Service Decomposition

Service Decomposition

Decide how to carve a system into deployable services — or whether to — and how those services find and call each other. This is the "application layer" of a design: it sits between the edge (dns/load-balancing/content-delivery) and the data tier. Getting the granularity wrong is one of the most expensive mistakes in distributed systems, in both directions.

The trap cuts both ways. One giant service can't scale teams or components independently; a swarm of tiny ones drowns you in network hops, partial failures, and undebuggable cross-service traces. The skill is finding the sweet spot for this system at this size — not maximizing service count.

When to reach for this

A second service is on the table; teams need to deploy independently; one part has a wildly different scaling profile than the rest; or an existing system is "too chatty" / hard to change. Also when someone proposes "microservices" by default.

When NOT to

A new product / small team / unproven domain — start with a monolith (or a modular monolith) and split later when a real seam and a real reason appear. Splitting prematurely buys distributed-systems cost (network failure, eventual consistency, ops) to solve a problem you don't have yet (YAGNI, GUIDE #7). Don't add a gateway/mesh/discovery layer before you have services that need them.

Clarify first

• Team & deploy independence — must parts ship on separate cadences/owners?
• Differential scale — does one component need 100× the others' resources?
• Coupling — which parts change together (belong together) vs evolve apart?
• Latency budget — how many in-process calls would become network hops?
• Consistency across the seam — can the split tolerate eventual consistency between services? (→ consistency-coordination)

The options

• Monolith — one deployable. Use when: new/small, fast iteration, strong cross-module transactions, one team.
• Modular monolith — one deployable, enforced internal module boundaries. Use when: you want clean seams without network cost yet — the best default for "we might split later."
• Microservices — many small deployables split by business capability / bounded context. Use when: independent deploy/scale/ownership genuinely pays for the distributed-systems tax.
• Comms style — decide only whether a given seam is synchronous (REST/gRPC, request/response, simple but couples availability) or asynchronous (decoupled but eventually consistent). This block decides sync-or-async per seam; it does not own the async mechanics — once a seam is async, invoke messaging-streaming for the delivery guarantees, ordering, backpressure, and DLQ semantics that make it safe. Don't pick async from the one-line "no immediate answer needed" rule without opening those failure modes. Most systems are a mix.
• Edge of the service tier — an API gateway / BFF (one front door: auth, routing, rate limiting, aggregation) vs direct exposure.
• Finding each other — service discovery (registry: DNS-based, or etcd/Consul/ZooKeeper) and optionally a service mesh (sidecars for mTLS, retries, traffic shaping) vs library-level clients.

Trade-offs

Behavior under stress

• Chatty services / fan-out: one user request becomes N synchronous internal calls; tail latency compounds and a single slow dependency stalls the chain. Mitigate: coarser boundaries, batching, async where an answer isn't needed now, and circuit breakers (→ resilience-failure).
• Distributed-transaction pain: a write spanning services can't be one ACID transaction. Mitigate: saga / outbox, or keep the data in one service so the transaction stays local (→ consistency-coordination).
• Retry storms across the mesh: naive retries amplify an overload into a cascade. Mitigate: backoff+jitter, budgets, circuit breakers (→ resilience-failure).
• Debuggability collapse: with no correlation IDs/tracing, a failure that crosses 6 services is invisible. Mitigate: distributed tracing + correlation IDs from day one (→ observability, distributed-logging).
• Gateway/discovery as SPOF: if the front door or registry dies, everything does. Mitigate: redundancy, client-side caching of discovery, health checks.

How to apply

1. Clarify the drivers — independent deploy? differential scale? team ownership? If none are real yet, stop: a (modular) monolith wins.
2. Default to the simplest that fits — monolith → modular monolith → extract the one seam that has a real reason — not "everything is a service".
3. Cut boundaries by business capability / bounded context, so data and the logic that owns it stay together (minimizes cross-service transactions).
4. Choose comms per call — sync only when the caller needs an immediate answer; async otherwise (→ messaging-streaming).
5. Add the front door + discovery only when warranted — gateway/BFF for auth/routing/aggregation; discovery/mesh when service count makes static wiring painful.
6. Stress-test the seams — walk chattiness, cross-service transactions, retry cascades, and tracing before committing.

Dos and don'ts

Do

• Start with a monolith / modular monolith; split a seam only when a real driver appears.
• Split by business capability so a service owns its data (keeps transactions local).
• Give every cross-service call a correlation ID + tracing from the start.
• Right-size: fewer, well-bounded services beat many tiny chatty ones.

Don't

• Don't choose microservices by default or for résumé reasons (GUIDE #7).
• Don't create a service whose write needs another service's data in the same transaction.
• Don't let the API gateway accrete business logic into a hidden monolith.
• Don't add a service mesh for a handful of services — the overhead outweighs it.

Numbers that matter

Each sync hop adds a same-datacenter round trip (~0.5 ms) plus the callee's own work. 10 serial hops is only ~5 ms of network — the real cost is the compounded p99/serialization/queueing/retry tail: each hop multiplies the chance of hitting a slow dependency, so end-to-end p99 degrades far faster than the mean. Size the call-graph depth and the per-hop p99 (not just the network) before splitting, with explicit p99 budgets. → back-of-the-envelope.

Interface sketch

A service boundary is a contract: the owned data (which service is the source of truth for each entity), the API it exposes (→ api-design), and the events it emits (→ messaging-streaming). Write down "who owns user records, who owns orders" before drawing service boxes — shared mutable data across a boundary is the #1 decomposition smell.

Choosing a provider

Default to the generic recipe above. If the user names a cloud, read references/providers/<provider>.md for the managed gateway / discovery / mesh mapping, limits, and trade-offs. If no file exists for that provider, the generic recipe is the answer.

Diagram

To visualize the service tier (gateway → services → their data, with sync vs async edges and trust boundaries), use the in-plugin architecture-diagram skill.

Related building blocks

• api-design — pairs with this: decomposition sets the boundaries, api-design defines each service's contract.
• messaging-streaming — pairs with this for async/event-driven comms between services (the alternative to sync coupling).
• consistency-coordination — owns the cross-service consistency story (saga/2PC) and the coordination theory behind service discovery (leader election, etcd/Consul/ZooKeeper); link there, don't re-teach.
• resilience-failure — depends on this: circuit breakers, timeouts, bulkheads keep a chatty mesh from cascading.
• observability + distributed-logging — feed into this: tracing + correlation IDs are what make a multi-service request debuggable.
• scaling-evolution — feeds into this: extracting a service is a scaling move on the "split a seam" rung.
• system-design — the orchestrator.

References

• references/deep-dive.md — bounded contexts & how to find seams, the strangler-fig migration from monolith, gateway vs BFF vs mesh, sync-vs-async decision detail, saga/outbox for cross-service writes, and the granularity anti-patterns (nano-services, distributed monolith, shared database).
• references/providers/{generic,aws,azure,gcp}.md — gateway / service-discovery / mesh service mappings, limits, and pitfalls per environment.