Rate limiting in Microservices - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Rate Limiting Purpose

Why is rate limiting important in a microservices architecture?

A To prevent a single client from overwhelming the system with too many requests

B To increase the number of requests a client can send without restrictions

C To allow unlimited access to all clients regardless of usage

D To reduce the number of microservices in the system

💡 Hint

Think about protecting system resources from overload.

Rate limiting controls how many requests a client can make in a time window to avoid system overload and ensure fair usage.

❓ Architecture

intermediate

Choosing Rate Limiting Strategy

Which rate limiting strategy is best suited for a distributed microservices system to ensure consistent limits across instances?

A Local in-memory counters on each service instance

B No rate limiting at all

C Centralized rate limiting using a shared Redis store

D Client-side rate limiting enforced by the user

💡 Hint

Consider how to keep counters consistent across multiple service instances.

Using a centralized store like Redis allows all instances to share rate limit counters, ensuring consistent enforcement.

❓ scaling

advanced

Scaling Rate Limiting for High Traffic

How can you design a rate limiting system that scales efficiently for millions of requests per second in a microservices environment?

A Implement a token bucket algorithm with sharded Redis clusters and local caching

B Use a single Redis instance to store all counters

C Store counters in local memory without synchronization

D Disable rate limiting during peak traffic

💡 Hint

Think about distributing load and reducing latency for counters.

Sharding Redis and using local caches reduces bottlenecks and latency, enabling high throughput rate limiting.

❓ tradeoff

advanced

Tradeoffs in Rate Limiting Granularity

What is a key tradeoff when choosing between user-level and IP-level rate limiting in microservices?

A User-level limits do not require authentication

B User-level limits are easier to implement but less fair

C IP-level limits always provide better security than user-level limits

D IP-level limits can block multiple users behind the same IP, causing unfair restrictions

💡 Hint

Consider shared network environments like offices or mobile carriers.

IP-level limits may block many users sharing an IP, while user-level limits require authentication but are more precise.

❓ estimation

expert

Estimating Capacity for Rate Limiting System

You expect 10 million requests per minute and want to enforce a limit of 100 requests per user per minute. Assuming 1 million unique users, estimate the minimum number of counters your rate limiting system must handle concurrently.

A 100 million counters

B 1 million counters

C 10 million counters

D 100 thousand counters

💡 Hint

Think about how many unique users you track, not total requests.

Each unique user requires one counter, so 1 million users means 1 million counters regardless of total requests.