How Should We Perform `Component Decomposition` for a System Design Document?

Component decomposition is the process of breaking a system into cohesive components with clearly defined responsibilities, interfaces, and dependencies. As a senior engineer, I start with functional requirements and important quality attributes rather than immediately splitting the system into microservices. The objective is to assign responsibilities so that components can evolve, scale, fail, and be tested with minimal unnecessary coupling.

First, I identify the main business capabilities and user journeys. For an online coding platform, these might include problem management, submission processing, code execution, identity and access management, progress tracking, and contest management. I then identify the responsibilities and data each capability owns. A submission service, for example, manages submission lifecycle and job creation, while a judge execution component handles compilation, sandbox execution, and test evaluation. Separating these responsibilities helps prevent business workflow logic from becoming entangled with resource-intensive and security-sensitive execution.

Next, I define component boundaries using cohesion, coupling, data ownership, change frequency, scalability, security, and failure isolation. Responsibilities that change together and need strong consistency often belong together. Responsibilities with substantially different scaling profiles, security requirements, or operational lifecycles may warrant separate components. I also identify bounded contexts and avoid creating components solely because individual functions or database tables exist. A component is a logical responsibility boundary; it does not automatically need to be a separately deployed microservice.

For each component, I document its purpose, responsibilities, inputs and outputs, API or event contracts, data ownership, dependencies, and failure behavior. I specify whether interactions are synchronous or asynchronous and clarify important guarantees such as idempotency, ordering, retry behavior, and consistency. For example, the submission service may synchronously validate and persist a submission, then publish a durable judging job. Judge workers consume jobs and publish results, while a result-processing component updates the submission record. The design must address the possibility that a process crashes between database persistence and message publication.

I then validate the decomposition against non-functional requirements. If code execution needs strong isolation and independent autoscaling, it should be separated from the user-facing API. If problem metadata is predominantly read-heavy, its delivery and caching strategy should not depend on the capacity of judge workers. If contest traffic requires priority scheduling, that requirement should influence the queue and worker boundaries. I use rough capacity estimates and failure scenarios to test whether the proposed boundaries make sense rather than assuming that more components automatically produce a more scalable architecture.

I prefer starting with a modular monolith or a small number of deployable services when the domain and scale allow it. A modular monolith can preserve clear internal boundaries without the network latency, distributed transactions, deployment overhead, and operational complexity of many microservices. I extract independently deployable components when there is a concrete reason, such as different scaling needs, stronger isolation, separate release cycles, or independent team ownership. This approach preserves the option to evolve the architecture as evidence accumulates.

A common mistake is decomposing a system by technical layers alone, such as controllers, services, and repositories, and then turning every layer into a separate service. Another is creating excessively small services that require many network calls to complete one business operation. The opposite mistake is building a single tightly coupled component that combines unrelated business rules, data ownership, and security responsibilities. I look for high cohesion within boundaries, explicit contracts between them, and as few unnecessary synchronous dependencies as possible.

Finally, I represent the decomposition in a component diagram and a responsibility table. I review whether each component has one clear purpose, whether every dependency is justified, whether data ownership is unambiguous, and whether the design supports the required failure and scaling behavior. I also document important architectural decisions and revisit boundaries when workload measurements, organizational ownership, or business requirements change. Good decomposition is not about finding a universally perfect number of components; it is about choosing boundaries that make the system easier to understand, change, operate, and scale.