Does this skill support different database types?

Yes, it provides implementation patterns for relational databases like PostgreSQL, search engines like Elasticsearch, and general aggregation logic for varied read model requirements.

What is a projection in event sourcing?

A projection is a specialized process that consumes an event stream and transforms that data into a specific format, typically stored in a database, optimized for fast and efficient querying.

How does it handle system restarts?

The skill includes persistent checkpointing patterns that record the last processed event position, allowing your projections to resume exactly where they left off without data loss or duplication.

Is this suitable for real-time applications?

Absolutely. It defines Live projection patterns specifically designed to update read models in real-time as events occur, making it ideal for live dashboards and reactive UIs.

Event Stream Projection Patterns

Name: Event Stream Projection Patterns
Author: ccf

byccf

Database Management

Implements high-performance read models and CQRS projections from event streams for optimized data retrieval.

About

This skill provides comprehensive guidance and templates for building projections in event-sourced systems. It helps developers transform raw event streams into optimized, queryable read models using various patterns like Live, Catchup, and Persistent projections. By providing boilerplate for checkpointing and event handling, it enables the creation of efficient materialized views, search indexes, and real-time dashboards while maintaining architectural consistency in CQRS environments.

Key Features

0 GitHub stars
Support for Live, Catchup, and Persistent projection types
Async Python base classes for scalable event processing
Architectural blueprints for CQRS read-side implementations
Ready-to-use templates for PostgreSQL and Elasticsearch integrations
Standardized checkpointing mechanisms for fault-tolerant processing

Use Cases

Generating real-time analytical dashboards and daily sales summaries
Synchronizing application state to search engines like Elasticsearch
Building optimized materialized views from distributed event logs

About

Key Features

0 GitHub stars
Support for Live, Catchup, and Persistent projection types
Async Python base classes for scalable event processing
Architectural blueprints for CQRS read-side implementations
Ready-to-use templates for PostgreSQL and Elasticsearch integrations
Standardized checkpointing mechanisms for fault-tolerant processing

Use Cases

Generating real-time analytical dashboards and daily sales summaries
Synchronizing application state to search engines like Elasticsearch
Building optimized materialized views from distributed event logs