如何合理地估算線程池大小？

這個問題雖然看起來很小，卻并不那么容易回答。大家如果有更好的方法歡迎賜教，先來一個天真的估算方法：假設(shè)要求一個系統(tǒng)的TPS（Transaction Per Second或者Task Per Second）至少為20，然后假設(shè)每個Transaction由一個線程完成，繼續(xù)假設(shè)平均每個線程處理一個Transaction的時間為4s。那么問題轉(zhuǎn)化為：

如何設(shè)計線程池大小，使得可以在1s內(nèi)處理完20個Transaction？

計算過程很簡單，每個線程的處理能力為0.25TPS，那么要達到20TPS，顯然需要20/0.25=80個線程。

很顯然這個估算方法很天真，因為它沒有考慮到CPU數(shù)目。一般服務(wù)器的CPU核數(shù)為16或者32，如果有80個線程，那么肯定會帶來太多不必要的線程上下文切換開銷。

再來第二種簡單的但不知是否可行的方法（N為CPU總核數(shù)）：

如果是CPU密集型應(yīng)用，則線程池大小設(shè)置為N+1
如果是IO密集型應(yīng)用，則線程池大小設(shè)置為2N+1
如果一臺服務(wù)器上只部署這一個應(yīng)用并且只有這一個線程池，那么這種估算或許合理，具體還需自行測試驗證。

接下來在這個文檔：服務(wù)器性能IO優(yōu)化 中發(fā)現(xiàn)一個估算公式：

最佳線程數(shù)目 = （（線程等待時間+線程CPU時間）/線程CPU時間 ）* CPU數(shù)目

比如平均每個線程CPU運行時間為0.5s，而線程等待時間（非CPU運行時間，比如IO）為1.5s，CPU核心數(shù)為8，那么根據(jù)上面這個公式估算得到：((0.5+1.5)/0.5)*8=32。這個公式進一步轉(zhuǎn)化為：

最佳線程數(shù)目 = （線程等待時間與線程CPU時間之比 + 1）* CPU數(shù)目

可以得出一個結(jié)論：

線程等待時間所占比例越高，需要越多線程。線程CPU時間所占比例越高，需要越少線程。

上一種估算方法也和這個結(jié)論相合。

一個系統(tǒng)最快的部分是CPU，所以決定一個系統(tǒng)吞吐量上限的是CPU。增強CPU處理能力，可以提高系統(tǒng)吞吐量上限。但根據(jù)短板效應(yīng)，真實的系統(tǒng)吞吐量并不能單純根據(jù)CPU來計算。那要提高系統(tǒng)吞吐量，就需要從“系統(tǒng)短板”（比如網(wǎng)絡(luò)延遲、IO）著手：

盡量提高短板操作的并行化比率，比如多線程下載技術(shù)
增強短板能力，比如用NIO替代IO
第一條可以聯(lián)系到Amdahl定律，這條定律定義了串行系統(tǒng)并行化后的加速比計算公式：

加速比=優(yōu)化前系統(tǒng)耗時 / 優(yōu)化后系統(tǒng)耗時

加速比越大，表明系統(tǒng)并行化的優(yōu)化效果越好。Addahl定律還給出了系統(tǒng)并行度、CPU數(shù)目和加速比的關(guān)系，加速比為Speedup，系統(tǒng)串行化比率（指串行執(zhí)行代碼所占比率）為F，CPU數(shù)目為N：

Speedup <= 1 / (F + (1-F)/N)

當N足夠大時，串行化比率F越小，加速比Speedup越大。

寫到這里，我突然冒出一個問題。

是否使用線程池就一定比使用單線程高效呢？

答案是否定的，比如Redis就是單線程的，但它卻非常高效，基本操作都能達到十萬量級/s。從線程這個角度來看，部分原因在于：

• 多線程帶來線程上下文切換開銷，單線程就沒有這種開銷
• 鎖

當然“Redis很快”更本質(zhì)的原因在于：Redis基本都是內(nèi)存操作，這種情況下單線程可以很高效地利用CPU。而多線程適用場景一般是：存在相當比例的IO和網(wǎng)絡(luò)操作。

所以即使有上面的簡單估算方法，也許看似合理，但實際上也未必合理，都需要結(jié)合系統(tǒng)真實情況（比如是IO密集型或者是CPU密集型或者是純內(nèi)存操作）和硬件環(huán)境（CPU、內(nèi)存、硬盤讀寫速度、網(wǎng)絡(luò)狀況等）來不斷嘗試達到一個符合實際的合理估算值。

最后來一個“Dark Magic”估算方法（因為我暫時還沒有搞懂它的原理），使用下面的類：

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 = createTask();
        start(task);
        start(task); // warm up phase
        long cpuTime = getCurrentThreadCPUTime();
        start(task); // test intervally
        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 " + createTask().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(createTask());
        }
        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(createTask());
        }
        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 createTask();

    /**
     * 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();
}

然后自己繼承這個抽象類并實現(xiàn)它的三個抽象方法，比如下面是我寫的一個示例（任務(wù)是請求網(wǎng)絡(luò)數(shù)據(jù)），其中我指定期望CPU利用率為1.0（即100%），任務(wù)隊列總大小不超過100,000字節(jié)：

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 SimplePoolSizeCalculatorImpl extends PoolSizeCalculator {

    @Override
    protected Runnable createTask() {
        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 SimplePoolSizeCalculatorImpl();
        poolSizeCalculator.calculateBoundaries(new BigDecimal(1.0), new BigDecimal(100000));
    }

}

/**
 * 自定義的異步IO任務(wù)
 * @author Will
 *
 */
class AsyncIOTask implements Runnable {

    @Override
    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();
        }
    }
}

得到的輸出如下：

Target queue memory usage (bytes): 100000
createTask() produced pool_size_calculate.AsyncIOTask which took 40 bytes in a queue
Formula: 100000 / 40
* Recommended queue capacity (bytes): 2500
Number of CPU: 4
Target utilization: 1
Elapsed time (nanos): 3000000000
Compute time (nanos): 47181000
Wait time (nanos): 2952819000
Formula: 4 * 1 * (1 + 2952819000 / 47181000)
* Optimal thread count: 256

推薦的任務(wù)隊列大小為2500，線程數(shù)為256，有點出乎意料之外。我可以如下構(gòu)造一個線程池：

ThreadPoolExecutor pool = 
new ThreadPoolExecutor(256, 256, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue(2500));

轉(zhuǎn)載 http://ifeve.com/how-to-calculate-threadpool-size/