Scalability

System Design Interview - Chapter 1 - Scale from zero to millions of users

System Design Interview - Chapter 1 - Scale from zero to millions of users

Translations: RU
A great generic plan for scaling any app from zero to millions of users. Single server setup Selection and usage of database Vertical scaling vs horizontal scaling approaches. And why you should prefer horizontal Adding load balancer for horizontal scaling Adding database replication for horizontal scaling Adding cache Adding CDN Stateless vs Stateful architecture and using external state storage Adding extra Data Centers Adding Message queue Adding Logging, Metrics, and Automation Scaling database (sharding) and futher steps… All of these is carefully but briefly disclosed in the Chapter 1 of the book:
Building Event-Driven Microservices - Chapter 2 - Event Driven Microservice Fundamentals

Building Event-Driven Microservices - Chapter 2 - Event Driven Microservice Fundamentals

Translations: RU
Introduction into Event-driven microservice (EDM) architecture consists of the following topics: two topologies content of events three types of events table-stream duality schemas for defining event data single writer principle features of event broker event broker vs message broker single source of truth principle scaling using containers and VMs microservice taxes that we should pay These topics are disclosed in the Chapter 2 of the book we are currently studying:
Designing Data-Intensive Applications - Chapter 1 - Reliable, Scalable, and Maintainable Applications

Designing Data-Intensive Applications - Chapter 1 - Reliable, Scalable, and Maintainable Applications

Translations: RU
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.