How does checkpointing work in these projections?

The skill includes logic to save the last processed event position (global position or sequence number), allowing the projector to safely resume from where it left off after a system restart.

Can I use this for real-time dashboards?

Absolutely. The skill covers 'Live' projection types that process events in real-time as they are appended to the stream, making them ideal for live updates.

What are projection patterns used for?

They are used to transform a series of events into a specific data format (a read model) that is optimized for specific queries, reporting, or search requirements.

Does this skill support different database types?

Yes, it provides templates and patterns for relational databases using SQL as well as NoSQL search engines like Elasticsearch.

Projection Patterns

Name: Projection Patterns
Author: pur3v4d3r

bypur3v4d3r

•

Database Management

Design and implement efficient read models and materialized views from event streams for event-sourced architectures.

This skill provides a comprehensive framework for building projections in event-sourced systems, enabling developers to transform raw event streams into optimized read-side databases. It covers various projection strategies including live subscriptions, catch-up processing, and persistent checkpointing, while offering ready-to-use templates for SQL databases, Elasticsearch indexing, and complex data aggregations. It is essential for implementing Command Query Responsibility Segregation (CQRS) patterns and improving query performance in distributed systems.

Key Features

01Architecture patterns for Event Store to Read Model transformations

02Full-text search integration templates for Elasticsearch

03Support for Live, Catchup, Persistent, and Inline projection types

04Pre-built templates for SQL-based order summaries and sales aggregations

05Reliable checkpointing logic for resume and restart capabilities

061 GitHub stars

Use Cases

01Building optimized CQRS read models for high-performance querying

02Generating real-time dashboards and search indexes from historical events

03Aggregating transactional data into daily or monthly reporting summaries

Key Features

01Architecture patterns for Event Store to Read Model transformations

02Full-text search integration templates for Elasticsearch

03Support for Live, Catchup, Persistent, and Inline projection types

04Pre-built templates for SQL-based order summaries and sales aggregations

05Reliable checkpointing logic for resume and restart capabilities

061 GitHub stars

Use Cases

01Building optimized CQRS read models for high-performance querying

02Generating real-time dashboards and search indexes from historical events

03Aggregating transactional data into daily or monthly reporting summaries