GraphQL Schema (Deep Workflow)
GraphQL concentrates complexity on the server: resolver graphs, N+1 fetches, schema evolution, and field-level authorization.
When to Offer This Workflow
Trigger conditions:
- - Designing a new GraphQL API or federated subgraph
- Latency or complexity incidents from client queries
- Need for safe schema deprecation and versioning
Initial offer:
Use six stages: (1) domain modeling, (2) operations surface, (3) performance patterns, (4) errors & partial results, (5) security & authz, (6) versioning & evolution). Confirm client patterns (Apollo, Relay) and gateway (if any).
Stage 1: Domain Modeling
Goal: Types reflect domain concepts; avoid dumping everything on Query; use input objects for mutations with validation.
Stage 2: Operations Surface
Goal: Queries for reads; mutations for writes; subscriptions only when justified (scaling and operational cost).
Pagination
- - Prefer cursor-based connections for large lists (Relay-style edges/nodes)
Stage 3: Performance Patterns
Goal: DataLoader or batching for N+1; query complexity/depth/cost limits; optional persisted queries for public APIs.
Stage 4: Errors & Partial Results
Goal: Document semantics of errors alongside partial data; map domain failures to structured extensions.
Stage 5: Security & Authz
Goal: Enforce authorization at field/object level—not only at the top resolver.
Stage 6: Versioning & Evolution
Goal: Prefer additive changes; @deprecated with migration window; in federation, clear ownership of types and entities.
Final Review Checklist
- - [ ] Schema reflects domain and operations
- [ ] Pagination and mutations idiomatic
- [ ] Batching and complexity limits in place
- [ ] Error behavior documented for clients
- [ ] Field-level authz enforced
- [ ] Deprecation policy defined
Tips for Effective Guidance
- - N+1 is the default failure mode—plan batching early.
- Pair with rest-best-practices when REST and GraphQL coexist at the edge.
Handling Deviations
- - Public APIs: consider persisted queries or allowlists to limit abusive queries.
GraphQL Schema(深度工作流)
GraphQL将复杂性集中在服务端:解析器图、N+1数据获取、schema演进以及字段级授权。
何时提供此工作流
触发条件:
- - 设计新的GraphQL API或联邦子图
- 客户端查询导致延迟或复杂性事故
- 需要安全的schema弃用和版本管理
初始建议:
使用六个阶段:(1) 领域建模,(2) 操作面,(3) 性能模式,(4) 错误与部分结果,(5) 安全与授权,(6) 版本管理与演进。确认客户端模式(Apollo、Relay)和网关(如有)。
阶段1:领域建模
目标: 类型反映领域概念;避免将所有内容堆在Query上;对变更操作使用带验证的输入对象。
阶段2:操作面
目标: 查询用于读取;变更用于写入;仅在合理时使用订阅(考虑扩展和运维成本)。
分页
- - 大型列表优先使用基于游标的连接(Relay风格的edges/nodes)
阶段3:性能模式
目标: 使用DataLoader或批处理解决N+1问题;设置查询复杂度/深度/成本限制;公共API可选持久化查询。
阶段4:错误与部分结果
目标: 记录errors与部分data的语义;将领域故障映射到结构化扩展。
阶段5:安全与授权
目标: 在字段/对象级别强制执行授权——不仅限于顶层解析器。
阶段6:版本管理与演进
目标: 优先采用增量变更;使用@deprecated并设定迁移窗口;在联邦中明确类型和实体的所有权。
最终审查清单
- - [ ] Schema反映领域和操作
- [ ] 分页和变更操作符合惯例
- [ ] 批处理和复杂度限制已就位
- [ ] 错误行为已向客户端文档化
- [ ] 字段级授权已强制执行
- [ ] 弃用策略已定义
有效指导技巧
- - N+1是默认的失败模式——尽早规划批处理。
- 当REST和GraphQL在边缘层共存时,搭配rest-best-practices使用。
处理偏差
- - 公共API:考虑使用持久化查询或白名单来限制滥用查询。