Wed May 24 2023
What Is Event-Driven Architecture and Why Use It?
Event-driven architecture (EDA) is a software design paradigm that promotes loose coupling between components by using events as the primary communication mechanism. In EDA, components interact by producing and consuming events. Events are asynchronously transmitted and received via an event broker such as Kafka, allowing for highly parallel and decoupled operations.
Event-driven architectures have increasingly taken the place of more traditional request-response architectures for web-scale application design. In a request-response architecture, components communicate through synchronous interactions. One component sends a request to another, which then processes the request and returns a response.
This direct communication establishes a tight coupling between components and a linear flow of control. The requesting component must wait for the response before continuing its operation. In contrast, event-driven architectures are based on asynchronous communication, allowing components to operate concurrently and independently.
What is an event?
Events can be anything from a user clicking a button to a sensor detecting a change in temperature to a financial transaction being processed. Each event carries relevant data that provides context and information about the occurrence, allowing other components to take appropriate action in response.
How does event-driven architecture work?
In an event-driven architecture, the components within the system communicate through the production and consumption of events. Here’s a high-level overview of how it works:
- Event producers are components that generate events based on certain triggers or changes in the system. Producers are responsible for creating and publishing events to an event bus or message broker.
- An event bus or message broker is a centralized service responsible for transmitting events between producers and consumers. The event bus ensures the asynchronous delivery of events and manages event routing, storage, and retrieval.
- Event consumers subscribe to events and act upon receiving them. Consumers process the events and may trigger further actions or events in response.
Event consumers process events asynchronously to prevent producers from becoming obstructed or delayed. This allows the system to scale and handle high volumes of events with low latency.
What are the benefits of event-driven architectures?
Event-driven architecture has become the preferred way to design complex, large-scale applications, primarily because of the advantages it offers compared to software built around the request-response model.
Some of the benefits motivating the choice of event-driven architecture include:
- Scalability: Asynchronous event processing allows components to work independently, enabling the system to handle high volumes of events without being constrained by the slowest component.
- Decoupling: EDA reduces direct dependencies between components, making it easier to develop, test, and maintain them, as well as introduce new features or replace existing ones.
- Resilience: Since components in an EDA are decoupled and communicate asynchronously, the failure of one component is less likely to cause a cascading failure throughout the entire system.
- Real-time Processing: EDA enables real-time processing of events, allowing businesses to react to changes in their environment quickly and effectively. This is particularly useful in industries where rapid response to events is critical, such as finance or IoT systems.
The disadvantages of event-driven architectures
While there are many benefits to choosing event-driven architectures, no architecture is perfect for every scenario. There are trade-offs that developers should keep in mind when choosing an architecture for their application.
- Complexity: Implementing an event-driven architecture can introduce complexity in the system due to the asynchronous nature of event processing. The added complexity can make it harder to understand and maintain the system.
- Eventual Consistency: As events are processed asynchronously, the system may not always be in a consistent state. This can lead to challenges in managing data consistency and ensuring that all components have an accurate view of the system state.
- Monitoring and Debugging: Monitoring and debugging an event-driven system can be more difficult compared to traditional request-response architectures. Developers may need to adopt new tools and techniques to trace the flow of events and identify issues.
Techniques for Event-Driven Migration
Building a new app around a microservice-based event-driven architecture is one thing. But what if your business relies on a monolithic app built around a request-response architecture? Is there a path to an event-driven migration that doesn’t risk excessive disruption or require the wholesale abandonment of existing systems?
The answer is yes, and the solution lies in using your legacy app and its databases as event sources for new services. There are various ways to achieve that goal, but Change Data Capture (CDC) is one of the most effective.
CDC is a technique for capturing changes made to one database and propagating those changes to another database. It typically uses the source database’s transaction log to detect changes like updates and inserts. It then extracts the relevant data to produce a stream of events in an event streaming solution like Apache Kafka. Event consumers—your app’s downstream services—can then access the events and incorporate the changes into their own databases.
Most relational databases support CDC, often with the help of a CDC tool like Debezium, including MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. Many cloud-native databases support CDC out-of-the-box.
The advantage of CDC is that changes are detected and propagated in real time, ensuring the legacy app and new event-driven services are consistent and that up-to-date information is always available to the newer services. Because event-driven architectures encapsulate functional responsibilities in separate components, it is possible to iterate on the legacy app and services that consume its events independently. Best of all, CDC makes event-driven migrations possible without disrupting the availability or operations of the legacy app.
Another benefit of using CDC for event-driven migration is that the event stream can be monitored and modified before it reaches event consumer services. An SDPM (Streaming Data Performance Monitoring) platform like Streamdal can tap into the event stream to detect anomalies, run custom functions on in-flight data, and detect and sanitize personally identifiable information (PII) before it propagates to downstream services.
Implementing Reliability with Streamdal
Regardless of the use case for migrating to event-driven architecture, you should ask yourself a few important questions before deploying brokers in production:
- Are you prepared to build the necessary components to ensure your platform is reliable?
- Does your team possess the knowledge or capability of learning to operate this technology?
- How much engineering hours can you break away from your core products to ensure your systems will work?
If you answered no to any of these, or you need to keep all hands on product and features, then you should consider adopting SDPM (Streaming Data Performance Monitoring). Streamdal has all the fundamental tools needed to ensure your event-driven systems are reliable and compliant.
Because our platform is open to anyone, and integrates with every messaging system, you can implement powerful reliability into your stack today. Want to see it in action? Book a demo today and we’ll show you how!
Event-driven architectures offer many advantages, making them an attractive option for businesses looking to build distributed, microservices-based apps around messaging solutions like Kafka, RabbitMQ, and NATS Messaging. If the benefits we’ve described outweigh the potential tradeoffs and align with your business goals, EDA can be a powerful tool for building scalable, responsive, and resilient software.
Jon is the Solutions Engineer at Streamdal. He has helped maintain and refine customer service and experience with over 10 years of experience in business operations, retail, and logistics. A lover of Fungi and Mycoremediation, and a fanatical asynchronous, event-driven enthusiast. Here at Streamdal he is the main point of contact for onboarding and integrations.
Wed May 31 2023
Why Use Anomaly Detection for Streaming Data?
Anomaly detection is one of the critical capabilities businesses need to maximize the value of streaming data; it allows them to quickly identify patterns, trends, and irregularities.