End-to-End Feature Design and Development Questions

Clarify goals & constraints

• Backward-compatibility for many internal/external clients, low downtime, observable rollout, automated verification.

High-level strategy

• Use semantic API versioning (major.minor) + OpenAPI/JSON Schema for contracts.
• Expose versions via API Gateway: /v1/resource (path versioning) and X-API-Version header for advanced routing.
• Store schemas in a central registry (JSON Schema or Avro) and generate server/client SDKs.

Introducing breaking changes safely

1. Publish a vN+1 OpenAPI contract and migration guide; bump major for breaking changes.
2. Implement new behavior under /vN+1 while keeping /vN active.
3. Provide compatibility adapters or transformation middleware to translate vN requests/responses when feasible.
4. Use feature flags and canary deployments (small percentage, internal teams first).

Run multiple versions concurrently

• API Gateway routes requests to versioned backend services or same service with versioned handlers.
• Shared business logic in libraries to avoid duplication; maintain stable public handlers per version.

Automate consumer compatibility testing

• Adopt consumer-driven contract testing (Pact) — consumers publish pacts to broker; CI verifies provider against all pacts.
• Generate automated integration tests from OpenAPI schemas (e.g., Dredd, schemathesis).
• Run contract verification in provider CI; fail builds on incompatibility.

Deprecation & minimal disruption

• Announce deprecation timeline in docs, release notes, and via response headers (Sunset, Deprecation, Link).
• Telemetry: track clients per version, endpoints, and error rates; notify top consumers directly.
• Provide migration support: SDK updates, code samples, and a "compatibility mode".
• After grace period, soft-disable with 429/410 responses plus migration hints; then remove.

Trade-offs

• Path versioning is explicit and cache-friendly; header versioning cleaner but harder for proxies.
• Maintaining multiple versions increases operational cost—mitigate with shared libs and automation.

This approach balances safety, automation, and developer experience appropriate for a full-stack engineer responsible for both client and server changes.

API contract (HTTP/JSON)

• Endpoint: GET /api/v1/products/{productId}/related?limit=8&context=cart
• Success: 200 OK

{
  "productId":"123",
  "generatedAt":"2026-03-02T12:00:00Z",
  "strategy":"collab_filter_v2",
  "items":[ {"id":"234","reason":"also_bought","score":0.87}, ... ]
}

• Errors: 5xx when service down; 503 with Retry-After for throttling; 200 with empty items allowed.

Service responsibilities

• Recommendation service: compute and serve ranked related items + explainers and generation timestamp.
• Product service: enrich IDs with display metadata (price, image) via batched calls.
• API gateway: auth, input validation, rate-limit, response caching.

Latency SLOs

• P95 ≤ 120ms for cache hits, P95 ≤ 400ms for cold path. Target client render-safe timeout = 600ms.

Caching policy & TTL

• CDN/edge cache: public GET TTL = 60s for high traffic pages.
• Edge-stale-while-revalidate: 30s.
• Service-level in-memory cache (Redis): TTL = 30s, hot items = 5–10m with LRU.
• Client-side: cache response for 15s; revalidate on focus.

Freshness requirements

• Near-real-time for inventory-sensitive fields (price/availability) — always rehydrate those from product service when displaying. Recommendations can be slightly stale (≤ 60s acceptable).

Fallback behavior

• If recommender unavailable: API returns 200 with fallback items: top-sellers or category-based recommendations with "strategy":"fallback_static" and generatedAt.
• If enrichment fails, return IDs with minimal metadata and frontend shows skeletons. Log incidents and emit alerts.

Evolving recommendation logic

• Versioned API path (/v1, /v2) and a "strategy" field in response so clients can opt-in.
• Backward-compatible schema: additive fields only; never remove keys without deprecation cycle.
• Feature flags and A/B test gates for rolling new algorithms.
• Contract tests + consumer-driven contract CI to detect breaking changes.
• Deprecation plan: announce, support both versions for 3 months, provide migration docs and server-side translation layer if needed.

Approach / decomposition criteria

• Domain boundaries: isolate functionality that is cohesive (search indexing, query parsing, ranking) and has clear inputs/outputs.
• Coupling & change frequency: pick components that change independently (ranking algorithms, filters).
• Data ownership & size: prefer read-heavy, compute-heavy pieces suited for separate scaling.
• Latency & scaling needs: separate to scale search independently.

Safe first steps (strangler-fig)

1. Add an API façade or gateway that routes search calls.
2. Implement a new Search service for read/query path only (non-destructive) behind a feature flag.
3. Start with read replicas or an index service that consumes data but keeps writes in the monolith.
4. Gradually flip traffic (canary) and monitor.

Data ownership & synchronization

• Primary source of truth: keep write ownership in the monolith initially.
• Use event-driven sync (domain events -> search indexer) or CDC to update the search DB/index (Elasticsearch/Opensearch).
• Avoid dual-write on the critical path; if needed use transactional outbox pattern to ensure reliability.

Integration patterns to stay functional

• Anti-corruption layer to translate models/API contracts.
• API gateway/proxy for routing and A/B testing.
• Backwards-compatible API versioning and feature flags for rollback.
• Circuit breakers and fallback to monolith search when service is unavailable.

As a full-stack dev I’d also update UI progressively (toggle new features per user cohort) and add end-to-end tests and observability for query correctness and latency.

Approach (brief)
Implement optimistic UI by immediately toggling the local "liked" state and showing a pending indicator, send an API request with an idempotency key, then reconcile the server response: confirm, update counts, or rollback on failure. Backend must support idempotency, conflict detection and return canonical state.

Frontend (React) — key steps

• Maintain local state: { liked: bool, likeCount: number, pending: bool, error: string }.
• On click: set pending=true and flip liked/count locally, generate uuid idempotencyKey, fire PATCH/POST fetch.
• When API returns success: set pending=false and apply server's canonical liked/likeCount.
• On failure or conflict: set pending=false, rollback to server state, show toast/error.

Example code:

// React handler (simplified)
async function toggleLike(postId) {
  const idempotencyKey = crypto.randomUUID();
  const prev = { liked, likeCount };
  setState(s => ({ ...s, pending:true, liked: !s.liked, likeCount: s.likeCount + (s.liked ? -1 : 1) }));
  try {
    const res = await fetch(`/api/posts/${postId}/like`, {
      method: 'POST',
      headers: { 'Content-Type':'application/json', 'Idempotency-Key': idempotencyKey },
      body: JSON.stringify({ like: !prev.liked })
    });
    const json = await res.json();
    if (!res.ok) throw new Error(json.error || 'Server error');
    // reconcile with canonical server state
    setState(s => ({ ...s, pending:false, liked: json.liked, likeCount: json.likeCount }));
  } catch (err) {
    // rollback and show error
    setState(s => ({ ...s, pending:false, liked: prev.liked, likeCount: prev.likeCount, error: err.message }));
  }
}

UI cues

• While pending show spinner or subtle opacity on button.
• Disable repeated clicks until pending resolves (or dedupe via idempotency).

Backend requirements

• Idempotency: accept an Idempotency-Key header; ensure repeated requests with same key produce same result and return previous response.
• Conflict detection: validate request vs canonical state (timestamps, version numbers, or compare client-sent expected state). Return 409 with canonical state when mismatch.
• Atomic update: increment/decrement counters and store per-user like records transactionally.
• Response: always return canonical { liked: bool, likeCount: int, version?: int }.

Trade-offs & notes

• Short optimistic window improves UX but requires careful rollback UX to avoid flicker.
• Use event/versioning (ETag, version field) for stronger conflict handling in complex flows.

Minimal signals to instrument (CRUD API) — from a full‑stack perspective

Logs (structured)

• Request logs: method, path, user-id, request-id, status, latency, error message.
  Why: root cause when something fails; correlates with traces.
  Alert: sudden spike > 5x errors/min over 5m (compared to 1h baseline).
• Audit logs: create/update/delete details (who, what, when).
  Why: security and data-change debugging.
  Alert: any unexpected bulk-delete or > N deletes/user in 1m.

Metrics (aggregated)

• Request rate (RPS) by endpoint and method.
  Why: traffic, capacity planning.
  Alert: drop > 50% of baseline RPS for 5m (possible outage).
• Error rate (%) per endpoint.
  Why: detect functional regressions.
  Alert: error rate > 1% sustained for 5m or absolute errors > 10/min.
• Latency histograms (p50/p95/p99).
  Why: user experience and SLOs.
  Alert: p95 latency > SLO threshold (e.g., 500ms) for 5m.
• Saturation metrics: CPU, memory, DB connection pool usage.
  Why: resource bottlenecks.
  Alert: DB connections > 80% or CPU > 90% for 5m.

Traces

• Distributed tracing for request path (frontend → API → DB). Capture spans for auth, DB calls, external services.
  Why: pinpoints slow component and latency sources.
  Alert: increase in average trace duration p95 > 2x baseline.

Rationale: logs for investigations, metrics for alerting/SLOs, traces for latency root cause. These cover detection, diagnosis, and recovery for production regressions.