物理部署
- Name Server是一个几乎无状态节点，可集群部署，节点之间无任何信息同步。
- Broker分为Master与Slave(主从配置)，一个Master可以对应多个Slave，但是一个Slave只能对应一个Master，Master与Slave的对应关系通过指定相同的BrokerName，不同的BrokerId来定义，BrokerId为0表示Master，非0表示Slave。Master也可以部署多个。每个Broker与Name Server集群中的所有节点建立长连接，定时注册Topic信息到所有Name Server。
- Producer与Name Server集群中的其中一个节点（随机选择）建立长连接，定期从Name Server取Topic路由信息，并向提供Topic服务的Master建立长连接，且定时向Master发送心跳。Producer完全无状态，可集群部署
- Consumer与Name Server集群中的其中一个节点（随机选择）建立长连接，定期从Name Server取Topic路由信息，并向提供Topic服务的Master、Slave建立长连接，且定时向Master、Slave发送心跳。Consumer既可以从Master订阅消息，也可以从Slave订阅消息，订阅规则由Broker配置决定。
编译安装
4.2.0版本：
- git clone https://github.com/apache/rocketmq.git
- 编译 mvn -Prelease-all -DskipTests clean install -U
旧版本： - git clone https://github.com/apache/incubator-rocketmq.git
- mvn clean package install -Prelease-all assembly:assembly -U
注意安装要求，maven是3.2.X否则可能会有问题(我的3.5.3运行quickstart的Consumer就有问题)
启动关闭:
cd distribution/target/apache-rocketmq
- nohup sh bin/mqnamesrv &
- nohup sh bin/mqbroker -n localhost:9876 autoCreateTopicEnable=true &,不加autoCreateTopicEnable=true我这边启动Producer报No route info of this topic错
- 使用tail -f ~/logs/rocketmqlogs/XXX.log或jps查看是否启动成功，Ctrl c退出
- 使用sh bin/mqshutdown broker和sh bin/mqshutdown namesrv关闭
jar包依赖
commons-cli-1.2.jar
commons-lang3-3.4.jar
fastjson-1.2.29.jar
guava-19.0.jar
javassist-3.20.0-GA.jar
jna-4.2.2.jar
logback-classic-1.0.13.jar
logback-core-1.0.13.jar
netty-all-4.0.36.Final.jar
openmessaging-api-0.1.0-alpha.jar
rocketmq-client-4.2.0-incubating-SNAPSHOT.jar
rocketmq-common-4.2.0-incubating-SNAPSHOT.jar
rocketmq-remoting-4.2.0-incubating-SNAPSHOT.jar
rocketmq-srvutil-4.2.0-incubating-SNAPSHOT.jar
rocketmq-tools-4.2.0-incubating-SNAPSHOT.jar
slf4j-api-1.7.5.jar
三种方式
- 同步——用在大规模分布场景下，像重要的通知消息，短信通知，短信市场系统等
public class SyncProducer {
public static void main(String[] args) throws Exception {
//Instantiate with a producer group name.
DefaultMQProducer producer = new
DefaultMQProducer("please_rename_unique_group_name");
//Launch the instance.
producer.start();
for (int i = 0; i < 100; i++) {
//Create a message instance, specifying topic, tag and message body.
Message msg = new Message("TopicTest" /* Topic */,
"TagA" /* Tag */,
("Hello RocketMQ " +
i).getBytes(RemotingHelper.DEFAULT_CHARSET) /* Message body */
);
//Call send message to deliver message to one of brokers.
SendResult sendResult = producer.send(msg);
System.out.printf("%s%n", sendResult);
}
//Shut down once the producer instance is not longer in use.
producer.shutdown();
}
}
- 异步——实时的业务场景
可以看出多了发送完的回调函数
public class AsyncProducer {
public static void main(String[] args) throws Exception {
//Instantiate with a producer group name.
DefaultMQProducer producer = new DefaultMQProducer("ExampleProducerGroup");
//Launch the instance.
producer.start();
producer.setRetryTimesWhenSendAsyncFailed(0);
for (int i = 0; i < 100; i++) {
final int index = i;
//Create a message instance, specifying topic, tag and message body.
Message msg = new Message("TopicTest",
"TagA",
"OrderID188",
"Hello world".getBytes(RemotingHelper.DEFAULT_CHARSET));
producer.send(msg, new SendCallback() {
@Override
public void onSuccess(SendResult sendResult) {
System.out.printf("%-10d OK %s %n", index,
sendResult.getMsgId());
}
@Override
public void onException(Throwable e) {
System.out.printf("%-10d Exception %s %n", index, e);
e.printStackTrace();
}
});
}
//Shut down once the producer instance is not longer in use.
producer.shutdown();
}
}
- 单向传输——对可靠性要求一般的场景，像日志搜集
public class OnewayProducer {
public static void main(String[] args) throws Exception{
//Instantiate with a producer group name.
DefaultMQProducer producer = new DefaultMQProducer("ExampleProducerGroup");
//Launch the instance.
producer.start();
for (int i = 0; i < 100; i++) {
//Create a message instance, specifying topic, tag and message body.
Message msg = new Message("TopicTest" /* Topic */,
"TagA" /* Tag */,
("Hello RocketMQ " +
i).getBytes(RemotingHelper.DEFAULT_CHARSET) /* Message body */
);
//Call send message to deliver message to one of brokers.
producer.sendOneway(msg);
}
//Shut down once the producer instance is not longer in use.
producer.shutdown();
}
}
逻辑架构
- 队列集合称为Topic，Consumer如果做广播消费，则一个consumer实例消费这个Topic对应的所有队列，如果做集群消费，则多个Consumer实例平均消费这个topic对应的队列集合。
有序消息
生产者
从运行结果看，默认四条消息队列(queueid0-3)。消息的id(0~9)会发送到不同的消息队列上
public static void main( String[] args ) throws Exception
{
//Instantiate with a producer group name.
MQProducer producer = new DefaultMQProducer("example_group_name");
//Launch the instance.
//多个地址用分号隔开；也可以设置成环境变量
((DefaultMQProducer) producer).setNamesrvAddr("127.0.0.1:9876");
producer.start();
String[] tags = new String[] {"TagA", "TagB", "TagC", "TagD", "TagE"};
for (int i = 0; i < 100; i++) {
int orderId = i % 10;
//Create a message instance, specifying topic, tag and message body.
Message msg = new Message("TopicTestjjj", tags[i % tags.length], "KEY" + i,
("Hello RocketMQ " + i).getBytes(RemotingHelper.DEFAULT_CHARSET));
SendResult sendResult = producer.send(msg, new MessageQueueSelector() {
@Override
public MessageQueue select(List<MessageQueue> mqs, Message msg, Object arg) {
Integer id = (Integer) arg;
//输出0~9
//System.out.println(id);
int index = id % mqs.size();// 如果队列数不变，同一个订单号取到的队列是同一个
return mqs.get(index);
}
}, orderId);//orderId传递给回调方法
System.out.printf("%s%n", sendResult);
}
//server shutdown
producer.shutdown();
}
消费者
消费者消费指定主题的消息，并注册监听器，打印消息并根据收到的消息顺序返回不同的消费状态
- 实现MessageListenerOrderly的consumeMessage方法
- CONSUME_FROM_LAST_OFFSET：第一次启动从队列最后位置消费，后续再启动接着上次消费的进度开始消费
- CONSUME_FROM_FIRST_OFFSET：第一次启动从队列初始位置消费，后续再启动接着上次消费的进度开始消费
- CONSUME_FROM_TIMESTAMP：第一次启动从指定时间点位置消费，后续再启动接着上次消费的进度开始消费
public static void main (String[] args) throws Exception {
DefaultMQPushConsumer consumer = new DefaultMQPushConsumer("example_group_name");
consumer.setNamesrvAddr("127.0.0.1:9876");
consumer.setConsumeFromWhere(ConsumeFromWhere.CONSUME_FROM_FIRST_OFFSET);
consumer.subscribe("TopicTest", "TagA || TagC || TagD");
consumer.registerMessageListener(new MessageListenerOrderly() {
AtomicLong consumeTimes = new AtomicLong(0);
@Override
public ConsumeOrderlyStatus consumeMessage(List<MessageExt> msgs,
ConsumeOrderlyContext context) {
context.setAutoCommit(false);
System.out.printf(Thread.currentThread().getName() + " Receive New Messages: " + msgs + "%n");
this.consumeTimes.incrementAndGet();
if ((this.consumeTimes.get() % 2) == 0) {
return ConsumeOrderlyStatus.SUCCESS;
} else if ((this.consumeTimes.get() % 3) == 0) {
return ConsumeOrderlyStatus.ROLLBACK;
} else if ((this.consumeTimes.get() % 4) == 0) {
return ConsumeOrderlyStatus.COMMIT;
} else if ((this.consumeTimes.get() % 5) == 0) {
context.setSuspendCurrentQueueTimeMillis(3000);
return ConsumeOrderlyStatus.SUSPEND_CURRENT_QUEUE_A_MOMENT;
}
return ConsumeOrderlyStatus.SUCCESS;
}
});
consumer.start();
System.out.printf("Consumer Started.%n");
}
广播模式
- 消费者设置：
//set to broadcast mode
consumer.setMessageModel(MessageModel.BROADCASTING);
定时消息
- 生产者
// This message will be delivered to consumer 10 seconds later.
message.setDelayTimeLevel(3);
- 消费者
消费者监听器中可以取得消息的存储时间，进而可以计算出延迟的时间
for (MessageExt message : messages) {
// Print approximate delay time period
System.out.println("Receive message[msgId=" + message.getMsgId() + "] "
+ (System.currentTimeMillis() - message.getStoreTimestamp()) + "ms later");
}
批量处理
- 能够提高小的消息的传送效率
- 限制1：same topic, same waitStoreMsgOK and no schedule support.
- 限制2：the total size of the messages in one batch should be no more than 1MiB.
String topic = "BatchTest";
List<Message> messages = new ArrayList<>();
messages.add(new Message(topic, "TagA", "OrderID001", "Hello world 0".getBytes()));
messages.add(new Message(topic, "TagA", "OrderID002", "Hello world 1".getBytes()));
messages.add(new Message(topic, "TagA", "OrderID003", "Hello world 2".getBytes()));
try {
producer.send(messages);
} catch (Exception e) {
e.printStackTrace();
//handle the error
}
- 太大则需要分块生产
public class ListSplitter implements Iterator<List<Message>> {
private final int SIZE_LIMIT = 1000 * 1000;
private final List<Message> messages;
private int currIndex;
public ListSplitter(List<Message> messages) {
this.messages = messages;
}
@Override public boolean hasNext() {
return currIndex < messages.size();
}
@Override public List<Message> next() {
int nextIndex = currIndex;
int totalSize = 0;
for (; nextIndex < messages.size(); nextIndex++) {
Message message = messages.get(nextIndex);
int tmpSize = message.getTopic().length() + message.getBody().length;
Map<String, String> properties = message.getProperties();
for (Map.Entry<String, String> entry : properties.entrySet()) {
tmpSize += entry.getKey().length() + entry.getValue().length();
}
tmpSize = tmpSize + 20; //for log overhead
if (tmpSize > SIZE_LIMIT) {
//it is unexpected that single message exceeds the SIZE_LIMIT
//here just let it go, otherwise it will block the splitting process这里直接让这个单独的“大”消息过去，否则会阻塞拆分的进程
if (nextIndex - currIndex == 0) {
//if the next sublist has no element, add this one and then break, otherwise just break如果下个子列表没有元素，则把这个“大”消息加进去，并把nextIndex+1
nextIndex++;
}
break;
}
if (tmpSize + totalSize > SIZE_LIMIT) {
break;
} else {
totalSize += tmpSize;
}
}
List<Message> subList = messages.subList(currIndex, nextIndex);
currIndex = nextIndex;
return subList;
}
}
//then you could split the large list into small ones:
ListSplitter splitter = new ListSplitter(messages);
while (splitter.hasNext()) {
try {
List<Message> listItem = splitter.next();
producer.send(listItem);
} catch (Exception e) {
e.printStackTrace();
//handle the error
}
}
消息过滤选择
- 一条消息只能有一个tag，因此通过consumer.subscribe("TOPIC", "TAGA || TAGB || TAGC");这种方式的选择性不强
- 可以通过向message中添加1到n条属性，消费者对属性或属性组合进行逻辑判断来过滤消息
// 生产者Set some properties.
msg.putUserProperty("a", String.valueOf(i));
//消费者
consumer.subscribe("TopicTest", MessageSelector.bySql("a between 0 and 3");