Performance Engineering: Scenario-Based Questions

Latency spikes in distributed systems are often due to unpredictable interactions between services, network hiccups, or load imbalances. Investigating requires tracing end-to-end behavior and correlating system metrics.

🔍 Investigation Workflow

• Reproduce: Identify specific times, endpoints, or users affected by high latency.
• Trace Calls: Use distributed tracing tools (e.g., OpenTelemetry, Jaeger) to identify slow segments.
• Correlate Metrics: Examine latency, throughput, and saturation using Prometheus, Datadog, or Cloud Monitoring.
• Inspect Logs: Review structured logs for errors, timeouts, or retries in the latency window.

🧪 Common Root Causes

• N+1 Queries: Multiple sequential calls increasing response time.
• Cold Starts: New instances or containers initializing slowly.
• GC Pauses: JVM or Python garbage collection blocking requests.
• Queue Bottlenecks: Backpressure in Kafka, SQS, or RabbitMQ.
• Network Issues: High latency between services across regions or AZs.

🧰 Diagnostic Tools

• Distributed Tracing: Jaeger, Zipkin, AWS X-Ray.
• Profiling: Pyroscope, Flamegraphs, eBPF tools (BPFTrace, Pixie).
• Network Inspection: mTR, tcpdump, VPC flow logs.
• Latency SLO Dashboards: Track P50, P95, and P99 latency with alerts.

✅ Resolution Techniques

• Introduce caching (local or distributed) to reduce load.
• Batch or debounce requests to limit overhead.
• Optimize DB queries and index usage.
• Use autoscaling and connection pool tuning to handle burst traffic.

🚫 Common Mistakes

• Focusing on average latency only — ignore P95/P99 at your peril.
• Ignoring upstream or downstream service health.
• Adding retries blindly, compounding latency under load.

📌 Real-World Insight

Latency in distributed systems rarely stems from one service. High maturity teams trace entire request paths, simulate spikes in staging, and design for graceful degradation during transient delays.