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这篇文章主要介绍了RocketMQ中如何实现并行模式，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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DefaultMQPushConsumerImpl.pullMessage中的PullCallback在接收到拉取的message之后，会调用ConsumeMessageService.submitConsumeRequest方法将消息“推”给listener来执行业务处理。RocketMQ支持并行和顺序两种消费模式，本文主要讲解并行模式ConsumeMessageConcurrentlyService的实现。该类包含一下关键属性：
public class ConsumeMessageConcurrentlyService implements ConsumeMessageService {
// ...
private final MessageListenerConcurrently messageListener; // 业务处理回掉
private final BlockingQueue consumeRequestQueue; // consumerExecutor的并发队列
private final ThreadPoolExecutor consumeExecutor;
private final String consumerGroup;
// ...
public ConsumeMessageConcurrentlyService(DefaultMQPushConsumerImpl defaultMQPushConsumerImpl,
MessageListenerConcurrently messageListener) {
this.defaultMQPushConsumerImpl = defaultMQPushConsumerImpl;
this.messageListener = messageListener;
this.defaultMQPushConsumer = this.defaultMQPushConsumerImpl.getDefaultMQPushConsumer();
this.consumerGroup = this.defaultMQPushConsumer.getConsumerGroup();
this.consumeRequestQueue = new LinkedBlockingQueue();
this.consumeExecutor = new ThreadPoolExecutor(
this.defaultMQPushConsumer.getConsumeThreadMin(),
this.defaultMQPushConsumer.getConsumeThreadMax(),
1000 * 60,
TimeUnit.MILLISECONDS,
this.consumeRequestQueue,
new ThreadFactoryImpl("ConsumeMessageThread_"));
this.scheduledExecutorService = Executors.newSingleThreadScheduledExecutor(new ThreadFactoryImpl("ConsumeMessageScheduledThread_"));
this.cleanExpireMsgExecutors = Executors.newSingleThreadScheduledExecutor(new ThreadFactoryImpl("CleanExpireMsgScheduledThread_"));
}
//...
} ConsumeMessageConcurrentlyService.submitConsumeRequest会将拉取到的message列表按配置的分批策略做分割，提交执行：
public void submitConsumeRequest( final Listmsgs, final ProcessQueue processQueue, final MessageQueue messageQueue, final boolean dispatchToConsume) { final int consumeBatchSize = this.defaultMQPushConsumer.getConsumeMessageBatchMaxSize(); // 配置的批次大小 if (msgs.size() <= consumeBatchSize) { // 拉取的消息列表长度小于配置的批次大小，一次性提交处理 ConsumeRequest consumeRequest = new ConsumeRequest(msgs, processQueue, messageQueue); try { this.consumeExecutor.submit(consumeRequest); } catch (RejectedExecutionException e) { this.submitConsumeRequestLater(consumeRequest); } } else { // 否则，将message分割后提交 for (int total = 0; total < msgs.size(); ) { List msgThis = new ArrayList (consumeBatchSize); for (int i = 0; i < consumeBatchSize; i++, total++) { if (total < msgs.size()) { msgThis.add(msgs.get(total)); } else { break; } } ConsumeRequest consumeRequest = new ConsumeRequest(msgThis, processQueue, messageQueue); try { this.consumeExecutor.submit(consumeRequest); } catch (RejectedExecutionException e) { for (; total < msgs.size(); total++) { msgThis.add(msgs.get(total)); } this.submitConsumeRequestLater(consumeRequest); } } } }
实际处理逻辑在ConsumeRequest.run方法中
// ConsumeRequest
public void run() {
if (this.processQueue.isDropped()) {
log.info("the message queue not be able to consume, because it's dropped. group={} {}", ConsumeMessageConcurrentlyService.this.consumerGroup, this.messageQueue);
return;
}
MessageListenerConcurrently listener = ConsumeMessageConcurrentlyService.this.messageListener;
ConsumeConcurrentlyContext context = new ConsumeConcurrentlyContext(messageQueue);
ConsumeConcurrentlyStatus status = null;
defaultMQPushConsumerImpl.resetRetryAndNamespace(msgs, defaultMQPushConsumer.getConsumerGroup());
ConsumeMessageContext consumeMessageContext = null;
// ....
long beginTimestamp = System.currentTimeMillis();
boolean hasException = false;
ConsumeReturnType returnType = ConsumeReturnType.SUCCESS;
try {
if (msgs != null && !msgs.isEmpty()) {
for (MessageExt msg : msgs) {
MessageAccessor.setConsumeStartTimeStamp(msg, String.valueOf(System.currentTimeMillis()));
}
}
// 1. 调用listener
status = listener.consumeMessage(Collections.unmodifiableList(msgs), context);
} catch (Throwable e) {
log.warn("consumeMessage exception: {} Group: {} Msgs: {} MQ: {}",
RemotingHelper.exceptionSimpleDesc(e),
ConsumeMessageConcurrentlyService.this.consumerGroup,
msgs,
messageQueue);
hasException = true;
}
long consumeRT = System.currentTimeMillis() - beginTimestamp;
// ...
if (!processQueue.isDropped()) {
// 2. 处理业务调用结果
ConsumeMessageConcurrentlyService.this.processConsumeResult(status, context, this);
} else {
// ...
}
}
}processConsumeResult将处理消费结果：
public void processConsumeResult(
final ConsumeConcurrentlyStatus status,
final ConsumeConcurrentlyContext context,
final ConsumeRequest consumeRequest
) {
int ackIndex = context.getAckIndex(); // 消费成功的index
if (consumeRequest.getMsgs().isEmpty())
return;
// ...
// 1. 消费失败msg处理
switch (this.defaultMQPushConsumer.getMessageModel()) {
case BROADCASTING:
for (int i = ackIndex + 1; i < consumeRequest.getMsgs().size(); i++) {
// 广播模式，仅打印消费失败的msg
MessageExt msg = consumeRequest.getMsgs().get(i);
log.warn("BROADCASTING, the message consume failed, drop it, {}", msg.toString());
}
break;
case CLUSTERING:
// 集群模式，首先尝试将消费失败的msg发回到broker，若失败则在本地尝试reconsume
List msgBackFailed = new ArrayList(consumeRequest.getMsgs().size());
for (int i = ackIndex + 1; i < consumeRequest.getMsgs().size(); i++) {
MessageExt msg = consumeRequest.getMsgs().get(i);
boolean result = this.sendMessageBack(msg, context);
if (!result) {
msg.setReconsumeTimes(msg.getReconsumeTimes() + 1);
msgBackFailed.add(msg);
}
}
if (!msgBackFailed.isEmpty()) {
consumeRequest.getMsgs().removeAll(msgBackFailed);
this.submitConsumeRequestLater(msgBackFailed, consumeRequest.getProcessQueue(), consumeRequest.getMessageQueue());
}
break;
default:
break;
}
// 2. 更新offset store
long offset = consumeRequest.getProcessQueue().removeMessage(consumeRequest.getMsgs());
if (offset >= 0 && !consumeRequest.getProcessQueue().isDropped()) {
this.defaultMQPushConsumerImpl.getOffsetStore().updateOffset(consumeRequest.getMessageQueue(), offset, true);
}
} 感谢你能够认真阅读完这篇文章，希望小编分享的“RocketMQ中如何实现并行模式”这篇文章对大家有帮助，同时也希望大家多多支持创新互联，关注创新互联行业资讯频道，更多相关知识等着你来学习!
