Building production systems that solve real problems

>> Architecture

• 1.Message queue (Redis) for stream ingestion and load distribution
• 2.Worker pool with auto-scaling based on queue depth
• 3.Optimized ML inference using ONNX runtime and GPU acceleration
• 4.WebSocket server for real-time alert delivery
• 5.PostgreSQL for alert storage with time-series optimization

>> Key Decisions

• ▹Redis over Kafka: Lower latency for real-time processing, simpler ops
• ▹ONNX runtime: 3x faster inference than PyTorch for production
• ▹Worker auto-scaling: Maintain <100ms latency during traffic spikes

>> Trade-offs

• ⚠Memory vs Latency: Keep 3 video frames in memory for temporal analysis
• ⚠Accuracy vs Speed: Optimized model to 95% accuracy for 10x throughput
• ⚠Cost vs Reliability: Redundant workers increase cost but prevent alert drops

>> Scalability

Horizontal scaling of workers handles 100+ concurrent streams. Queue prevents backpressure. Redis Cluster for HA.

>> Architecture

• 1.Shared database with tenant_id partitioning for cost efficiency
• 2.Row-level security (RLS) policies for data isolation
• 3.Redis for per-tenant configuration caching
• 4.API gateway with tenant routing and rate limiting
• 5.Background workers for async operations (email, reports)

>> Key Decisions

• ▹Shared DB over database-per-tenant: 10x cost savings, acceptable security with RLS
• ▹PostgreSQL partitioning: Query performance scales linearly with tenant count
• ▹Redis caching: Reduced DB load by 70% for config-heavy operations

>> Trade-offs

• ⚠Isolation vs Cost: Shared DB reduces cost but requires strict RLS policies
• ⚠Flexibility vs Complexity: Custom per-tenant configs increase system complexity
• ⚠Performance vs Consistency: Eventual consistency for non-critical updates

>> Scalability

Partitioned tables scale to 1000+ tenants. Read replicas for analytics. Sharding strategy planned for 10k+ tenants.