IntermediateTECHNICAL
You’ve worked with logging and monitoring in your backend services. Which tools or stacks did you use (for example, centralized logging, metrics, tracing), and how did those influence how you designed your endpoints and error handling?
Backend Developer
General

Sample Answer

On my last team, we standardized on the ELK stack for logs, Prometheus + Grafana for metrics, and Jaeger for distributed tracing across about 40 microservices. That stack heavily influenced how I designed endpoints and error handling. Every request gets a correlation ID injected at the edge and propagated through HTTP headers and Kafka messages. In handlers, I log in a structured way (JSON with service, endpoint, userId, requestId, latency, and key business fields), and I’m very deliberate about log levels: warnings for client mistakes, errors only for genuine server issues. That reduced noisy “error” logs by ~60%. For metrics, I expose RED metrics (rate, errors, duration) per endpoint and business KPIs, like signup conversion. When something spikes, tracing lets us see the slow span within seconds instead of digging through logs for hours, which cut mean time to resolution by roughly 30–40%.

Keywords

ELK + Prometheus/Grafana + Jaeger stack across microservicesCorrelation IDs and structured JSON loggingClear log level strategy and RED metrics per endpointMeasurable reduction in noise and MTTR
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