Design a rate limiter in HLD - Architecture Diagram

System Overview - Design a rate limiter

This system controls how many requests a user can make in a given time. It helps protect services from too many requests at once. The key need is to limit requests fairly and quickly without slowing down the service.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Rate Limiter Service
  |
  +--> Cache (for counters)
  |
  v
Backend Service
  |
  v
Database

Components

User

client

Sends requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Receives requests and forwards them to Rate Limiter Service

Rate Limiter Service

service

Checks if user request count is within allowed limits

Cache

cache

Stores request counts per user for fast access

Backend Service

service

Processes allowed requests

Database

database

Stores user data and long-term logs

Request Flow - 12 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Rate Limiter Service

Rate Limiter Service → Cache

Cache → Rate Limiter Service

Rate Limiter Service → Cache

Rate Limiter Service → API Gateway

API Gateway → Backend Service

Backend Service → Database

Backend Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Rate Limiter cannot quickly check or update request counts, causing possible incorrect rate limiting or slower responses

Mitigation:Fallback to database for counts with slower response; add cache replication and monitoring to reduce downtime

Architecture Quiz - 3 Questions

Which component first checks if a user’s request count is within limits?

A API Gateway

B Rate Limiter Service

C Load Balancer

D Cache

Design Principle

This design uses a fast cache to track request counts for quick decisions, preventing overload. The Load Balancer and API Gateway separate concerns for scalability and security. Fallbacks ensure reliability even if cache fails.

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