Prometheus

Performance Monitoring for Cloud-Native Apps Modern cloud-native apps run across many services, containers, and regions. Performance data helps teams understand user experience, stay reliable, and move fast. A good monitoring setup shows what happens now and why something changes. What to monitor Latency: track P50, P95, and P99 for user requests. Slow tails often reveal hidden bottlenecks. Error rate: measure failed responses and exceptions per service. Throughput: requests per second and goodput per path. Resource saturation: CPU, memory, disk, and network limits, plus container restarts. Dependency health: databases, caches, queues, and external APIs. Availability and SLOs: align dashboards with agreed service levels. How to instrument and collect data Use OpenTelemetry for traces and context propagation across services. Capture metrics with a time-series database (for example Prometheus style metrics). Include basic logs with structured fields to join traces and metrics when needed. Keep sampling sane for traces to avoid overwhelming backends while still finding root causes. Visualization and alerts Build dashboards that show a service map, latency bands, error rates, and saturation in one view. Alert on SLO breaches, sudden latency spikes, or rising error rates. Correlate traces with metrics to identify the slowest span and its service. Use dashboards to compare deployed versions during canary periods. Practical steps you can start today Define clear SLOs and SLIs for critical user journeys. Instrument core services first, then expand to downstream components. Enable tracing with sampling that fits your traffic and costs. Review dashboards weekly and drill into high-lidelity traces when issues occur. Test alerts in a staging or canary release to avoid noise. A quick example Imagine a page request that slows down after a code change. The trace shows a longer database call in Service A. Metrics reveal higher latency and a growing queue in a cache. With this view, you can roll back the change or optimize the query, then re-check the metrics and traces to confirm improvement. ...

Observability, Metrics, and Tracing in Modern Apps Observability is more than collecting logs. It is the practice of turning raw data into a story about how your app behaves in production. Modern apps run across services, clouds, and containers. With good observability, teams detect issues quickly, understand user impact, and improve performance. Metrics form the baseline. They are numerical measurements that answer “how much” and “how fast.” Common metrics include request latency, error rate, throughput, and resource saturation. Defining SLOs and alert thresholds helps teams act before customers notice. Tools like Prometheus or cloud-native services collect time series data and visualize it in dashboards. When teams agree on a small, meaningful set of metrics, responders can prioritize improvements without chasing noise. ...