A preliminary understanding of CAP Theory

+ C (Consistency), consistency: all nodes see the same data at the same time (all nodes see the same data at the same time); also known as atomicity or transaction consistency, which requires that all operations of a transaction either commit successfully, and if one operation fails, the whole transaction fails + A (Availability), availability: a guarantee that every request receives a response about whether it was successful or failed, to be able to ensure that every request can receive a successful or failed response to this operation; usually need to ensure availability through distributed data redundancy, load balancing, etc.; + P (Partition tolerance): partition tolerance: the system continues to operate despite arbitrary message loss or failure of part of the system Generally speaking, partition tolerance is a basic requirement for distributed systems. The CAP principle means that these three elements can only satisfy the highlights at the same time. According to the above, in a distributed system, partition tolerance is generally a basic requirement, so the rest can only strike a balance between consistency and availability. The following figure shows the options for different tradeoffs: resources: http://en.wikipedia.org/wiki/CAP_theorem http://www.julianbrowne.com/article/viewer/brewers-cap-theorem