التسجيل المنظم ومستويات السجل

Plain text log lines are easy to write but hard for machines to query. Structured logging emits each entry as key-value data, usually JSON, that tools can index and filter precisely.

Consider a typical free-text log. Extracting the user or order requires fragile regex parsing.

The same event as JSON exposes each field explicitly, ready to query.

Structured logs let you ask precise questions, like all orders over $50 by user 42, without brittle text parsing. They power dashboards and alerts directly.

Levels rank importance. Common levels from lowest to highest:

• DEBUG: detailed diagnostics
• INFO: normal events
• WARN: unexpected but recoverable
• ERROR: failures needing attention

Reserve ERROR for genuine failures and WARN for anomalies. Logging everything at ERROR makes real problems invisible in the noise.

Production usually runs at INFO and above, while development enables DEBUG. The level acts as a volume knob you can turn without code changes.

Attach contextual keys like a request ID to every log in a flow so you can correlate entries across a single operation.

Structured fields make it easy to accidentally log passwords or tokens. Redact sensitive values before emitting the entry.

Use consistent field names across services, e.g. always userId, never sometimes uid. Consistency makes cross-service queries possible.

Structured logs flow cleanly into collectors and search systems like the ELK stack, where indexed fields enable fast filtering and visualization.

What is the main advantage of structured (JSON) logs over plain text?

You learned structured logging:

• Emit logs as key-value JSON, not free text
• Use levels (DEBUG/INFO/WARN/ERROR) as a volume knob
• Add correlating context like request IDs
• Keep field names consistent and redact secrets