eclipse mysql 线程池_如何合理地估算线程池大小？

分手后的思念是犯贱 2022-10-31 01:34 194阅读 0赞

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：

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：

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：

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：

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：

package threadpool;

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：

package threadpool;

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));

/\*\*

\* 自定义的异步IO任务

\* @author Will

\*/

class AsyncIOTask implements Runnable \{

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();

得到如下输出：

Target queue memory usage (bytes): 100000

createTask() produced threadpool.AsyncIOTask which took 40 bytes in a queue

Formula: 100000 / 40

\* Recommended queue capacity (bytes): 2500

Number of CPU: 8

Target utilization: 1

Elapsed time (nanos): 3000000000

Compute time (nanos): 280801800

Wait time (nanos): 2719198200

Formula: 8 \* 1 \* (1 + 2719198200 / 280801800)

\* Optimal thread count: 88

推荐的任务队列大小为2500，线程数为88。依次为依据，我们就可以构造这样一个线程池：

ThreadPoolExecutor pool = new ThreadPoolExecutor(88, 88, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue(2500));

可以将这个文件打包成可执行的jar文件，这样就可以拷贝到测试/正式环境上执行。

xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

4.0.0

threadpool

dark-magic

1.0-SNAPSHOT

jar

dark\_magic

http://maven.apache.org

UTF-8

dark-magic

maven-assembly-plugin

false

jar-with-dependencies

threadpool.SimplePoolSizeCaculatorImpl

make-assembly

package

assembly

![700715fa29e8a9cdf64661f8c51b5f49.png][]

来源：

www.cnblogs.com/cjsblog/p/9068886.html

参考：

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觉得不错，别忘了随手点赞+转发哦！

[700715fa29e8a9cdf64661f8c51b5f49.png]: /images/20221024/57a408888e6244cdbb768b70e6f93b02.png

eclipse mysql 线程池_如何合理地估算线程池大小？

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