projection-patterns

To build read models from event streams for CQRS, you project events into queryable materialized views. This requires a projection runtime with event store access to translate incoming events into structured data for dashboards and reporting.

Live projections process events in real-time for immediate consistency, while catchup projections rebuild read models from historical event streams. Both types require checkpointing to track progress and ensure replayability across your event store.

Multi-table projections across domains are handled by generating cross-domain read models from your event stream. The projection runtime performs transactional updates to maintain consistency when materializing events into multiple database tables.

You need a projection runtime with event store access and checkpointing, but external stores like databases or search indexes are optional. Projections can materialize views directly or write to external stores for scalable analytics and reporting.

To ensure projection replayability, implement proper checkpointing within your projection runtime. Checkpointing tracks the event stream position, allowing catchup projections to rebuild read models deterministically from the event store without data loss.

Projections are applicable for scalable analytics and dashboards by transforming event streams into materialized views. Multi-table read model generation supports reporting needs, while persistent projections maintain updated views for continuous querying.