Build Event-Sourced Projections and Read Models
Builds CQRS read models and materialized views from event streams for optimized queries and real-time dashboards.
Why it matters
Optimize query performance and enable real-time data insights by building robust projection patterns and read models for event-sourced systems.
Outcomes
What it gets done
Create materialized views from event streams.
Implement CQRS read models for efficient querying.
Develop real-time dashboards and search indexes from events.
Aggregate data across multiple event streams.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-projection-patterns | bash Overview
Projection Patterns
Provides a guide to building CQRS read models and materialized views from event streams for query optimization and dashboards. Use when building CQRS read models, materialized views, or search indexes from event streams.
What it does
Provides a comprehensive guide to building projections and read models for event-sourced systems.
When to use - and when NOT to
Use this skill when building CQRS read models, creating materialized views from events, optimizing query performance, implementing real-time dashboards, building search indexes from events, or aggregating data across streams. Not a fit for tasks unrelated to projection patterns or for systems that don't use event sourcing/CQRS architecture.
Inputs and outputs
Covers building read models optimized for query patterns rather than write patterns (the core CQRS separation), creating materialized views that project raw event streams into query-ready structures, tuning projections for query performance, building real-time dashboard data sources fed by event streams, constructing search indexes derived from domain events, and aggregating data across multiple event streams into unified views. Points to a detailed implementation playbook for concrete patterns and examples when deeper guidance beyond the core concepts is needed.
Who it's for
Backend engineers working with event-sourced or CQRS architectures who need to build query-optimized read models and materialized views from event streams rather than querying the event store directly.
FAQ
Common questions
Discussion
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