RabbitMQ

RabbitMQ

转载：http://sishuok.com/forum/blogPost/list/5782.html

 

1. introduction:

      RabbitMQ is a message broker. In essence, it accepts messages from producers, and delivers them to consumers. In-between, it can route, buffer, and persist the messages according to rules you give it.

    A queue is the name for a mailbox. It lives inside RabbitMQ. Although messages flow through RabbitMQ and your applications, they can be stored only inside a queue. A queue is not bound by any limits, it can store as many messages as you like - it's essentially an infinite buffer. Many producers can send messages that go to one queue - many consumers can try to receive data from one queue.

    To send, we must declare a queue for us to send to; then we can publish a message to the queue:

     channel.queueDeclare(QUEUE_NAME, false, false, false, null);

    String message = "Hello World!";

    channel.basicPublish("", QUEUE_NAME, null, message.getBytes());

    System.out.println(" [x] Sent '" + message + "'");

     Declaring a queue is idempotent - it will only be created if it doesn't exist already. The message content is a byte array, so you can encode whatever you like there.

2. Work Queues:

     The main idea behind Work Queues (aka: Task Queues) is to avoid doing a resource-intensive task immediately and having to wait for it to complete. Instead we schedule the task to be done later. We encapsulate a task as a message and send it to a queue. A worker process running in the background will pop the tasks and eventually execute the job. When you run many workers the tasks will be shared between them.

     

     One of the advantages of using Task Queue is the ability to easily parallelise work. If we are building up a backlog of work, we can just add more workers and that way, scale easily.

Message acknowledgment：

    Doing a task can take a few seconds. You may wonder what happens if one of the consumers starts a long task and dies with it only partly done. With our current code, once RabbitMQ delivers a message to the customer it immediately removes it from memory. In this case, if you kill a worker we will lose the message it was just processing. We'll also lose all the messages that were dispatched to this particular worker but were not yet handled.

But we don't want to lose any tasks. If a worker dies, we'd like the task to be delivered to another worker.

In order to make sure a message is never lost, RabbitMQ supports message acknowledgments. An ack(nowledgement) is sent back from the consumer to tell RabbitMQ that a particular message has been received, processed and that RabbitMQ is free to delete it.

If a consumer dies without sending an ack, RabbitMQ will understand that a message wasn't processed fully and will redeliver it to another consumer. That way you can be sure that no message is lost, even if the workers occasionally die.

There aren't any message timeouts; RabbitMQ will redeliver the message only when the worker connection dies. It's fine even if processing a message takes a very, very long time.

Message durability：

    We have learned how to make sure that even if the consumer dies, the task isn't lost. But our tasks will still be lost if RabbitMQ server stops.

When RabbitMQ quits or crashes it will forget the queues and messages unless you tell it not to. Two things are required to make sure that messages aren't lost: we need to mark both the queue and messages as durable.

    First, we need to make sure that RabbitMQ will never lose our queue. In order to do so, we need to declare it as durable:

boolean durable = true;

channel.queueDeclare("hello", durable, false, false, null);

     RabbitMQ doesn't allow you to redefine an existing queue with different parameters and will return an error to any program that tries to do that.
     At this point we're sure that the task_queue queue won't be lost even if RabbitMQ restarts. Now we need to mark our messages as persistent - by setting MessageProperties (which implements BasicProperties) to the value PERSISTENT_TEXT_PLAIN.

import com.rabbitmq.client.MessageProperties;

channel.basicPublish("", "task_queue", 

            MessageProperties.PERSISTENT_TEXT_PLAIN,

            message.getBytes());

Note on message persistence：

     Marking messages as persistent doesn't fully guarantee that a message won't be lost. Although it tells RabbitMQ to save the message to disk, there is still a short time window when RabbitMQ has accepted a message and hasn't saved it yet. Also, RabbitMQ doesn't do fsync(2) for every message -- it may be just saved to cache and not really written to the disk. The persistence guarantees aren't strong, but it's more than enough for our simple task queue. If you need a stronger guarantee you can wrap the publishing code in a transaction.

Fair dispatch：

    You might have noticed that the dispatching still doesn't work exactly as we want. For example in a situation with two workers, when all odd messages are heavy and even messages are light, one worker will be constantly busy and the other one will do hardly any work. Well, RabbitMQ doesn't know anything about that and will still dispatch messages evenly.

This happens because RabbitMQ just dispatches a message when the message enters the queue. It doesn't look at the number of unacknowledged messages for a consumer. It just blindly dispatches every n-th message to the n-th consumer.

     In order to defeat that we can use the basicQos method with the prefetchCount = 1 setting. This tells RabbitMQ not to give more than one message to a worker at a time. Or, in other words, don't dispatch a new message to a worker until it has processed and acknowledged the previous one. Instead, it will dispatch it to the next worker that is not still busy.

int prefetchCount = 1;

channel.basicQos(prefetchCount);

 

3. Publish/Subscribe:

     Essentially, published messages are going to be broadcast to all the receivers.

Exchanges:

     The core idea in the messaging model in RabbitMQ is that the producer never sends any messages directly to a queue.

     Instead, the producer can only send messages to an exchange. An exchange is a very simple thing. On one side it receives messages from producers and the other side it pushes them to queues. The exchange must know exactly what to do with a message it receives. Should it be appended to a particular queue? Should it be appended to many queues? Or should it get discarded. The rules for that are defined by the exchange type.

     There are a few exchange types available: direct, topic, headers and fanout.

     channel.exchangeDeclare("logs", "fanout");

 

Nameless exchange：

     In previous parts of the tutorial we knew nothing about exchanges, but still were able to send messages to queues. That was possible because we were using a default exchange, which we identify by the empty string ("").

     Recall how we published a message before:

      channel.basicPublish("", "hello", null, message.getBytes());

     The first parameter is the the name of the exchange. The empty string denotes the default or nameless exchange: messages are routed to the queue with the name specified by routingKey, if it exists.

Temporary queues：

     Giving a queue a name is important when you want to share the queue between producers and consumers.

     We want to hear about all log messages, not just a subset of them. We're also interested only in currently flowing messages not in the old ones. To solve that we need two things.

     Firstly, whenever we connect to Rabbit we need a fresh, empty queue. To do this we could create a queue with a random name, or, even better - let the server choose a random queue name for us.

     Secondly, once we disconnect the consumer the queue should be automatically deleted.

     In the Java client, when we supply no parameters to queueDeclare() we create a non-durable, exclusive, autodelete queue with a generated name:

      String queueName = channel.queueDeclare().getQueue();

     At that point queueName contains a random queue name. For example it may look like amq.gen-U0srCoW8TsaXjNh73pnVAw==.

Bindings：

     We've already created a fanout exchange and a queue. Now we need to tell the exchange to send messages to our queue. That relationship between exchange and a queue is called a binding.

      channel.queueBind(queueName, "logs", "");

4. Routing：

Bindings：

     In previous examples we were already creating bindings. You may recall code like:

      channel.queueBind(queueName, EXCHANGE_NAME, "");

     A binding is a relationship between an exchange and a queue. This can be simply read as: the queue is interested in messages from this exchange.

     Bindings can take an extra routingKey parameter.

      channel.queueBind(queueName, EXCHANGE_NAME, "black");

     The meaning of a binding key depends on the exchange type. The fanout exchanges, which we used previously, simply ignored its value.

Direct exchange:

     We will use a direct exchange instead. The routing algorithm behind a direct exchange is simple - a message goes to the queues whose binding key exactly matches the routing key of the message.

     The routing algorithm behind a direct exchange is simple - a message goes to the queues whose binding key exactly matches the routing key of the message.

5. Topics

     Messages sent to a topic exchange can't have an arbitrary routing_key - it must be a list of words, delimited by dots. The words can be anything, but usually they specify some features connected to the message.

     There can be as many words in the routing key as you like, up to the limit of 255 bytes.

     The binding key must also be in the same form. The logic behind the topic exchange is similar to a direct one - a message sent with a particular routing key will be delivered to all the queues that are bound with a matching binding key. However there are two important special cases for binding keys:

     * (star) can substitute for exactly one word.

     # (hash) can substitute for zero or more words.

6. Remote procedure call (RPC)

     But what if we need to run a function on a remote computer and wait for the result? Well, that's a different story. This pattern is commonly known as Remote Procedure Call or RPC.