Distributed Systems

In a monolithic architecture, executing a complex business transaction—such as fulfilling an e-commerce order—is straightforward. All data resides in a single relational database, allowing developers to wrap multiple database writes across inventory, payments, and shipping inside a single ACID transaction. If an error occurs at any point, a SQL ROLLBACK instantly restores system consistency. However, modern cloud-native systems adopt a Microservices Architecture, where each service owns its data and exposes distinct API boundaries. In this distributed paradigm, a single end-to-end business operation spans multiple independent microservices and database engines.

In modern distributed software engineering, Event-Driven Architecture (EDA) has become the backbone of scalable microservice systems. Services regularly emit domain events—such as OrderCreated, PaymentProcessed, or UserRegistered—to communicate state changes across service boundaries without tight coupling. However, implementing reliable event publishing in a microservices environment introduces a subtle yet catastrophic engineering problem: How do you guarantee that a database update and its corresponding event publication both succeed or both fail together?

In the early days of web development, building a software application was straightforward: you wrote code, packaged it into a single executable or deployable archive, and ran it on a server. This approach, known as the Monolithic Architecture, served the industry well for decades. However, as applications grew into massive enterprise platforms with hundreds of developers and millions of concurrent users, monoliths began to show their limits. Deployments became slow and risky, databases became bottlenecks, and codebases grew too complex for any single developer to comprehend.