How does this skill help with CQRS implementation?

It provides the architectural scaffolding and logic needed to separate write operations from read operations, ensuring the read side (projections) stays synchronized with the event store.

Can I use this skill for real-time dashboards?

Yes, the skill includes templates for live and persistent projections that update read models instantly as new events occur, perfect for low-latency dashboards.

What is the benefit of a catch-up projection?

Catch-up projections allow you to process historical events to rebuild a read model from scratch or update it after a period of downtime without losing data integrity.

Does it support multiple database types?

Absolutely. The patterns are database-agnostic and include specific examples for PostgreSQL (Relational), Elasticsearch (Search), and general aggregation logic.

What are projection patterns in event sourcing?

Projection patterns are techniques used to transform a stream of events into a structured 'read model' or data state that is specifically optimized for querying.

CQRS Projection Patterns

Name: CQRS Projection Patterns
Author: EngineerWithAI

byEngineerWithAI

データベース管理

Builds efficient read models and materialized views from event streams using CQRS and event sourcing patterns.

概要

This skill provides comprehensive architectural guidance and code templates for implementing the read side of CQRS systems. It facilitates the transformation of raw event logs into query-optimized data stores, covering various projection types including live subscriptions, catch-up processing, and persistent checkpoints. Whether you are building real-time dashboards, full-text search indexes with Elasticsearch, or aggregated reporting tables in SQL, these patterns ensure data consistency and high-performance querying in event-driven architectures.

主な機能

0 GitHub stars
Implementation patterns for Live, Catchup, Persistent, and Inline projections
Standardized projector architecture with robust checkpoint management
Guided workflows for event replays and read model rebuilding
Templates for SQL-based materialized views and complex data aggregations
Elasticsearch integration patterns for high-performance search indexing

ユースケース

Synchronizing relational data stores with search engines like Elasticsearch
Building real-time analytical dashboards from transaction logs
Optimizing query performance in high-scale Event Sourced systems

概要

主な機能

0 GitHub stars
Implementation patterns for Live, Catchup, Persistent, and Inline projections
Standardized projector architecture with robust checkpoint management
Guided workflows for event replays and read model rebuilding
Templates for SQL-based materialized views and complex data aggregations
Elasticsearch integration patterns for high-performance search indexing

ユースケース

Synchronizing relational data stores with search engines like Elasticsearch
Building real-time analytical dashboards from transaction logs
Optimizing query performance in high-scale Event Sourced systems