Design a rate limiter in HLD - System Design Guide

Problem Statement

When a service receives too many requests in a short time, it can become overwhelmed, causing slow responses or crashes. Without control, a few users or faulty clients can consume all resources, making the system unavailable for others.

Solution

A rate limiter controls how many requests a user or client can make in a given time window. It tracks request counts and blocks or delays requests that exceed the allowed limit, protecting the system from overload and ensuring fair usage.

Architecture

Client 1

→Rate Limiter

↓

→Storage

This diagram shows clients sending requests to a rate limiter, which checks request counts stored in a storage system before forwarding allowed requests to the backend.

Trade-offs

✓ Pros

→

Prevents system overload by limiting request rates.

→

Ensures fair resource usage among users.

→

Protects backend services from spikes and abuse.

→

Can improve overall system stability and user experience.

✗ Cons

→

Adds latency due to request checking.

→

Requires additional storage and synchronization for counters.

→

Complexity increases with distributed systems needing consistent state.

Use when your system faces high traffic with potential bursts or abuse, typically above hundreds or thousands of requests per second, or when backend stability is critical.

Avoid if your system has very low traffic (under 100 requests per second) or if strict request limits would harm user experience more than occasional overload.

Real World Examples

Amazon

Amazon uses rate limiting on its APIs to prevent excessive calls from a single client that could degrade service for others.

Twitter

Twitter applies rate limiting to control how many tweets or API requests a user can make in a time window to prevent spam and abuse.

Stripe

Stripe enforces rate limits on payment API calls to protect backend systems from sudden spikes and ensure transaction reliability.

Alternatives

Circuit Breaker

Stops requests temporarily after failures rather than limiting request rate.

Use when: Choose when backend failures or errors are the main concern, not request volume.

Load Balancing

Distributes requests evenly across servers but does not limit request rate per client.

Use when: Choose when you want to spread load but do not need to restrict client request frequency.

Backpressure

Slows down request processing dynamically rather than outright rejecting excess requests.

Use when: Choose when you want to degrade service gracefully instead of hard blocking.

Summary

Rate limiting prevents system overload by controlling request frequency per client.

It ensures fair usage and protects backend services from spikes and abuse.

Implementing rate limiting improves system stability and user experience under high load.

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