RabbitMQ客户端参数性能测试(1)

最近我在公司上线了rabbitmq,替换了原来阿里出的rocketmq(别说我黑阿里的东西,这玩意真的都是坑),我并不想告诉你rabbitmq安装过程是怎么样的,去看官网就知道,戳这里

看看网上说rabbitmq效率多高多高,但是怎么测试也只有15000Qps,还是用golang的客户端来测试访问的中间没有任何处理逻辑,悲催的是java原生客户端只能跑到11000Qps,(好吧,我承认我也黑了java),看似基本上这个问题也不大,但是我相信优化的空间应该还是蛮大的,所以做了一下测试,先上代码吧:

package com.enniu.rabbitmq;
import com.rabbitmq.client.AMQP;
import com.rabbitmq.client.AMQP.BasicProperties;
import com.rabbitmq.client.Channel;
import com.rabbitmq.client.Connection;
import com.rabbitmq.client.ConnectionFactory;
import com.rabbitmq.client.Consumer;
import com.rabbitmq.client.DefaultConsumer;
import com.rabbitmq.client.Envelope;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.TimeoutException;
import java.util.concurrent.atomic.AtomicLong;
/**
 * Created by coffee on 15/10/28.
 */
public class Main {
  static final String exchangeName = "testblock";
  static final String routingKey = "testblock";
  static final String queueName = "testblock";
  private static int producterConnection_size = 1; //消息生产者连接数
  private static int consumerConnection_size = 1; //消费者连接数
  private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
  private static int qos = 1; //Qos设置
  private static long sleep_time = 0; //模拟每条消息的处理时间
  private static boolean autoAck = true; //是否默认Ack
  private static Logger logger = LoggerFactory.getLogger(Main.class);
  public static void main(String[] args) throws Exception {
    final AtomicLong count = new AtomicLong(10000000000L);
    ConnectionFactory factory = new ConnectionFactory();
    factory.setUsername("test");
    factory.setPassword("test");
    factory.setVirtualHost("/test");
    factory.setHost("10.0.88.88");
    factory.setPort(5672);
    //启动监控程序
    Thread t = new Thread(new Runnable() {
      @Override
      public void run() {
        long c = count.get();
        while (c != 0){
          try{
            Thread.sleep(1000);
            long c1 = count.get();
            logger.debug("每秒消费为:{}Qps",c-c1);
            c=c1;
          }catch (Exception e){
          }
        }
      }
    });
    t.start();
    //启动
    for (int i=0;i<producterConnection_size;i++){
      Connection conn1 = factory.newConnection();
      Thread t1 = producter(conn1, count.get());
      t1.start();
    }
    //启动consumer
    for (int i=0;i<consumerConnection_size;i++){
      Connection conn1 = factory.newConnection();
      Thread t2 = consumer(conn1, count);
      t2.start();
    }
  }
  public static Thread consumer(final Connection conn, final AtomicLong count) throws Exception {
    return new Thread(new Runnable() {
      @Override
      public void run() {
        logger.debug("start consumer");
        try {
          final CountDownLatch cdl = new CountDownLatch(1000);
          for(int i = 0;i<consumer_size;i++) {
          final Channel channel = conn.createChannel();
          channel.basicQos(0, qos, false);
          Consumer consumer = new DefaultConsumer(channel) {
            @Override
            public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException {
              if (count.decrementAndGet() == 0) {
                channel.basicCancel(consumerTag);
                cdl.countDown();
                try {
                  channel.close();
                } catch (TimeoutException e) {
                  e.printStackTrace();
                }
              }
              try {
                Thread.sleep(sleep_time);
              } catch (InterruptedException e) {
              }
              if (!autoAck){
                getChannel().basicAck(envelope.getDeliveryTag(), true);
              }
            }
          };
            String consumerTag = channel.basicConsume(queueName,autoAck, "testConsumer" + i, consumer);
            logger.debug("consumerTag is {}", consumerTag);
          }
          cdl.await();
        } catch (Exception e) {
        }
      }
    });
  }
  public static Thread producter(final Connection conn, final long count) throws Exception {
    return new Thread(new Runnable() {
      @Override
      public void run() {
        logger.debug("start send Message");
        try {
          Channel channel = conn.createChannel();
          channel.exchangeDeclare(exchangeName, "direct", true);
          channel.queueDeclare(queueName, true, false, false, null);
          channel.queueBind(queueName, exchangeName, routingKey);
          BasicProperties properties = new BasicProperties.Builder().deliveryMode(2).build();
          for (long i = 0; i < count; i++) {
            byte[] messageBodyBytes = ("{\"merchantsId\":13}").getBytes();
            channel.basicPublish(exchangeName, routingKey, properties, messageBodyBytes);
//            logger.debug("add message {}",i);
          }
          channel.close();
          conn.close();
        } catch (Exception e) {
          e.printStackTrace();
        }
      }
    });
  }
}

说说我的测试环境,两台vm安装rabbitmq做集群,配置是CPU:2core,mem:2GB,disk:187G sata
客户端是我的macpro,雷电口转千兆网卡接入测试网络.但是据说中间有一个交换机是百兆的(哭死),所以只能按照百兆连接来计算.

测试一:

测试内容:

启动一个生产者,无消费者

配置:

private static int producterConnection_size = 1; //消息生产者连接数
private static int consumerConnection_size = 0; //消费者连接数
private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
private static int qos = 1; //Qos设置
private static long sleep_time = 0; //模拟每条消息的处理时间
private static boolean autoAck = true; //是否默认Ack

结果:

自己看图,基本上不用我解释

测试二:

测试内容:

启动三个生产者,无消费者

配置:

private static int producterConnection_size = 3; //消息生产者连接数
private static int consumerConnection_size = 0; //消费者连接数
private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
private static int qos = 1; //Qos设置
private static long sleep_time = 0; //模拟每条消息的处理时间
private static boolean autoAck = true; //是否默认Ack

结果:

另外我还发现mq已经启动了流控,看来他已经认为我发送过快.

由此可见生产者的个数对整体的消息publish没有太大的影响,至少在单机的情况下是这样,而且publish的时候会一直抖动,不会一直保持在一个范围内,应该是流控机制引起的,具体怎么优化流控还请各位大牛指教,我暂时还没有什么头绪.

测试三:

测试内容:

启动无生产者,一个消费者,Qos为0.默认Ack,接收到消息马上返回,不休眠

配置:

private static int producterConnection_size = 0; //消息生产者连接数
private static int consumerConnection_size = 1; //消费者连接数
private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
private static int qos = 0; //Qos设置
private static long sleep_time = 0; //模拟每条消息的处理时间
private static boolean autoAck = true; //是否默认Ack

结果:

测试四:

测试内容:

启动无产者,一个消费者,Qos为1.默认Ack,接收到消息马上返回,不休眠

配置:

private static int producterConnection_size = 0; //消息生产者连接数
private static int consumerConnection_size = 1; //消费者连接数
private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
private static int qos = 1; //Qos设置
private static long sleep_time = 0; //模拟每条消息的处理时间
private static boolean autoAck = true; //是否默认Ack

结果:

貌似这里没有什么差别,消费能力都是在10k/s左右,看来Qos设置为1还是不设置并不影响消费能力,可以吧Qos加大看看效果.

测试五:

测试内容:

启动无产者,一个消费者,Qos为10.默认Ack,接收到消息马上返回,不模拟业务处理时间

配置:

private static int producterConnection_size = 0; //消息生产者连接数
private static int consumerConnection_size = 1; //消费者连接数
private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
private static int qos = 10; //Qos设置
private static long sleep_time = 0; //模拟每条消息的处理时间
private static boolean autoAck = true; //是否默认Ack

结果:

这里Qos的增加并没有对消费效率产生影响,其实这是说得通的,Qos本质是控制consumer处理消息的缓存大小,

==但是如果在网络比较差得情况下Qos=1和Qos=10对消费会有很大的差异==.
例如,消息从mq传递到consumer需要50ms,处理只需要5ms的时候,如果Qos=1,那么就必须等到这条消息消费完了再分配下一条消息,这样一条消息处理的整体时间是50*2+5=105ms,但是如果这时Qos=10的话,相当于当一条消息到达之后不用等消息处理完,可以就再分配下一条消息,这样基本上保证时时刻刻都有消息都消费,不需要等待网络传输的时间当缓存消息达到10条的时候正好可以吧传输的50ms冲抵掉.

今天最最后一个测试,就是publish对consumer的影响到底多大.

测试六:

测试内容:

启动一个产者,一个消费者,Qos为10.默认Ack,接收到消息马上返回,不模拟业务处理时间

配置:

private static int producterConnection_size = 1; //消息生产者连接数
private static int consumerConnection_size = 1; //消费者连接数
private static final int consumer_size = 1;//每个消费者连接里面开启的consumer数量
private static int qos = 10; //Qos设置
private static long sleep_time = 0; //模拟每条消息的处理时间
private static boolean autoAck = true; //是否默认Ack