"For real-time tracking, I'd use:

1. Redis Geo for spatial queries:
   - Store driver locations with GEOADD
   - Query nearby drivers with GEORADIUS
   - O(log N) complexity for spatial queries

2. WebSocket connections:
   - Persistent connections for customers tracking orders
   - Push location updates every 5 seconds
   - Automatic reconnection on network issues

3. Location update optimization:
   - Only update when driver moves > 50 meters
   - Batch updates to reduce database writes
   - Use time-series database for historical data"

"Driver assignment considers multiple factors:

1. Scoring algorithm:
   - Distance to restaurant (40% weight)
   - Driver rating (30% weight)
   - Completion rate (20% weight)
   - Current order load (10% weight)

2. Assignment flow:
   - Find drivers within 5km of restaurant
   - Calculate score for each driver
   - Send request to highest scoring driver
   - 30-second timeout, then try next driver

3. Optimization:
   - Batch matching every 30 seconds
   - Consider multiple orders per driver
   - Route optimization for multi-pickup"

"Multi-pronged approach:

1. Dynamic Pricing:
   - Increase delivery fees by 20-50%
   - Based on demand/supply ratio
   - Transparent to customers

2. Driver Incentives:
   - Peak hour bonuses
   - Guaranteed minimum earnings
   - Push notifications to offline drivers

3. System Scaling:
   - Auto-scale to 3x capacity
   - Increase cache hit rate
   - Batch order processing

4. Customer Communication:
   - Show estimated wait times
   - Suggest off-peak ordering
   - Offer scheduled delivery"

-- Emphasize geospatial capabilities
CREATE INDEX idx_restaurants_location 
ON restaurants USING GIST(location);

-- Time-based partitioning for orders
CREATE TABLE orders_2024_01 PARTITION OF orders
FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

-- Sharding strategy
"Shard orders by order_id hash (16 shards)
Shard users by user_id hash (8 shards)
Shard locations by city_id for locality"

"For multi-pickup optimization:
- Traveling Salesman Problem (TSP)
- Use approximation algorithms (2-opt, genetic)
- Consider time windows for each pickup
- Real-time traffic integration"

"Accurate ETA requires:
- Real-time traffic data (Google Maps API)
- Historical delivery times
- Restaurant preparation time
- Driver speed patterns
- Weather conditions"

"Detect fraudulent orders:
- Unusual delivery locations
- High-frequency ordering
- Payment method patterns
- Customer behavior analysis
- ML-based risk scoring"

"My food delivery design includes:

1. Real-time tracking: Redis Geo + WebSockets
2. Smart matching: Multi-factor driver scoring
3. Scalability: Sharded databases, auto-scaling services
4. Performance: < 30s driver assignment, 5s location updates

Key tradeoffs:
- Greedy vs optimal driver assignment (chose greedy for speed)
- Location update frequency (5s for balance)
- Dynamic pricing for supply/demand balance"