什么是网络营销中最古老的一种,正版搜索引擎优化,巩义网站建设案例,wordpress删除页面地址如何合理地估算线程池大小#xff1f; 这个问题虽然看起来很小#xff0c;却并不那么容易回答。大家如果有更好的方法欢迎赐教#xff0c;先来一个天真的估算方法#xff1a;假设要求一个系统的TPS#xff08;Transaction Per Second或者Task Per Second#xff09;至少为… 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TPSTransaction 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20/0.2580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N1如果是IO密集型应用则线程池大小设置为2N1 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1.5sCPU核心数为8那么根据上面这个公式估算得到((0.51.5)/0.5)*832。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FCPU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 voidcalculateBoundaries(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), newBigDecimal(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 查看源代码 打印帮助 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, newLinkedBlockingQueue(2500));
