How do you monitor system health and detect anomalies early?

Long Answer

Monitoring system health and catching anomalies before users are affected requires a layered approach: tracking infrastructure, applications, and real user experience. My strategies blend observability, metrics, logging, tracing, and proactive alerting to keep systems resilient.

1) Core health checks

At the infrastructure level, I monitor CPU, memory, disk, and network utilization. Application-level health includes response time, error rate, request throughput, and queue lengths. For databases, I track query latency, connection pools, and replication lag. These provide the baseline “vital signs.”

2) Metrics and SLIs/SLOs

Metrics are tied to Service Level Indicators (SLIs) like availability, latency, and error rates. These map to Service Level Objectives (SLOs) that set acceptable performance thresholds. For example, “99.9% of requests complete under 300ms.” This turns metrics into business-relevant signals.

3) Proactive anomaly detection

Static thresholds often miss subtle issues. I use time-series anomaly detection (Prometheus rules, Datadog Watchdog, AWS CloudWatch Anomaly Detection) to detect deviations from historical baselines. Seasonality-aware alerts reduce false positives. Correlation of metrics across layers—CPU spikes with error rate jumps—improves signal quality.

4) Distributed tracing and logs

Complex systems need end-to-end tracing (Jaeger, OpenTelemetry) to follow requests across microservices. Tracing highlights slow dependencies before they become outages. Centralized log aggregation (ELK stack, Splunk) provides searchable evidence of anomalies and helps link errors to root causes.

5) Synthetic monitoring and RUM

Synthetic probes simulate user actions—logins, checkout flows—across regions to catch problems early. Real User Monitoring (RUM) adds real-world performance data from browsers, highlighting anomalies in Core Web Vitals. This dual approach ensures both proactive detection and real-world validation.

6) Alerting and escalation

Alerts are tiered: warnings for approaching thresholds, criticals for user-impacting events. To prevent noise, I tune thresholds, aggregate alerts, and define runbooks with clear escalation paths. For SEV1 incidents, alerts page the on-call engineer immediately, followed by incident response protocols.

7) Continuous improvement

Every anomaly becomes a learning opportunity. Post-mortems review missed signals, refine metrics, and adjust dashboards. Metrics evolve alongside new features, ensuring ongoing coverage. Automation (self-healing scripts, auto-scaling) further reduces downtime.

In summary, system health monitoring is not just dashboards—it is a proactive cycle of measuring, analyzing, alerting, and improving. By tracking vital metrics, using anomaly detection, and escalating effectively, issues are resolved before users feel the pain.

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Table

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Common Mistakes

• Monitoring only infrastructure, ignoring application and user-facing metrics.
  
• Relying on static thresholds without anomaly detection, causing missed signals.
  
• Alert fatigue from too many low-value notifications.
  
• No correlation between metrics, logs, and traces, slowing down root cause analysis.
  
• Overlooking synthetic monitoring, so broken user flows go undetected.
  
• Ignoring SLIs/SLOs, leading to metrics that lack business context.
  
• Not running post-mortems, causing repeated failures and missed improvements.
  
  

Sample Answers

Junior:
“I track CPU, memory, and uptime. I use simple alerting when usage is high or servers go down. I also add health checks on key endpoints.”

Mid:
“I monitor infrastructure and app metrics with Prometheus and Grafana. I track latency, error rates, and throughput, and add synthetic probes to test logins. Alerts are severity-based and link to runbooks for faster triage.”

Senior:
“My approach covers infrastructure, apps, databases, and user experience. I tie metrics to SLIs/SLOs, use anomaly detection, and integrate logs + distributed tracing. Alerts are prioritized to prevent fatigue, with clear escalation and incident playbooks. Post-mortems ensure continuous improvement.”

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Evaluation Criteria

Interviewers expect layered monitoring strategies: infrastructure, application, database, and user experience. Strong answers mention SLIs/SLOs, anomaly detection, synthetic monitoring, tracing, and alerting with escalation paths. Red flags include vague reliance on “server health,” ignoring user experience, or no plan for alert fatigue. The best candidates highlight proactive detection, correlation of signals, and continuous learning via post-mortems, showing both technical mastery and operational maturity.

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Preparation Tips

• Set up a demo Prometheus + Grafana stack and monitor CPU, latency, and error rates.
  
• Add anomaly detection alerts in CloudWatch or Datadog.
  
• Practice distributed tracing with OpenTelemetry.
  
• Configure synthetic probes for critical flows like checkout.
  
• Review how to calculate SLIs and set realistic SLOs.
  
• Simulate an incident: trigger alerts, follow a runbook, escalate, and document.
  
• Explore real-user monitoring tools to capture Core Web Vitals
  
• Be ready to explain how you prevent alert fatigue and balance sensitivity with accuracy.
  
  

Real-world Context

An e-commerce site once relied only on server metrics, missing that checkout latency had doubled. Adding synthetic monitoring caught failures 10 minutes earlier, reducing lost sales. A SaaS provider’s static thresholds missed gradual memory leaks; anomaly detection flagged the pattern, preventing outages. Another team faced alert fatigue with hundreds of warnings daily; tuning thresholds and mapping to SLOs cut noise by 60%. In fintech, combining tracing with metrics identified a slow third-party API before it became a SEV1 incident. These cases prove proactive, layered monitoring reduces downtime and protects customers.

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Key Takeaways

• Monitor infrastructure, apps, databases, and user experience.
  
• Use SLIs/SLOs to give business context to metrics.
  
• Apply anomaly detection, not just static thresholds.
  
• Add synthetic monitoring to simulate user flows.
  
• Prioritize alerts, escalate fast, and refine with post-mortems.
  
  

Practice Exercise

Scenario:
You manage a large-scale SaaS platform where slow API responses can affect thousands of users.

Tasks:

1. Define SLIs for availability, latency, and error rates; set SLOs (e.g., 99.9% requests <300ms).
   
2. Configure Prometheus + Grafana dashboards with CPU, memory, latency, throughput, and database lag.
   
3. Add anomaly detection for latency spikes and memory leaks.
   
4. Integrate distributed tracing (OpenTelemetry) to map service dependencies.
   
5. Set synthetic probes for login and checkout flows across regions.
   
6. Configure alerting tiers: Slack for warnings, PagerDuty for SEV1 issues.
   
7. Write a runbook for API timeouts and escalation procedures.
   
8. Conduct a simulated incident, then run a post-mortem to refine thresholds.
   
   

Deliverable:
A monitoring system that proactively detects anomalies, prioritizes alerts, and ensures user experience remains stable through early detection and fast escalation.