5) {
111                 throw new IllegalStateException("Test not accurate");
112             }
113             expired = false;
114             start = System.currentTimeMillis();
115             Timer timer = new Timer();
116             timer.schedule(new TimerTask() {
117                 public void run() {
118                     expired = true;
119                 }
120             }, testtime);
121             while (!expired) {
122                 task.run();
123             }
124             start = System.currentTimeMillis() - start;
125             timer.cancel();
126         } while (Math.abs(start - testtime) > EPSYLON);
127         collectGarbage(3);
128     }
129 
130     private void collectGarbage(int times) {
131         for (int i = 0; i < times; i++) {
132             System.gc();
133             try {
134                 Thread.sleep(10);
135             } catch (InterruptedException e) {
136                 Thread.currentThread().interrupt();
137                 break;
138             }
139         }
140     }
141 
142     /**
143      * Calculates the memory usage of a single element in a work queue. Based on
144      * Heinz Kabbutz' ideas
145      * (http://www.javaspecialists.eu/archive/Issue029.html).
146      *
147      * @return memory usage of a single {@link Runnable} element in the thread
148      * pools work queue
149      */
150     public long calculateMemoryUsage() {
151         BlockingQueue queue = createWorkQueue();
152         for (int i = 0; i < SAMPLE_QUEUE_SIZE; i++) {
153             queue.add(creatTask());
154         }
155 
156         long mem0 = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();
157         long mem1 = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();
158 
159         queue = null;
160 
161         collectGarbage(15);
162 
163         mem0 = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();
164         queue = createWorkQueue();
165 
166         for (int i = 0; i < SAMPLE_QUEUE_SIZE; i++) {
167             queue.add(creatTask());
168         }
169 
170         collectGarbage(15);
171 
172         mem1 = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();
173 
174         return (mem1 - mem0) / SAMPLE_QUEUE_SIZE;
175     }
176 
177     /**
178      * Create your runnable task here.
179      *
180      * @return an instance of your runnable task under investigation
181      */
182     protected abstract Runnable creatTask();
183 
184     /**
185      * Return an instance of the queue used in the thread pool.
186      *
187      * @return queue instance
188      */
189     protected abstract BlockingQueue createWorkQueue();
190 
191     /**
192      * Calculate current cpu time. Various frameworks may be used here,
193      * depending on the operating system in use. (e.g.
194      * http://www.hyperic.com/products/sigar). The more accurate the CPU time
195      * measurement, the more accurate the results for thread count boundaries.
196      *
197      * @return current cpu time of current thread
198      */
199     protected abstract long getCurrentThreadCPUTime();
200 
201 }

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：

 1 package threadpool;
 2 
 3 import java.io.BufferedReader;
 4 import java.io.IOException;
 5 import java.io.InputStreamReader;
 6 import java.lang.management.ManagementFactory;
 7 import java.math.BigDecimal;
 8 import java.net.HttpURLConnection;
 9 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), 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     public void run() {
45         HttpURLConnection connection = null;
46         BufferedReader reader = null;
47         try {
48             String getURL = "http://baidu.com";
49             URL getUrl = new URL(getURL);
50 
51             connection = (HttpURLConnection) getUrl.openConnection();
52             connection.connect();
53             reader = new BufferedReader(new InputStreamReader(
54                     connection.getInputStream()));
55 
56             String line;
57             while ((line = reader.readLine()) != null) {
58                 // empty loop
59             }
60         }
61 
62         catch (IOException e) {
63 
64         } finally {
65             if(reader != null) {
66                 try {
67                     reader.close();
68                 }
69                 catch(Exception e) {
70 
71                 }
72             }
73             connection.disconnect();
74         }
75 
76     }
77 
78 }

得到如下输出：

Target queue memory usage (bytes): 100000
createTask() produced threadpool.AsyncIOTask which took 40 bytes in a queue
Formula: 100000 / 40
* Recommended queue capacity (bytes): 2500
Number of CPU: 8
Target utilization: 1
Elapsed time (nanos): 3000000000
Compute time (nanos): 280801800
Wait time (nanos): 2719198200
Formula: 8 * 1 * (1 + 2719198200 / 280801800)
* Optimal thread count: 88

推荐的任务队列大小为2500，线程数为88。依次为依据，我们就可以构造这样一个线程池：

ThreadPoolExecutor pool = new ThreadPoolExecutor(88, 88, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue<Runnable>(2500));

可以将这个文件打包成可执行的jar文件，这样就可以拷贝到测试/正式环境上执行。

 1 <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 2   xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
 3     <modelVersion>4.0.0</modelVersion>
 4 
 5     <groupId>threadpool</groupId>
 6     <artifactId>dark-magic</artifactId>
 7     <version>1.0-SNAPSHOT</version>
 8     <packaging>jar</packaging>
 9 
10     <name>dark_magic</name>
11     <url>http://maven.apache.org</url>
12 
13     <properties>
14         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
15     </properties>
16 
17     <dependencies>
18         
19     </dependencies>
20 
21     <build>
22         <finalName>dark-magic</finalName>
23 
24         <plugins>
25             <plugin>
26                 <artifactId>maven-assembly-plugin</artifactId>
27                 <configuration>
28                     <appendAssemblyId>false</appendAssemblyId>
29                     <descriptorRefs>
30                         <descriptorRef>jar-with-dependencies</descriptorRef>
31                     </descriptorRefs>
32                     <archive>
33                         <manifest>
34                             <!-- 此处指定main方法入口的class -->
35                             <mainClass>threadpool.SimplePoolSizeCaculatorImpl</mainClass>
36                         </manifest>
37                     </archive>
38                 </configuration>
39                 <executions>
40                     <execution>
41                         <id>make-assembly</id>
42                         <phase>package</phase>
43                         <goals>
44                             <goal>assembly</goal>
45                         </goals>
46                     </execution>
47                 </executions>
48             </plugin>
49         </plugins>
50     </build>
51 </project>