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This article mainly introduces what cap refers to, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

The full name of cap is CAP principle or CAP Theorem, which means that in a distributed system, consistency (Consistency), availability (Availability) and partition fault tolerance (Partition tolerance) can only achieve two points at the same time, and it is impossible to take into account all three.

CAP principle, also known as CAP theorem, refers to that in a distributed system, Consistency (consistency), Availability (availability) and Partition tolerance (partition fault tolerance) can not be obtained at the same time.

Consistency (C): whether all data backups in a distributed system have the same value at the same time. (equivalent to all nodes accessing the same latest copy of data)

Availability (A): ensure that every request has a response regardless of success or failure.

Partition tolerance (P): the loss or failure of any information in the system will not affect the continued operation of the system.

The essence of the CAP principle is either AP, CP, or AC, but there is no CAP. If there is no copy of the data in a distributed system, then the system must meet the strong consistency condition, because there is only one data, there will be no data inconsistency, and the two elements C and P have, but if the system has a network partition condition or downtime, it will inevitably cause some data to be inaccessible, and the availability conditions cannot be met, that is, the CP system will be obtained in this case. But CAP cannot be satisfied at the same time.

Therefore, when designing a distributed architecture, a trade-off must be made. At present, it is generally through the final consistency of each node in the distributed cache to improve the performance of the system, and through the use of asynchronous data replication technology between multiple nodes to achieve clustered data consistency. NOSQL like memcached is usually used as a means of implementation. Although memcached can also be a distributed cluster environment, for a piece of data, it is always stored on a memcached server. In the event of a network failure or a server crash, all data stored on this server will be inaccessible. Because the data is stored in memory, restarting the server will cause all data to be lost. Of course, you can also implement a set of mechanisms to synchronize and persist data between distributed memcached, but it is very difficult to implement.

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