Performance & Caching Interview Questions

Performance & Caching affects schema shape, resolver logic, caching, and client contracts. Design APIs for evolvability—version fields carefully and document breaking vs non-breaking changes.

Use CDN edge caching, normalized client caches, or server-side cache keys based on query shape. Invalidate or TTL appropriately so Performance & Caching does not serve stale data after mutations.

Return structured errors with codes and messages safe for clients. Log server-side details, map validation failures clearly, and avoid leaking stack traces in production Performance & Caching endpoints.

API keys, OAuth2, JWT, or session cookies depending on clients. Performance & Caching should enforce auth at the gateway or resolver layer and scope data access per user or role.

Use cursor-based pagination for large or real-time datasets; offset pagination only when datasets are small and stable. Document limits and include pageInfo metadata in Performance & Caching responses.

OpenAPI, GraphQL schema SDL, Postman collections, and generated client SDKs. Keep docs close to code so Performance & Caching contracts stay accurate as the API evolves.

Contract tests between services, mocked dependencies in unit tests, and staging environments that mirror production. Verify Performance & Caching behavior under load and failure injection.

Use the official Graphql docs for Performance & Caching, language or framework references, and reputable community guides. Bookmark release notes and migration guides when upgrading versions, since Performance & Caching behavior can change between releases.

Copying snippets without understanding why Performance & Caching works leads to fragile code. Beginners often skip error handling, tests, or edge cases. Slow down, trace execution step by step, and validate assumptions with small experiments.

Start with the problem Performance & Caching solves, show a minimal working example, and list the team conventions around it. Point them at official docs and one trusted internal example rather than random snippets.

Performance & Caching shows up often in production Graphql work—misunderstanding it leads to bugs, performance issues, or security gaps. Interviewers want clear explanations plus practical judgment.

Describe scaffolding a feature, configuring defaults, or validating input where Performance & Caching is required. Call out what goes wrong if the team skips conventions around it.

Read the official overview, run a minimal sandbox, learn key terms and common errors, then expand with a small project. Hands-on practice beats memorizing Performance & Caching definitions.

Follow team conventions, split concerns into testable units, handle edge cases, and document assumptions. Review similar modules in the codebase, add observability, and ship incrementally with feature flags if Performance & Caching is risky.

Map the Performance & Caching attack surface (injection, broken auth, data exposure, DoS). Layer defenses—validation, rate limits, least privilege, encryption, and regular audits.

Measure first, then improve the hottest Performance & Caching paths with batching, connection pooling, async I/O, better algorithms, or sharding. Re-check p95/p99 after each change and skip micro-tweaks without clear gains.

Use expand/contract or strangler patterns, dual-write/dual-read where needed, feature flags, and rollback plans. Validate parity with shadow traffic before decommissioning the old Performance & Caching path.

State whether Performance & Caching needs strong consistency or can tolerate eventual consistency. Discuss partitions, quorum, conflict resolution, and user-visible anomalies during failures.

Pick availability and latency indicators, set achievable objectives, watch burn rate, and decide when reliability work outranks features. Tie those budgets to release decisions for Performance & Caching.

Define clear ownership boundaries for Performance & Caching, failure domains, caching, and observability. Plan capacity, multi-region needs if relevant, and explicit trade-offs between consistency, latency, and cost.