I’ve discussed using event messages to carry payload data, to help with resynchronizing a failed independent component with its loosely coupled neighbours. However, this could very easily lead to extremely large and inefficient messages, for what should ideally be very simple events. This will invariably lead to performance and scalability issues, not to mention affecting the cost of provisioning. Also, since message publishers are unconcerned with how many subscribers there might be, publishing large messages is an irresponsible development practice with potentially unforeseeable consequences. I’ve outlined some alternatives we’ve assessed in attempting to resolve this issue.
The first strategy is, as we’ve discussed, including a data payload within the event itself. For example, consider an order processor component raising an event to indicate successfully closing an order. The event might also include some customer data, which is not strictly necessary, just to indicate the order has been made. As I’ve indicated, this strategy is really only viable for small message sizes. If we were to attempt to include an entire order with the event, the overall payload size would dwarf the event notification and result in the issues we’ve already mentioned.
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A second, leaner approach is to include a REST URL to reference the payload data. Thus subscribers can choose to consume the related payload data as required using the REST URL. In our example, a downstream payment processor component might need customer data from the order. By requesting the order customer details via the supplied REST URL, the payment processor potentially has access to the entire order. I like this pattern as it virtually eliminates the size associated with any payload and also leverages the caching benefits of REST, ensuring a flexible, efficient mechanism for very large payloads.
Note: Consider it a tip to include a version determining parameter in the URL, to ensure the reference within the event remains immutable in the face of any later changes to the data.
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The third strategy is a little more difficult to explain and involves the use of a mediator object. A mediator is an abstraction used to lower direct coupling between interdependent components. The mediator is responsible for abstracting any communications between components and can be implemented in many different ways depending on its use and the data involved. Communications can be heavily cached, synchronous, asynchronous it really doesn’t matter. It is sufficient for the consuming component to know, the mediator is responsible for executing a required task which is ultimately under the purview of another component. A mediator may make a variety of communications but only ever to a single component, thus ownership of the mediator is clear.
This strategy is only to be considered where some behaviour is required, not just data, which is the responsibility of another component. Within our order example, it might be to obtain the customer’s billing address for the payment processor say. This could be considered behaviour because the customer’s billing address is unlikely to be a property of the newly closed order, and hence it would require a lookup based on the customer (a responsibility of the customer management component). The mediating object might orchestrate the process, acquiring data from the customer management component to do this.
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