如何合理地估算線程池大小？

時間 2020-03-24

標籤如何合理估算線程大小简体版

原文原文鏈接

蔣小強
http://ifeve.com/how-to-calcu...

如何合理地估算線程池大小？html

這個問題雖然看起來很小，卻並不那麼容易回答。你們若是有更好的方法歡迎賜教，先來一個天真的估算方法：假設要求一個系統的TPS（Transaction Per Second或者Task Per Second）至少爲20，而後假設每一個Transaction由一個線程完成，繼續假設平均每一個線程處理一個Transaction的時間爲4s。那麼問題轉化爲：java

如何設計線程池大小，使得能夠在1s內處理完20個Transaction？面試

計算過程很簡單，每一個線程的處理能力爲0.25TPS，那麼要達到20TPS，顯然須要20/0.25=80個線程。後端

很顯然這個估算方法很天真，由於它沒有考慮到CPU數目。通常服務器的CPU核數爲16或者32，若是有80個線程，那麼確定會帶來太多沒必要要的線程上下文切換開銷。服務器

再來第二種簡單的但不知是否可行的方法（N爲CPU總核數）：網絡

若是是CPU密集型應用，則線程池大小設置爲N+1
若是是IO密集型應用，則線程池大小設置爲2N+1

若是一臺服務器上只部署這一個應用而且只有這一個線程池，那麼這種估算或許合理，具體還需自行測試驗證。多線程

接下來在這個文檔：服務器性能IO優化中發現一個估算公式：架構

最佳線程數目 = （（線程等待時間+線程CPU時間）/線程CPU時間）* CPU數目

好比平均每一個線程CPU運行時間爲0.5s，而線程等待時間（非CPU運行時間，好比IO）爲1.5s，CPU核心數爲8，那麼根據上面這個公式估算獲得：((0.5+1.5)/0.5)*8=32。這個公式進一步轉化爲：異步

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

能夠得出一個結論：ide

線程等待時間所佔比例越高，須要越多線程。線程CPU時間所佔比例越高，須要越少線程。

上一種估算方法也和這個結論相合。

一個系統最快的部分是CPU，因此決定一個系統吞吐量上限的是CPU。加強CPU處理能力，能夠提升系統吞吐量上限。但根據短板效應，真實的系統吞吐量並不能單純根據CPU來計算。那要提升系統吞吐量，就須要從「系統短板」（好比網絡延遲、IO）着手：

儘可能提升短板操做的並行化比率，好比多線程下載技術
加強短板能力，好比用NIO替代IO

第一條能夠聯繫到Amdahl定律，這條定律定義了串行系統並行化後的加速比計算公式：

加速比=優化前系統耗時 / 優化後系統耗時

加速比越大，代表系統並行化的優化效果越好。Addahl定律還給出了系統並行度、CPU數目和加速比的關係，加速比爲Speedup，系統串行化比率（指串行執行代碼所佔比率）爲F，CPU數目爲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。

寫到這裏，我忽然冒出一個問題。

是否使用線程池就必定比使用單線程高效呢？

答案是否認的，好比Redis就是單線程的，但它卻很是高效，基本操做都能達到十萬量級/s。從線程這個角度來看，部分緣由在於：

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

固然「Redis很快」更本質的緣由在於：Redis基本都是內存操做，這種狀況下單線程能夠很高效地利用CPU。而多線程適用場景通常是：存在至關比例的IO和網絡操做。

因此即便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，都須要結合系統真實狀況（好比是IO密集型或者是CPU密集型或者是純內存操做）和硬件環境（CPU、內存、硬盤讀寫速度、網絡情況等）來不斷嘗試達到一個符合實際的合理估算值。

最後來一個「Dark Magic」估算方法（由於我暫時尚未搞懂它的原理），使用下面的類：

package pool_size_calculate;

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();

}

而後本身繼承這個抽象類並實現它的三個抽象方法，好比下面是我寫的一個示例（任務是請求網絡數據），其中我指按期望CPU利用率爲1.0（即100%），任務隊列總大小不超過100,000字節：

package pool_size_calculate;

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));
    }

}

/**
 * 自定義的異步IO任務
 * @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();
        }

    }

}

獲得的輸出以下：

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

推薦的任務隊列大小爲2500，線程數爲256，有點出乎意料以外。我能夠以下構造一個線程池：

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

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