Analytics Dashboard - Phase 2: Backend Query API

[Michael-CNS]

Analytics Dashboard - Phase 2: Backend Query API

Part of epic: #3
Depends on: #4

Objective

Build the complete set of API endpoints in the analytics service to retrieve and aggregate simulation data from ArangoDB.

Tasks

Endpoints to implement:

• GET /snapshots - List all available simulation runs with metadata
• GET /snapshots/{run_id} - Get detailed info for a specific run
• GET /snapshots/{run_id}/statistics - Aggregate statistics (mean/median/min/max RSRP, coverage %, etc.)
• GET /snapshots/{run_id}/distribution - RSRP distribution data for histogram (bins and counts)
• GET /snapshots/{run_id}/reports - Paginated UE reports with filters (query params: page, limit, min_rsrp, max_rsrp, cell)
• GET /snapshots/{run_id}/reports/{user_id} - Individual UE full report (all cells visible)
• GET /snapshots/{run_id}/cell-load - Cell load distribution (UEs per cell)
• GET /compare?run_id_1=X&run_id_2=Y - Compare two runs (statistics delta)

Query Logic:

• Write efficient AQL queries for each endpoint
• Implement server-side aggregation (don't load all UEs into memory)
• Add pagination helpers for large result sets
• Implement filtering logic (RSRP range, location bounds, etc.)
• Add response models/schemas for type safety

Acceptance Criteria

• All endpoints return correct data
• Queries execute in <2 seconds for 30k UE scenarios
• Pagination works correctly
• Filtering parameters work as expected
• Proper error handling for invalid run_ids or parameters
• API documentation (OpenAPI/Swagger) available

Technical Notes

• Use AQL for aggregations (don't pull all data to Python)
• Consider indexes on run_id, best_rsrp if queries are slow
• Return lightweight responses (don't include full cell configs in every response)