Serverless Architecture
A deep dive into the event-driven, globally distributed infrastructure that powers Aevum Encyclopedia's 2.4M+ articles with sub-50ms latency and 99.99% uptime.
Overview
Aevum Encyclopedia operates on a fully serverless architecture designed for massive horizontal scaling, operational simplicity, and cost efficiency. By leveraging edge computing, event-driven functions, and managed data services, we eliminate infrastructure overhead while maintaining academic-grade reliability.
System Architecture
Our architecture follows a request-flow model optimized for read-heavy encyclopedia workloads, with write operations routed through async pipelines for consistency.
Each tier is independently scalable, versioned, and monitored. Cold starts are mitigated through provisioned concurrency and edge-first execution patterns.
Core Components
API Gateway
Handles authentication, rate limiting, request validation, and intelligent routing to appropriate function pools.
GraphQL + RESTEdge Functions
Lightweight, stateless compute instances deployed across 300+ global PoPs. Executes business logic, transforms payloads, and orchestrates service calls.
Deno / Node.jsAI Inference Pipeline
Async workers that process new contributions, verify claims against knowledge graphs, and generate summaries or translations.
Python / ONNX RuntimeEvent Bus
Decouples services using pub/sub messaging. Triggers downstream workflows like cache invalidation, search indexing, and audit logging.
WebSockets / KafkaData & Caching Strategy
Encyclopedia content is inherently read-heavy. We implement a multi-tier caching architecture to minimize database load and ensure instant article retrieval.
| Layer | TTL | Technology | Purpose |
|---|---|---|---|
| Edge Cache | 5–30 min | Cloudflare Workers KV / Fastly | Static assets, popular articles, metadata |
| Regional Cache | 1–4 hours | Redis Cluster | Session data, rate limits, draft buffers |
| Primary Store | ∞ | PostgreSQL (Partitioned) | Structured article data, relationships |
| Search Index | Real-time | OpenSearch / Meilisearch | Full-text, semantic, and faceted search |
Performance & Scaling
Cold Start Mitigation
We maintain a warm pool of 500+ provisioned function instances across critical geographic regions. Combined with async initialization and V8 isolates, P99 cold starts are reduced to <40ms.
Auto-Scaling Rules
- Scale up at 70% CPU or 80% memory utilization
- Scale down to baseline after 5 min of idle time
- Burst capacity reserved for major content releases or viral traffic
- Read/write path separation prevents query storms from blocking contributions
Security Model
Zero-trust principles govern all internal and external communications. Functions operate with least-privilege IAM roles, and all data is encrypted in transit (TLS 1.3) and at rest (AES-256).
Authentication is handled via JWTs with short-lived access tokens and refresh rotation. Editor privileges are scoped using RBAC matrices evaluated at the gateway layer before any compute execution.
Deployment & CI/CD
Infrastructure is defined as code using Terraform and Pulumi. Function code follows a GitOps workflow with automated testing, linting, and canary deployments.
export default {
provider: "cloudflare-workers",
edge: {
regions: ["iad", "lhr", "tyo", "syd", "fra"],
provisioned_concurrency: 500,
timeout: "10s",
memory: "256MB"
},
functions: {
"article.get": {
handler: "./src/handlers/article.get.ts",
cache: true,
ttl: "5m"
},
"article.update": {
handler: "./src/handlers/article.update.ts",
queue: true, // async pipeline
retry: { max: 3, backoff: "exponential" }
}
}
}Each commit triggers integration tests against a mirrored staging environment. Production rollouts use automatic rollback if error rates exceed 0.5% or latency P95 degrades by >15%.