⚡ Bolt: Optimize ConsumableService.get_statistics with aggregation#31
⚡ Bolt: Optimize ConsumableService.get_statistics with aggregation#31
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- Replaced O(N) Python loop with a single MongoDB aggregation pipeline using $facet. - Reduced database roundtrips and network traffic by calculating statistics on the server. - Added unit tests in tests/unit/test_consumable_service_stats.py to verify correctness. - Leveraged existing department scoping in mongodb.aggregate. Co-authored-by: Woschj <81321922+Woschj@users.noreply.github.com>
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This optimization replaces an inefficient Python-side loop in the
ConsumableService.get_statisticsmethod with a single MongoDB aggregation pipeline. By using the$facetoperator, we can calculate category counts, location counts, and stock level statistics (sufficient, warning, critical) in one database call. This significantly reduces memory usage and network overhead, especially for large datasets.The change includes:
get_statisticsmethod inapp/services/consumable_service.py.tests/unit/test_consumable_service_stats.pyverifying the new implementation..jules/bolt.mdwith learnings about using$facetfor multi-grouping statistics.PR created automatically by Jules for task 1986748458157367353 started by @Woschj