Event-driven architecture (EDA) is a design pattern for creating real-time applications across industries — from streaming and messaging to banking and logistics. It provides many technical benefits, including asynchronous communication and high scalability.
It also offers business benefits, such as increased employee productivity, customer acquisition, and revenue. This article delves into EDA, covering its core concepts and common patterns and highlighting its usefulness to developers.
Defining event-driven architecture
Event-driven architecture allows developers to build applications that respond to real-time events. The design pattern consists primarily of decoupled, autonomous components that enable applications to be highly scalable, flexible, and resilient.
Asynchronous APIs (async APIs) are critical to the operation of event-based architectures. They belong to a category of APIs that includes streaming, message queues, and event logs. As events occur, the system immediately sends information about them to anything that needs it, such as downstream systems, devices, and third-party applications; these can then react to the events in real time.
Why use event-driven architecture?
There are many technical benefits to EDA, such as:
- Real-time processing and responsiveness: EDA allows systems to respond to events in real-time with low latency — a requirement for many web and mobile applications. For example, a chatbot application needs to follow and understand conversations, responding to user input instantly. Multiplayer online gaming applications need real-time event processes to synchronize player actions and adjust game content dynamically. With EDA, applications can respond to events as they occur.
- High scalability, agility, and flexibility: Components in an EDA application communicate through events instead of direct calls. This decoupling allows components to operate independently, making for highly scalable and flexible systems. Teams can adapt the event-driven system to meet fast-evolving business needs and add new components without disrupting others.
- Complex event processing: EDA is ideal for applications that require complex event processing or dynamic workflows. For example, a fraud detection platform must monitor real-time data and detect suspicious patterns to block risky transactions immediately. A logistics application must track shipments and respond promptly to delivery delays and route changes. EDA enables these applications to process complex and ongoing events in real-time.
- Reliability and resilience: Components in an EDA application operate autonomously, making the system quite resilient. When one or two components fail, others will continue to operate, allowing the application to avoid catastrophic failures. This decoupling of components helps ensure an application’s reliability.
Now that you know some of the benefits of EDA, let’s look at some core concepts.
Core concepts in event-driven architecture
Event-based architecture is quite complex, and these are the core elements and concepts to get familiar with:
- Events: Events are noticeable changes or occurrences within a system. They vary greatly and depend on the domain and application. A user creating an account, a sensor detecting a motion, and a shopping platform marking an item as shipped are all examples of events.
- Event producers: These components generate and publish events in a system. They communicate the events to other parts of the system.
- Event consumers: These receive and process events generated by event producers. Based on the events, they perform actions or trigger specific processes.
- Event sourcing: This is a pattern in which the system stores an application’s state as a sequence of events instead of its current state. The system associates each change with an event containing the timestamp and nature of the change.
- Event broker: An event broker is an intermediary that routes events between event producers and event consumers. It ensures that the system distributes events properly. Popular open source event brokers include Apache Kafka and RabbitMQ.
- Event bus: This mechanism acts as middleware, decoupling event producers from event consumers. It facilitates the communication of events between components in a system. Brokers like Apache Kafka and RabbitMQ can also be used as event buses.
- Asynchronous communication means that event producers and consumers are decoupled and operate independently. This decoupling is typically done through an event broker or event bus.
These core concepts form the foundation of EDA patterns that make real-time applications possible.
Event-driven architecture patterns
In EDA, patterns are technologies and techniques that define the generation, capturing, processing, and distribution of events in a system. There are quite a few EDA patterns, for example:
- Event streaming: This pattern involves publishing events to an event log or persistent stream for interested consumers to subscribe to. The pattern allows the system to capture and process events in real-time. Data flows continuously from one or more sources, like sensors, applications, or databases.
- Event sourcing: This pattern stores all application state changes as a sequence of immutable events in an event log or journal (append-only). It allows a system to reconstruct the current state at any point in time by replaying events from the beginning in the order they occurred.
- Event mesh: The event mesh pattern involves using interconnected brokers or a mesh network to form a distributed system that allows events to route dynamically across environments. An event mesh allows an application to share information with other applications regardless of where they’re deployed, like private or public clouds, edge devices, or data centers.
- Publish/subscribe (pub/sub): This pattern allows different system components to communicate asynchronously. It has three main components: a message broker, publishers, and subscribers. Publishers trigger events within the system; subscribers register with the system’s message broker to get notifications about specific events; and the message broker receives events from publishers and routes them to the subscribers interested in them.
- Command Query Responsibility Segregation (CQRS): The CQRS pattern separates an application’s read and write operations into two data models so that you can design and optimize them independently. This pattern is often used along with the event-sourcing pattern, improving performance for reading and writing data.
Most of these patterns require a distributed streaming platform or message broker software for implementation in applications. For example, Apache Kafka, Amazon Kinesis, RabbitMQ, and Solace are platforms commonly used for event streaming, event sourcing, and pub/sub patterns. These EDA patterns leverage events and asynchronous communication, allowing developers to build highly scalable and responsive real-time applications.
Discover the potential of event-driven architecture
Businesses in nearly every industry need real-time applications; EDA is crucial to fulfilling that need. It helps developers build innovative applications for a wide range of industries and use cases. The possibilities for EDA applications are endless, from e-commerce order processing and online gaming to predictive maintenance and automated smart homes.
