How would you design API architecture balancing performance & versioning?

Long Answer

Balancing API architecture for performance, versioning, and backward compatibility is a design problem of trade-offs. The aim is a contract that evolves safely while many client applications—web, mobile, partner integrations—consume it at different cadences. The blueprint below prioritizes fast reads, predictable changes, and safe rollout.

1) Layered entry with an API gateway

‍Front the system with a gateway (or service mesh ingress) to centralize cross-cutting concerns: TLS, auth, rate limiting, request/response transformation, and observability. The gateway also enforces API versioning rules (path v1, header Accept: application/vnd.company.v2+json, or GraphQL schema version tags) and routes to the correct backend. Co-locate edge caching and coarse feature flags here to keep hot paths snappy.

2) Contracts first, evolution always

‍Design resources and operations around stable business concepts. Prefer additive evolution: new fields, optional parameters, new endpoints. Breaking changes require a new major version with a migration window. Publish a deprecation policy (e.g., 6–12 months), changelogs, and migration guides. For backward compatibility, implement tolerant readers (ignore unknown fields) and server-side defaulting so older clients keep working.

3) Versioning strategy

‍Choose one primary mechanism and stick to it.

• Path versioning for coarse changes (/v1, /v2)—simple for routing and docs.
  
• Media type or header versioning for fine-grained evolution—keeps URLs stable.
  
• GraphQL: favor schema evolution with deprecations and field hiding; support persisted queries per client.
  Expose compatibility matrices so multi-client apps know which SDK and server versions match.
  
  

4) Performance optimization

‍Push read performance with layered caching: CDN for public GETs, gateway cache for authenticated reads with cache keys (user/tenant), and micro-cache at the service. Use conditional requests (ETag/If-None-Match) and proper Cache-Control. Apply pagination, filtering, and sparse fieldsets to reduce payloads. For high-throughput domains, use CQRS: command writes flow to storage; reads query a denormalized view (search index, read model). Offload slow work via async jobs, webhooks, and event streams. Keep p99 latency budgets and error budgets per endpoint.

5) Data compatibility & schema discipline

‍Adopt schema linting and contract testing. For REST, validate OpenAPI against consumer tests; for GraphQL, publish schemas and run client builds against them. Use semantic versioning for SDKs. Enforce strict backward-compatible rules in CI (no field removals, no type narrowing). Store sample fixtures per version; run replay tests to catch regressions.

6) Multi-client concerns

‍Support multiple client applications through client capabilities headers (e.g., X-Client-Features: receipts,v3-search). The server tailors behavior without forking versions. Release changes behind server-side feature flags and progressive rollout by client id. Provide mobile-friendly payloads (compress, gzip/br), HTTP/2 or HTTP/3, and idempotent POSTs for flaky networks.

7) Observability, SLAs, and safety nets

‍Instrument every endpoint with RED/USE metrics, distributed tracing, and structured logs including version, client id, and tenant. Add circuit breakers and timeouts to keep the API responsive under partial failures. Provide error contracts that don’t change between versions, with stable codes and remediation hints.

8) Migration & deprecation playbook

‍Announce breaking changes early, ship dual-write/dual-read where feasible, and run shadow traffic to verify v2 parity. Host parallel docs; ship SDKs with adapters or polyfills. Track adoption dashboards for each client cohort; only remove v1 when thresholds are met. Maintain a small “compat layer” for a grace period to avoid a big-bang cutover.

This approach lets your API architecture stay fast and reliable while API versioning and backward compatibility evolve predictably—no nasty surprises, even in a busy, polyglot client ecosystem.

Common Mistakes

Forking new API versions for tiny changes, creating version sprawl. Mixing path and header versioning randomly. Shipping breaking field removals without a deprecation window. Over-mocking clients and skipping contract testing, so integrations break in prod. Ignoring backward compatibility by changing error formats or status codes. Pushing huge payloads without pagination or sparse fields, tanking performance for mobile clients. Skipping CDN/ETag so servers burn CPU on cached reads. Lacking observability by version/client id, making rollbacks blind. Finally, “big-bang” migrations that force all multiple client applications to flip at once.

Sample Answers (Junior / Mid / Senior)

Junior:

‍“I’d put a gateway in front, use path API versioning (/v1), and evolve additively. For performance, I’d add caching, ETags, and pagination. I’d avoid breaking changes and keep old clients working.”

Mid:

‍“I’d standardize on header/media versioning, keep URLs stable, and publish a deprecation policy. A CDN + gateway cache, sparse fields, and CQRS read models keep latency low. We run contract tests against OpenAPI so multiple clients upgrade safely.”

Senior:

‍“Architecture: gateway for auth/routing, consistent versioning policy, and a compat layer. Read scale via CDN + CQRS; writes async where safe. Backward compatibility by additive changes, tolerant readers, and server defaults. We ship SDKs with adapters, run shadow traffic for v2, monitor adoption by client cohort, and enforce breaking-change checks in CI.”

Evaluation Criteria

Interviewers look for: a coherent API architecture with a single, enforced versioning strategy; explicit backward compatibility rules (additive changes, deprecation windows, tolerant readers); concrete performance tactics (CDN, caching, pagination, CQRS, compression); multi-client rollout plans (capability headers, feature flags, SDKs); strong contract governance (OpenAPI/GraphQL checks, consumer/provider contract testing); and ops hygiene (tracing, SLAs, error contracts). Vague “we’ll just add v2” answers score low; detailed policies, migration playbooks, and measurable guardrails score high.

Preparation Tips

Draft an OpenAPI and write consumer tests for two fake mobile clients; break a field and confirm CI blocks it. Implement both path and header API versioning in a toy service; pick one, document trade-offs, and lock it in. Add CDN/ETag locally (e.g., Varnish/NGINX) and measure p95 deltas with and without sparse fields. Prototype CQRS by adding a denormalized read model; watch query times drop. Create a deprecation policy template and a migration checklist (docs, SDK, telemetry, sunset headers). Add tracing with version/client tags and build a dashboard by cohort. Rehearse a 60–90s pitch covering performance, versioning, and backward compatibility.

Real-world Context

A fintech platform moved from ad-hoc changes to header API versioning and published a 9-month deprecation policy; support tickets fell by half while p95 dropped after enabling CDN + ETag. A retail app added CQRS read models and sparse fields, cutting product search latency by 60% for mobile. A SaaS vendor broke an error format; consumer contract tests caught it in CI, not in prod. A marketplace shipped v2 behind capability headers; only clients advertising v2-search saw new ranking, avoiding a big-bang. Across teams, the winning combo was consistent versioning, additive evolution, and relentless performance work.

Key Takeaways

• Pick one API versioning approach and enforce it.
  
• Evolve additively; honor deprecation windows and tolerant readers.
  
• Win performance with caching, ETag, pagination, CQRS, compression.
  
• Use contract testing and telemetry tagged by version/client.
  
• Roll out safely with capability headers, flags, and SDK support.
  

Practice Exercise

Scenario: You own a public REST API used by iOS, Android, web, and partners. You must ship a ranking change and 3 new fields, keep old clients working, and improve p95 by 30% without a big-bang cutover.

Tasks:

1. Choose a single API versioning mode (path or header/media) and write a 1-page deprecation policy (sunset header, timelines, changelog).
   
2. Make changes additively: keep old fields, add new ones as optional with server defaults; update OpenAPI.
   
3. Implement CDN + gateway cache, ETag/If-None-Match, pagination defaults, and gzip/br compression; record p95 before/after.
   
4. Add sparse fieldsets (?fields=) and filtering to reduce payload.
   
5. Ship client capability headers (X-Client-Features) to opt into new ranking; progressively enable by cohort.
   
6. Wire contract tests for two mock clients and fail CI on breaking diffs; tag telemetry by version/client.
   
7. Create a rollback plan and a dashboard tracking adoption and latency by client.
   

Deliverable: A merged report (docs + metrics) and a 60–90s verbal walkthrough that proves your API architecture balances performance, versioning, and backward compatibility for multiple client applications.