projection-patterns

To build read models from event streams, you apply projection logic that consumes events and updates denormalized query stores. This creates optimized, query-friendly views for fast lookups, dashboards, and reporting in event-sourced systems.

To keep materialized views updated reliably, you need ordered event processing combined with checkpoints. This tracks the last processed event, ensuring your read models stay current and can resume safely without missing updates.

To handle multi-table updates during event projection, you should use transactions. This ensures that all related tables in your denormalized query store are updated atomically, maintaining data consistency across complex reporting views.

Duplicate data on replay occurs when projection handlers are not idempotent. You must implement idempotent handlers so applying the same event multiple times yields the same read model state, allowing safe rebuilds of persistent projections.

You need to rebuild persistent projections when your read model schema changes or data becomes corrupted. Because projections are derived from event streams, you can replay events from the beginning using checkpoints to restore the correct state.

Yes, you can use event projections to populate search indexes and daily sales reports. By denormalizing and aggregating event stream data, projections create specialized query stores optimized for analytics and user-facing lookups.