Evolvability

Designing Data-Intensive Applications - Chapter 4 - Encoding and Evolution

Designing Data-Intensive Applications - Chapter 4 - Encoding and Evolution

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 I prepared an overview and mind-map.

Chapter 4:

  • What is evolvability. Backward and Forward compatibility
  • Approaches to encode data:
    • JSON, XML, and their binary variants
    • Thrift and Protobuf
    • Apache Avro
  • Models of data flow
    • Through databases
    • Through services: REST, SOAP, RPC and the future
    • Through message brokers - when they are better and when they are not

Much more details in the mind-map:

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. (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

Download full mind map (PDF)