Response Time

Every popular software should have a Rate Limiter. It prevents DDOS attack, reduces cost and prevents servers from being overloaded.

There are some tricky questions to be considered during implementation of Rate Limiter:

Where to put Rate Limiter: client-side, server-side, gateway?
Algorithms for rate limiting. There are many algorithms with pros and cons: Token bucket, Leaking bucket, Fixed window counter, Sliding window log, Sliding window counter. Your business needs will define the right algorithm.
How are rate limiting rules created?
Where are the rules stored?
How to handle requests that are rate limited?

These questions are disclosed in a very interesting Chapter 4 of the book:

Earlier this year the book club of our company has studied excellent book:

Martin Kleppmann - Designing Data-Intensive Applications

This is the best book I have read about building complex scalable software systems. 💪

As usually (to better learn) I prepared an overview and mind-map.

Chapter 1:

Building blocks of the apps
What is Reliability, Scalability and Maintainability. Examples and definitions.
- Faults and Failures
- Performance, Load, Latency and Response Time
- Operability, Simplicity, Evolvability
Why you should randomly kill your servers 😅
How Twitter delivers 12,000 tweets per second to 300,000 readers per second. (VERY interesting!)
How much money Amazon loses for each 100ms delay in their response time
How to quickly calculate percentiles for monitoring response time in PROD