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.

Practice

1. What is the primary purpose of a rate limiter in system design?

easy

A. To control the number of requests a user can make in a given time

B. To increase the speed of database queries

C. To store user data securely

D. To balance load between multiple servers

Solution

1. Step 1: Understand the role of rate limiter

   A rate limiter restricts how many requests a user or client can send in a certain time to prevent overload.

2. Step 2: Identify the correct purpose

   Among the options, only controlling request rate matches the rate limiter's function.

3. Final Answer:

   To control the number of requests a user can make in a given time -> Option A

4. Quick Check:

   Rate limiter = control request rate [OK]

Hint: Rate limiter limits requests per time window [OK]

Common Mistakes:

• Confusing rate limiter with load balancer
• Thinking it speeds up database queries
• Assuming it stores user data

2. Which data structure is most suitable to implement a sliding window rate limiter?

easy

A. Stack

B. Hash Map

C. Queue

D. Binary Tree

Solution

1. Step 1: Recall sliding window mechanism

   Sliding window rate limiter tracks timestamps of requests in a time window, removing old ones as time moves.

2. Step 2: Choose data structure for efficient insert and remove

   A queue allows adding new timestamps at the end and removing old timestamps from the front efficiently, matching sliding window needs.

3. Final Answer:

   Queue -> Option C

4. Quick Check:

   Sliding window = queue for timestamps [OK]

Hint: Sliding window needs FIFO structure like queue [OK]

Common Mistakes:

• Using stack which is LIFO, not suitable
• Choosing hash map without order
• Picking binary tree which is complex here

3. Consider a rate limiter allowing 3 requests per 10 seconds using sliding window. If requests come at seconds 1, 3, 7, and 9, which request will be rejected?

medium

A. Request at second 9

B. Request at second 3

C. Request at second 7

D. Request at second 1

Solution

1. Step 1: Track requests in 10-second window

   Requests at 1, 3, 7 are allowed as they are within limit 3 per 10 seconds.

2. Step 2: Check request at second 9

   At second 9, previous requests at 1, 3, 7 are still within 10 seconds window (from -1 to 9). So 3 requests already made, this 4th request exceeds limit and is rejected.

3. Final Answer:

   Request at second 9 -> Option A

4. Quick Check:

   4th request in 10s window = rejected [OK]

Hint: Count requests in last 10 seconds; 4th exceeds limit [OK]

Common Mistakes:

• Ignoring requests older than 10 seconds
• Allowing all requests without limit
• Counting requests incorrectly

4. A rate limiter uses a fixed window counter but sometimes allows bursts of requests at window edges. What is the likely cause?

medium

A. Sliding window algorithm is used

B. Queue data structure is not used

C. Rate limit is set too low

D. Fixed window resets counters abruptly causing bursts

Solution

1. Step 1: Understand fixed window behavior

   Fixed window counts requests in fixed intervals, resetting count at window end.

2. Step 2: Identify burst cause

   Requests near end of one window and start of next can both be allowed, causing bursts.

3. Final Answer:

   Fixed window resets counters abruptly causing bursts -> Option D

4. Quick Check:

   Fixed window reset causes bursts [OK]

Hint: Fixed window resets cause bursts at edges [OK]

Common Mistakes:

• Confusing sliding window with fixed window
• Blaming rate limit value instead of algorithm
• Ignoring window reset behavior

5. You need to design a distributed rate limiter for millions of users with low latency. Which approach best balances accuracy and scalability?

hard

A. Centralized fixed window counter on a single server

B. Distributed sliding window using local caches and periodic sync

C. Per-user token bucket stored only in client devices

D. No rate limiting, rely on server hardware scaling

Solution

1. Step 1: Consider scalability and accuracy needs

   Millions of users require distributed design to avoid bottlenecks and reduce latency.

2. Step 2: Evaluate options

   Centralized fixed window causes bottleneck; client-only token bucket is insecure; no rate limiting risks overload. Distributed sliding window with local caches and sync balances accuracy and scalability.

3. Final Answer:

   Distributed sliding window using local caches and periodic sync -> Option B

4. Quick Check:

   Distributed sliding window = scalable + accurate [OK]

Hint: Use distributed sliding window with local caches [OK]

Common Mistakes:

• Choosing centralized approach causing bottlenecks
• Relying on client-only enforcement
• Ignoring rate limiting and risking overload