Event-Driven Microservices - Quick Guide

---

---

Event-Driven Microservices - Introduction

Event-Driven Microservices (EDM) are microservices that interact using events. Events represent the changes in the state of the system, for example, process a transaction, place an order, complete a payment, etc.

Event-Driven Microservices create a loosely coupled system where services do not depend on direct communication but respond to events only when these events occur. We use EDM systems in large-scale and distributed environments where real-time processing is required.

Characteristics of Event-Driven Microservices

Some of the important characteristics of Event-Driven Microservices (EDM) are listed below −

• Asynchronous Communication − Services communicate asynchronously in the EDM system. We do not wait for responses and instead react to events as these events occur. It improves efficiency and performance of the System.
• Loose Coupling − EDM services are decoupled and communicate through events. Each of these services can run independently. It reduces the risk of cascading failures.
• Scalability − We can scale up services in an EDM independently based on requirement.
• Real-Time Processing − We can use EDM systems in real time because EDM processes events as these events occur. We use EDM in applications for instant reactions. Examples are: payment processing, transaction systems, and real-time monitoring.
• Event-Driven Workflow − EDM generates services using events instead of calling these services directly. So services only act when an event occurs.

Architecture of Event-Driven Microservices

There are some important terms in the architecture of Event-Driven Microservices, such as Event Producer, Event Broker, Event Consumer, and Message Queues. These are explained below −

• Event Producer − It generates events. When an action occurs, like a customer placing an order and a payment being processed. Then the event producer sends an event to the event broker for further processing.
• Event Router − It routes events between producers and consumers. The tools like Apache Kafka, RabbitMQ, and AWS EventBridge act as brokers. Events are delivered to the correct consumers.
• Event Consumer − It is made up of services that react to events. These services listen for events and perform actions based on the event data, like updating databases, notifying users, and processing payments.
• Message Queues − Message queues are required to send deliveries. If the services are not available, then message queues send the deliveries to the consumers. These queues balance load and manage event traffic.

Types of Events in EDM

There are two types of events in the EDM systems −

• Domain Events − These are business-level events. These represent changes in the state, like an order being placed, a payment being completed, a user registering, etc.
• Integration Events − These events are used in communication between microservices. So different services can synchronize data and trigger actions across the system. For example, these events notify the shipping service to prepare for delivery, when an order is placed.

EDM Event Workflow

When an event occurs, for example a new transaction is generated by an event producer. The event broker distributes it to its event consumers. These consumers process the event based on their function. This is an event workflow in the EDM system −

• Event Generation − Customer places an order in an e-commerce platform. The order event is generated by the producer.
• Event Routing − The event broker routes the order event to multiple services (consumers) like inventory management, payment processing, and notification services.
• Event Processing − Each service processes the event. Inventory management updates stock. The payment service processes the payment. The notification service sends a confirmation email.
• Real-Time Response − The system updates all services in real-time without waiting for any single service to complete its task.

Advantages and Disadvantages of Event-Driven Microservices

There are various advantages and disadvantages of Event-Driven Microservices (EDM), some of which are highlighted below −

Advantages of Event-Driven Microservices

Following are the advantages of using Event-Driven Microservices −

• Real-Time Processing − Services react only when events occur in EDM. We use EDM for real-time applications.
• Scalability − We can scale up each microservice; it optimizes resource use based on requirements.
• Loose Coupling − Services are independent. It reduces risks of cascading failures. We can also update any of these services.
• Efficiency − Services do not need to wait for responses because of the loosely coupled system. It improves system performance.

Disadvantages of Event-Driven Microservices

Following are the disadvantages of using Event-Driven Microservices −

• Complexity − We need to manage many independent services. It can increase system complexity.
• Data Consistency − We need to keep data consistent across services in an asynchronous architecture. But it can be challenging.
• Security − We need to secure communication between services to prevent unauthorized access.

Event-Driven Architecture vs Microservices Architecture

The following table highlights how an event-driven architecture is different from a microservices architecture −

Conclusion

Event-Driven Microservices architecture is a scalable and real-time solution for modern applications that need to react instantly to changing conditions. Its loose coupling and asynchronous communication make it well-suited for large, distributed systems. Although EDM has some complexity in event handling and data consistency, but its benefits such as real-time processing and service independence outweigh these challenges.

Architecture of Event-Driven Microservices

We can send and receive event messages using Event-Driven Microservices (EDM). Producers generate and publish events to notify other services when actions and changes occur in the system. At the same time, other services consume these events to take relevant actions to updates in real-time.

The main advantages of EDM systems are their asynchronous communication and loosely coupled design. Services subscribe to and react to events as these occur rather than waiting to request data at the time of need. EDM is loose coupling to keep independence between microservices. So, we can operate and scale any services individually without impacting each other.

EDM has loosely coupled architecture with event-driven basics. EDM can address the limitations of traditional approaches. We will discuss these solutions in this chapter.

REST-Driven Approach

We call components one another and wait for responses in the REST-based synchronous system −

But there are some problems with this approach that include the following −

• Dependence on Responses − If module 1 waits for a response from Module 2. Then it can have delays if Module 2 is slow and unavailable.
• Service Downtime − If a service goes down. Then its dependent services are disrupted.
• Network Latency − Data transfer can have delays for large payloads requiring pagination.
• Heavy Load Impact − Services under heavy load may respond slowly. So it reduces performance.

We use EDM solutions when systems become less efficient due to these synchronous connections.

Event-Driven Microservices Example

Suppose we have a reporting application GIB API used to send sales reports to the government. This application fetches all sales data from another API. In the beginning, with a low volume of transactions, we built the API using a simple REST approach.

But when the data grows, the volume of transactions increases unexpectedly. The REST approach has a problem, so we need an event-driven solution.

Solution 1: Converting to Event Messaging

We apply the event-driven approach for efficient report generation. This plan is easy as given below −

• Generate events for new transactions − An event is published whenever a new transaction item is created.
• Asynchronous data fetching − Related data is fetched and transformed into report content.
• Storing and retrieving reports − Report data is stored in a relational database (PostgreSQL). Report strings are concatenated and saved as files for later access.

Through this asynchronous event messaging. The reporting application can process data without the need for constant direct REST calls.

There is another performance issue after converting the process to an asynchronous model. The reporting API is still overloading the transaction API because it requests transaction details for each item.

Solution 2: Fat Event

We can use the fat event approach to resolve the above issue. Each event message has all relevant transaction details. We embed the data directly within each event. So it eliminates the need for additional REST calls. It creates a fully asynchronous EDM architecture.

{
   "identifier": 2,
   "detail": "sale transaction item",
   "created_date": 4587997988425,
   "last_modified_date": 1845298016540
}

Complete Event-Driven Architecture

The entire system is more responsive with fat events. We have all necessary information within events. Each service can operate independently without depending on other services in real-time.

Solution 3: Outbox Pattern

We consider the data processed as financial transactions. Keeping accuracy is important. This required a reliable method to ensure all events are delivered and processed without any data loss. Here, we applied the outbox pattern.

What is the Outbox Pattern?

We first saved event messages in a database table in the outbox pattern, instead of publishing event messages directly. A job then batches these events and sends these periodically.

Outbox Pattern Workflow

• Event Generation − The business module publishes an event.
• Persisting Events − The event service stores the event message in a relational database (RDBMS).
• Triggering the Job − A scheduler initiates the Send Event Messages job.
• Publishing Messages − The event service retrieves the stored events and publishes these using message brokers like RabbitMQ.

Advantages of the Outbox Pattern

• Persistent Events − It has ACID properties because of events saved in a relational database, so data is persistence.
• Recovery of Lost Events − If an event fails to publish. It can be resent from the database.

Disadvantages of the Outbox Pattern

• Increased Complexity − Since it adds another layer to manage within the architecture.
• Delayed Publishing − Events may be published at intervals rather than in real time.

Benefits of EDM Architecture

Given below are some of the benefits of using the EDM architecture −

• Loosely Coupled Structure − Each microservice is independent, so it is flexible and modular.
• Complete Isolation − Each microservice can function without synchronous calls to other services.
• Asynchronous Processing − It improves overall system responsiveness and efficiency.
• Performance Gain − Services can react to events as these occur.

The main advantage of EDM is loose coupling. It preserves the independence of microservices.

EDM has a Single Point of Failure

If you use a message broker like RabbitMQ in EDM, it can have a point of failure.

But you can solve this using one of these techniques −

• Cluster Configuration − You can set up RabbitMQ as a cluster to prevent single points of failure.
• Durable Queues − You can configure queues to persist.
• Message Persistence − You can store messages for later retrieval, even in case of a temporary broker outage.

Other instances in the cluster can take over, if an error occurs. So the persisted messages can be recovered.

Duplicate Event Messages

An event may be published more than once, sometimes. But you can use the following technique to prevent issues from duplicate messages −

Idempotent Consumers − Consumers should be used to handle duplicate events. It should first check if these have already processed an event before taking action.

In microservice architecture, you need to maintain loose coupling for effective communication. You can use event-driven (EDM) approaches to reduce dependency on direct REST calls. EDM avoids the issues of synchronous communication, like waiting for responses and handling service outages.

We set up a RabbitMQ cluster with persisted messages. Services can publish events without requiring immediate acknowledgment. Fat events have all needed data to improve efficiency. The outbox pattern has reliable event delivery, even in cases of network issues and temporary outages.

Conclusion

EDM is used to keep microservices independent and loosely coupled. You can handle real-time data in EDM with efficiency and scalability. But, if microservices become interdependent. Then the architecture risks transforming into a distributed monolith. So you should keep loose coupling using an event-driven design.

Use Cases of Event-Driven Microservices

Event-driven architecture (EDA) is used in applications where you require flexibility and processes can react in real-time. We can create, detect and process events using EDM. An event is a change in state, like user input, system notifications, data updates, etc. These events trigger different responses in the system.

Event producers generate events and send them to a message broker (also known as event router and event bus). The message broker propagates the events to the consumers.

Components are loosely coupled in the EDM architecture. Following are the components in the EDM architecture –

• Event Producers − These generate events and send them to the event router.
• Event Routers − These do event routing between producers and consumers.
• Event Consumers − These receive events and do services.
• Event Store − It records all events for audit, debug, and replay purposes.

Event-Driven Microservices have several use-cases, some of which we will highlight in this chapter.

EDM for E-Commerce Platform

We use EDA with microservices to communicate asynchronously. There are advantages because it is a loosely coupled system. So it communicates between independent services. As traditional request-response can fail but EDM does not fail because EDM has a loosely coupled asynchronous system. Services can operate independently using a publish-subscribe model in EDM.

For example, suppose there is an e-commerce platform where you can sell, bill, and warehousing services to fulfill orders. We use EDM to process a sales event without waiting for the availability of downstream services like billing. We fill the service and go offline temporarily. The event will be in the queue and processed once the service resumes. So it avoids disruptions for end users.

EDM for real-Time Customer

We can use EDA to power real-time and customer-facing updates. We connect internal event-driven systems to customer-facing interfaces. We give feedback to users when events occur.

For example, let's consider an online store. If a payment fails, EDA can update the user with the reason (e.g., credit card declined, network issues). There is no dependency required for request-response calls. It improves user experience. So we can update users with real-time information on important transactions.

EDM for Real-Time Data Analysis

We can use EDA on real-time data analysis. We process events as these occur. EDM minimizes delays in data processing, so we analyze in real time. For example, event-driven dashboards can give you real-time updates on package locations, transport delays, etc. We can use data from IoT sensors on vehicles and monitor shipments for up-to-the-minute status and updates to customers and operators alike.

EDM Internet of Things (IoT) Applications

We can use EDA in IoT applications. Devices generate events (e.g., sensor readings, status changes). These devices can send data to subscribed consumers using EDA to monitor and control in real-time.

For example, let's consider the supply chain with IoT where sensors send updates on location, temperature, etc. We use EDA to get these updates. So we can track shipments in real time and respond to disruptions, like deviations in temperature.

EDM for Fraud Detection and Security Monitoring

EDA can detect fraud because of suspicious activities and patterns. It responds to these activities and patterns. EDM prevents unauthorized transactions and security breaches. For example, in a banking application, if an unusual transaction occurs (e.g., a large withdrawal in an unfamiliar location). EDA can trigger an event that alerts both the user and bank fraud detection team for verification.

EDM for Inventory and Supply Chain Management

EDA keeps optimal stock levels because it triggers updates to inventory in real time. So there will be efficient supply chain operations.

For example, in retail, EDA prevents stockouts because it tracks inventory in real-time. When the quantity of an item falls below a given threshold. Then an event is triggered to order more stock and alert a warehouse manager.

EDM for Healthcare Monitoring and Alerts

We can use EDA healthcare for monitoring. So hospitals can respond to critical patient data. For example, patient monitoring devices emit events when vital signs cross given thresholds. EDA routes these alerts to doctors. So they respond to critical conditions.

EDM for End-to-End Transaction Tracking

We can monitor transactions throughout their lifecycle. So it gives you transparency and proactive management. For example, in a payment processing system, each stage of a transaction (authorization, capture, settlement) emits events. We can track and validate these. So we can identify issues at any stage, like failed payments and delays.

EDM for Cross-Region Data Synchronization

We can data replication across multiple regions using EDM. So there will be consistency and redundancy in distributed systems. For example, a video streaming platform can use EDA to synchronize user watchlists and progress data across regions. When a user in one region updates their watchlist. Then an event is triggered to replicate this data in other regions too.

EDM for Customer Notifications

EDA is instrumental in driving real-time. So can respond to customers actions in targeted marketing.

For example, in an e-commerce setting, if a customer adds an item to their cart but leaves the site without purchasing. Then EDA can trigger a follow-up email reminder and a discount notification. It encourages them to complete the purchase.

Conclusion

We use EDA in various applications because of its real-time services. It gives you asynchronous communication between event producers, routers, and consumers. Each component can operate independently because of loosely coupled services.

We can use EDA in e-commerce, real-time analytics, IoT, fraud detection, and healthcare monitoring. Applications can process events as they happen in EDA, so we can track transactions in real-time to optimize inventory and respond to critical patient data.

Communication of Event-Driven Microservices

Event-Driven Communication (EDC) forms a foundational part of distributed systems. Components within an application to respond to specific events. So we service these independently based on the occurrence of events. We do not need to wait on direct commands and continuous requests. We can handle these in real-time processing in interconnected environments. We use EDC in various industries like e-commerce, finance, and IoT.

There are various core components in Event-Driven Communication. Each component is used to interact with services. Events are generated, routed, and processed. These components are required to service these events. We operate these services asynchronously and independently.

Listed below are the components of Event-Driven Communication −

• Event Generation
• Event Routing
• Event Consumption
• Event Processing
• Message Storage
• Scalability and Load Management
• Monitoring and Observability
• Security Management

We will explain these components in this chapter −

Event Generation in Event-Driven Communication

Event generation is the starting point in Event-Driven Communication. Events are produced when there is a state change within the system. These events capture specific actions, like when a user logs in, a payment is processed, an order is placed, etc. This data is then sent to the system to process.

The Event Generation component is used to perform the following functions −

• Monitor for actions and state changes, like completed payments, updated inventory, etc.
• Generate structured events to include relevant details.
• Send events to an event broker. These events are stored until these are routed to the relevant services.

This component forms the entry point for Event-Driven Communication. So the flow of data and reactions begin.

Event Routing in Event-Driven Communication

Once an event is generated. We route these to their consumers. Event routing ensures that each event reaches the relevant services that need to process it. It is known as event broker and message router. Examples are: Apache Kafka, RabbitMQ, AWS EventBridge, etc. We can handle high volumes of events and send these events correctly.

The event routing component of the EDC handles the following functions −

• It ensures reliable delivery of events from producers to their consumers.
• It routes events based on defined conditions.
• It handles delays and manages event traffic.

Event Consumption in Event-Driven Communication

After events are routed. Services take these events and take action. Each event consumer waits for events to arrive and reacts accordingly. Consumers can include any service that needs to perform an operation, like updating records, sending notifications, and initiating additional processes based on the event.

The Event Consumption component is responsible for the following functions −

• It listens for events that match their triggers.
• It executes predefined actions, like database updates, notifications, API calls, etc.
• It acts independently without depending on other services for direct communication.

Services are loosely coupled and respond only when relevant events are received.

Event Processing in Event-Driven Communication

Event processing transforms raw event data into actionable insights and operations. This component uses logic to incoming events, like verifying transactions, validating user actions, and filtering information for analysis. We need to handle processing in real-time.

The Event Processing component handles the following functions −

• It uses business logic to analyze and act on incoming events.
• It manages resources to handle large data volumes.
• It has low-latency processing for applications, like payment processing and monitoring.

Message Storage in Event-Driven Communication

Message storage temporarily holds events. It ensures their reliability. We store messages. Systems can manage events that arrive faster than these can be processed and also if there are temporary service outages. This component acts as a buffer. So we retrieve these events later by services if these are available.

Here is a list of some of the functions that message storage can handle −

• It temporarily stores events until consumers are ready.
• It ensures reliable event storage to prevent data loss.
• It gives fault tolerance and load balancing across consumers.

Scalability and Load Management in Event-Driven Communication

We can have various types of events in Event-Driven Communication systems because of its scalability and load management. We can scale up resources to handle changes in traffic without delays. This component oversees resource allocation and workload distribution to keep consistent performance.

• Monitor event traffic to manage resource allocation.
• Distribute workload across services
• Scale resources dynamically to match the volume of events in real-time.

Monitoring in Event-Driven Communication

We monitor Event-Driven Communication to ensure that events flow between components. This component gives insights into event processing, identifying delays and issues that may impact system performance.

• Track event flow to detect and address problems
• Generate metrics on performance, latency, and resource utilization
• Drill down into events for debugging

Security Management in Event-Driven Communication

Security is important in Event-Driven Communication. Data moves between components. There can be unauthorized access and data breaches. This can compromise the entire system. So security management controls to protect event data and ensure authorized access only.

• Authenticate and authorize access to the system
• Encrypt data to secure it from unauthorized access
• Monitor for unusual activities and security breaches

Advantages and Disadvantages of EDC

The following table highlights some of the advantages and disadvantages of Event-Driven Communication −

Conclusion

Event-Driven Communication is used to manage dynamic applications in real-time. Through asynchronous messaging. Systems can process events independently. Each service operates only when necessary. We use EDC in finance, e-commerce, healthcare, and IoT.

Integration of Event-Driven Microservices

We can use existing systems into an Event-Driven Architecture (EDA). We can build systems to handle unpredictable workloads. So it delivers faster responses to customer interactions. It also gives you real-time insights for data-driven decisions.

Why Integrate Systems into Event-Driven Model?

Traditional architectures operate on a request-response basis. It requires direct and synchronous communication between services. So there is limitation in this structure and scalability problem. It increases downtime and delays data processing. Whereas, EDA operates asynchronously. It broadcasts and processes events in real-time. For example, when a retailer moves from a request-driven system to EDA. Then its sales, inventory, and customer service functions stay up-to-date in real-time without overloading any single system component.

EDA also improves scalability because it has decoupled components within the architecture. This loose coupling means that a failure in one part of the system does not cause cascading failures. So the rest of the system continues running. Systems gain asynchronous data flow. EDA also reduced dependencies.

Key Components for Integration of EDA with Existing Systems

We need to introduce / update various components when integrating EDA with existing systems. These are some important components −

• Event Broker (Message Broker) − The event broker acts as a central hub. There can be asynchronous communication in the systems. Examples of brokers are: Apache Kafka, AWS EventBridge, and RabbitMQ. These bridge traditional architectures with event-driven workflows.
• Event Router − This routes events to their consumers based on defined conditions. It prevents data congestion because it directs events to the right systems while filtering out irrelevant ones.
• Event Consumers and Producers − Existing systems and new applications act as consumers and producers by either publishing events to the broker or subscribing to receive these. We require custom adapters to convert data into event formats for modern EDA.
• Event Store − It keeps historical records of events for traceability, auditing, and debugging. Event stores, like Kafka. Systems can replay events as needed. It supports data integrity and recovery.
• Data Synchronization Services − For real-time data consistency across legacy and modern systems, these services change from existing databases and push these to the event bus. So all systems remain up-to-date.

Steps to Integrate EDM with Existing Systems

Following are the steps to integrate EDM with existing systems −

• Identify Integrating Points − First we find areas where real-time data can improve business processes. We look for repetitive tasks and areas where delays impact customer experience.
• Set up an Event Broker − We implement an event broker to handle the load from data streams. It acts as a bridge to the new architecture. We use platforms like Kafka. We integrate with legacy systems using connectors.
• Define Events and Data Models − We find which events will be published and standardize the data models. We can create schemas for both legacy and modern applications. We use tools like Confluent Schema Registry for Kafka or AWS Schema Registry.
• Decouple Legacy Processes − We start with non-critical functions to produce and consume events. For example, a retail company may start with asynchronous sales transactions before fully transitioning inventory and supply chain systems.
• Implement Idempotency and Deduplication − We manage duplicate events. Consumers should be idempotent. So processing the same event multiple times gives the same output.
• Monitor and Optimize − We use monitoring tools to track event flow, latency, and error rates. We set up metrics to measure integration success. It improves processes as needed.

Examples of EDA-Legacy Integration

Some of the examples of EDA-legacy integration are given below −

• E-commerce Platforms (Sales and Inventory Management) − Retailers like Shopify and Amazon use EDA to decouple inventory and sales services from ordering systems. So sales transactions can asynchronously trigger inventory updates in real-time stock without synchronous dependencies.
• Banking and Payment Systems − Citi Commercial Cards adopted EDA for its API platform. So transactions trigger asynchronous updates across account balances, statements and fraud alerts.
• Logistics and Supply Chain − EDA supports logistics companies like UPS in managing tough supply chains. We can integrate existing tracking systems with an event-driven approach.

Benefits and Challenges of EDA Integration

Following are the benefits of EDA integration −

• Increased scalability − We can scale up Individual components without affecting the entire system.
• Real-Time responsiveness − Systems respond to data changes. It improves customer experience and service delivery.
• Enhanced resilience − Decoupled services help isolate failures and keep system stability.
• Reduced operational complexity − We can replace synchronous processes using EDA. So it improves service interactions.

Following are the challenges of EDA integration −

• Complexity in transitioning − We integrate EDA with legacy systems. But it requires architectural changes and careful data governance.
• Testing and validation − We can test asynchronous interactions and event flows. But these are tougher than traditional request-driven methods.
• Reliability and order consistency − Events should be processed in the correct order and exactly once. So it requires configuration and error handling.

Conclusion

We can integrate existing systems with an event-driven architecture. EDA is the choice to do this. We can integrate legacy systems into a modern event-driven framework. But it needs planning, strategic component selection, and a phased approach.