System Design: Scenario-Based Questions

Performance degradation under load is a common system design challenge. Addressing it requires data-driven diagnostics and the application of proven scaling patterns.

🔍 Bottleneck Identification

• Use APM tools: Identify slow endpoints, DB queries, or CPU-bound processes (e.g., Datadog, New Relic).
• Analyze system metrics: Look for spikes in CPU, memory, disk I/O, or network throughput.
• Check logs: Timeouts, retries, and errors can indicate backend strain.
• Database load: Profile queries and indexes. Use EXPLAIN plans to detect inefficiencies.

📈 Common Bottlenecks

• Monolithic services: Single-process applications under stress.
• Database contention: Locking, slow joins, or unindexed reads.
• Session storage: Sticky sessions blocking stateless scaling.
• Cache misses: Overloaded databases due to poor cache utilization.

📦 Scalability Solutions

• Horizontal scaling: Add more instances behind a load balancer.
• Read replicas: Offload DB reads from the primary node.
• CDN: Cache static assets and API responses at the edge.
• Job queues: Offload intensive processing from web tier (e.g., Celery, SQS).
• Split services: Break large monoliths into independently scalable microservices.

🧪 Testing for Scale

• Use load testing tools: k6, JMeter, or Locust.
• Test autoscaling behavior with synthetic traffic.
• Track latency percentiles (P50, P95, P99) under load.

✅ Best Practices

• Decouple components using queues and caches.
• Design stateless services for horizontal scaling.
• Pre-warm caches and enable circuit breakers where applicable.

🚫 Common Pitfalls

• Scaling the frontend/backend without database optimization.
• Neglecting internal service-to-service latency.
• Reactive scaling without observability and capacity planning.

📌 Real-World Insight

Engineering teams often find that addressing scalability is less about raw power and more about architecture choices. Smart caching, decoupling, and service isolation have 10x more impact than scaling CPUs alone.