Observability and Distributed Tracing for Modern Apps

Observability and Distributed Tracing for Modern Apps Observability helps teams understand how an app behaves in real life. It uses three pillars: metrics, traces, and logs. Metrics give numbers for latency, throughput, and error rate. Traces show how a request travels across services. Logs provide context about events and decisions. Together, they help you see the health of your system and spot issues fast. Distributed tracing maps the path of a request across microservices. Each request starts a trace with multiple spans for work done by different services. For example, a user opening a page may go through a frontend, an API gateway, an auth service, a database call, and a cache. The trace helps you see which step added delay or failed. ...

September 22, 2025 · 2 min · 343 words

Observability as a Product: Measuring What Matters

Observability as a Product: Measuring What Matters Observability is often viewed as a toolkit of dashboards and alerts. If we treat it as a product, we focus on outcomes, users, and measurable improvements. Teams can discuss what matters, not just what is comfortable to monitor. The goal is to turn telemetry into feedback that drives better product decisions. Measuring what matters means choosing signals that connect to user value and business results. Consider these axes: ...

September 22, 2025 · 2 min · 250 words

Observability for Modern Systems Logs Metrics Tracing

Observability for Modern Systems: Logs, Metrics, Tracing Observability is a practical way to understand modern software. It rests on three pillars: logs, metrics, and tracing. Together they help teams see what a system did, why it behaved a certain way, and how to fix it efficiently. Good observability is not only about collecting data; it is about making signals easy to interpret when problems arise. Logs tell the story of events as they happen. Use structured formats and consistent fields like timestamp, level, service, and a unique request_id to tie signals across services. Keep logs concise and avoid exposing sensitive data. A line might look like: {“timestamp”:“2025-09-22T12:00:01Z”,“level”:“ERROR”,“service”:“checkout”,“request_id”:“abc123”,“message”:“payment failed”,“code”:“PAY-401”}. When you search later, these fields help you filter and correlate across machines. ...

September 22, 2025 · 2 min · 384 words