System Design HLD Example: URL Shortener (TinyURL and Bitly)

Scenario context

A URL shortener is a read-heavy system (100:1 ratio) that maps long URLs to short, unique aliases. The core scaling challenge is generating unique IDs without database contention—solved using a Range-Based ID Generator or a Distributed Counter with Base62 encoding. A 301/302 redirect strategy combined with aggressive Redis caching ensures sub-10ms response times. This walkthrough focuses on **Lessons Learned…

This walkthrough focuses on Core Mechanism & Implementation.

Before making a design choice, clarify:

• This walkthrough focuses on Lessons Learned from Production URL Shortener Systems .
• • In production deployments, the top 1% of links receive over 95% of redirect traffic.
• • Standard LRU caching handles this well under steady state, but viral link events — a celebrity post, a product launch — can cause sudden cache misses on previously cold links.

Concepts in play: system-design-hld-architectures, caching, distributed-locking, event-driven-architecture

Stage 1 — Production Considerations & Trade-offs

• Observable outcome: - Range-Based Allocation solves the distributed counter bottleneck: each server claims a block of IDs from ZooKeeper and increments an in-memory counter locally, with zero per-request network coordin…
• After working through the stages, you should be able to explain: - In production deployments, the top 1% of links receive over 95% of redirect traffic.
• • Standard LRU caching handles this well under steady state, but viral link events — a celebrity post, a product launch — can cause sudden cache misses on previously cold links.
• • • A URL shortener is a read-heavy system (100:1 read/write ratio).

Observable outcome: - - A URL shortener is a read-heavy system (100:1 read/write ratio).

End-to-end outcome

This walkthrough focuses on Lessons Learned from Production URL Shortener Systems .

Alternatives considered

The source focuses on one primary design. A second architecture is not compared, so no alternative is invented here.

Failure and degraded modes

Production Considerations & Trade-offs

• Detection: Observable outcome: - Range-Based Allocation solves the distributed counter bottleneck: each server claims a block of IDs from ZooKeeper and increments an in-memory counter locally, with zero per-req…
• User or operator impact: Observable outcome: - Range-Based Allocation solves the distributed counter bottleneck: each server claims a block of IDs from ZooKeeper and increments an in-memory counter locally, with zero per-req…

• Observable outcome: - Range-Based Allocation solves the distributed counter bottleneck: each server claims a block of IDs from ZooKeeper and increments an in-memory counter locally, with zero per-request network coordin…