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Aggregating Messages with Aggregator Processors

Introduction:
In this section, we will explore the concept of message aggregation in Apache Camel using the Aggregator processors. Message aggregation involves combining multiple messages into a single message based on a certain condition or criteria. This functionality is particularly useful when dealing with split messages that need to be reassembled or when merging related messages for further processing. Let’s dive into message aggregation with the Aggregator processors in Camel, along with code samples.

3.2.3.1 Basic Message Aggregation with the Aggregator Processor:
The Aggregator processor in Camel allows you to aggregate messages based on a specific condition or correlation strategy. Let’s consider a simple example of aggregating messages based on a shared correlation ID:

Java
from("direct:input")
.aggregate(header("correlationId"), new MyAggregationStrategy())
.completionSize(5)
.to("direct:aggregatedOutput");

In this example, the aggregate statement defines the aggregation process. The header("correlationId") specifies the header used for correlation, indicating which messages should be aggregated together. The MyAggregationStrategy class implements the logic for aggregating messages. The completionSize(5) indicates that the aggregation should be completed when five messages with the same correlation ID have been received. The aggregated message is then sent to the “direct:aggregatedOutput” endpoint.

3.2.3.2 Advanced Aggregation Strategies:
The Aggregator processor allows you to define custom aggregation strategies to handle more complex scenarios. You can implement your own logic to determine when the aggregation should be completed and how the aggregated message should be created. Let’s consider an example:

Java
from("direct:input")
.aggregate(header("orderId"), new MyCustomAggregationStrategy())
.completionPredicate(exchangeProperty("aggregationComplete").isEqualTo(true))
.completionTimeout(5000)
.to("direct:aggregatedOutput");

In this example, the aggregate statement uses the “orderId” header for correlation. The MyCustomAggregationStrategy class defines a custom logic for aggregating messages. The completionPredicate specifies that the aggregation should be completed when the exchange property “aggregationComplete” is set to true. Additionally, the completionTimeout sets a timeout of 5000 milliseconds, ensuring that the aggregation is completed even if the expected number of messages is not reached. Finally, the aggregated message is sent to the “direct:aggregatedOutput” endpoint.

3.2.3.3 Splitting and Aggregating Messages:
In some scenarios, you may need to split a message into multiple parts, perform individual processing on each part, and then aggregate the results. This can be achieved using the Splitter and Aggregator processors together. Let’s see an example:

Java
from("direct:input")
.split(body().tokenize(","))
.to("direct:individualProcessing")
.end()
.aggregate(header("originalId"), new MyAggregationStrategy())
.completionSize(5)
.to("direct:aggregatedOutput");

In this example, the split statement splits the message body based on a delimiter (“,”) and sends each part to “direct:individualProcessing” for individual processing. The end statement marks the end of the split operation. After individual processing, the Aggregator processor is used to aggregate the processed messages based on the “originalId” header. Once the completion size of 5 is reached, the aggregated message is sent to the “direct:aggregatedOutput” endpoint.

Conclusion:
In this section, we explored the

concept of message aggregation in Apache Camel using the Aggregator processors. These processors allow you to combine multiple messages into a single message based on specific conditions or correlation strategies. By utilizing the Aggregator processor, you can efficiently handle scenarios involving split messages or merging related messages for further processing. Understanding how to leverage these processors will enable you to build robust and efficient integration solutions. In the next section, we will delve into error handling and fault tolerance mechanisms in Camel routes.

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About Author
Ozzie Feliciano CTO @ Felpfe Inc.

Ozzie Feliciano is a highly experienced technologist with a remarkable twenty-three years of expertise in the technology industry.

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