Event-driven architecture (EDA) is an increasingly popular design pattern in application development. Organizations in many industries use it to create innovative, real-time applications. We’ve previously discussed core concepts and common event-driven architecture patterns.
Now that we’ve looked at the basics, we’ll delve into how some companies have tailored EDA implementation to suit their exact needs, including the tools that made it happen. We’ve also thrown in some tips to help you make the most of your event-based architecture.
Tools for building event-driven architectures
Building an event-driven architecture involves combining different EDA patterns and implementing various technologies, which requires proper tools. The tools below are crucial to building and maintaining responsive and scalable event-based architectures:
- Event streaming platforms — Event streaming platforms enable communication between various components in an event-driven system. They process the high volume of data generated by events and allow applications to respond to events as they occur in real time. Examples: Apache Kafka, Amazon Kinesis, Confluent Platform, and Apache Pulsar.
- Message brokers — Message brokers serve as intermediaries, facilitating the exchange of messages between different system components. Event streaming systems focus on communicating event data, while message brokers handle a wide range of message types. Examples: RabbitMQ, Google Pub/Sub, and Amazon SQS (Simple Queue Service).
- Stream processing frameworks — These frameworks perform real-time processing on the constant flow of event data. They ingest the data coming from the event broker. They then transform and analyze the event data to trigger specific actions or generate insights. Examples: Apache Flink, Apache Storm, and Apache Spark Streaming.
- API gateways — An API gateway serves as an entry point for all client requests, communicating those requests to the application’s backend services. Most API gateways handle a wide range of tasks for the event-driven system, such as routing requests (events), authentication and authorization, event publishing (to an event broker or event bus), and traffic management. Example: Tyk’s API Gateway.
- Monitoring and analytics — Continuous monitoring ensures the health and performance of the system and facilitates event processing. Analytics can identify bottlenecks in event flows and errors in various parts of the system. They both enable visibility and observability in EDA-based applications. Examples: Grafana and Splunk.
Organizations worldwide use different combinations of these tools to create real-time applications.
Examples of real-world EDA-driven applications
Companies around the world have created amazing real-time applications with EDA. A few examples:
- Ridesharing applications — Uber’s platform is built on EDA. The company uses Apache Kafka for its messaging queues, processing trillions of messages and petabytes of data every day. Kafka enables dynamic pricing, real-time updates for drivers and riders, and capturing and storing log data. Uber uses Apache Flink for its exactly-once events processing system — the framework processes streams of unbounded data in near real time.
- E-commerce and online marketplace platforms — Walmart, Target, and Shopify have e-commerce platforms that run on EDA. They leverage Apache Kafka for real-time capabilities like inventory management, order processing, and order tracking. eBay uses Kafka to handle real-time processes like tracking user activity, auction bidding, and disaster recovery.
- Streaming services — Apache Kafka serves as the Netflix platform’s message broker, processing millions of events every second. Kafka handles events, messages, and stream processing in real time. Spotify adopted Google Cloud Pub/Sub to implement an EDA for its music streaming platform. The pub/sub pattern delivers events throughout the Spotify platform, like opening the app or sharing a playlist.
- Banking systems and applications — EDA powers many of ING’s banking operations and applications using a combination of technologies including Apache Kafka and Apache Flink. Kafka takes on tasks like processing stock price updates and sending investment alerts to customers in real time. An EDA based on Apache Flink also drives ING’s stream data platform.
These examples only scratch the surface of EDA’s use in applications. It’s also vital to real-time applications in logistics, online gaming, healthcare, social networking, and financial trading.
Best practices when developing event-based architectures
Creating event-driven architectures involves combining many tools, technologies, and techniques. To make the most out of EDA, consider following these best practices:
- Make sure events are well-named and identifiable — Name each based on a specific purpose and use consistent — but not generic — naming conventions across the board (for example, event headers, metadata, and schemas). Implement unique event IDs to ensure every event is identifiable.
- Don’t overdo the events — Events are the stars of an EDA, but a system with too many becomes overly complex and more difficult to test and debug. Create events thoughtfully, focusing on significant or necessary changes in the system.
- Decouple components as much as possible using event brokers — EDA components should operate independently, communicating through asynchronous messages. Loosely couple components, particularly your event producers and event consumers. You can employ loose coupling by implementing event brokers with tools like Apache Kafka or RabbitMQ.
- Choose your message delivery semantics carefully — You have several semantics options, including exactly-once, at-most-once, at-least-once, and eventual consistency. You should consider your options carefully because the choice directly impacts how many temporary inconsistencies occur between events and services. It also affects your choice of messaging framework.
- Choose the proper messaging framework — You’ll need one that can handle your preferred message delivery semantics. For example, you can use Apache Kafka to implement a number of methods, including exactly-once, at-most-once, and at-least-once. Kafka also supports eventual consistency. RabbitMQ currently offers all the above, except for exactly-once.
- Implement event monitoring, logging, and error handling — Monitoring helps ensure your EDA-based application is optimized, performs well, and remains reliable. Event logging allows you to create audit trails for compliance and security audits. Error handling strengthens the system’s stability by enabling it to continue operating when errors or exceptions occur.
- Continuously test your architecture and applications — Continuous testing is crucial for event-based applications for many reasons, including:
- Asynchronous workflows — In an EDA, event processing can happen long after publication, making it challenging to find problems with event workflows. Continuous testing helps developers overcome the asynchronous nature of the EDA system, as they can locate and identify problems faster.
- Eventual consistency — Some event-driven systems aim for eventual consistency, which can lead to inconsistencies (services could temporarily have different versions of data). Continuous testing helps ensure that data communicated throughout the system remains consistent and uncorrupted.
Building better EDA-based applications
We’ve highlighted real-world examples of event-driven applications and some standard tools for building them. We’ve also covered several best practices you should follow to ensure the quality, performance, and reliability of your event-driven architectures.
No matter your industry or development goals, this article can serve as a good starting point for your adventure into EDA-based, real-time applications.
