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duanbiao2000
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projection-patterns

Translate event streams into queryable read models and materialized projections.

3|1|Updated Feb 3, 2026
Search Tags:#real-time#cqrs#projection#event-sourcing#data-architecture#read-models
One-click install
npx skills add https://github.com/duanbiao2000/obsidianDoc26 --skill projection-patterns-duanbiao2000
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: projection-patterns
Source: https://github.com/duanbiao2000/obsidianDoc26/tree/main/agents-main/plugins/backend-development/skills/projection-patterns
Command: npx skills add https://github.com/duanbiao2000/obsidianDoc26 --skill projection-patterns-duanbiao2000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Converts event streams into queryable read models and materialized projections to enable fast, real-time access to domain data.

Core Features & Use Cases

  • Build live and catchup projections to support current-state queries
  • Create and maintain materialized views across streams with idempotent processing
  • Implement multi-table and multi-store projections with transactional integrity
  • Support projection types like Live, Catchup, Persistent, and Inline for varied consistency guarantees

Quick Start

Define a projection by implementing a Projection subclass and wire it into a Projector to begin processing events.

Frequently Asked Questions about projection-patterns

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

FAQPage Schema
How do I create read models from event streams for real-time queries?▼

To create read models from event streams, implement a Projection subclass and wire it into a Projector to process events into queryable materialized views with deterministic processing and idempotent replay.

What is the best way to maintain materialized views across multiple streams with transactional integrity?▼

Maintaining materialized views across streams requires multi-table and multi-store projections with transactional updates, ensuring idempotent processing and checkpointing for fault tolerance during rebuilds.

How does idempotent replay work when rebuilding projections in CQRS architectures?▼

Idempotent replay in CQRS ensures deterministic projection processing by reprocessing events safely, using checkpointing to track progress and guaranteeing transactional updates across tables during fault recovery.

When do I need different projection types like Live, Catchup, and Persistent for event sourcing?▼

Different projection types provide varied consistency guarantees: Live projections support current-state queries, Catchup handles historical data, while Persistent and Inline projections balance real-time access with rebuild requirements.

Can I use projection patterns for real-time dashboards and analytics pipelines?▼

Yes, projection patterns support real-time dashboards and analytics pipelines by translating event streams into up-to-date views, enabling fast queryable insights across multiple streams with checkpointing for fault tolerance.

What are the limitations of inline projections compared to persistent projections in event-sourced systems?▼

Inline projections offer immediate consistency but may impact write performance, while Persistent projections decouple processing to support catchup and rebuilds, trading immediate consistency for improved fault tolerance and scalability.

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