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cqrs-to-event-sourcing

Explains why CQRS implementations necessarily converge on event sourcing through C-to-Q synchronization analysis.

Updated Jun 23, 2026
Search Tags:#architecture#domain-driven design#cqrs#event sourcing#read model#event store#dual-write problem
One-click install
npx skills add https://github.com/j5ik2o/marp-ai-base --skill cqrs-to-event-sourcing-j5ik2o
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cqrs-to-event-sourcing
Source: https://github.com/j5ik2o/marp-ai-base/tree/main/.agents/skills/cqrs-to-event-sourcing
Command: npx skills add https://github.com/j5ik2o/marp-ai-base --skill cqrs-to-event-sourcing-j5ik2o

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? When implementing CQRS, teams struggle to synchronize the command side with the query side, especially when the read model contains computed values that do not exist in the command-side database. This Skill provides a step-by-step logical analysis showing why triggers, polling, and event queues each fail, and why event sourcing becomes the inevitable design conclusion. ## Core Features & Use Cases - Synchronization Method Analysis: Evaluates triggers, polling, and event notification queues, identifying the limits of each (unsyncable computed values, missing change detection, scalability, and the dual-write problem). - CQRS Misconception Correction: Clarifies that "CQRS does not require model separation" applies only to non-CQRS regions, while command/query model separation is mandatory within CQRS regions. - Architecture Decision Flow: Provides a decision flowchart and review checklist for CQRS adoption, C-to-Q synchronization design, and event sourcing validation. - Use Case: When designing a cart system where the query side needs computed values like total price that are not stored in the command-side database, use this Skill to determine why an event store must become the single source of truth. ## Quick Start Ask the AI to explain whether CQRS requires event sourcing and how to synchronize the command side with the query side.

Frequently Asked Questions about cqrs-to-event-sourcing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Does CQRS require event sourcing?▼

CQRS does not formally require event sourcing, but serious implementations converge on it. Triggers cannot sync computed values, polling cannot detect changes and does not scale, and event queues cause dual-write problems, leaving event sourcing as the practical resolution.

How do I synchronize the command side and query side in CQRS?▼

Options include database triggers, polling, and event notification queues. Triggers fail for computed values absent from the command database, polling lacks change detection and scalability, and queues introduce dual-write risk, so an event store as the source of truth is recommended.

What is the dual-write problem in CQRS?▼

The dual-write problem occurs when writing to both a relational database and a message queue, which cannot share one transaction. One write can succeed while the other fails, causing inconsistency. Event sourcing avoids this by making the event store the single write target.

Why can't database triggers sync computed values to the read model?▼

Computed values like cart totals are produced by domain object behavior and never stored in the command-side database. Reproducing the same calculation in SQL duplicates business logic, and storing computed results distorts repository interfaces.

Does CQRS mean I must split models everywhere in my system?▼

No. The correct interpretation is that a system can be divided into CQRS and non-CQRS regions. Within CQRS regions, command and query model separation is mandatory; non-CQRS regions can use conventional CRUD.

When is event sourcing not needed for CQRS?▼

If the command side and query side share identical data with no computed or joined values, CQRS itself is unnecessary and conventional CRUD suffices. Event sourcing becomes necessary only when read models require derived data.

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