Monitoring and logging in a microservices architecture require centralizing logs and metrics from every service into a single place, since a request can span many services and per-service log files alone aren't enough to diagnose issues.
Key Points: • A centralized logging stack like ELK (Elasticsearch, Logstash, Kibana) or a managed equivalent aggregates logs from all services so they can be searched and correlated in one place. • Metrics collection with Prometheus and dashboards in Grafana track service-level indicators like latency, error rate, and throughput over time. • Distributed tracing with a tool like Jaeger or Zipkin ties together the spans of a single request as it crosses multiple services, using a shared trace ID. • Structured logging (consistent JSON fields, including the trace ID) makes it possible to correlate a specific log line with the exact request and service that produced it. • Alerting rules on key metrics, like error rate or latency percentiles, let the team react to problems before users report them.
Example: When a checkout request fails intermittently, a trace ID in the structured logs lets an engineer pull every log line related to that exact request across the Order, Payment, and Inventory services in Kibana, while a Grafana dashboard shows whether the failures correlate with a spike in database latency.
Interview Tip: A concise interview answer is:
"I'd centralize logs with something like the ELK stack, collect metrics with Prometheus and visualize them in Grafana, and add distributed tracing with Jaeger or Zipkin so a single request can be followed across every service it touches. Structured logging with a shared trace ID is what ties all three together for real observability."