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This article mainly explains "what is the theory of CAP and BASE". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "what is the theory of CAP and BASE".

CAP

CAP principle, also known as CAP theorem, refers to the consistency (Consistency), availability (Availability), partition fault tolerance (Partition tolerance) in a distributed system. The CAP principle means that these three elements can only achieve two points at the same time, and it is impossible to take all three into consideration.

Consistency

In a distributed environment, consistency refers to whether data can be consistent among multiple replicas. Under the requirement of consistency, when a system performs update operations in a consistent state of data, it should ensure that the data of the system is still in a constant state.

For a system that distributes copies of data on different distributed nodes, if the data of node An is updated and the update is successful, the data on node B is not updated accordingly. therefore, when reading the data of node B, the old data (or dirty data) is still obtained. In a distributed system, after the update operation of a data item is executed successfully, all users can read its latest value, so such a system is considered to have strong consistency.

Usability

Availability means that the service provided by the system must be available all the time, and the result can always be returned in a limited time for every operation request of the user.

Within a limited time

For an operation request from a user, the system must be able to return the corresponding processing result within a specified period of time, and if this time range is exceeded, the system is considered unavailable. In addition, "limited time" refers to the operating indicators designed at the beginning of the system design, usually there are great differences between different systems, in any case, for the user request, the system must have a reasonable response time, otherwise the user will be disappointed with the system.

Return the result

"return result" is another very important indicator of usability, which requires the system to return a normal response result after completing the processing of the user's request. The normal response result usually clearly reflects the processing result of the team request, that is, success or failure, rather than a return result that confuses the user.

Partition fault tolerance

Partition fault tolerance restricts a distributed system to have the following characteristics: when a distributed system encounters any network partition failure, it still needs to be able to provide services that meet consistency and availability, unless the whole network environment fails.

The fault tolerance (P) of a distributed system partition must be satisfied. So the general distributed system implements CP or AP.

BASE

BASE is the abbreviation of three phrases: Basically Available (basic available), Soft state (soft state), and Eventually consistent (ultimate consistency). BASE theory is the result of a tradeoff between consistency and usability in CAP. The core idea of BASE theory is that even if strong consistency can not be achieved, each application can adopt appropriate ways to make the system achieve final consistency according to its own business characteristics.

Basic availability

Basic availability means that distributed systems are allowed to lose part of their availability in the event of unpredictable failures. Note that this is by no means equivalent to the system being unavailable. For example:

Loss of response time. Normally, an online search engine needs to return the corresponding query results to the user within 0.5 seconds, but due to the failure, the response time of the query results is increased by 1 / 2 seconds.

Loss of system function: under normal circumstances, when shopping on an e-commerce site, consumers can successfully complete almost every order, but at the peak of shopping during some festivals, due to the surge in consumer shopping behavior, in order to protect the stability of the shopping system, some consumers may be led to a downgraded page.

Soft state

Soft state means that the data in the system is allowed to have an intermediate state, and it is considered that the existence of the intermediate state will not affect the overall availability of the system, that is, there is a delay in the process of allowing the system to synchronize data between replicas of different nodes.

Final consistency

The ultimate consistency emphasizes that all copies of data can reach a consistent state after a period of synchronization. Therefore, the essence of ultimate consistency is that the system needs to ensure that the final data can achieve consistency, but does not need to guarantee the strong consistency of system data in real time.

Generally speaking, BASE theory is oriented to large-scale, highly available and scalable distributed systems, which is contrary to the traditional ACID characteristics. It is completely different from the strong consistency model of ACID, but achieves availability at the expense of strong consistency, and allows data to be inconsistent for a period of time, but finally reach a consistent state. But at the same time, in the actual distributed scenario, different business units and components have different requirements for data consistency, so in the specific distributed system architecture design process, ACID features and BASE theory are often combined.

CAP of zookeeper

What ZooKeeper guarantees here is CP. Zookeeper availability cannot be guaranteed.

The availability of each service request cannot be guaranteed. The access request to ZooKeeper at any time can get consistent data results, and the system is fault-tolerant to network segmentation, but it can not guarantee the availability of each service request. Therefore, ZooKeeper cannot guarantee the availability of services.

Clusters are not available during leader elections. When using ZooKeeper to obtain the list of services, when the master node loses contact with other nodes because of a network failure, the remaining nodes will re-elect leader. The problem is that the election leader takes too long, 30-120s, and the entire zk cluster is not available during the election period, which leads to the paralysis of the registration service during the election period. Although the service can be finally restored, the long-term unavailability of registration caused by the long election time cannot be tolerated. Therefore, ZooKeeper cannot guarantee the availability of services.

Thank you for your reading, the above is the content of "what is the theory of CAP and BASE". After the study of this article, I believe you have a deeper understanding of what the theory of CAP and BASE is, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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