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LetsGit.IT/Categories/Monoliths
Monolithseasy

Structured logging: what is it and why is it useful in a monolith?

Tags
#monoliths#logging#observability
Back to categoryPractice quiz

Answer

Structured logging means logs are emitted as machine-readable fields (e.g., JSON) like `level`, `message`, `requestId`, `userId`. It’s useful because you can reliably search, filter, and correlate logs across many code paths without parsing random text.

Advanced answer

Deep dive

Expanding on the short answer — what usually matters in practice:

  • Context (tags): monoliths, logging, observability
  • Scaling: what scales horizontally vs vertically, where bottlenecks appear.
  • Reliability: retries/circuit breakers/idempotency, observability (logs/metrics/traces).
  • Evolution: keep changes cheap (boundaries, contracts, tests).
  • Explain the "why", not just the "what" (intuition + consequences).
  • Trade-offs: what you gain/lose (time, memory, complexity, risk).
  • Edge cases: empty inputs, large inputs, invalid inputs, concurrency.

Examples

A tiny example (an explanation template):

// Example: discuss trade-offs for "structured-logging:-what-is-it-and-why-is-it-use"
function explain() {
  // Start from the core idea:
  // Structured logging means logs are emitted as machine-readable fields (e.g., JSON) like `le
}

Common pitfalls

  • Too generic: no concrete trade-offs or examples.
  • Mixing average-case and worst-case (e.g., complexity).
  • Ignoring constraints: memory, concurrency, network/disk costs.

Interview follow-ups

  • When would you choose an alternative and why?
  • What production issues show up and how do you diagnose them?
  • How would you test edge cases?

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