High demand events, like exam result releases, concert ticket sales, or airdrop token claims, can cause websites to crash due to server overload. When millions of people try to access the same site at once, without proper controls in place, this can lead to a terrible user experience, giving bots and scalpers an unfair advantage.
One effective way to manage this traffic is by implementing rate limiting—a method of controlling how many requests a user, IP address, or application can make to a server within a set period. This helps protect your system from overloads, attacks, and ensures a fair user experience.
Rate Limiting Strategies:
Captcha Challenges: Ensure users are human by requiring verification.
Virtual Queueing: Place users in a queue to manage heavy traffic.
API Throttling: Limit or temporarily block abusive users.
Rate Limiting Algorithms:
Fixed Window: Sets strict limits within set time intervals (e.g., 100 requests per minute).
Sliding Window: Uses a rolling time window for more balanced request distribution.
Token Bucket: Gives clients a set number of tokens; each request uses one token, and tokens regenerate over time.
Leaky Bucket: Throttles bursts of requests, allowing a steady flow of traffic.
Example with Express.js application
Example with flask application
Best Practices:
Set Sensible Limits: Tailor rate limits to typical usage and business needs.
Provide Clear Responses: If a user exceeds the limit, return a helpful message with an HTTP 429 status.
Allow Exemptions: Let trusted users or admins have higher limits when necessary.
Use Rate Limit Headers: Communicate limits to clients with headers like X-RateLimit-Limit.
Monitor and Adjust: Regularly track traffic and adjust limits based on real-time trends.
By implementing rate limiting, you ensure that your application remains reliable, fair, and protected from misuse during peak traffic events.