Cqrs on IT Comparison

Single Model vs CQRS: One Data Model vs Split Read/Write Models

Sun, 06 Sep 2026 09:53:08 +0900

Overview

A single model uses one unified set of classes and one schema for both reading and writing data, while CQRS (Command Query Responsibility Segregation) splits the system into separate write models (commands) and read models (queries) that can use different schemas, storage, or even databases. The choice matters because it trades simplicity and consistency for scalability and query flexibility as read and write demands diverge.

Comparison Diagram

Comparison Table

Aspect	Single Model	CQRS
Request entry point	One API/service handles reads and writes through the same code path	Requests are split upfront into command handlers and query handlers
Data model shape	One set of classes/entities represents the domain for every operation	Separate write model (rich domain logic) and read model (denormalized, query-optimized)
Write path	Write validates and persists directly to the shared schema	Command handler validates, applies business rules, persists to the write store
Read path	Read queries the same schema writes use, often requiring joins	Query handler reads from a precomputed, often denormalized read store
Sync between models	Not applicable — there is only one model, so no sync is needed	Read store is updated via events or projections after each write, introducing lag
Consistency guarantee	Strong consistency by default since reads see writes immediately	Eventual consistency between write and read sides unless engineered otherwise
Scaling behavior	Read and write load scale together since they share infrastructure	Read and write sides scale independently to match different load profiles
Operational complexity	Low — one schema, one deployment, one mental model to maintain	Higher — multiple stores, projection/event pipelines, and eventual-consistency debugging

Key Differences

Single model keeps one schema for everything; CQRS splits into a write model and a read model
CQRS trades immediate consistency for eventual consistency via projections or events
Single model is simpler to reason about; CQRS adds operational overhead from syncing multiple stores
CQRS enables independent scaling of reads and writes; single model scales them together
Query flexibility is higher in CQRS since read models can be shaped per use case

When to Use Each

Single Model

Event Sourcing vs State-Based Persistence: Storing Changes vs Storing Current State

Tue, 04 Aug 2026 05:10:16 +0900

Overview

Event sourcing persists every change to an entity as an immutable, ordered event log, deriving current state by replaying that history. State-based persistence instead stores and overwrites a single current record, discarding prior values on each update. The choice affects auditability, storage growth, and how much replay/projection machinery you need to build.

Comparison Diagram

Comparison Table

Aspect	Event Sourcing	State-Based Persistence
Write operation	Appends a new immutable event to the log; nothing is ever modified in place	Updates or overwrites the existing row/record with new values
Data representation	Sequence of domain events describing what happened	Single current snapshot of the entity’s fields
Reading current state	Replay events from the start (or from a snapshot) to derive current state	Direct read of the stored row, no reconstruction needed
Concurrency conflicts	Detected by checking the expected event stream version before appending	Detected via optimistic locking (row version/timestamp) or DB-level locks
Historical/audit trail	Native and complete — every past state is reconstructable	Not retained by default; requires a separate audit log or triggers
Schema evolution	Old event versions must be upcast or handled explicitly by consumers	Existing rows are migrated or altered directly to the new shape
Storage growth	Grows unbounded with every change; needs snapshotting/compaction to stay fast	Stays roughly proportional to the number of entities, not their history
Query complexity	Ad-hoc queries need dedicated projections/read models built from the events	Supports direct ad-hoc SQL queries against the current data

Key Differences

Event sourcing stores an append-only event log; state-based persistence stores only the current row
Rebuilding state requires replay in event sourcing, versus a simple read in state-based systems
Event sourcing gives a native audit trail; state-based needs bolt-on logging to recover history
Concurrency is resolved via event versioning instead of row-level locking
Storage grows unbounded without snapshots in event sourcing, while state-based storage stays compact

When to Use Each

Event Sourcing

CQRS vs CRUD: Splitting Reads and Writes vs One Unified Model

Tue, 04 Aug 2026 05:09:25 +0900

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

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