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This article mainly explains "how to understand cap Theorem". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Let the editor take you to learn how to understand the cap Theorem.

CAP theorem is the foundation of designing distributed system.

CAP theorem points out that a distributed system cannot satisfy the following three points at the same time:

1. Consistency (Consistent)

two。 Availability (Availability)

3. Partition tolerance (Partition Tolerance)

These three points are very important for designing distributed web services.

First of all, let's talk about the meaning of these three points. in order to understand their meaning, we first need to know that the CAP theorem is proposed for a distributed web services system, such a system is composed of N multi-nodes. For convenience of illustration, we assume that the system has only two nodes {N1 ~ N2}.

Consistency means that operations on nodes are atomic, and changes to data on one node are synchronized on all nodes, during which there can be no other operations. For example, a write operation on N1 must be atomic, that is, writing on N1 and synchronizing to N2 at the same time, the whole process is atomic, and there can be no read operations in this writing process, otherwise inconsistent results may be read (for example, N1 has modified the data but N2 is not synchronized yet).

Availability means that once a node receives a request (such as a web request), it must respond. The content of the response can be successfully fetched data or a failure message. For example, when N1 receives a request, it must return a request result or a failed result, and if it does not give any response, it violates usability.

Partition tolerance means that any message is allowed to be lost between nodes. Communication between nodes sends messages, which may be lost in the network, which is objective. For example, N1 and N2 send messages to each other in a local area network. No matter what protocol (tcp,udp, etc.) is used, message packets may be lost between them. In theory, in the worst case, all packets will be lost.

So the CAP theorem says that it is impossible for a distributed system to guarantee the atomicity of the operation and respond to the request among the network nodes with lost messages at the same time. For example, if atomicity does not satisfy availability, if you write data on N1, N2 needs to synchronize data, assuming that all messages between N1 and N2 are lost (worst-case scenario), and the data on N2 is inconsistent, to ensure the atomicity of this write operation, you need to wait until the data synchronization on N2 is completed, at this time, no other operations can be carried out, the requests accepted by nodes can not be responded to, and the system cannot meet availability.

At this point, I believe you have a deeper understanding of "how to understand cap Theorem". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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