多线程与高并发（九）：单机压测工具JMH，单机最快MQ

单机压测工具JMH

JMH Java准测试工具套件

什么是JMH

官网

http://openjdk.java.net/projects/code-tools/jmh/

创建JMH测试

1.创建Maven项目，添加依赖

<?xml version="1.0" encoding="UTF-8"?><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><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><encoding>UTF-8</encoding><java.version>1.8</java.version><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target></properties><groupId>mashibing.com</groupId><artifactId>HelloJMH2</artifactId><version>1.0-SNAPSHOT</version><dependencies><!-- https://mvnrepository.com/artifact/org.openjdk.jmh/jmh-core --><dependency><groupId>org.openjdk.jmh</groupId><artifactId>jmh-core</artifactId><version>1.21</version></dependency><!-- https://mvnrepository.com/artifact/org.openjdk.jmh/jmh-generator-annprocess --><dependency><groupId>org.openjdk.jmh</groupId><artifactId>jmh-generator-annprocess</artifactId><version>1.21</version><scope>test</scope></dependency></dependencies></project>

2.idea安装JMH插件 JMH plugin v1.0.3

3.由于用到了注解，打开运行程序注解配置

compiler -> Annotation Processors -> Enable Annotation Processing

4.定义需要测试类PS (ParallelStream)

package com.mashibing.jmh;import java.util.ArrayList;
import java.util.List;
import java.util.Random;public class PS {static List<Integer> nums = new ArrayList<>();static {Random r = new Random();for (int i = 0; i < 10000; i++) nums.add(1000000 + r.nextInt(1000000));}static void foreach() {nums.forEach(v->isPrime(v));}static void parallel() {nums.parallelStream().forEach(PS::isPrime);}static boolean isPrime(int num) {for(int i=2; i<=num/2; i++) {if(num % i == 0) return false;}return true;}
}

5.写单元测试

这个测试类一定要在 test package下面

package com.mashibing.jmh;import org.openjdk.jmh.annotations.*;import static org.junit.jupiter.api.Assertions.*;public class PSTest {@Benchmark@Warmup(iterations = 1, time = 3)@Fork(5)@BenchmarkMode(Mode.Throughput)@Measurement(iterations = 1, time = 3)public void testForEach() {PS.foreach();}
}

6.运行测试类，如果遇到下面的错误：

   ERROR: org.openjdk.jmh.runner.RunnerException: ERROR: Exception while trying to acquire the JMH lock (C:\WINDOWS\/jmh.lock): C:\WINDOWS\jmh.lock (拒绝访问。), exiting. Use -Djmh.ignoreLock=true to forcefully continue.at org.openjdk.jmh.runner.Runner.run(Runner.java:216)at org.openjdk.jmh.Main.main(Main.java:71)

这个错误是因为JMH运行需要访问系统的TMP目录，解决办法是：

打开RunConfiguration -> Environment Variables -> include system environment viables

7.阅读测试报告

JMH中的基本概念

Warmup
预热，由于JVM中对于特定代码会存在优化（本地化），预热对于测试结果很重要
Mesurement
总共执行多少次测试
Timeout
Threads
线程数，由fork指定
Benchmark mode
基准测试的模式
Benchmark
测试哪一段代码

官方样例：
http://hg.openjdk.java.net/code-tools/jmh/file/tip/jmh-samples/src/main/java/org/openjdk/jmh/samples/

Disruptor单机最快MQ

内存里的高效队列

介绍

主页：http://lmax-exchange.github.io/disruptor/

源码：https://github.com/LMAX-Exchange/disruptor

GettingStarted: https://github.com/LMAX-Exchange/disruptor/wiki/Getting-Started

api: http://lmax-exchange.github.io/disruptor/docs/index.html

maven: https://mvnrepository.com/artifact/com.lmax/disruptor

Disruptor的特点

对比ConcurrentLinkedQueue : 链表实现

JDK中没有ConcurrentArrayQueue

Disruptor是数组实现的

无锁，高并发，使用环形Buffer，直接覆盖（不用清除）旧的数据，降低GC频率

实现了基于事件的生产者消费者模式（观察者模式）

RingBuffer

环形队列

RingBuffer的序号，指向下一个可用的元素

采用数组实现，没有首尾指针

对比ConcurrentLinkedQueue，用数组实现的速度更快

假如长度为8，当添加到第12个元素的时候在哪个序号上呢？用12%8决定

当Buffer被填满的时候到底是覆盖还是等待，由Producer决定

长度设为2的n次幂，利于二进制计算，例如：12%8 = 12 & (8 - 1) pos = num & (size -1)

Disruptor开发步骤

定义Event - 队列中需要处理的元素
定义Event工厂，用于填充队列

这里牵扯到效率问题：disruptor初始化的时候，会调用Event工厂，对ringBuffer进行内存的提前分配

GC产频率会降低
定义EventHandler（消费者），处理容器中的元素

事件发布模板

long sequence = ringBuffer.next();  // Grab the next sequence
try {LongEvent event = ringBuffer.get(sequence); // Get the entry in the Disruptor// for the sequenceevent.set(8888L);  // Fill with data
} finally {ringBuffer.publish(sequence);
}

使用EventTranslator发布事件

//===============================================================EventTranslator<LongEvent> translator1 = new EventTranslator<LongEvent>() {@Overridepublic void translateTo(LongEvent event, long sequence) {event.set(8888L);}};ringBuffer.publishEvent(translator1);//===============================================================EventTranslatorOneArg<LongEvent, Long> translator2 = new EventTranslatorOneArg<LongEvent, Long>() {@Overridepublic void translateTo(LongEvent event, long sequence, Long l) {event.set(l);}};ringBuffer.publishEvent(translator2, 7777L);//===============================================================EventTranslatorTwoArg<LongEvent, Long, Long> translator3 = new EventTranslatorTwoArg<LongEvent, Long, Long>() {@Overridepublic void translateTo(LongEvent event, long sequence, Long l1, Long l2) {event.set(l1 + l2);}};ringBuffer.publishEvent(translator3, 10000L, 10000L);//===============================================================EventTranslatorThreeArg<LongEvent, Long, Long, Long> translator4 = new EventTranslatorThreeArg<LongEvent, Long, Long, Long>() {@Overridepublic void translateTo(LongEvent event, long sequence, Long l1, Long l2, Long l3) {event.set(l1 + l2 + l3);}};ringBuffer.publishEvent(translator4, 10000L, 10000L, 1000L);//===============================================================EventTranslatorVararg<LongEvent> translator5 = new EventTranslatorVararg<LongEvent>() {@Overridepublic void translateTo(LongEvent event, long sequence, Object... objects) {long result = 0;for(Object o : objects) {long l = (Long)o;result += l;}event.set(result);}};ringBuffer.publishEvent(translator5, 10000L, 10000L, 10000L, 10000L);

使用Lamda表达式

package com.mashibing.disruptor;import com.lmax.disruptor.RingBuffer;
import com.lmax.disruptor.dsl.Disruptor;
import com.lmax.disruptor.util.DaemonThreadFactory;public class Main03
{public static void main(String[] args) throws Exception{// Specify the size of the ring buffer, must be power of 2.int bufferSize = 1024;// Construct the DisruptorDisruptor<LongEvent> disruptor = new Disruptor<>(LongEvent::new, bufferSize, DaemonThreadFactory.INSTANCE);// Connect the handlerdisruptor.handleEventsWith((event, sequence, endOfBatch) -> System.out.println("Event: " + event));// Start the Disruptor, starts all threads runningdisruptor.start();// Get the ring buffer from the Disruptor to be used for publishing.RingBuffer<LongEvent> ringBuffer = disruptor.getRingBuffer();ringBuffer.publishEvent((event, sequence) -> event.set(10000L));System.in.read();}
}

ProducerType生产者线程模式

ProducerType有两种模式 Producer.MULTI和Producer.SINGLE

默认是MULTI，表示在多线程模式下产生sequence

如果确认是单线程生产者，那么可以指定SINGLE，效率会提升

如果是多个生产者（多线程），但模式指定为SINGLE，会出什么问题呢？

等待策略

1，(常用）BlockingWaitStrategy：通过线程阻塞的方式，等待生产者唤醒，被唤醒后，再循环检查依赖的sequence是否已经消费。

2，BusySpinWaitStrategy：线程一直自旋等待，可能比较耗cpu

3，LiteBlockingWaitStrategy：线程阻塞等待生产者唤醒，与BlockingWaitStrategy相比，区别在signalNeeded.getAndSet,如果两个线程同时访问一个访问waitfor,一个访问signalAll时，可以减少lock加锁次数.

4，LiteTimeoutBlockingWaitStrategy：与LiteBlockingWaitStrategy相比，设置了阻塞时间，超过时间后抛异常。

5，PhasedBackoffWaitStrategy：根据时间参数和传入的等待策略来决定使用哪种等待策略

6，TimeoutBlockingWaitStrategy：相对于BlockingWaitStrategy来说，设置了等待时间，超过后抛异常

7，（常用）YieldingWaitStrategy：尝试100次，然后Thread.yield()让出cpu

8，（常用）SleepingWaitStrategy : sleep

消费者异常处理

默认：disruptor.setDefaultExceptionHandler()

覆盖：disruptor.handleExceptionFor().with()