Can I use this for real-time dashboards?

Yes, the skill includes patterns for live projections that update read models as events occur, making it ideal for real-time data visualization and analytics.

What are projection patterns in event sourcing?

Projection patterns are methods used to transform a stream of events into a specific read model or state that is optimized for querying, rather than relying on the raw event log for data retrieval.

How does this skill help with CQRS?

It provides standardized templates and logic for the 'Read' side of CQRS (Command Query Responsibility Segregation), ensuring your queryable databases stay in sync with your event store.

How are projection failures handled?

The skill emphasizes persistent projections with checkpointing, allowing your system to save its position in the event stream and resume processing from the last successful state after a failure.

Does it support different database types?

The skill includes implementation patterns for relational databases like PostgreSQL as well as search engines like Elasticsearch, allowing for flexible data storage strategies.

Projection Patterns for Event Sourcing

Name: Projection Patterns for Event Sourcing
Author: as4584

byas4584

データベース管理

Build efficient read models and materialized views from event streams to optimize query performance and implement CQRS architectures.

概要

This skill provides a comprehensive framework for implementing projection patterns in event-sourced systems. It assists developers in creating real-time read models, building search indexes, and generating aggregated reports by processing event streams. Whether you are implementing Command Query Responsibility Segregation (CQRS) or need to optimize query performance with materialized views, this skill offers production-ready templates for Python-based projectors, checkpointing strategies, and integrations with databases like PostgreSQL and Elasticsearch.

主な機能

Automated checkpoint management for reliable event processing
Real-time event stream processing patterns
0 GitHub stars
CQRS read model implementation and architecture
Multi-type projection support (Live, Catchup, Persistent, Inline)
Database and search index synchronization for PostgreSQL and Elasticsearch

ユースケース

Implementing historical data rebuilds for new reporting requirements
Building high-performance dashboards from transactional event logs
Synchronizing relational data with full-text search engines

概要

主な機能

Automated checkpoint management for reliable event processing
Real-time event stream processing patterns
0 GitHub stars
CQRS read model implementation and architecture
Multi-type projection support (Live, Catchup, Persistent, Inline)
Database and search index synchronization for PostgreSQL and Elasticsearch

ユースケース

Implementing historical data rebuilds for new reporting requirements
Building high-performance dashboards from transactional event logs
Synchronizing relational data with full-text search engines