感謝網友【蔣小強】投稿。
如何合理地估算線程池大小？
這個問題雖然看起來很小，卻並不那麼容易回答。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：假設要求一個系統的TPS（Transaction Per Second或者Task Per Second）至少爲20，然後假設每個Transaction由一個線程完成，繼續假設平均每個線程處理一個Transaction的時間爲4s。那麼問題轉化爲：
如何設計線程池大小，使得可以在1s內處理完20個Transaction？
計算過程很簡單，每個線程的處理能力爲0.25TPS，那麼要達到20TPS，顯然需要20/0.25=80個線程。
很顯然這個估算方法很天真，因爲它沒有考慮到CPU數目。一般服務器的CPU核數爲16或者32，如果有80個線程，那麼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爲CPU總核數）：
- 如果是CPU密集型應用，則線程池大小設置爲N+1
- 如果是IO密集型應用，則線程池大小設置爲2N+1
如果一臺服務器上只部署這一個應用並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。
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：
1 |
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 |
比如平均每個線程CPU運行時間爲0.5s，而線程等待時間（非CPU運行時間，比如IO）爲1.5s，CPU核心數爲8，那麼根據上面這個公式估算得到：((0.5+1.5)/0.5)*8=32。這個公式進一步轉化爲：
1 |
最佳線程數目
= （線程等待時間與線程CPU時間之比 + 1）* CPU數目 |
可以得出一個結論：
線程等待時間所佔比例越高，需要越多線程。線程CPU時間所佔比例越高，需要越少線程。
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，所以決定一個系統吞吐量上限的是CPU。增強CPU處理能力，可以提高系統吞吐量上限。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。那要提高系統吞吐量，就需要從“系統短板”（比如網絡延遲、IO）着手：
- 儘量提高短板操作的並行化比率，比如多線程下載技術
- 增強短板能力，比如用NIO替代IO
第一條可以聯繫到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：
1 |
加速比=優化前系統耗時
/ 優化後系統耗時 |
加速比越大，表明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：
1 |
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
當然“Redis很快”更本質的原因在於：Redis基本都是內存操作，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和網絡操作。
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，都需要結合系統真實情況（比如是IO密集型或者是CPU密集型或者是純內存操作）和硬件環境（CPU、內存、硬盤讀寫速度、網絡狀況等）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（因爲我暫時還沒有搞懂它的原理），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();
}然後自己繼承這個抽象類並實現它的三個抽象方法，比如下面是我寫的一個示例（任務是請求網絡數據），其中我指定期望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();
}
}
}
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
ThreadPoolExecutor pool =
new ThreadPoolExecutor(256, 256, 0L, TimeUnit.MILLISECONDS, new LinkedBlockingQueue(2500));