quality-observability-performance

OpenTelemetry provides a vendor-neutral framework for collecting traces, metrics, and logs across distributed systems. Instrument your services by adding OpenTelemetry SDKs, configuring exporters to send telemetry to backends like Prometheus or Jaeger, and adding trace spans around critical operations to track requests end-to-end.

Service Level Objectives (SLOs) define target reliability commitments; Service Level Indicators (SLIs) measure actual performance against those targets. Error budgets quantify how much unreliability you can tolerate within your SLO window, enabling data-driven decisions on feature development versus stability work.

Performance engineering combines CPU and memory profiling, load testing, and capacity planning to locate bottlenecks before they reach production. Collect baseline metrics, simulate realistic traffic patterns, analyze resource utilization hotspots, and validate improvements iteratively.

Yes. Structured logging pairs with OpenTelemetry traces and Prometheus metrics to create unified observability. Output logs as JSON with consistent fields, correlate them to trace IDs, and route all signals to centralized analysis platforms for complete system visibility.

A monitoring backend is essential for collecting and storing metrics and traces at scale. Prometheus collects metrics; tracing systems like Jaeger store distributed traces. Both integrate with OpenTelemetry exporters to enable production-grade observability and alerting on SLIs.

Frontend performance optimization includes measuring Core Web Vitals, profiling JavaScript execution, reducing bundle size, and caching strategies. Pair these with backend observability to correlate frontend delays with service latency, enabling end-to-end performance tuning.