Crisp answer: They cover different layers of the observability stack. Nagios handles host and service availability checks. Grafana visualises time-series metrics from Prometheus and other sources. Logscale stores and searches log data. Together they cover uptime, performance, and diagnostics.
The three pillars of observability:
Metrics: What is the system doing right now? (numbers over time)
Tools: Prometheus, CloudWatch, Graphite
Visualised in: Grafana
Logs: What happened and when? (events, errors, audit trail)
Tools: Logscale, Elasticsearch, Loki, CloudWatch Logs
Traces: How did a request flow through the system? (distributed tracing)
Tools: Jaeger, Tempo, AWS X-Ray
How they work together in an incident:
1. Nagios fires: web01 check_http returns CRITICAL
→ On-call engineer gets paged
2. Engineer opens Grafana:
→ Checks the Kubernetes cluster overview dashboard
→ Sees error rate spike at 14:32, pod restart count increasing
→ Drills into the specific service dashboard
3. Engineer opens Logscale:
→ Searches for errors around 14:32 in the affected service
→ Finds: "Database connection pool exhausted"
→ Traces the root cause to a slow query spike
4. Fix deployed, Nagios clears, Grafana shows error rate back to normal
What to say in the interview:
"They cover different dimensions of observability. Nagios answers 'is it up' via active health checks and pages when something goes down. Grafana answers 'how is it performing' by visualising metrics over time. Logscale answers 'what went wrong' by searching through log events. In practice an incident starts with a Nagios page or a Grafana alert, and the investigation moves into Logscale to understand why. I am most experienced with the Prometheus and Grafana side of this from my homelab, and I pick up new tools quickly — Logscale's query model is conceptually similar to tools I already use."