Design a rate limiter in HLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of a rate limiter in system design?

A rate limiter controls the number of requests a user or system can make in a given time to prevent overload and ensure fair resource usage.

beginner

Name two common algorithms used to implement rate limiting.

Token Bucket and Leaky Bucket are two popular algorithms for rate limiting.

intermediate

Explain how the Token Bucket algorithm works in simple terms.

Tokens are added to a bucket at a fixed rate. Each request uses a token. If no tokens are left, requests are blocked or delayed.

intermediate

What is the difference between fixed window and sliding window rate limiting?

Fixed window counts requests in fixed time slots, which can cause bursts at edges. Sliding window tracks requests over a moving time frame for smoother control.

advanced

Why is distributed rate limiting more complex than single-node rate limiting?

Because requests can come to multiple servers, they need to share state or coordinate to enforce limits consistently across the system.

Which rate limiting algorithm allows bursts of traffic up to a certain limit?

A Round Robin

B Fixed Window

C Token Bucket

D Sliding Window

Token Bucket allows bursts by accumulating tokens that can be used quickly.

What problem does rate limiting primarily solve?

A Preventing system overload

B Encrypting data

C Caching responses

D Load balancing

Rate limiting prevents too many requests from overwhelming a system.

In a fixed window rate limiter, what issue can occur at window boundaries?

A Data loss

B Request bursts

C Slow response

D Memory leak

Requests can burst at the edges of fixed windows causing uneven load.

Which component is essential for distributed rate limiting?

A Single server

B Local cache only

C Static IP addresses

D Shared state or coordination

Distributed systems need shared state or coordination to enforce limits consistently.

Leaky Bucket algorithm is best described as:

A Requests are processed at a steady rate regardless of bursts

B Tokens accumulate for bursts

C Requests are dropped randomly

D Requests are queued indefinitely

Leaky Bucket processes requests at a fixed rate, smoothing bursts.

Describe how you would design a rate limiter for an API that must handle millions of users fairly and efficiently.

Think about how to track requests per user and how to share state across servers.

Explain the trade-offs between fixed window and sliding window rate limiting methods.

Consider how requests are counted over time.