Database Query Optimization

MongoDB aggregations can be optimized by enforcing cursor-based processing, which streams results in batches to maintain memory efficiency. This approach avoids loading large result sets into memory all at once during multi-location data retrieval.

Eliminating N+1 query patterns requires replacing iterative lookups with direct field access on the location field. This avoids costly $lookup joins and significantly speeds up group-by operations across multiple locations.

Per-location gaming day offsets are handled within batch processing during cursor streaming, ensuring accurate analytics across many locations without requiring expensive join operations or separate offset calculations.

Apply allowDiskUse and maxTimeMS when executing large cursor-based aggregations across multi-location date ranges to prevent memory overflow errors and enforce query execution timeouts for scalable analytics.

Cursor-based aggregation improves performance and memory efficiency for 7d and 30d reporting dashboards by streaming large multi-location results in batches rather than executing memory-intensive single returns.