Architecture

Overview CRUD (Create, Read, Update, Delete) models an application around a single data model that handles both reads and writes through uniform operations. CQRS (Command Query Responsibility Segregation) instead splits reads and writes into separate paths, often with different models and stores optimized for each. The choice matters most as a system’s read/write patterns diverge or its business logic grows too complex for a single model to represent cleanly. Comparison Diagram CRUDCQRSClientModel / APIDatabaseone path, one modelClientCommandQueryWrite ModelRead ModelWrite DBRead DBsync via eventssplit paths, split models Comparison Table Aspect CRUD CQRS Request handling Single endpoint set (GET/POST/PUT/DELETE) hits one code path for both reads and writes Requests split into distinct Command (write) and Query (read) channels with separate handlers Data model One model represents the entity for both reading and writing Separate write model (domain/aggregate) and read model (denormalized view) per side Storage Single database or table serves both reads and writes Optional separate stores per side, e.g. relational for writes, cache or search index for reads Consistency Strongly consistent by default since reads hit the same store just written to Read side is often eventually consistent, synced from the write side via events Business logic placement Validation and rules scattered across create/update handlers Rules concentrated in command handlers that enforce invariants before state changes Scaling Read and write load scale together since they share the same path Read and write sides can be scaled and optimized independently Implementation overhead Minimal; straightforward to build, test, and reason about Higher; requires sync mechanism and handling of eventual consistency Key Differences CRUD uses a unified model for reads and writes; CQRS separates commands and queries into distinct paths CRUD read-after-write is immediate since storage is shared; CQRS’s read side is often eventually consistent CRUD business logic lives in generic handlers; CQRS pushes rules into explicit command handlers CQRS allows independent scaling of reads and writes; CRUD scales both together CRUD is simpler to build; CQRS trades that simplicity for flexibility at the cost of architectural complexity When to Use Each CRUD ...

Overview Monolithic architecture packages an entire application as a single deployable unit, while microservices architecture splits it into independently deployable distributed services. The choice shapes everything from how teams organize their work to how failures propagate and how the system scales under load. Comparison Diagram Monolith Microservices UI Layer Business Logic Data Access Shared DB single deployable unit API Gateway Orders Users Payments Inventory db db db db independent, own datastores Comparison Table Aspect Monolithic Architecture Microservices Architecture Deployment unit Single deployable artifact containing all modules Multiple independently deployable services Inter-component communication In-process function calls Network calls (REST, gRPC, or messaging) Data storage Typically one shared database Each service owns and manages its own database Scaling approach Scale the entire application as one unit Scale individual services independently based on load Fault isolation A bug or crash can bring down the whole application Failures can be isolated to a single service when designed well Release and deployment process Single build/deploy pipeline with coordinated releases Independent CI/CD pipeline per service, deployed on its own schedule Technology stack flexibility One language and framework for the entire app Polyglot — each service can pick its own stack Operational overhead Low — one application to host and monitor High — requires service discovery, orchestration, and distributed tracing Key Differences Monolith code runs as a single process; microservices communicate as independent processes over the network. A monolith centers on one shared database, while microservices decentralize data ownership per service. Microservices allow granular scaling of just the components under load, unlike a monolith that scales as a whole. Splitting into services buys better fault containment but introduces real operational complexity. Well-designed microservices offer stronger fault isolation than a monolith, where one bug can crash everything. When to Use Each Monolithic Architecture ...