projection-patterns

Build read models from event streams by using projection patterns that process event data into query-optimized structures. This Skill provides Python templates for creating CQRS read sides and materialized views that aggregate and denormalize data for efficient querying.

The best way to create materialized views from event sourcing data is through aggregating projections. This Skill offers patterns to maintain up-to-date views by continuously processing event streams and updating dedicated read models with summarized information like order totals and item counts.

Yes, you can use Elasticsearch for search projections in an event-driven architecture. This Skill includes specific Python code examples for building Elasticsearch search projections that index event stream data, enabling optimized and denormalized search queries.

You need asyncpg or a similar database driver to implement projections for event-sourced systems when writing to PostgreSQL. This Skill uses asyncpg as a dependency to handle asynchronous database operations for creating and updating multi-table projections and read models.

Optimize queries for real-time order summary dashboards by building dedicated read models that aggregate event streams. This Skill provides templates to project order status, total amounts, and item counts into a materialized view, avoiding expensive runtime event replay calculations.

Use projection patterns instead of querying the event store directly when read performance is critical. By denormalizing event stream data into query-optimized structures and materialized views, projections eliminate the need to replay entire event histories for every query request.