Understanding of cap

1.CAP Theorem ：
  In a distributed system ,Consistency（ Uniformity ）、Availability（ Usability ）、Partition tolerance（ Partition tolerance ）, Three can't get at the same time
  Uniformity （c）： All nodes can access the latest data
  Usability （A）： Every request can be answered , Whether the request succeeds or fails
  Partition tolerance （P）： Except for all overall network failures , No other fault can make the whole system unavailable
 
2.CAP The theory is that in a distributed storage system , At most, we can only achieve the above two points . And because of the current network hardware will certainly appear delay packet loss and other problems , So partition tolerance is what we have to achieve , So we can only make trade-offs between consistency and availability

CA： If you don't ask for P（ Zoning is not allowed ）, be C（ Strong consistency ） and A（ Usability ） It can be guaranteed . But give up P At the same time, it means giving up the scalability of the system , That is, distributed nodes are limited , There is no way to deploy child nodes , This is against the original intention of distributed system design
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CP: If you don't ask for A（ You can use ）, Each request requires strong consistency between servers , and P（ Partition ） Will cause unlimited synchronization time ( That is to wait for the data synchronization to access the service normally ), In case of network failure or message loss, etc , It's about sacrificing the user experience , Wait for all data to be consistent before allowing users to access the system
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AP： To be highly available and allow partitioning , You need to give up consistency . Once zoning happens , Nodes may lose contact with each other , For high availability , Each node can only provide services with local data , This will lead to global data inconsistency .