Serverless Observability: Scenario-Based Questions

Serverless functions abstract away infrastructure, but still require observability to troubleshoot errors, track performance, and analyze behavior under scale. Monitoring focuses on logs, metrics, and traces.

📊 Key Metrics to Monitor

• Invocation Count: Number of times the function is called (helps detect traffic patterns).
• Error Rate: Percentage of failed executions.
• Duration: Execution time per invocation (watch for latency spikes).
• Cold Starts: Additional latency due to function initialization.
• Throttles: Requests denied due to concurrency limits.

🛠 Tooling Per Platform

• AWS Lambda: Use CloudWatch Logs, Metrics, X-Ray (for tracing), and CloudTrail for audit events.
• GCP Cloud Functions: Use Cloud Logging (Stackdriver), Monitoring, and Trace.
• Third-party: Datadog, New Relic, or Lumigo for unified observability.

🧪 Debugging Strategies

• Log structured data for easier parsing and filtering.
• Use trace IDs to link logs, metrics, and traces for a single request.
• Set up alerts on high error rates, timeouts, or cost anomalies.
• Replay or test events locally using SAM (AWS) or Functions Framework (GCP).

✅ Best Practices

• Use standardized logging libraries and middleware.
• Log external calls and downstream dependency latency.
• Define custom metrics to track business logic outcomes.
• Enable tracing headers across services for distributed observability.

🚫 Common Pitfalls

• Logging unbounded data (e.g., full payloads) — increases costs and reduces signal.
• Relying only on logs without metrics or tracing.
• Ignoring cold start metrics for time-sensitive workloads.

📌 Real-World Insight

Teams embracing serverless must evolve observability beyond traditional node-based metrics. Structured logging, distributed tracing, and high-cardinality alerting are the foundation of reliable serverless operations.