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2025•Engineering Note

The Microservice Trap: When Splitting Too Soon Slows You Down

What is the “Microservice Trap”?

The microservice trap is when a team adopts microservices before they have the product, team maturity, or operational tooling to support them—resulting in a distributed monolith that is harder to build, deploy, and debug than a well‑structured monolith.

Typical drivers:

  • Copying architectures from Big Tech without similar scale.
  • “We need to scale” confusion (traffic vs. team/process scaling).
  • Premature optimization and cargo‑culting.

Symptoms You’re in the Trap

  • More time on plumbing than product: service meshes, IAM, sidecars, CI/CD, but features crawl.
  • Chatty services & tight coupling: lots of cross‑service calls, cascading failures.
  • Versioning hell: breaking API changes ripple through multiple repos.
  • Observability gaps: no end‑to‑end trace to debug a user request.
  • Incident surface area grows: each deploy might break unrelated flows.
  • Duplicated domains and data: inconsistent business rules across services.

When Microservices Make Sense

Choose microservices only when you can honestly say “yes” to most of these:

  • Clear bounded contexts (DDD) and stable domain boundaries.
  • Independent scaling needs (e.g., image processing vs. CRUD traffic).
  • Team autonomy: teams can own services end‑to‑end (code → run → on‑call).
  • Operational maturity: automated CI/CD, infrastructure as code, observability, SLOs.
  • Asynchronous workflows: events/queues where eventual consistency is acceptable.

If not, start with a modular monolith.

Safer Starting Point: The Modular Monolith

A modular monolith gives you clear module boundaries in a single deployable unit.

Key practices:

  • Enforce boundaries in code: module folders/packages + dependency rules (e.g., ArchUnit, Deptry, ESLint import rules).
  • Explicit interfaces: define service‑like boundaries (ports/adapters) internally.
  • Database schema per module (same DB, isolated schemas or tables).
  • Event bus in‑process: publish/subscribe internally first (handlers remain in one repo).
  • Independent test suites: unit + contract tests per module.

This keeps latency, complexity, and cost down—while preparing you to split later.

Migration Strategy: From Modular Monolith to Microservices

  1. Stabilize boundaries: confirm module APIs rarely change with consumers.
  2. Extract the high‑pain module first: pick where scaling/latency is real (e.g., media, search).
  3. Introduce async first: publish domain events; consumers handle their own data.
  4. Strangle pattern: proxy calls through a facade while moving functionality out iteratively.
  5. Own the platform: templates, golden paths, and paved‑road tooling for new services.
  6. Hard SLOs & tracing: define budgets, add tracing/metrics before cutting the cord.
  7. Data split last: move data ownership carefully (CDC, dual‑write mitigation, backfills).

Anti‑Patterns to Avoid

  • “One DB per service” too early: creates data duplication and complex transactions.
  • Synchronous spiderweb: REST fan‑out chains; use async or a composed backend‑for‑frontend.
  • Shared library as hidden coupling: breaking changes across 20 services. Prefer versioned, small SDKs or contracts.
  • Global transactions across services: embrace eventual consistency and idempotency.
  • Too many repos without tooling: prefer a monorepo or strong dependency management.

Architecture Checklist

If you’re monolith‑first:

  • Modules with clear ownership and interfaces.
  • Contract tests between modules.
  • Centralized logging and request correlation IDs.
  • Feature flags and dark launches.
  • Synthetic checks per critical flow.

If you’re going microservices:

  • CI/CD templates with rollback, canaries, and runtime configs.
  • Observability: logs + metrics + distributed tracing end‑to‑end.
  • API versioning strategy (semantic + deprecation policy).
  • Async messaging with DLQs, retries, and idempotency keys.
  • SLOs, error budgets, and on‑call rotations.

Team & Process Guardrails

  • Conway’s Law aware: map services to long‑lived teams, not vice versa.
  • Docs as contracts: ADRs for boundaries, data ownership, and event definitions.
  • Paved roads: scaffolding CLI, templates, and lint rules to reduce variance.
  • Change review: architectural review only at boundary changes, not feature PRs.

Quick Decision Framework

Ask these in order:

  1. What pain are we solving that a monolith cannot? (be specific)
  2. Which module has independent scaling or latency needs?
  3. Can a queue + worker solve it inside the monolith first?
  4. Do we have the platform/observability to run N services?
  5. What’s the rollback plan if extraction fails?

If you stall on any question—stay modular, not micro.

Conclusion

Microservices are a trade‑off, not a destination. Start with a well‑designed modular monolith, mature your platform, then extract only what hurts. You’ll ship faster now and still have a clean runway for future scale.