projection-patterns

To build read models from event streams, you implement projectors that subscribe to event stores and translate events into materialized views. This Skill provides Python templates for constructing CQRS read sides, enabling efficient data querying without impacting the write side.

Projections in event sourcing include live, catchup, persistent, and inline types. Each type handles the flow from the event store to the read model differently, allowing you to optimize data querying and materialized view updates based on specific system requirements.

You integrate Elasticsearch with projections by using event handlers to process event streams and update search indices. This Skill includes specific Python code templates for Elasticsearch integration, enabling advanced search capabilities alongside your standard database materialized views.

You need materialized views in an event-sourced system when query performance is critical and reconstructing state from raw event streams is too slow. Building projections allows you to pre-compute data into read models, enabling efficient data querying for application interfaces.

CQRS read-side implementation requires database interactions for storing and updating materialized views from event streams. This involves handling projection logic like multi-table updates and aggregations, ensuring your read models remain synchronized with underlying events.