How does checkpointing work in these patterns?

Checkpointing saves the last processed event position for each projection, allowing the system to resume processing from the correct spot after a restart or failure.

Does this skill support specific databases?

Yes, it includes implementation templates and best practices for relational databases like PostgreSQL and search engines like Elasticsearch.

What are projection patterns in event sourcing?

Projection patterns are methods used to transform a sequential stream of events into a specific data format optimized for querying, often referred to as a read model.

When should I use the CQRS Projection Patterns skill?

Use this skill when building systems where the write model (event log) needs to be decoupled from the read model to improve query performance, scalability, or specialized searchability.

What is the difference between a Live and Catchup projection?

Live projections process events in real-time as they occur, whereas Catchup projections process historical events to build or rebuild a read model from the beginning of the event stream.

CQRS Projection Patterns

Name: CQRS Projection Patterns
Author: Tahir-yamin

byTahir-yamin

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データベース管理

Builds efficient read models and materialized views from event streams to optimize query performance in event-sourced architectures.

概要

This skill provides specialized patterns and implementation templates for building projections in event-driven systems. It focuses on CQRS (Command Query Responsibility Segregation) principles, enabling developers to transform raw event logs into query-optimized read models across various databases like PostgreSQL or Elasticsearch. These patterns facilitate real-time catch-up subscriptions, persistent checkpointing, and complex data aggregations, ensuring high-performance read-sides and eventual consistency for distributed systems.

主な機能

Support for multiple projection types including Live, Catchup, and Persistent
Advanced data aggregation patterns for real-time reporting and analytics
Robust checkpoint management for resilient event processing
Ready-to-use templates for SQL databases and Elasticsearch integration
3 GitHub stars
Standardized projection architecture for event-sourced systems

ユースケース

Implementing the read-side of a CQRS-based microservice architecture
Generating real-time business dashboards and metrics from raw event logs
Synchronizing relational database records with search indexes for full-text search

概要

主な機能

Support for multiple projection types including Live, Catchup, and Persistent
Advanced data aggregation patterns for real-time reporting and analytics
Robust checkpoint management for resilient event processing
Ready-to-use templates for SQL databases and Elasticsearch integration
3 GitHub stars
Standardized projection architecture for event-sourced systems

ユースケース

Implementing the read-side of a CQRS-based microservice architecture
Generating real-time business dashboards and metrics from raw event logs
Synchronizing relational database records with search indexes for full-text search