共计 9989 个字符,预计需要花费 25 分钟才能阅读完成。
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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