🎟️ Events, Ticketing & Creators · Deployment · Pro
High-demand Ticket Sale Deployment
Where a ticketing system runs to survive a big on-sale: waiting room at the CDN edge, autoscaled services, and databases sized for the spike.
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Drawing diagram…
What this diagram shows
- The waiting room runs at the CDN edge, in front of everything
- Services scale out ahead of the announced on-sale time
- Seat holds live in Redis, orders in a database cluster
Prompt used
Deployment for a high-demand ticket sale on AWS: Cloudflare runs the virtual waiting room and bot protection at the edge. Behind it an Application Load Balancer sends traffic to EKS, where catalogue, inventory and order services are pre-scaled before the on-sale. ElastiCache Redis holds seats; Aurora PostgreSQL with read replicas stores orders. SQS queues ticket delivery emails.
Mermaid code
flowchart TB
FANS[Fans] --> CF[Cloudflare - waiting room and bot protection]
CF --> ALB[Application Load Balancer]
subgraph AWS[AWS Mumbai]
subgraph EKS[EKS - pre-scaled before on-sale]
CAT[Catalogue x10]
INV[Inventory x20]
ORD[Orders x20]
MAIL[Ticket Mailer]
end
REDIS[(ElastiCache Redis - seat holds)]
AUR[(Aurora PostgreSQL + read replicas)]
SQS[SQS - ticket emails]
end
ALB --> CAT
ALB --> INV
ALB --> ORD
INV --> REDIS
ORD --> AUR
CAT --> AUR
ORD --> SQS
SQS --> MAILRelated templates
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