Best Practices for Real-Time Metrics in Microservices
Practical guide to instrumenting, collecting, tracing, and alerting real-time metrics in microservices—naming, dashboards, SLOs, and scaling.
Practical guide to instrumenting, collecting, tracing, and alerting real-time metrics in microservices—naming, dashboards, SLOs, and scaling.
Map requests across microservices to locate latency and root causes. Use traces, spans, and OpenTelemetry best practices to reduce MTTD and MTTR.
How guess-and-redeploy inflates costs, hurts reliability, and wastes developer time — and how production-safe debugging, live breakpoints, and AI observability stop it.
Compare six affordable, easy-to-integrate anomaly detection tools for small development teams, focusing on setup speed, real-time alerts, integrations, and pricing.
Identify hidden dependencies, stale maps, and async tracing blind spots in microservices — and fix them with tracing, service meshes, and AI tools.
Practical observability for startups: logs, metrics, traces with OpenTelemetry, cost-saving sampling, AI-driven detection, and CI/CD integration.
Practical RCA steps for production: define clear problems, collect logs and traces, map events, prioritize fixes, and validate changes with monitoring.
Live breakpoints let you debug production apps without pausing them by injecting lightweight instrumentation that captures snapshots of variables, call stacks, and context.
Small dev teams need a clear production debugging playbook — structured logs, focused alerts, fast triage, tracing, fixes, and post-mortems to prevent repeat outages.
Explore the key differences between application monitoring and observability, and learn how they work together to enhance system reliability.
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