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Monolith vs microservices & distributed basics

P38.distribution.01 · Audience: guest, it-ml, language-pro · Prerequisites: APIs & service design

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The moment a call crosses a network, it can fail in a new way: it might have happened, but you never heard back. This module is about designing for that world — when to split a system at all, and the two primitives that make distributed calls survivable: retry with backoff and idempotency.

Step 1 / 3 — Monolith first, split on a force
ⓘ Concept: Distribution is a cost, not a goal
A well-structured monolith is simpler to build, test and run — one database, one transaction, no network. Split into services only when a concrete force demands it: independent scaling, team autonomy, or fault isolation — and along a clean domain seam (the same boundary you'd draw as a port), not "one service per noun".

Why it matters — Splitting too early buys distributed-systems pain for no benefit; splitting on a real force (scale, team autonomy, isolation) along a clean seam is what pays off.

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🎓 Practice ladder

2 graded rungs · ~24 min

Build a retry-with-backoff wrapper and an idempotency wrapper, then reason about monoliths vs microservices in an interview-style prompt.

Rung 1 — idempotent retries & backoff

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Rung 2 — monolith vs microservices (interview)

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That completes P38 — and with it the whole Production Engineering domain: you can package, gate, observe, and shape a Python system into resilient services.

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Production Engineering on an ML Model · P43

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