Wall Street, NYC

Distributed Rate Limiter for High-Concurrency Environments with Redis: A Token Bucket Approach

Imagine a stock trading platform where users constantly access real-time stock prices, check portfolio values, and make buy/sell trades. To maintain system performance and prevent abuse, the platform limits the number of API requests each user can make per second.

Problem: Managing High Volume of Requests Per User

In this scenario:

1. High-Frequency Requests: Active users frequently send API requests to refresh stock prices, check account status, and perform trades. Users may inadvertently or deliberately send requests in quick succession.
2. Risk of Server Overload: If not managed, excessive requests can overwhelm the server, leading to slow response times, system crashes, or unfair usage where certain users monopolize server resources.
3. Enforcing Fair Usage: To prevent abuse and ensure fairness, the platform limits each user to a maximum of 10 requests per second. If a user exceeds this rate, they are temporarily blocked from sending additional requests until the limit resets in the next second.

The goal is to implement a rate-limiting mechanism that controls the number of requests per user while handling concurrency efficiently…