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5.4. Guaranteed Delivery
Guaranteed delivery means that once a message is placed into a message channel, the messaging system guarantees that the message will reach its destination, even if parts of the application should fail. In general, messaging systems implement the guaranteed delivery pattern, shown in Figure 5.4, “Guaranteed Delivery Pattern”, by writing messages to persistent storage before attempting to deliver them to their destination.
Figure 5.4. Guaranteed Delivery Pattern
Components that support guaranteed delivery
The following Apache Camel components support the guaranteed delivery pattern:
In JMS, the
deliveryPersistentquery option indicates whether or not persistent storage of messages is enabled. Usually it is unnecessary to set this option, because the default behavior is to enable persistent delivery. To configure all the details of guaranteed delivery, it is necessary to set configuration options on the JMS provider. These details vary, depending on what JMS provider you are using. For example, MQSeries, TibCo, BEA, Sonic, and others, all provide various qualities of service to support guaranteed delivery.
In ActiveMQ, message persistence is enabled by default. From version 5 onwards, ActiveMQ uses the AMQ message store as the default persistence mechanism. There are several different approaches you can use to enabe message persistence in ActiveMQ.
The simplest option (different from Figure 5.4, “Guaranteed Delivery Pattern”) is to enable persistence in a central broker and then connect to that broker using a reliable protocol. After a message is been sent to the central broker, delivery to consumers is guaranteed. For example, in the Apache Camel configuration file,
META-INF/spring/camel-context.xml, you can configure the ActiveMQ component to connect to the central broker using the OpenWire/TCP protocol as follows:
<beans ... > ... <bean id="activemq" class="org.apache.activemq.camel.component.ActiveMQComponent"> <property name="brokerURL" value="tcp://somehost:61616"/> </bean> ... </beans>
If you prefer to implement an architecture where messages are stored locally before being sent to a remote endpoint (similar to Figure 5.4, “Guaranteed Delivery Pattern”), you do this by instantiating an embedded broker in your Apache Camel application. A simple way to achieve this is to use the ActiveMQ Peer-to-Peer protocol, which implicitly creates an embedded broker to communicate with other peer endpoints. For example, in the
camel-context.xmlconfiguration file, you can configure the ActiveMQ component to connect to all of the peers in group,
GroupA, as follows:
<beans ... > ... <bean id="activemq" class="org.apache.activemq.camel.component.ActiveMQComponent"> <property name="brokerURL" value="peer://GroupA/broker1"/> </bean> ... </beans>
broker1is the broker name of the embedded broker (other peers in the group should use different broker names). One limiting feature of the Peer-to-Peer protocol is that it relies on IP multicast to locate the other peers in its group. This makes it unsuitable for use in wide area networks (and in some local area networks that do not have IP multicast enabled).
A more flexible way to create an embedded broker in the ActiveMQ component is to exploit ActiveMQ's VM protocol, which connects to an embedded broker instance. If a broker of the required name does not already exist, the VM protocol automatically creates one. You can use this mechanism to create an embedded broker with custom configuration. For example:
<beans ... > ... <bean id="activemq" class="org.apache.activemq.camel.component.ActiveMQComponent"> <property name="brokerURL" value="vm://broker1?brokerConfig=xbean:activemq.xml"/> </bean> ... </beans>
activemq.xmlis an ActiveMQ file which configures the embedded broker instance. Within the ActiveMQ configuration file, you can choose to enable one of the following persistence mechanisms:
The ActiveMQ Journal component is optimized for a special use case where multiple, concurrent producers write messages to queues, but there is only one active consumer. Messages are stored in rolling log files and concurrent writes are aggregated to boost efficiency.