0x6 Java系列：如何合理地估算線程池大小？

如何合理地估算線程池大小？

這個(gè)問題雖然看起來很小，卻并不那么容易回答。大家如果有更好的方法歡迎賜教，先來一個(gè)天真的估算方法：假設(shè)要求一個(gè)系統(tǒng)的TPS（Transaction Per Second或者Task Per Second）至少為20，然后假設(shè)每個(gè)Transaction由一個(gè)線程完成，繼續(xù)假設(shè)平均每個(gè)線程處理一個(gè)Transaction的時(shí)間為4s。那么問題轉(zhuǎn)化為：

如何設(shè)計(jì)線程池大小，使得可以在1s內(nèi)處理完20個(gè)Transaction？

計(jì)算過程很簡單，每個(gè)線程的處理能力為0.25TPS，那么要達(dá)到20TPS，顯然需要20/0.25=80個(gè)線程。

很顯然這個(gè)估算方法很天真，因?yàn)樗鼪]有考慮到CPU數(shù)目。一般服務(wù)器的CPU核數(shù)為16或者32，如果有80個(gè)線程，那么肯定會帶來太多不必要的線程上下文切換開銷。

再來第二種簡單的但不知是否可行的方法（N為CPU總核數(shù)）：

·??????如果是CPU密集型應(yīng)用，則線程池大小設(shè)置為N+1

·??????如果是IO密集型應(yīng)用，則線程池大小設(shè)置為2N+1

如果一臺服務(wù)器上只部署這一個(gè)應(yīng)用并且只有這一個(gè)線程池，那么這種估算或許合理，具體還需自行測試驗(yàn)證。

接下來在這個(gè)文檔：服務(wù)器性能IO優(yōu)化中發(fā)現(xiàn)一個(gè)估算公式：

最佳線程數(shù)目=?（（線程等待時(shí)間+線程CPU時(shí)間）/線程CPU時(shí)間）* CPU數(shù)目

比如平均每個(gè)線程CPU運(yùn)行時(shí)間為0.5s，而線程等待時(shí)間（非CPU運(yùn)行時(shí)間，比如IO）為1.5s，CPU核心數(shù)為8，那么根據(jù)上面這個(gè)公式估算得到：((0.5+1.5)/0.5)*8=32。這個(gè)公式進(jìn)一步轉(zhuǎn)化為：

最佳線程數(shù)目=?（線程等待時(shí)間與線程CPU時(shí)間之比+ 1）* CPU數(shù)目

可以得出一個(gè)結(jié)論：

線程等待時(shí)間所占比例越高，需要越多線程。線程CPU時(shí)間所占比例越高，需要越少線程。

上一種估算方法也和這個(gè)結(jié)論相合。

一個(gè)系統(tǒng)最快的部分是CPU，所以決定一個(gè)系統(tǒng)吞吐量上限的是CPU。增強(qiáng)CPU處理能力，可以提高系統(tǒng)吞吐量上限。但根據(jù)短板效應(yīng)，真實(shí)的系統(tǒng)吞吐量并不能單純根據(jù)CPU來計(jì)算。那要提高系統(tǒng)吞吐量，就需要從“系統(tǒng)短板”（比如網(wǎng)絡(luò)延遲、IO）著手：

·??????盡量提高短板操作的并行化比率，比如多線程下載技術(shù)

·??????增強(qiáng)短板能力，比如用NIO替代IO

第一條可以聯(lián)系到Amdahl定律，這條定律定義了串行系統(tǒng)并行化后的加速比計(jì)算公式：

加速比=優(yōu)化前系統(tǒng)耗時(shí)/?優(yōu)化后系統(tǒng)耗時(shí)

加速比越大，表明系統(tǒng)并行化的優(yōu)化效果越好。Addahl定律還給出了系統(tǒng)并行度、CPU數(shù)目和加速比的關(guān)系，加速比為Speedup，系統(tǒng)串行化比率（指串行執(zhí)行代碼所占比率）為F，CPU數(shù)目為N：

Speedup <= 1 / (F + (1-F)/N)

當(dāng)N足夠大時(shí)，串行化比率F越小，加速比Speedup越大。

寫到這里，我突然冒出一個(gè)問題。

是否使用線程池就一定比使用單線程高效呢？

答案是否定的，比如Redis就是單線程的，但它卻非常高效，基本操作都能達(dá)到十萬量級/s。從線程這個(gè)角度來看，部分原因在于：

·??????多線程帶來線程上下文切換開銷，單線程就沒有這種開銷

·??????鎖

當(dāng)然“Redis很快”更本質(zhì)的原因在于：Redis基本都是內(nèi)存操作，這種情況下單線程可以很高效地利用CPU。而多線程適用場景一般是：存在相當(dāng)比例的IO和網(wǎng)絡(luò)操作。

所以即使有上面的簡單估算方法，也許看似合理，但實(shí)際上也未必合理，都需要結(jié)合系統(tǒng)真實(shí)情況（比如是IO密集型或者是CPU密集型或者是純內(nèi)存操作）和硬件環(huán)境（CPU、內(nèi)存、硬盤讀寫速度、網(wǎng)絡(luò)狀況等）來不斷嘗試達(dá)到一個(gè)符合實(shí)際的合理估算值。

最后來一個(gè)“Dark Magic”估算方法（因?yàn)槲視簳r(shí)還沒有搞懂它的原理），使用下面的類：

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();

}

然后自己繼承這個(gè)抽象類并實(shí)現(xiàn)它的三個(gè)抽象方法，比如下面是我寫的一個(gè)示例（任務(wù)是請求網(wǎng)絡(luò)數(shù)據(jù)），其中我指定期望CPU利用率為1.0（即100%），任務(wù)隊(duì)列總大小不超過100,000字節(jié)：

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));

}

}

/**

*?自定義的異步IO任務(wù)

* @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();

}

}

}

得到的輸出如下：

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

推薦的任務(wù)隊(duì)列大小為2500，線程數(shù)為256，有點(diǎn)出乎意料之外。我可以如下構(gòu)造一個(gè)線程池：

ThreadPoolExecutor pool =

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

ifeve.com/how-to-calculate-threadpool-size/

版權(quán)聲明：本文內(nèi)容由網(wǎng)絡(luò)用戶投稿，版權(quán)歸原作者所有，本站不擁有其著作權(quán)，亦不承擔(dān)相應(yīng)法律責(zé)任。如果您發(fā)現(xiàn)本站中有涉嫌抄襲或描述失實(shí)的內(nèi)容，請聯(lián)系我們jiasou666@gmail.com 處理，核實(shí)后本網(wǎng)站將在24小時(shí)內(nèi)刪除侵權(quán)內(nèi)容。

版權(quán)聲明：本文內(nèi)容由網(wǎng)絡(luò)用戶投稿，版權(quán)歸原作者所有，本站不擁有其著作權(quán)，亦不承擔(dān)相應(yīng)法律責(zé)任。如果您發(fā)現(xiàn)本站中有涉嫌抄襲或描述失實(shí)的內(nèi)容，請聯(lián)系我們jiasou666@gmail.com 處理，核實(shí)后本網(wǎng)站將在24小時(shí)內(nèi)刪除侵權(quán)內(nèi)容。