如何合理地估算线程池大小?
如何合理地估算线程池大小?
感谢网友【蒋小强】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:
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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:
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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:
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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:
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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:
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package pool_size_calculate; |
002 |
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import java.math.BigDecimal; |
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import java.math.RoundingMode; |
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import java.util.Timer; |
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import java.util.TimerTask; |
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import java.util.concurrent.BlockingQueue; |
008 |
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/** |
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* A class that calculates the optimal thread pool boundaries. It takes the |
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* desired target utilization and the desired work queue memory consumption as |
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* input and retuns thread count and work queue capacity. |
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* |
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* @author Niklas Schlimm |
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* |
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*/ |
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public abstract class PoolSizeCalculator { |
018 |
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/** |
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* The sample queue size to calculate the size of a single {@link Runnable} |
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* element. |
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*/ |
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private final int SAMPLE_QUEUE_SIZE = 1000 ; |
024 |
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/** |
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* Accuracy of test run. It must finish within 20ms of the testTime |
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* otherwise we retry the test. This could be configurable. |
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*/ |
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private final int EPSYLON = 20 ; |
030 |
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/** |
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* Control variable for the CPU time investigation. |
033 |
*/ |
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private volatile boolean expired; |
035 |
036 |
/** |
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* Time (millis) of the test run in the CPU time calculation. |
038 |
*/ |
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private final long testtime = 3000 ; |
040 |
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/** |
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* Calculates the boundaries of a thread pool for a given {@link Runnable}. |
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* |
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* @param targetUtilization |
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*
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) { |
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throw new IllegalStateException( "Test not accurate" ); |
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} |
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expired = false ; |
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start = System.currentTimeMillis(); |
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Timer timer = new Timer(); |
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timer.schedule( new TimerTask() { |
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public void run() { |
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expired = true ; |
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} |
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}, testtime); |
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while (!expired) { |
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task.run(); |
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} |
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start = System.currentTimeMillis() - start; |
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timer.cancel(); |
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} while (Math.abs(start - testtime) > EPSYLON); |
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collectGarbage( 3 ); |
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} |
064 |
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private void collectGarbage( int times) { |
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for ( int i = 0 ; i < times; i++) { |
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System.gc(); |
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try { |
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Thread.sleep( 10 ); |
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} catch (InterruptedException e) { |
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Thread.currentThread().interrupt(); |
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break ; |
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} |
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} |
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} |
076 |
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/** |
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* Calculates the memory usage of a single element in a work queue. Based on |
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* Heinz Kabbutz' ideas |
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* |
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* @return memory usage of a single {@link Runnable} element in the thread |
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* pools work queue |
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*/ |
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public long calculateMemoryUsage() { |
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BlockingQueue queue = createWorkQueue(); |
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for ( int i = 0 ; i < SAMPLE_QUEUE_SIZE; i++) { |
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queue.add(creatTask()); |
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} |
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long mem0 = Runtime.getRuntime().totalMemory() |
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- Runtime.getRuntime().freeMemory(); |
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long mem1 = Runtime.getRuntime().totalMemory() |
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- Runtime.getRuntime().freeMemory(); |
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queue = null ; |
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collectGarbage( 15 ); |
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mem0 = Runtime.getRuntime().totalMemory() |
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- Runtime.getRuntime().freeMemory(); |
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queue = createWorkQueue(); |
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for ( int i = 0 ; i < SAMPLE_QUEUE_SIZE; i++) { |
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queue.add(creatTask()); |
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} |
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collectGarbage( 15 ); |
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mem1 = Runtime.getRuntime().totalMemory() |
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- Runtime.getRuntime().freeMemory(); |
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return (mem1 - mem0) / SAMPLE_QUEUE_SIZE; |
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} |
107 |
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/** |
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* Create your runnable task here. |
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* |
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* @return an instance of your runnable task under investigation |
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*/ |
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protected abstract Runnable creatTask(); |
114 |
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/** |
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* Return an instance of the queue used in the thread pool. |
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* |
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* @return queue instance |
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*/ |
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protected abstract BlockingQueue createWorkQueue(); |
121 |
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/** |
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* Calculate current cpu time. Various frameworks may be used here, |
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* 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. |
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* |
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* @return current cpu time of current thread |
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*/ |
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protected abstract long getCurrentThreadCPUTime(); |
131 |
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} |
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:
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package pool_size_calculate; |
02 |
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import java.io.BufferedReader; |
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import java.io.IOException; |
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import java.io.InputStreamReader; |
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import java.lang.management.ManagementFactory; |
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import java.math.BigDecimal; |
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import java.net.HttpURLConnection; |
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import java.net.URL; |
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import java.util.concurrent.BlockingQueue; |
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import java.util.concurrent.LinkedBlockingQueue; |
12 |
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public class SimplePoolSizeCaculatorImpl extends PoolSizeCalculator { |
14 |
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@Override |
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protected Runnable creatTask() { |
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return new AsyncIOTask(); |
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} |
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@Override |
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protected BlockingQueue createWorkQueue() { |
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return new LinkedBlockingQueue( 1000 ); |
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} |
24 |
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@Override |
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protected long getCurrentThreadCPUTime() { |
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return ManagementFactory.getThreadMXBean().getCurrentThreadCpuTime(); |
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} |
29 |
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public static void main(String[] args) { |
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PoolSizeCalculator poolSizeCalculator = new SimplePoolSizeCaculatorImpl(); |
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poolSizeCalculator.calculateBoundaries( new BigDecimal( 1.0 ), new BigDecimal( 100000 )); |
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} |
34 |
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} |
36 |
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/** |
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* 自定义的异步IO任务 |
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* @author Will |
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* |
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*/ |
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class AsyncIOTask implements Runnable { |
43 |
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@Override |
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public void run() { |
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HttpURLConnection connection = null ; |
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BufferedReader reader = null ; |
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try { |
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String getURL = "http://baidu.com" ; |
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URL getUrl = new URL(getURL); |
51 |
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connection = (HttpURLConnection) getUrl.openConnection(); |
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connection.connect(); |
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reader = new BufferedReader( new InputStreamReader( |
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connection.getInputStream())); |
56 |
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String line; |
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while ((line = reader.readLine()) != null ) { |
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// empty loop |
60 |
} |
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} |
62 |
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catch (IOException e) { |
64 |
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} finally { |
66 |
if (reader != null ) { |
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try { |
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reader.close(); |
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} |
70 |
catch (Exception e) { |
71 |
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} |
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} |
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connection.disconnect(); |
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} |
76 |
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} |
78 |
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} |
得到的输出如下:
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Target queue memory usage (bytes): 100000 |
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createTask() produced pool_size_calculate.AsyncIOTask which took 40 bytes in a queue |
03 |
Formula: 100000 / 40 |
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* Recommended queue capacity (bytes): 2500 |
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Number of CPU: 4 |
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Target utilization: 1 |
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Elapsed time (nanos): 3000000000 |
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Compute time (nanos): 47181000 |
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Wait time (nanos): 2952819000 |
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Formula: 4 * 1 * (1 + 2952819000 / 47181000) |
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* 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:
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ThreadPoolExecutor pool = |
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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本文链接地址: 如何合理地估算线程池大小?