Rate limiting in Microservices - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is rate limiting in system design?

Rate limiting is a technique to control how many requests a user or client can make to a service in a given time. It helps prevent overload and abuse.

intermediate

Name two common algorithms used for rate limiting.

Two common algorithms are Token Bucket and Leaky Bucket. They help decide when to allow or reject requests based on limits.

beginner

Why is rate limiting important in microservices?

Rate limiting protects microservices from too many requests that can cause crashes or slowdowns. It ensures fair use and system stability.

intermediate

What is the difference between fixed window and sliding window rate limiting?

Fixed window counts requests in fixed time blocks (like per minute). Sliding window tracks requests continuously over time for smoother limits.

advanced

How can distributed rate limiting be implemented across multiple servers?

Distributed rate limiting can use a shared data store like Redis to keep counters consistent across servers, ensuring limits apply globally.

What is the main goal of rate limiting?

A Increase the number of requests a user can make

B Prevent system overload by limiting requests

C Store user data securely

D Improve database query speed

Rate limiting controls request rates to prevent overload and abuse.

Which algorithm allows bursts of requests but controls average rate over time?

A Sliding window

B Fixed window

C Token bucket

D Round robin

Token bucket allows bursts by accumulating tokens and controls average rate.

In microservices, why might you use a shared Redis store for rate limiting?

A To synchronize rate limits across servers

B To cache images

C To store user passwords

D To log errors

Redis can hold counters shared by all servers to enforce global limits.

What happens when a client exceeds the rate limit?

A The request is rejected or throttled

B The request is delayed indefinitely

C The client is permanently banned

D The request is accepted and processed

Requests over the limit are usually rejected or delayed to protect the system.

Which rate limiting method provides smoother control by tracking requests continuously?

A Leaky bucket

B Fixed window

C Token bucket

D Sliding window

Sliding window tracks requests over a moving time frame for smoother limits.

Explain how rate limiting helps maintain system stability in microservices.

Think about what happens if too many requests hit a service at once.

Describe the difference between fixed window and sliding window rate limiting techniques.

Consider how requests are counted over time.