Businesses are facing unparalleled demands for the instantaneous processing of data and for smooth interactions among their various systems. As a software development engineer working with high-performance, cloud-based business applications, I have experienced how those applications can be transformed when the event-driven architectures (EDAs) used to build them are made to work well and are built to scale. When they aren’t, the opposite happens. Businesses pay for cloud resources and get poor performance, with the EDAs as the poor performers.
The Power of Event-Driven Design
How we understand system interactions is fundamentally altered when using event-driven architectures. Request-response patterns are still prevalent, but in many cases, systems can be thought of as reacting to changes in state or “events” as they occur. This is fundamentally how EDAs work and makes them so appealing for modern business requirements that demand all sorts of real-time processing. And with all sorts of real-time processing comes the advantageous decoupling of system components. When I think about the application of EDA in distributed systems, what I see is powerful real-time event processing and powerful component processing that can vary in design from tightly coupled to loosely decoupled systems.
From my experience constructing dispersed systems and microservices that scale, I’ve found that EDA can succeed or fail based on several interconnected factors. At the core of EDA is a well-designed event schema. In API design, event schemas must be treated as first-class citizens. This entails having strict versioning policies and ensuring backward compatibility. Equally important is the idea that events must carry sufficient context to allow for meaningful downstream processing. Although there are several ways to accomplish all these things, one effective approach involves using JSON Schema for validation and sorting out with event semantics to let collapse to different levels for different consumers if need be.
Managing event processing at scale seems the most daunting when it comes to implementing an event-driven architecture (EDA). I’ve dealt with this aspect mostly by adopting sophisticated routing and processing patterns. One of the most important things I’ve learned from my mistakes is the necessity of dead letter queues—not just for events that failed to deliver but also for events that make it to the other side but can’t be processed (think of them as event processing error logs).
Another crucial piece for reliable delivery is to implement retries with exponential backoff. I have to say, though, that in my experience, using an even slower first retry (after approximately 30 seconds) and making sure our event processors are idempotent (i.e., can process the same event without causing harmful effects if the event is delivered more than once) has served us much better than the EDA “retry for successes” mantra you might find in the literature.
Practical Implementation Strategies
Implementing enterprise EDAs can be done architecturally in certain patterns. One of those patterns is event sourcing. For critical business domains where you need to be able to audit (i.e., retrace your decisions) or do “temporal queries” (i.e., questions about time), event sourcing has tremendous value. That said, event sourcing closely pairs with Command Query Responsibility Segregation (CQRS), a pattern that separates read and write concerns in a system. When using event sourcing and CQRS, you can achieve them with scale and reliability on the cloud because cloud vendors offer managed services for event storage and processing.
Ensuring that reliable events are delivered means making use of the cloud-native message queuing and event streaming services. These platforms provide the foundation for asynchronous processing, which is key to having a system that can work under peak loads or partial outages—asynchronous processing guarantees that not every part of your system has to be working for its event-driven architecture to function correctly.
Cloud-native platforms allow for such architectures to exist. Without them, you’d have a poor-performing system with a handful of threads holding up an event-driven house of cards. Events and messages should be delivered and received without having to worry about the parts of your system that are not currently operational. If they fail to deliver, you should have a way to detect the failure without having to wade through the system logs to find the event that started everything.
Looking Ahead: The Future of EDAs
As companies transform their structures, event-driven architectures will increasingly play a pivotal role. The growth of serverless computing and edge processing opens up new and exciting possibilities for event-driven systems. I base my anticipation of emerging trends in machine learning-driven event routing, edge-based processing for reduced latency, and enhanced event schema evolution mechanisms on my experience with cloud platforms and distributed systems. We’re also seeing rapid development in tools for debugging and monitoring event-driven systems. These new tools are making it easier to maintain and troubleshoot complex event-driven architectures.
Implementing event-driven architectures is not a one-size-fits-all proposition. You have to think carefully about your particular business context and the technical requirements that are unique to your situation. But if you get it right, the event-driven foundation is something that you can build on to create large, reliable, and responsive systems that have the kind of architectural agility needed to adapt to changing business demands.
