Shulou(Shulou.com)05/31 Report--

In this issue, the editor will bring you about how to understand the principle of distributed CAP. The article is rich in content and analyzes and narrates it from a professional point of view. I hope you can get something after reading this article.

The biggest difficulty of distributed system is how to synchronize the state of each node. CAP theorem is not only the basic theorem in this aspect, but also the starting point for understanding distributed systems.

In theoretical computer science, CAP Theorem (CAP theorem), also known as Brewer's theorem (because Eric Brewer, a computer scientist at the University of California, proposed in 1998), points out that it is impossible for a distributed computing system to satisfy the following three points at the same time:

Consistency (Consistency), which is equivalent to all nodes accessing the same latest data copy

Availability (Availability), which can get a correct response for every request-but there is no guarantee that the data obtained is up-to-date.

Partition fault tolerance (Partition tolerance), in terms of practical effect, partition is equivalent to the time limit for communication. If the system cannot achieve data consistency within the time limit, it means that a partition situation has occurred and a choice must be made between C and A for the current operation.

According to the theorem, a distributed system can only satisfy two of the three terms, but not all three.

Partition fault tolerance (Partition Tolerance)

Most distributed systems are distributed in multiple subnetworks. Each subnetwork is called a partition. Partition fault tolerance means that interval communication may fail. For example, if one server is in China and the other is in the United States, these are the two zones that may not be able to communicate with each other.

In the figure above, G1 and G2 are two servers that span regions. G1 sends a message to G2, which may not be received. This situation must be taken into account when designing the system.

Generally speaking, partition fault tolerance is unavoidable, so it can be considered that the P of CAP is always true. The CAP theorem tells us that the rest of C and A cannot be done at the same time.

Consistency (Consistency)

Consistency means that read operations after writing must return this value. Take millet for example: if a record is v0, the user initiates a write operation to G1 and changes it to v1.

Next, the result of the user's read operation is also v1, which is called consistency.

The problem is that it is possible for the user to initiate a read to G2 and return v0 because the value of G2 has not changed. The results of G1 and G2 read operations are inconsistent.

In order to change G2 to v1, it is necessary to ask G1 to send a message to G2 during the G1 write operation, asking G2 to also change to v1.

In this way, the user can also get v1 by initiating a read operation to G2.

Availability (Availability)

Usability means that as long as the user makes a request, the user must respond.

The user can choose to initiate a read operation to G1 or G2. No matter which server it is, as long as it receives a request, it must tell the user whether it is v0 or v1, otherwise it will not meet the availability.

The contradiction between consistency and usability

Why can't consistency and availability be established at the same time? The answer is simple because communication may fail (that is, partition fault tolerance occurs).

If the consistency of G2 is guaranteed, G1 must lock the read and write operations of G2 during write operations. Only after data synchronization can read and write be reopened. During the lock-up period, G2 cannot read or write, that is, it loses availability.

If the availability of G2 is guaranteed, it is inevitable that G2 cannot be locked, so consistency is not established.

To sum up, G2 cannot achieve consistency and availability at the same time. Only one target can be selected when designing the system. If you pursue consistency, you cannot guarantee the availability of all nodes; if you pursue the availability of all nodes, you cannot achieve consistency.

The above is the editor for you to share how to understand the principle of distributed CAP, if you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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