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>
转载：
http://ifeve.com/how-to-calculate-threadpool-size/
http://www.importnew.com/17384.html
https://www.cnblogs.com/cherish010/p/8334952.html