8.1 Logs, metrics and traces
When something breaks you look at data, not at guesses.
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The same thing, three names
One idea, sold under a different brand by each provider. Learn it once and you can read any job advertisement.
- On AWS
- CloudWatch + X-Ray
- On Azure
- Azure Monitor + Application Insights
- On Google Cloud
- Cloud Logging/Monitoring + Cloud Trace
When something breaks, the difference between looking at data and guessing is this topic: logs answer what happened, metrics answer how much and how often, traces answer where time went across services. Structured logging with correlation IDs is what makes the three joinable. It opens the observability module because alerting and reliability targets both stand on these signals. The cost lesson is cardinality — metrics dimensions multiply, and a label with unbounded values turns a cheap metric into an expensive one quietly.
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The three signals and what each answers
Logs say what happened, metrics say how much and how often, and traces say where the time went across services. Reaching for the wrong one is why some investigations take all night.
Structured logging and correlation IDs
Logs written as structured records rather than sentences can be searched and aggregated, and an identifier carried through a request is what lets you follow one user's journey across services.
Metrics, dimensions and cardinality cost
Every distinct combination of labels on a metric is stored separately, so a label with many possible values multiplies cost quickly. This is the most common way monitoring bills grow unexpectedly.
Distributed tracing across services
A trace follows one request through every service it touches and shows where the time was spent. In a system of several services it is often the only way to answer why something was slow.
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