What are the 15 most commonly used principles in big data's architecture design?

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Architectural principles

1. Numb1 design. Never less than two, usually three.

2. Roll back the design. Make sure that the system can roll back to any version that has not been released before.

3. Disable design. You can turn off any published functionality.

4. Monitoring design. Monitoring must be considered at the design stage, not added after the implementation is complete.

5. Design multi-live data center. Don't be limited by a data center solution.

6. Use mature technology. Only use technology that really works.

7. Asynchronous design. Synchronous calls are made only when absolutely necessary.

8. Stateless system. Use status only when the business really needs it.

9. Horizontal expansion, non-vertical upgrade. Never be lazy about a bigger, faster system.

10. The design has at least two steps at the forefront. Consider the next action plan before scalability issues occur.

11. Buy non-core. If it's not what you do best and can't provide a differentiated competitive advantage, buy it directly.

12. Use commercial hardware. In most cases, the cheap one is the best.

13. Xiao Gou Jian, Xiao release, try and make mistakes. All R & D should be built small, iterated constantly, so that the system continues to grow.

14. Isolate the fault. The design of fault isolation is realized, and the fault propagation and cross influence are avoided through open circuit protection.

15. Automation. The process of designing and building automation. If a machine can do it, don't rely on people.

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