Templates/E-commerce High Traffic

E-commerce High Traffic

E-commerceAdvancedOrg-scale commerce

Flash-sale ready storefront with CDN edge caching, search indexing, event-driven fulfillment, and Stripe payment webhooks. Handles spiky traffic via layered caching and async order processing.

Recommended for: Commerce teams preparing for flash-sale traffic

14 nodes18 connectionsEdge cachingEvent-driven fulfillmentSearch-first browsing

Scope Notes

Intended for Commerce teams preparing for flash-sale traffic at Advanced complexity and Org-scale commerce scope. Treat the graph as a starting shape rather than a fixed stack: keep the topology and the signals below, then swap providers to match your platform.

Edge cachingEvent-driven fulfillmentSearch-first browsing

Review Checklist

0 of 12 reviewed

Use Case

High-traffic stores, flash sales, marketplace platforms

Best Fit Scenarios

  • Seasonal spikes and marketing campaigns
  • Catalogs requiring search relevance tuning
  • Operations teams needing resilient order pipelines

Stack Breakdown

CDNNext.jsExpressKafkaPostgreSQLElasticsearch

Architecture Layers

1Edge/CDN
2Storefront + reverse proxy
3API services
4Event bus
5Data & search
6Payments, identity & failure isolation

Components by Category

infra

CDNNginxVault

frontend

Next.js

backend

ExpressNestJSAuth Service

database

RedisPostgreSQLElasticsearch

async

KafkaQueue WorkerDead Letter Queue

external

Stripe

Why This Topology Works

The CDN absorbs read traffic while Kafka decouples order writes from both the fulfillment worker and the search index. Elasticsearch is a derived read model written only through the event stream, so the catalog cannot silently diverge from PostgreSQL.

Scaling Notes

Kafka consumers scale on their own lag, so fulfillment and indexing grow independently. Redis fronts session reads, and the payments path is a synchronous Stripe API call with an inbound payment_intent.succeeded webhook.

Observability

Order failures park in the DLQ and page the on-call owner; replay is an operator action. Trace the payment round trip separately from the event-driven fulfillment path.

Typical Bottlenecks

  • Inventory contention on hot SKUs during flash sales
  • Kafka consumer lag between order capture and fulfillment
  • Elasticsearch index freshness versus catalog change rate

Async Flow and Reliability

Checkout returns as soon as the order is accepted; fulfilment, indexing and notifications run as consumers off Kafka. Unprocessable events go to the DLQ and are replayed only after the root cause is fixed.

Upgrade Path

Add inventory reservation before scaling ad spend, then split catalog and order APIs into separate services and introduce per-tenant rate limiting at the edge.