如何合理地估算线程池大小？ · java学习摘记

[如何合理地估算线程池大小？](http://ifeve.com/how-to-calculate-threadpool-size/) 感谢网友【[蒋小强](http://weibo.com/u/1761654130)】投稿。 **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](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](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](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](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``));` | **原创文章，转载请注明：** 转载自[并发编程网 – ifeve.com](http://ifeve.com/)**本文链接地址:** [如何合理地估算线程池大小？](http://ifeve.com/how-to-calculate-threadpool-size/)