projection-patterns

To build read models from event streams, you implement projection patterns that subscribe to events and update materialized views. This Skill provides Python templates for creating queryable projections like order summaries to optimize CQRS read-side performance.

A projection pattern in event sourcing is a mechanism that processes event streams to build and maintain denormalized read models. It translates append-only event data into materialized views, allowing you to query current state without replaying the entire event history.

You optimize CQRS query performance by creating aggregating projections that maintain pre-calculated materialized views. This reduces database load by serving real-time dashboard queries directly from optimized read models instead of computing aggregates on demand.

Yes, you can create Elasticsearch search projections. The Skill includes specific Python templates for building search projections alongside basic, order summary, aggregating, and multi-table projections to handle various querying needs.

You should use multi-table projections when your read model requires complex data relationships that cannot be captured in a single flat table. They allow you to populate multiple interconnected tables simultaneously from a single event stream for comprehensive querying.