如何合理地估算线程池大小？

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mb5ff5909699060 2018-11-09 14:06:00

文章标签 java 线程池大小 ide 线程池加速比 文章分类 代码人生

如何合理地估算线程池大小？

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：

`1`	`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：

`1`	`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：

`1`	`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：

`1`	`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：

`001`	`package` `pool_size_calculate;`

`002`

`003`	`import` `java.math.BigDecimal;`

`004`	`import` `java.math.RoundingMode;`

`005`	`import` `java.util.Timer;`

`006`	`import` `java.util.TimerTask;`

`007`	`import` `java.util.concurrent.BlockingQueue;`

`008`

`009`	`/**`

`010`	`* A class that calculates the optimal thread pool boundaries. It takes the`

`011`	`* desired target utilization and the desired work queue memory consumption as`

`012`	`* input and retuns thread count and work queue capacity.`

`013`	`*`

`014`	`* @author Niklas Schlimm`

`015`	`*`

`016`	`*/`

`017`	`public` `abstract` `class` `PoolSizeCalculator {`

`018`

`019`	`/**`

`020`	`* The sample queue size to calculate the size of a single {@link Runnable}`

`021`	`* element.`

`022`	`*/`

`023`	`private` `final` `int` `SAMPLE_QUEUE_SIZE =` `1000``;`

`024`

`025`	`/**`

`026`	`* Accuracy of test run. It must finish within 20ms of the testTime`

`027`	`* otherwise we retry the test. This could be configurable.`

`028`	`*/`

`029`	`private` `final` `int` `EPSYLON =` `20``;`

`030`

`031`	`/**`

`032`	`* Control variable for the CPU time investigation.`

`033`	`*/`

`034`	`private` `volatile` `boolean` `expired;`

`035`

`036`	`/**`

`037`	`* Time (millis) of the test run in the CPU time calculation.`

`038`	`*/`

`039`	`private` `final` `long` `testtime =` `3000``;`

`040`

`041`	`/**`

`042`	`* Calculates the boundaries of a thread pool for a given {@link Runnable}.`

`043`	`*`

`044`	`* @param targetUtilization`

`045`	`* 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) {

`046`	`throw` `new` `IllegalStateException(``"Test not accurate"``);`

`047`	`}`

`048`	`expired =` `false``;`

`049`	`start = System.currentTimeMillis();`

`050`	`Timer timer =` `new` `Timer();`

`051`	`timer.schedule(``new` `TimerTask() {`

`052`	`public` `void` `run() {`

`053`	`expired =` `true``;`

`054`	`}`

`055`	`}, testtime);`

`056`	`while` `(!expired) {`

`057`	`task.run();`

`058`	`}`

`059`	`start = System.currentTimeMillis() - start;`

`060`	`timer.cancel();`

`061`	`}` `while` `(Math.abs(start - testtime) > EPSYLON);`

`062`	`collectGarbage(``3``);`

`063`	`}`

`064`

`065`	`private` `void` `collectGarbage(``int` `times) {`

`066`	`for` `(``int` `i =` `0``; i < times; i++) {`

`067`	`System.gc();`

`068`	`try` `{`

`069`	`Thread.sleep(``10``);`

`070`	`}` `catch` `(InterruptedException e) {`

`071`	`Thread.currentThread().interrupt();`

`072`	`break``;`

`073`	`}`

`074`	`}`

`075`	`}`

`076`

`077`	`/**`

`078`	`* Calculates the memory usage of a single element in a work queue. Based on`

`079`	`* Heinz Kabbutz' ideas`

`080`	`* (http://www.javaspecialists.eu/archive/Issue029.html).`

`081`	`*`

`082`	`* @return memory usage of a single {@link Runnable} element in the thread`

`083`	`* pools work queue`

`084`	`*/`

`085`	`public` `long` `calculateMemoryUsage() {`

`086`	`BlockingQueue queue = createWorkQueue();`

`087`	`for` `(``int` `i =` `0``; i < SAMPLE_QUEUE_SIZE; i++) {`

`088`	`queue.add(creatTask());`

`089`	`}`

`090`	`long` `mem0 = Runtime.getRuntime().totalMemory()`

`091`	`- Runtime.getRuntime().freeMemory();`

`092`	`long` `mem1 = Runtime.getRuntime().totalMemory()`

`093`	`- Runtime.getRuntime().freeMemory();`

`094`	`queue =` `null``;`

`095`	`collectGarbage(``15``);`

`096`	`mem0 = Runtime.getRuntime().totalMemory()`

`097`	`- Runtime.getRuntime().freeMemory();`

`098`	`queue = createWorkQueue();`

`099`	`for` `(``int` `i =` `0``; i < SAMPLE_QUEUE_SIZE; i++) {`

`100`	`queue.add(creatTask());`

`101`	`}`

`102`	`collectGarbage(``15``);`

`103`	`mem1 = Runtime.getRuntime().totalMemory()`

`104`	`- Runtime.getRuntime().freeMemory();`

`105`	`return` `(mem1 - mem0) / SAMPLE_QUEUE_SIZE;`

`106`	`}`

`107`

`108`	`/**`

`109`	`* Create your runnable task here.`

`110`	`*`

`111`	`* @return an instance of your runnable task under investigation`

`112`	`*/`

`113`	`protected` `abstract` `Runnable creatTask();`

`114`

`115`	`/**`

`116`	`* Return an instance of the queue used in the thread pool.`

`117`	`*`

`118`	`* @return queue instance`

`119`	`*/`

`120`	`protected` `abstract` `BlockingQueue createWorkQueue();`

`121`

`122`	`/**`

`123`	`* Calculate current cpu time. Various frameworks may be used here,`

`124`	`* depending on the operating system in use. (e.g.`

`125`	`* http://www.hyperic.com/products/sigar). The more accurate the CPU time`

`126`	`* measurement, the more accurate the results for thread count boundaries.`

`127`	`*`

`128`	`* @return current cpu time of current thread`

`129`	`*/`

`130`	`protected` `abstract` `long` `getCurrentThreadCPUTime();`

`131`

`132`	`}`

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：

`01`	`package` `pool_size_calculate;`

`02`

`03`	`import` `java.io.BufferedReader;`

`04`	`import` `java.io.IOException;`

`05`	`import` `java.io.InputStreamReader;`

`06`	`import` `java.lang.management.ManagementFactory;`

`07`	`import` `java.math.BigDecimal;`

`08`	`import` `java.net.HttpURLConnection;`

`09`	`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`	`@Override`

`45`	`public` `void` `run() {`

`46`	`HttpURLConnection connection =` `null``;`

`47`	`BufferedReader reader =` `null``;`

`48`	`try` `{`

`49`	`String getURL =` `"http://baidu.com"``;`

`50`	`URL getUrl =` `new` `URL(getURL);`

`51`

`52`	`connection = (HttpURLConnection) getUrl.openConnection();`

`53`	`connection.connect();`

`54`	`reader =` `new` `BufferedReader(``new` `InputStreamReader(`

`55`	`connection.getInputStream()));`

`56`

`57`	`String line;`

`58`	`while` `((line = reader.readLine()) !=` `null``) {`

`59`	`// empty loop`

`60`	`}`

`61`	`}`

`62`

`63`	`catch` `(IOException e) {`

`64`

`65`	`}` `finally` `{`

`66`	`if``(reader !=` `null``) {`

`67`	`try` `{`

`68`	`reader.close();`

`69`	`}`

`70`	`catch``(Exception e) {`

`71`

`72`	`}`

`73`	`}`

`74`	`connection.disconnect();`

`75`	`}`

`76`

`77`	`}`

`78`

`79`	`}`

得到的输出如下：

`01`	`Target queue memory usage (bytes): 100000`

`02`	`createTask() produced pool_size_calculate.AsyncIOTask which took 40 bytes in a queue`

`03`	`Formula: 100000 / 40`

`04`	`* Recommended queue capacity (bytes): 2500`

`05`	`Number of CPU: 4`

`06`	`Target utilization: 1`

`07`	`Elapsed time (nanos): 3000000000`

`08`	`Compute time (nanos): 47181000`

`09`	`Wait time (nanos): 2952819000`

`10`	`Formula: 4 * 1 * (1 + 2952819000 / 47181000)`

`11`	`* 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：

`1`	`ThreadPoolExecutor pool =`

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

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如何合理地估算线程池大小？

如何合理地估算线程池大小？

如何合理地估算线程池大小？

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