关于java:如何合理地估算线程池大小

这个问题尽管看起来很小，却并不那么容易答复。

大家如果有更好的办法欢送赐教，先来一个天真的估算办法：

假如要求一个零碎的TPS（Transaction Per Second或者Task Per Second）至多为20，而后假如每个Transaction由一个线程实现，持续假如均匀每个线程解决一个Transaction的工夫为4s。

那么问题转化为：如何设计线程池大小，使得能够在1s内解决完20个Transaction？

计算过程很简略，每个线程的解决能力为0.25TPS，那么要达到20TPS，显然须要20/0.25=80个线程。

很显然这个估算办法很天真，因为它没有思考到CPU数目。个别服务器的CPU核数为16或者32，如果有80个线程，那么必定会带来太多不必要的线程上下文切换开销。

再来第二种简略的但不知是否可行的办法（N为CPU总核数）：

如果是CPU密集型利用，则线程池大小设置为N+1
如果是IO密集型利用，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利用并且只有这一个线程池，那么这种估算或者正当，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工夫）/线程CPU工夫）* CPU数目

比方均匀每个线程CPU运行工夫为0.5s，而线程等待时间（非CPU运行工夫，比方IO）为1.5s，CPU外围数为8，那么依据下面这个公式估算失去：((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工夫之比 + 1）* CPU数目

能够得出一个论断：线程等待时间所占比例越高，须要越多线程。线程CPU工夫所占比例越高，须要越少线程。

上一种估算办法也和这个论断相合。

一个零碎最快的局部是CPU，所以决定一个零碎吞吐量下限的是CPU。加强CPU解决能力，能够进步零碎吞吐量下限。但依据短板效应，实在的零碎吞吐量并不能单纯依据CPU来计算。那要进步零碎吞吐量，就须要从“零碎短板”（比方网络提早、IO）着手：

尽量进步短板操作的并行化比率，比方多线程下载技术
加强短板能力，比方用NIO代替IO

第一条能够分割到Amdahl定律，这条定律定义了串行零碎并行化后的减速比计算公式：

减速比=优化前零碎耗时 / 优化后零碎耗时

减速比越大，表明零碎并行化的优化成果越好。Addahl定律还给出了零碎并行度、CPU数目和减速比的关系，减速比为Speedup，零碎串行化比率（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，减速比Speedup越大。

写到这里，我忽然冒出一个问题。

是否应用线程池就肯定比应用单线程高效呢？

答案是否定的，比方Redis就是单线程的，但它却十分高效，基本操作都能达到十万量级/s。从线程这个角度来看，局部起因在于：

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”更实质的起因在于：Redis根本都是内存操作，这种状况下单线程能够很高效地利用CPU。而多线程实用场景个别是：存在相当比例的IO和网络操作。

所以即便有下面的简略估算办法，兴许看似正当，但实际上也未必正当，都须要联合零碎真实情况（比方是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、网络情况等）来一直尝试达到一个符合实际的正当估算值。

最初来一个“Dark Magic”估算办法（因为我临时还没有搞懂它的原理），应用上面的类：

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();

}

而后本人继承这个抽象类并实现它的三个形象办法，比方上面是我写的一个示例（工作是申请网络数据），其中我指定冀望CPU利用率为1.0（即100%），工作队列总大小不超过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工作
 * @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();
        }

    }

}

失去如下输入：

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

举荐的工作队列大小为2500，线程数为88。顺次为根据，咱们就能够结构这样一个线程池：

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

能够将这个文件打包成可执行的jar文件，这样就能够拷贝到测试/正式环境上执行。

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>threadpool</groupId>
    <artifactId>dark-magic</artifactId>
    <version>1.0-SNAPSHOT</version>
    <packaging>jar</packaging>

    <name>dark_magic</name>
    <url>http://maven.apache.org</url>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>

    </dependencies>

    <build>
        <finalName>dark-magic</finalName>

        <plugins>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <appendAssemblyId>false</appendAssemblyId>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                    <archive>
                        <manifest>
                            <!-- 此处指定main办法入口的class -->
                            <mainClass>threadpool.SimplePoolSizeCaculatorImpl</mainClass>
                        </manifest>
                    </archive>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>assembly</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

起源：

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

参考：

http://ifeve.com/how-to-calcu…\
http://www.importnew.com/1738…\
https://www.cnblogs.com/cheri…

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