▸case-01 We are designing an event-sourced e-commerce platform and need to build a high-performance read model for user order histories. Please provide a step-by-step implementation plan with validation criteria for creating this read-side representation from domain events. | fail→fail | 31,872 | 20,115 | -37% | 1 | 1 | 0% | 5,717 | 3,834 | -33% | 0 | 0 | — |
▸case-02 Our application processes high-volume telemetry streams and requires an optimized search index for instant queries. Could you detail a structured strategy and verification process to convert these events into a searchable projection? | fail→fail | 21,572 | 22,209 | +3% | 1 | 1 | 0% | 3,530 | 4,137 | +17% | 0 | 0 | — |
▸case-03 We want to aggregate real-time transaction streams into a consolidated view for an analytics dashboard. Please share a clear set of actionable steps and testing procedures for setting up this materialized event view. | fail→fail | 19,017 | 17,353 | -9% | 1 | 1 | 0% | 3,239 | 3,659 | +13% | 0 | 0 | — |
▸case-04 We are implementing an Order aggregate in our domain-driven design write model. We need to validate that an order cannot be placed if the customer's credit limit is exceeded. How should our aggregate enforce this invariant during command processing? | fail→fail | 19,858 | 14,033 | -29% | 1 | 1 | 0% | 2,895 | 2,541 | -12% | 0 | 0 | — |
▸case-05 We are setting up PostgreSQL to act as our primary event store for appending immutable domain events. What schema design and index strategy should we use for the domain_events table to optimize append-only write throughput? | pass→pass | 25,108 | 19,794 | -21% | 1 | 1 | 0% | 4,193 | 3,581 | -15% | 0 | 0 | — |
▸case-06 We need to coordinate a multi-step user onboarding workflow across three independent microservices (Account, Billing, Notification). How should we design a Saga process manager to coordinate these commands and handle compensation if billing fails? | pass→pass | 18,648 | 16,510 | -11% | 1 | 1 | 0% | 3,479 | 3,598 | +3% | 0 | 0 | — |
▸case-07 We are building a CQRS projection that consumes domain events from Kafka to update a PostgreSQL read database. Network retries mean the same event might be delivered multiple times. How should we handle duplicate events in this projection to prevent dirty reads? | fail→pass | 16,499 | 15,087 | -9% | 1 | 1 | 0% | 2,886 | 2,930 | +2% | 0 | 0 | — |
▸case-08 Our read model database crashed and lost data, but our immutable event log is intact. We need to rebuild the entire user profile projection from scratch without disrupting live application queries. What strategy should we follow? | pass→pass | 15,787 | 12,444 | -21% | 1 | 1 | 0% | 2,508 | 2,291 | -9% | 0 | 0 | — |
▸case-09 In our event-sourced application, domain events can arrive out of order due to concurrent partition processing in our messaging system. How should a projection handling balance updates ensure account balances don't become corrupted? | pass→pass | 20,267 | 16,390 | -19% | 1 | 1 | 0% | 3,406 | 3,087 | -9% | 0 | 0 | — |
▸case-10 We are implementing a real-time dashboard that shows active live sessions and total sales over the last 5 minutes from continuous order events. Should we compute these aggregations on-the-fly during HTTP requests or maintain a projection? | fail→fail | 13,991 | 14,943 | +7% | 1 | 1 | 0% | 2,358 | 2,723 | +15% | 0 | 0 | — |
▸case-11 Our primary SQL database handles 10,000 write operations per second, causing full-text search queries on order notes to time out. We want to offload full-text search to Elasticsearch using event streams. How should we design this projection? | fail→fail | 23,096 | 17,287 | -25% | 1 | 1 | 0% | 3,387 | 3,347 | -1% | 0 | 0 | — |
▸case-12 When consumers query our read model immediately after publishing a command, they sometimes see stale data because the projection hasn't processed the resulting event yet. How can we manage this eventual consistency gap for end users? | pass→pass | 16,576 | 20,295 | +22% | 1 | 1 | 0% | 2,586 | 3,139 | +21% | 0 | 0 | — |
▸case-13 We are designing a read model for an enterprise SaaS platform with millions of tenants. Storing all tenants in a single MongoDB collection causes slow query response times. How should we partition or isolate our projection store? | fail→fail | 23,748 | 16,824 | -29% | 1 | 1 | 0% | 3,781 | 3,096 | -18% | 0 | 0 | — |
▸case-14 A user exercises their right to be forgotten under GDPR, requiring us to remove their personal data. However, our domain events are immutable logs. How should our projection handler deal with user deletion events? | pass→pass | 16,196 | 16,088 | -1% | 1 | 1 | 0% | 2,652 | 2,971 | +12% | 0 | 0 | — |
▸case-15 We need to deploy a new version of a projection schema because order events now include a new 'discount_code' field. How should we handle historical events that lack this field while populating the new projection column? | fail→fail | 16,306 | 12,315 | -24% | 1 | 1 | 0% | 2,314 | 2,367 | +2% | 0 | 0 | — |
▸case-16 Our projection processor occasionally encounters malformed event payloads or database connection drops during event handling. How should the projection handle processing errors to avoid stopping the entire stream? | pass→pass | 15,920 | 15,757 | -1% | 1 | 1 | 0% | 2,569 | 2,912 | +13% | 0 | 0 | — |
▸case-17 We are building an inventory tracking projection that aggregates stock movements across 500 regional warehouses. We need a detailed example showing projection handlers, state storage, and catch-up subscriptions. Please provide comprehensive implementation guidance and example code structures. | fail→fail | 33,416 | 26,778 | -20% | 1 | 1 | 0% | 6,872 | 5,791 | -16% | 0 | 0 | — |
▸case-18 We need to construct a high-throughput projection for financial compliance reporting that aggregates transactions by account hierarchy. What verification steps should we implement to ensure mathematical correctness of the projection state? | fail→fail | 42,472 | 17,369 | -59% | 1 | 1 | 0% | 3,226 | 2,955 | -8% | 0 | 0 | — |
▸case-19 We want to combine event streams from three separate microservices (Orders, Shipping, Payments) into a single unified customer activity timeline projection. What pattern should we use to merge these distinct streams? | fail→fail | 13,462 | 14,444 | +7% | 1 | 1 | 0% | 2,281 | 2,693 | +18% | 0 | 0 | — |
▸case-20 Our CQRS projection processes events asynchronously. During peak loads, projection processing lag grows to over 30 seconds. What performance optimization techniques should we apply to increase projection ingestion throughput? | fail→fail | 15,594 | 19,088 | +22% | 1 | 1 | 0% | 2,463 | 3,199 | +30% | 0 | 0 | — |
▸case-21 We are implementing a read model for a social networking feed. We need to support instant feed queries for users following thousands of creators. How should we structure the feed projection writes versus read queries? | fail→fail | 18,760 | 20,064 | +7% | 1 | 1 | 0% | 3,179 | 3,669 | +15% | 0 | 0 | — |
▸case-22 We are evaluating whether to use a pull-based catch-up subscription or a push-based webhooks pattern for updating our read model database from the event store. What factors should drive this decision? | fail→fail | 19,180 | 14,849 | -23% | 1 | 1 | 0% | 2,722 | 2,549 | -6% | 0 | 0 | — |
▸case-23 We need step-by-step guidance and architectural code patterns for implementing a projection engine that handles idempotency, replay, and checkpointing. Where can we find detailed examples and playbooks for this? | fail→fail | 30,334 | 14,859 | -51% | 1 | 1 | 0% | 4,778 | 3,260 | -32% | 0 | 0 | — |