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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

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 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 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));

文章目录
  1. 1. 如何合理地估算线程池大小?
  2. 2. 如何设计线程池大小,使得可以在1s内处理完20个Transaction
  3. 3. 是否使用线程池就一定比使用单线程高效呢?