Edge Computing
Edge runtimes move logic closer to users—with strict CPU/time limits, different APIs than full Node, and tenant isolation requirements.
When to Offer This Workflow
Trigger conditions:
- - Auth, redirects, or personalization at the CDN layer
- HTML rewriting, A/B assignment, or bot mitigation at the edge
- Global latency SLOs for read-heavy paths
Initial offer:
Use six stages: (1) workload fit, (2) edge vs origin split, (3) data & state, (4) security & tenancy, (5) limits & cost, (6) testing & rollout). Confirm platform (Workers, Lambda@Edge, Fastly Compute, etc.).
Stage 1: Workload Fit
Goal: Prefer short, CPU-light, request-scoped work—not long jobs or huge body buffering.
Exit condition: Explicit list of what remains on origin (heavy SSR, large uploads).
Stage 2: Edge vs Origin Split
Goal: Document what runs where: geo headers, redirects, cache key logic, A/B bucketing, partial HTML injection.
Practices
- - Cache
Vary and cookie behavior documented to avoid wrong personalization leakage
Stage 3: Data & State
Goal: If using edge KV/Durable Objects/regional stores, state consistency (eventual vs strong) and rate of round-trips to origin.
Stage 4: Security & Tenancy
Goal: Validate JWT/session at edge; isolate tenants; never embed secrets in deploy bundles visible to clients.
Stage 5: Limits & Cost
Goal: Wall-clock CPU limits, request size caps, egress pricing; graceful degradation or fallback to origin.
Stage 6: Testing & Rollout
Goal: Canary per region/PoP; synthetics from multiple locations; compare p95 vs origin-only path.
Final Review Checklist
- - [ ] Workload fits edge constraints
- [ ] Edge vs origin responsibilities documented
- [ ] State/consistency strategy clear
- [ ] Multi-tenant security reviewed
- [ ] Limits, cost, fallback documented
- [ ] Multi-region validation performed
Tips for Effective Guidance
- - Edge runtimes differ from full Node—verify available APIs (fs, streams, crypto).
- Read platform-specific cold-start and isolate model docs.
Handling Deviations
- - Hybrid: edge for headers/cache only; heavy compute stays on origin.
边缘计算
边缘运行时将逻辑迁移至更靠近用户的位置——需遵循严格的CPU/时间限制、与完整Node环境不同的API以及租户隔离要求。
何时提供此工作流
触发条件:
- - 在CDN层进行身份验证、重定向或个性化处理
- 在边缘层执行HTML重写、A/B测试分配或机器人缓解
- 针对高读取路径的全局延迟SLO要求
初始方案:
采用六个阶段:(1)工作负载适配、(2)边缘与源站拆分、(3)数据与状态、(4)安全与租户隔离、(5)限制与成本、(6)测试与部署。确认平台选择(Workers、Lambda@Edge、Fastly Compute等)。
阶段1:工作负载适配
目标: 优先处理短时、CPU轻量、请求级任务——避免长时间作业或大型主体缓冲。
退出条件: 明确列出保留在源站的任务(重型SSR、大文件上传)。
阶段2:边缘与源站拆分
目标: 记录各组件运行位置:地理标头、重定向、缓存键逻辑、A/B分组、部分HTML注入。
实践要点
- - 记录缓存Vary和Cookie行为,避免个性化信息错误泄露
阶段3:数据与状态
目标: 若使用边缘KV/Durable Objects/区域存储,需明确状态一致性(最终一致vs强一致)及与源站往返的速率。
阶段4:安全与租户隔离
目标: 在边缘层验证JWT/会话;隔离租户;切勿将客户端可见的密钥嵌入部署包。
阶段5:限制与成本
目标: 设定CPU墙钟时间限制、请求大小上限、出站流量定价;实现优雅降级或回退至源站。
阶段6:测试与部署
目标: 按区域/PoP进行金丝雀发布;从多个位置进行合成测试;对比p95与纯源站路径的性能。
最终审查清单
- - [ ] 工作负载符合边缘约束条件
- [ ] 边缘与源站职责已记录
- [ ] 状态/一致性策略明确
- [ ] 多租户安全性已审查
- [ ] 限制、成本、回退方案已记录
- [ ] 多区域验证已完成
有效指导建议
- - 边缘运行时与完整Node环境不同——需验证可用API(fs、streams、crypto)。
- 阅读平台特定的冷启动和隔离模型文档。
偏差处理
- - 混合方案:边缘仅处理标头和缓存;重型计算保留在源站。