What is event-driven architecture?

Event-driven architecture (EDA) is a software design pattern used in microservices or other decoupled services or apps. Each service in the ecosystem can asynchronously publish and subscribe to events via an event broker.

EDA provides a flexible, scalable, and real-time approach to processing actions and responses quickly. It is ideal for managing high volumes and high-velocity data in real time with a minimum time lag. It can handle complex event processing, such as pattern matching or aggregation over time windows.

Let's start by looking at some of the benefits of event-driven architecture.

Less impactful failure

A failure at any service will not impact any other service since event-driven architecture uses decoupled applications. The event broker is the conduit between services, storing published events until a consumer calls for them. The event broker also manages any surges in published events.

Independently build and deploy applications

The event broker speeds up your development process because event producers and consumers don’t need to work together. The event broker stores the event and delivers it once the system returns online. Since applications are loosely coupled, you don’t need custom code to poll, filter, and route events. The event broker automatically filters and pushes events to consumers.

Audit with ease

Auditing is easier and more reliable since every change in the system is stored as an event. The event broker creates an accurate record of every change, acting as your centralized command center, deciding who can publish events, subscribe to the broker, and access your data.

Reduced costs

EDA uses a push-based system, so everything happens only when the event producer pushes an event to the event broker. This means the system is not polling periodically to check for an event, consuming less network bandwidth, less computing power, and less idle fleet capacity.

Disadvantages of event-driven architecture

More complexity

Event-driven architecture deals with a large ecosystem of event producers and event consumers transmitting events across different microservices. Managing this complex network can be a daunting task for any developer. They have to consider many factors like event lifecycles, event discovery, and ensuring the code works across systems, just to name a few.

Troubleshooting challenges

Debugging and testing require more resources and time compared to traditional architecture. Tracing an event from its producer to its consumer can be difficult because of event-driven architecture's distributed and decoupled nature.

Monitoring difficulties

Event-driven architecture requires a proper schema so developers can understand exactly how distributed, decoupled components interact. This needs a comprehensive set of monitoring tools and an alerting mechanism that provides developers with a complete view of the event flow.

To ensure success, consider the following when developing an event-driven architecture:

Event source durability: Your event source should be reliable. It must guarantee delivery if you need to process every single event.
Performance control requirements: Your application should be able to handle the asynchronous nature of event routers.
Event flow tracking: Focus on dynamic tracking via monitoring services and avoid static tracking techniques using code analysis.
Event source data: If you need to rebuild the state, your event source should be deduplicated and ordered.

Event-driven architecture vs. Traditional architecture

Traditional architecture

Traditional architecture follows a request-response pattern where a server transmits a query and waits for a response. This is great when working with static data but not convenient for real-time processing where data changes.

Some key characteristics of traditional architecture:

The service should be online when pull requests are made, or the request will go unfulfilled.
Making changes to the system is complicated and time-consuming.
Any data changes erase previous information about state changes.
It is unable to record changes from multiple sources simultaneously.
It supports synchronized communication and recording of events, ensuring the information is consistent.

Event-driven architecture (EDA)

EDA functions in real time, so all event consumers subscribed to events receive them as produced by the producers.

Some key characteristics of EDA:

The system does not need to know consumers are online for events to be published.
Changes can be made to a specific component without impacting the entire process flow.
The system records and retains the history of every event.
EDA can record data while executing processes, allowing continuous intelligence in a computer system.
EDA does not follow a chronological alignment of timelines, making it difficult to synchronize information.

Event-driven architecture use cases

Marketplaces & Auctions

Event-driven architecture allows marketplaces & auction organizations to enlist different vendors for services like order validation, stock management, and payment processing. Services produce and consume data for new events, which are then sent to an event broker. The event broker pushes each new event to the relevant services automatically.

Because it uses decoupled applications, event-driven architecture also gives organizations the flexibility to introduce changes to the system. For example, if the organization introduces new suppliers or distributors, only that specific process will be changed. The remaining operation will continue functioning, causing minimal or no interruption to the overall customer experience.

IoT Device Control

Event-driven architecture can manage and process data from multiple sources in real-time, making it an ideal system for running IoT devices. It can publish, receive, and process a wide range of IoT commands simultaneously, allowing the system to trigger the appropriate response promptly without repeatedly asking for information through polls.

For example, if a sensor picks up a change in the state (like a change in temperature, location, or volume), the system can trigger the appropriate response. In this case, it would be notifying the appropriate personnel.

Moreover, developers can modify existing code and introduce new processes without impacting the currently running processes.

Financial services

Event-driven architecture is used in financial services to monitor fraud, process transactions, and provide real-time analytics. Every event, like account activity, credit score checks, or fraud detection, is bound by a set of rules decided while implementing EDA. Depending on this, the system can trigger actions like blocking suspicious transactions, validating an application form, or sending notifications to a customer's phone.

Any updates to the system can be made without disrupting the other operations as well. So you can update your fraud detection software by shutting down only those processes that depend on the service, leaving the others free to function freely.

Logistics and transportation

Event-driven architecture is ideal for tracking shipments, monitoring inventory, and providing real-time customer and logistics company updates. The logistics industry manages many individual devices across a wide region. With EDA, developers can build an event mesh across all the services and platforms, allowing better synchronization between different parts of the organization to serve and collect data across multiple web and mobile apps.

Also, as the amount of data invariably increases, the organization can scale without losing previous information or disrupting the current workflows using a more agile approach to development.

Components of event-driven architecture

Event producers

An event producer creates and publishes events. They are also known as event emitters or event agents. The event producer does not know the event consumer, and vice versa. Event producers do not need to belong to the same software ecosystem as the event consumer. An example of an event producer is the front end of a website, IoT devices, SaaS applications, etc.

Event consumers

Event consumers, also known as event sinks, listen for specific events. When they receive a notification that the event has occurred, they can perform actions like starting a workflow, running an analysis, and updating a database. They are a downstream component that comes into play after the event broker receives the event from the consumer.

Multiple consumers can use the same events. Applications can act as event producers and consumers and do not need to belong to the same software ecosystem as the event producer. Examples of event consumers include service endpoints, message queues, databases, event processors, or other microservices.

Event brokers

An event broker (aka event bus) is a middleware component that enables the exchange of messages between publishers and subscribers in a distributed system. Event brokers act as the intermediary between event producers and event consumers. They enable asynchronous communication between the two parties, i.e., allow producers and consumers to send communication over the messaging system to each other without waiting for a response. They also allow different systems to send messages to each other, improving interoperability between systems.

Event brokers support both push and pull consumption models. In the push consumption model, consumers subscribe to specific events, and the event broker pushes the appropriate events to the consumer. In the pull consumption model, consumers request events from the event broker. Apache Kafka, RabbitMQ, or AWS EventBridge are examples of event brokers.

Event schema

An event schema shows the structure of an event and shares information about the event’s data. For example, when you place an order on an e-commerce website, an “OrderCreated” event is generated with information like product ID, quantity, and shipping address. It can also include information like the data type — for example, "number of items" is an integer, "shopping address" is a text string, etc.

Event schemas provide consistency in how events are produced and consumed, which is important to determine how events are routed and processed within the system. They are stored in a common location called the schema registry. When creating event schemas, you can create sparse events with little data about the event or an event with a full-state description, like the example above.

Event processing

Event processing is the capture, enrichment, formatting, and release of events, the subsequent routing and processing of released events, and the consumption of the processed events. There are four common styles of event processing:

Simple event processing (SEP):
Simple event processing is commonly used to coordinate real-time workflows when a notable state change occurs, significantly reducing latency and costs. Simple events occur separately from other events. The final event message may contain data required to initiate a response.
Event stream processing (ESP):
This drives real-time information consumption around the enterprise by processing event data streams to identify relationships and patterns and support timely decision-making. Stream event processing is ideal for IoT workloads where decisions must be made instantly.
Complex event processing (CEP):
This type of event processing is used when multiple events have to occur before any action takes place. No further action will be taken unless all the criteria are met. Complex event processing usually uses more advanced language interpreters, software pattern definitions, and correlations. It is used mostly to identify business opportunities, threats, and anomalies.
Publish/Subscribe processing (Pub/Sub):
When an event is published, it is sent to all event consumers who subscribe to it. Once the event is received, it cannot be replayed, and no new subscriber can view it.

Event-driven architecture relies heavily on event processes to facilitate communication and cooperation between different applications that may not be compatible. Event processes enable swift reactions to singular events, combinations of multiple events, and targeted observations of these events.

Implementing event-driven architecture

Choosing an event broker

It is important to find the right event broker for your system since it defines your applications' architecture, design, and operations. When choosing your event broker, assess its functional and operational features to find the right one for your application.

Event broker capabilities and features can be divided into the following broad categories:

Client connectivity features like protocol support, transaction support, and software development and integration features regulate how event producers and consumers connect to the event broker and process event messages.
Message delivery features establish relationships between event messages and event brokers. These features include message structure, topic organization, topic partitioning, metadata and message persistence semantics, routing, subscriptions, and quality of service.
Broker deployment features are the features enabled to satisfy several non-functional tasks, like throughput, resilience, latency, and recoverability while deploying and configuring event brokers.
Management and operations capabilities are the features available for managing, monitoring, and securing the event broker system and event-driven applications like application programming interfaces (APIs), command-line interfaces (CLI), and user interfaces (UIs).

The categories are further subdivided into many attributes and conditions influencing the application design and its resulting capabilities. When selecting the event broker, consider the organization’s requirements, the capabilities of individual brokers, and where you're willing to meet halfway.

Event brokers are divided into three types based on their functions and capabilities:

Queue-oriented event brokers: When you need multiprotocol client connectivity and flexible topic structure definition without message retention and replay.
Log-oriented event brokers: When you need longer message retention, replay, and/or very high throughput, you can tolerate a flat topic structure.
Subscription-oriented event brokers are perfect for cloud-based event-driven applications hosted on a single cloud provider's environment. Note that this event broker only works if you are willing to design your application around the unordered, at least once delivery model.

As you can see, not all event brokers are the same. It is important to clearly understand their capabilities before selecting one. Here are a few things to consider:

Keep the event broker configurations simple.
Consider the main features when selecting the broker type.
Don't be afraid to use multiple event brokers if required.
Use well-defined schema management.

Defining the event schema

An event schema ensures that certain events follow a predetermined behavior, allowing consistency throughout the system. To create an event schema:

Identify the type of event you want to define. They can be classified into system, business, or user events.
Define the event properties that the event will contain. This includes both mandatory and optional properties. They should be specific and capture all the important information that needs to be transmitted.
Specify the data format in which the event will be transmitted and ensure all system components support it. You can use various formats like JSON, XML, or binary formats.
Define the event schema versioning strategy. This helps maintain backward compatibility when changes are made to the event schema. The strategy should cover how you will version the event schema, handle its evolution, and ensure different versions of the event schema are compatible.
Share the event schema with all the relevant components to ensure they understand the event structure and how to process it.
Test the event schema with various use cases and confirm the event schema works for different scenarios. Verify that all components can process the event correctly.

Best practices when defining event schema

Always define the events to support the business's requirements in the business domain and make sure they represent notable changes or actions within the domain.
Always use a standardized format like JSON or Avro to ensure they can easily function between different systems and languages.
Use a consistent naming convention and structure for your events to make them easy to understand and maintain.
Document your event schema and include information about the event's purpose, data, and any relevant business rules or constraints. This will help any developer easily understand and maintain your event schema.
Identify how in-depth each event needs to be defined based on the system requirements. Do you need high-level events representing business actions or more granular events representing individual changes or updates?
Keep the event schema simple. Do not overcomplicate it with unnecessary details or complex data structures.

Designing event producers and consumers

Event producers and consumers are designed to fulfill specific requirements:

Event producers generate and publish events to the event broker.
Event consumers subscribe to or receive events as the event producer publishes them.

System requirements like the event-driven architecture's scale, performance, and reliability can also influence the design of event producers and consumers.

Best practices when designing an event producer

Choose a format that is efficient, easy to parse, and able to support the types of events being produced. Some possible candidates are JSON, XML, or Avro.
Every event should contain all the pertinent information consumers require to process events independently of any external dependencies, including metadata or contextual information.
To improve reliability, design events so they can be processed multiple times without any problems.

Best practices when designing an event consumer

Consider the specific requirements of your system when choosing an event processing model, like the rate and volume of events and the time required by the consumer to process them.
Implement fail-safes like resending events so that your consumer can handle the workload if any component fails to perform its defined action.
Ensure consumers can function simultaneously. This significantly improves the performance and scalability of the event-driven architecture style.

Deploying and scaling the event-driven system

The deployment process in an event-driven architecture should always remain consistent between services to ensure microservices continue to function seamlessly. Microservices testing and deployment should be carried out at the teams' discretion.

Here are a few considerations to keep in mind during deployment:

Reset event consumer offsets.
Purge event stores.
Review and update event schemas.
Delete event streams.

Tools are available to perform the above functions, enabling further automation and supporting team autonomy.

Best practices for event-driven architecture patterns

Open communication between developers and architects:
This is non-negotiable when creating an effective event-driven architecture. Both parties bring different perspectives about event opportunities and how to incorporate them.
Prepare for scaling the system:
Keep scalability in mind when building the event architecture. This will enable reactive and flexible processes and rapid changes to how activities flow from one component to another.
Apply API best practices like taxonomies, portfolios, and portals:
Taxonomies create an event hierarchy subscribed to by one or more event consumers. Portfolios identify and organize events within business domains. An event portal creates a space for event producers and consumers to connect and collaborate on event usage policy and subscriptions to event streams.
Design the event architecture to span across multiple clouds:
Carefully consider which event technologies to use and how to connect them across different cloud infrastructures. This way, the event architecture can span multiple clouds and technologies.
Define quality of service (QOS) and contextual guidelines.
When implementing event producers or event consumers, define QOS guidelines (like where to have delivery guarantees, sequence preservation, and event persistence) and contextual guidelines (which application acts as the event producer or consumer).

Best practices for testing and monitoring event-driven architecture

Event throughput:
Continuously monitor the number of events produced, processed, and transmitted through the system and the time taken to perform these actions. This way, detecting and responding to any issues becomes easier.
Event processing latency:
Monitor the processing time of events at specific stages of the event processing pipeline or during the entire process to ensure minimal latency and operational efficiency.
Event error rate:
Monitor the error rate to ensure the event workflow is working reliably by tracking the number of events that fail to be processed or are rejected by event consumers.
Log events and metadata:
Maintain a record of events as they are produced and processed, along with any metadata or contextual information associated with the events.
Create alerts:
Set up alerts to detect and inform stakeholders about problems in the workflow, like high error rates, high latency, or low throughput.

Best practices for performance optimization in event-driven architecture

Use performance modeling

A performance model helps:

Developers build scalable deployment models
Make architectural and design optimizations to reduce latency
Design tests to validate performance and throughput

The performance model should cover event inventory, infrastructure modeling, and the maximum workload the system can expect.

Leverage caching

Caches create a faster data layer for data look-ups, reducing the cost of sending and receiving database queries. The event stream data pipeline updates the cache almost immediately. It allows event processors to access the data instead of querying the database or making service calls to systems of record every time.

Collocate processing

Event-driven architecture performs best when event producers, event consumers, and event brokers are collocated. The only downside is the entire application will go down if the data center goes down. You can avoid this by setting up mirrors for the event architecture.

Important security considerations for event-driven architecture

Similar to point-to-point architecture, restricting services to only produce or consume events related to specific queues helps protect important data in an event-driven architecture. Set these restrictions by providing clear guidelines about which services can write and consume events.

Comparison of event-driven architecture and traditional architecture in the real world

Event-driven architecture

Logistics

A German delivery giant operates one of the biggest Kafka clusters in the European continent. The company utilizes it to make real-time analysis and monitoring available throughout its logistics process.

By leveraging a cloud-based event-driven architecture, the company can quickly and securely duplicate data, which can be integrated into Kafka, allowing the organization to make real-time decisions and improve visibility into every stage of the process. It streamlines its operations and provides customers with immediate updates on the delivery status by processing more than a billion events every day, tracking each shipment from pickup to delivery.

The solution implemented by the company optimizes how containers are distributed and arranged while enabling real-time management of parcel and delivery traffic.

Transportation

One of the largest transportation services companies in the United States uses event-stream processing to run its connected fleet. In 2020, the connected fleet architecture used 700 GB of data daily to publish 850+ events with 50+ event streams, producing 2 billion pings per day in its vehicle tracking system.

The company installed sensors to collect information on each vehicle, like GPS location, speed, fuel level, mileage, and engine temperature. This data is streamed to an event broker, which processes and analyzes the data instantaneously.

Today, it uses this system design to run diagnostics on trucks nationwide. It enables drivers and other employees to make data-driven decisions regarding their fleet using real-time insights about fleet performance and availability. The real-time capabilities of this architecture also enabled the company to improve its operational efficiency.

Microkernel architecture pattern

eCommerce

The world's largest online retailer (Amazon) used to have a single, monolithic application for its retail website, making upgrading and scaling difficult. As the company grew, this became a problem. In 2001, it refactored its system into small, independently running, service-specific applications.

Developers pulled out specific lines of code that served a single, functional purpose and divided them into separate microservices in a web service interface. For example, they developed a single service for the Buy button on a product page, a single service for the tax calculator function, and so on.

This change to a microservices architecture allowed for quicker development and easier maintenance, leading to the development of additional solutions to the company's portfolio. Without the transition to microservices, the eCommerce giant could not have grown to become one of the most valuable companies in the world.

Conclusion

Fundamentally, event-driven software architecture allows developers to create real-time applications that are more scalable, flexible, and responsive. By following the best practices and staying up-to-date on the latest frameworks, developers can successfully integrate EDA into their workflow and create high-performing, modern applications.

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