Logs, metrics, and traces

Logs describe discrete events, metrics summarize numeric behavior over time, and traces connect work across service boundaries. Together they help answer what happened, how much, where, and for whom.

At a glance

Question	Practical answer
When is it useful?	A slow checkout trace identifies the database span, metrics show latency increased for all users, and structured logs reveal timeout details for one request ID.
What should you do?	Instrument one request with a correlation ID, duration metric, structured start/error logs, and spans across two functions or services.
How do you know it worked?	From one alert you can move to the affected metric, a representative trace, and relevant logs without searching by guesswork or exposing personal data.
Common failure	Collecting everything creates cost and noise; define questions, retention, sampling, cardinality limits, and privacy rules first.

flowchart LR
  A[Question] --> B[Logs, metrics, and traces]
  B --> C[Small example]
  C --> D[Evidence]

The important idea is not to stop at a definition: connect the concept to a small example and observable evidence.

Worked example

A slow checkout trace identifies the database span, metrics show latency increased for all users, and structured logs reveal timeout details for one request ID.

Before acting, write the success signal. Change one condition at a time, observe the result, and record assumptions. For Logs, metrics, and traces, this separates what you know from what you are merely guessing.

Practice in 20–30 minutes

Goal: Instrument one request with a correlation ID, duration metric, structured start/error logs, and spans across two functions or services.

1. Record the starting state and your prediction.
2. Implement the smallest version without adding unnecessary tools.
3. Change exactly one input or constraint and repeat.
4. Save a command, screenshot, output, or checklist as evidence.

Expected result: From one alert you can move to the affected metric, a representative trace, and relevant logs without searching by guesswork or exposing personal data.

What can go wrong

Collecting everything creates cost and noise; define questions, retention, sampling, cardinality limits, and privacy rules first.

When the result differs from your prediction, do not change many things at once. Check inputs, versions, environment, permissions, and logs, then repeat from the smallest example.

Definition of done

• I can explain the concept in my own words.
• I completed the small example and kept evidence.
• I know one failure mode and how to check it.
• Someone else can repeat the work without guessing missing steps.

Go deeper

Use the linked resource or repository at the end of the page when you need a full implementation. Check current versions before applying commands to a real project.