Start observability by following one request

A user reports that submitting a form sometimes takes too long. The application has logs, a server CPU chart, and an error counter. None of those immediately answers which dependency delayed that particular submission.

That is where I would start an observability project: with a question someone actually needs to answer. Instrument one important journey well enough to explain its behaviour, then expand from what the investigation reveals.

Give each signal a job

OpenTelemetry provides a common approach to producing and exporting telemetry. Its signals documentation distinguishes traces, metrics, and logs, among other signals.

For the slow-form example, I would assign them complementary questions:

• Metrics: How often are submissions slow, and when did that change?
• Traces: Which steps consumed time in a particular submission?
• Logs: Which relevant events or errors occurred during those steps?

The observability primer explains the broader goal of understanding a system through its outputs. My practical starting point would be a single route with a clear success condition, rather than collecting every possible measurement at once.

Preserve the connection between steps

A request might pass through a web handler, a database query, and an external service. If those operations produce unrelated telemetry, the person investigating has to reconstruct the sequence manually.

Context propagation carries the information used to associate work across boundaries. OpenTelemetry's context-propagation guide explains how trace context connects spans and can support correlation with logs.

I would first check the existing framework and library instrumentation. Then I would add application spans only where they express a meaningful operation, such as validating an import or generating a report. A span for every tiny helper function can make a trace harder to read.

For work that moves into a queue, verify the producer-to-consumer boundary explicitly. A useful trace should remain understandable when processing continues after the original HTTP response.

Keep the data useful and bounded

The temptation during debugging is to record entire inputs. That can make telemetry expensive and expose information that was never needed to diagnose the problem.

I would begin with a small field set: service name, environment, operation, outcome, duration, and a correlation identifier where appropriate. Avoid placing secrets or full message bodies in logs by default. Also review metric labels: unconstrained values such as individual request identifiers can produce far more distinct series than a dashboard needs.

These are proposed design constraints, not measurements from this portfolio. The right retention and sampling settings depend on the service's traffic, risk, and investigation requirements.

Make collection part of the deployment

The OpenTelemetry Collector can receive, process, and export telemetry. Its documentation describes a vendor-neutral component that can sit between applications and backends.

I would treat its configuration as deployable code: review changes, test connectivity, and understand what happens when the destination is unavailable. Telemetry should help operate the application without quietly becoming an unbounded source of memory use or retries.

Rehearse a small incident

Introduce a controlled delay into a test dependency. Then ask someone to identify the affected route, the delayed step, and the relevant event using the collected signals.

If they cannot connect those pieces, another chart is unlikely to solve the problem. Improve the missing context or naming and repeat the exercise. A successful rehearsal gives the team something concrete: a path from a user's symptom to an explanation they can act on.