How do you implement the aggregator pattern to combine multiple messages into one?

1 Why use the aggregator pattern?

The aggregator pattern can be useful when you need to collect and process data from different sources, such as multiple microservices, sensors, or events. For example, you may want to aggregate the results of a parallel computation, or you may want to collect and analyze sensor data from different locations. By using the aggregator pattern, you can avoid sending many small messages over the network, which can be inefficient and costly. Instead, you can send one large message that contains all the relevant data.

2 How does the aggregator pattern work?

The aggregator pattern consists of three main components: a splitter, an aggregator, and a correlator. The splitter is responsible for dividing a large message into smaller ones, and sending them to different destinations. The aggregator is responsible for receiving the smaller messages, and combining them into one. The correlator is responsible for identifying which messages belong to the same group, and ensuring that they are aggregated correctly. The correlator can use various strategies, such as sequence numbers, message IDs, or headers.

3 What are some examples of the aggregator pattern?

One example of the aggregator pattern is the scatter-gather pattern, which is used to invoke multiple services in parallel, and aggregate their responses. For example, you may want to query multiple product catalogs, and return a single list of results to the client. The splitter can send a request to each catalog service, and the aggregator can merge the responses into one. The correlator can use the request ID or a correlation ID to match the responses with the original request.

Another example of the aggregator pattern is the compose method pattern, which is used to combine multiple messages into one method invocation. For example, you may want to update multiple fields of a customer record, and send a single update request to the database. The splitter can send each field update as a separate message, and the aggregator can compose them into one update method call. The correlator can use the customer ID or a sequence number to group the messages together.

4 What are some best practices for the aggregator pattern?

When utilizing the aggregator pattern, it is important to use a reliable messaging system to ensure messages are delivered and acknowledged. Additionally, a timeout mechanism should be employed to handle incomplete or missing messages, as well as avoid blocking the aggregator. To prevent data loss in case of failures, a persistent store should be used to keep track of the message groups and their state. Furthermore, a consistent and clear correlation strategy is essential to avoid mixing up or duplicating messages. Lastly, a scalable and flexible aggregator design should be employed to manage varying message sizes and volumes.

5 How do you test the aggregator pattern?

In order to test the aggregator pattern, you need to ensure that the splitter divides the large message into smaller ones and sends them to the right destinations, that the correlator identifies and groups the messages correctly and assigns them a unique identifier, and that the aggregator receives and combines the messages correctly to produce the expected output. Additionally, it is important to verify that the aggregator handles incomplete or missing messages gracefully, triggering the appropriate actions, and performs well under different load and concurrency scenarios without causing bottlenecks or errors.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?