Observability
OpenTelemetry, Prometheus, Grafana, Datadog, Dynatrace, New Relic.
OpenTelemetry for Performance Engineers: A Practical Start
A practical introduction to OpenTelemetry's traces, metrics, and logs, and how to instrument a service for meaningful performance analysis.
Read →Prometheus and Grafana Basics for Performance Monitoring
How Prometheus's pull-based metrics model and PromQL work, and how to build Grafana dashboards that actually answer performance questions.
Read →The RED Method: Rate, Errors, Duration for Service Monitoring
How the RED method gives a simple, consistent framework for monitoring any request-driven service, and how it complements the USE method.
Read →Distributed Tracing Explained: Spans, Context, and Sampling
How distributed tracing actually works under the hood — spans, trace context propagation, and sampling strategies — explained from first principles.
Read →Structured Logging Best Practices for Debuggable Systems
Why structured logging (key-value fields, not free text) matters for debugging at scale, and practical conventions worth adopting.
Read →The USE Method: Utilization, Saturation, Errors for Resource Monitoring
How Brendan Gregg's USE method systematically checks system resources for performance bottlenecks, and how it pairs with the RED method.
Read →APM Tool Comparison: Datadog, Dynatrace, and New Relic
A practical comparison of how Datadog, Dynatrace, and New Relic approach instrumentation, AI-assisted root-cause analysis, and pricing.
Read →Building SLO Dashboards That Drive Real Decisions
How to design an SLO dashboard that actually informs the ship/freeze decisions error budgets are meant to enable, not just display pretty graphs.
Read →Synthetic Monitoring vs Real User Monitoring (RUM)
How synthetic monitoring and real user monitoring complement each other for understanding production performance, and when to rely on each.
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