A classic URL shortener architecture — the go-to system design interview example. Load balancer, cache, database, ID generation, and analytics pipeline.
Copy this Mermaid code into Cybewave Studio or any Mermaid-compatible editor.
graph TD
Client[Client] --> LB[Load Balancer]
LB --> App1[App Server 1]
LB --> App2[App Server 2]
LB --> App3[App Server 3]
App1 --> Cache[(Redis Cache)]
App2 --> Cache
App3 --> Cache
App1 --> DB[(PostgreSQL)]
App2 --> DB
App3 --> DB
Cache -.->|Cache Miss| DB
App1 --> IDGen[ID Generator - Snowflake]
App2 --> IDGen
App3 --> IDGen
DB --> ReadReplica[(Read Replica)]
App1 --> Analytics[Analytics Service]
Analytics --> Kafka[Kafka]
Kafka --> ClickHouse[(ClickHouse)]
subgraph URL Shortener Flow
direction LR
Create[POST /shorten] --> IDGen
IDGen --> Store[Store in DB + Cache]
Redirect[GET /:id] --> Cache
Cache --> Return[302 Redirect]
endMaster system design interviews with a proven template.
Classic URL shortener design with all the components interviewers expect: LB, cache, DB, ID generation, analytics.
Load balancer with multiple app servers, read replicas, Redis caching, and async analytics pipeline.
Snowflake ID generator, Kafka for event streaming, ClickHouse for analytics — production-grade choices.
Ask the AI to add rate limiting, custom domains, link expiration, or A/B testing to practice deeper design.
This template shows a URL shortener with load balancer, app servers, Redis cache, PostgreSQL, read replicas, Snowflake ID generator, and a Kafka + ClickHouse analytics pipeline. You can ask the AI to generate other classic designs like social feeds, chat systems, or notification services.
Yes. The template covers the key areas interviewers evaluate: scalability (LB + replicas), caching strategy (Redis), data model (PostgreSQL), unique ID generation (Snowflake), and analytics. Customize it to practice different scenarios.
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