Scalability & Architecture: Scenario-Based Questions

Scaling failures often come down to one thing: bottlenecks. Whether compute, database, or I/O — knowing how to find and fix them is essential to growth and reliability.

📉 Common Bottleneck Areas

• CPU: High utilization during peak traffic or large computation.
• Memory: Leaks, unbounded caches, or large data loads.
• Database: Slow queries, locking, max connections.
• Network: Latency spikes, DNS issues, throughput caps.
• Disk I/O: Logging overload, read/write contention.

🔍 Bottleneck Identification Tools

• APM: New Relic, Datadog, Dynatrace
• System Metrics: Prometheus, CloudWatch, Node Exporter
• DB Profiling: EXPLAIN plans, pg_stat_statements, slow query logs
• Tracing: OpenTelemetry, Jaeger, Zipkin

⚙️ Scaling Tactics

• Introduce read replicas, horizontal sharding for DBs.
• Split monoliths into independently scalable services.
• Use CDN or edge caching for static content.
• Apply autoscaling policies for CPU/RAM thresholds.

✅ Best Practices

• Set baseline metrics early for comparison.
• Benchmark in pre-prod before full rollout.
• Use chaos engineering to stress test known limits.
• Design for failure — implement timeouts, retries, fallbacks.

🚫 Common Pitfalls

• Scaling before understanding root cause of slowness.
• Assuming more hardware fixes poorly written code.
• Underestimating DB or cache hot keys and write skews.

📌 Final Insight

Bottlenecks define your ceiling. Finding them early and solving them holistically ensures your system scales gracefully under pressure.