Rate limits

Introduction

Rate limits are mechanisms used to control the amount of incoming or outgoing traffic to or from a network, application, or API over a specified period. They are essential for:

Preventing Abuse: Limiting the number of requests a user or system can make prevents misuse and overloading of resources.

Ensuring Fair Usage: Ensuring that all users have equal access to the service and no single user monopolizes the resources.

Maintaining Performance: Keeping the system responsive and stable by avoiding excessive load.

Security: Protecting against DDoS (Distributed Denial of Service) attacks by limiting the request rate.

Usually, rate limits are expressed as the highest quantity of requests that can be made in a given minute, hour, or day. Following a request that exceeds the limit is typically throttled (delayed) or refused, frequently accompanied by an HTTP status code (429 Too Many Requests), indicating that the limit has been reached.

Implementation

Implementing rate limits can involve various strategies, such as:

Fixed Window: A set limit within a fixed time frame (e.g., 100 requests per minute).

Sliding Window: Similar to a fixed window but more flexible, resetting the window dynamically as requests come in.

Token Bucket: Requests are allowed if there are enough tokens in the bucket, which refills at a set rate over time.

Leaky Bucket: Requests are processed at a constant rate, with excess requests queued and processed later.

While offering a better user experience, comprehending and appropriately implementing rate limits contributes to maintaining the services' integrity and dependability.

Rate Limiting Strategies with Examples

1. Fixed Window

Description: In a fixed window rate limiter, a set limit is applied within a fixed time frame. For instance, a user might be allowed to make up to 100 requests per minute.

Example Implementation:

NodeJS

Python

2. Sliding Window

Description: The sliding window algorithm provides more flexibility by dynamically adjusting the window as requests come in. The window slides forward with each new request.

Example Implementation:

NodeJS

Python

3. Token Bucket

Description: In the token bucket algorithm, requests are allowed if there are enough tokens in the bucket. Tokens refill at a set rate over time.

Example Implementation:

NodeJS

Python

4. Leaky Bucket

Description: The leaky bucket algorithm processes requests at a constant rate. Excess requests are queued and processed later.

Example Implementation:

NodeJS

Python

This document provides an overview of rate limiting strategies and practical examples of their implementations. You can use these examples as a basis for integrating rate limiting into your own applications.

Introduction#

Implementation#

Rate Limiting Strategies with Examples#

1. Fixed Window#

2. Sliding Window#

3. Token Bucket#

4. Leaky Bucket#

Introduction

Implementation

Rate Limiting Strategies with Examples

1. Fixed Window

2. Sliding Window

3. Token Bucket

4. Leaky Bucket