如何合理地估算线程池大小？
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2021-01-21 22:20
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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：
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：
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。
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。
另外，关注公众号Java技术栈，在后台回复：面试，可以获取我整理的 Java 系统面试题和答案，包括多线程、JVM 等，非常齐全。
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 threadpool;
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 threadpool;
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 {
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(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。
"http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
4.0.0
threadpool
dark-magic
1.0-SNAPSHOT
jar
dark_magic
http://maven.apache.org
UTF-8
dark-magic
maven-assembly-plugin
false
jar-with-dependencies
threadpool.SimplePoolSizeCaculatorImpl
make-assembly
package
assembly
来源：
www.cnblogs.com/cjsblog/p/9068886.html
参考：
http://ifeve.com/how-to-calculate-threadpool-size/
http://www.importnew.com/17384.html
https://www.cnblogs.com/cherish010/p/8334952.html
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