projection-patterns

Build read models from event streams by applying projection patterns that process events into denormalized tables. This skill provides Python templates to create projectors for materialized views, enabling fast querying in event-driven architectures.

Projections differ by processing timing: live projections compute on-demand, catchup projections rebuild from event history, and persistent projections continuously update read models. This skill outlines when to use each type for optimizing CQRS query performance.

Create an OrderSummaryProjection by using provided Python templates to aggregate order events into a denormalized read model table. This pattern transforms event streams into pre-calculated summaries for fast querying on the CQRS read side.

You need projections for query performance optimization in CQRS when read-side queries become slow or complex. Projections materialize event stream data into structured views, preventing expensive real-time event aggregation during reads.

Yes, you can use these projection patterns for search indexing and multi-table updates. The skill includes reusable templates for building projectors that populate search indexes and synchronize multiple read models from a single event stream.

Inline projections in event-driven architecture can introduce transactional coupling between write and read sides, potentially causing latency in event processing. This skill details alternative projection types to avoid these bottlenecks.