Rate Limiting

A collection of rate limiting algorithms implemented in Go using Redis as the backend store.
These algorithms help control the number of requests a user or client can make in a given time window.

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Algorithms Implemented

1. Fixed Window
2. Sliding Logs
3. Leaky Bucket
4. Sliding Window
5. Token Bucket

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Overview of Algorithms

1. Fixed Window

• Description: Counts requests per fixed time interval.
• Pros:
  • Memory efficient
  • Easy to implement
  • Resets quota at the end of a window
• Cons:
  • Burst traffic at window edges may exceed quota
  • Not smooth for high-frequency requests

2. Sliding Logs

• Description: Stores timestamps of requests to enforce limits over a rolling window.
• Pros: Accurate, supports sliding window rate limiting
• Cons: High memory usage due to storing logs

3. Leaky Bucket

• Description: Requests are processed at a fixed rate; excess requests queue or leak out.
• Pros: Smooths traffic, simple concept
• Cons: Can delay requests, requires queue management

4. Sliding Window

• Description: Combines Fixed Window and Sliding Logs to allow smoother rate limiting
• Pros: More accurate than Fixed Window, supports bursts
• Cons: Slightly more complex than Fixed Window

5. Token Bucket

• Description: Each user has a bucket of tokens; each request consumes one token. Tokens refill at a fixed rate.
• Pros:
  • Supports bursts
  • Smooths traffic
  • Memory efficient
• Cons:
  • Slightly more complex than Fixed Window
  • Token refill timing must be handled carefully

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Prerequisites

• Go 1.20+
• Redis (local or cloud instance)

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Getting Started

1. Clone the repository:

git clone https://github.com/FarajGopher/Rate-limiting.git
cd rate-limiting