An Interactive Guide To Rate Limiting

UpdatedJune 6, 2025

Passionate about all areas of computer science, currently exploring DevOps, SRE and Linux

Comments (2)

Great article, I'm following you, I hope I can learn such vivid knowledge similar to this one from your follow-up articles in the future

BaseOrange1y ago

Dear respected author,Hello, I would like to request permission to reprint one of your articles on my blog (translated into Chinese). I will give full credit to the original source. Is that acceptable? Looking forward to your reply.

BaseOrange1y ago

I need to add one more thing. I won't make any profit by reprinting your article, and my blog doesn't have any advertising revenue either. I just think the article you wrote is relatively clear.

Sagyam Thapa1y ago

Sure I will be delighted. Do send me the link to the translated article. Not that I can read Chinese but I would love to see the translation anyways.

BaseOrange1y ago

I appreciate it. I'll get back to you as soon as possible.

BaseOrange1y ago

Hello, I have translated the article and published it on my blog. To honor the copyright of the original author, I have placed the copyright notice at the top of the article. I hope this translation provides value to the readers, and I welcome any feedback. URL：log.660066.xyz/2025/06/13/interactive-guide-to-rate-limiting

Sagyam Thapa1y ago

BaseOrange

Thank you for the translation.

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Sagyam's Blog

7 posts

Introduction

Rate limiting is a must have strategy in every back-end app. It prevent one user from overusing a resource and degrading the quality of service for other users. Here are some benefits of rate limiting

It presents resource starvation
Reduces server hosting cost
Provides basic protection against DDoS

I have made four interactive app that let’s you play around with common rate limiting algorithms.

Token bucket

Working:

A bucket holds fixed number tokens
Tokens are added to bucket at fixed rate
When a request comes in:
- If a token is available, it’s removed from the bucket and the request is allowed.
- If no tokens are available, the request is rejected or delayed.
Allows for occasional short burst if tokens are available

I have created an app that let’s you play with leaky bucket algorithm.

Leaky bucket

Working

Think of it as a bucket leaking at a fixed rate
Incoming requests are added to the bucket
Requests are processed (or "leak") at a constant rate
If the bucket is full when a new request arrives, the request is dropped
Smooths out bursts; outputs requests at a steady rate

I have made an app that let’s you play with leaky bucket algorithm.

Fixed window counter

Working:

Time is divided into fixed size windows (e.g., 1 minute)
A counter tracks the number of requests per client/IP in the current window
If the count exceeds the limit, further requests are rejected until the next window
Simple and efficient, but allows burst traffic spike at end/start

I have created an app that let’s you play with fixed bucket algorithm.

Sliding window counter

Working:

Keeps a timestamped log of each request
When a request comes in, logs are checked to count how many requests were made in the last X seconds
If under the limit, the request is allowed and logged; otherwise, it’s rejected

I have created an app that let’s you play with sliding bucket algorithm.

Command Palette

Comments (2)

More from this blog

Introduction

Token bucket

Working:

Leaky bucket

Working

Fixed window counter

Working:

Sliding window counter

Working: