Ultimate consistency: What business scenarios are applicable to? How to ensure the consistency of the final data?

In-depth analysis of final consistency: application scenarios and technology implementation

In distributed system architecture, data consistency issues have always been a core challenge for developers. This article will focus on final consistency (AP model), explore its application and implementation strategies in actual business, and briefly compare final consistency and strong consistency (CP model).

The article begins with a brief description of the consistency difficulties in distributed transactions, as well as CAP and BASE theories. Strong consistency (CP model) is usually achieved by integrating multiple transactions into one atomic transaction, ensuring that all transactions are either fully successful or all failed. This model is suitable for scenarios where data consistency is extremely demanding, such as transfer of funds in financial transactions.

However, the concept of ultimate consistency (AP model) is relatively complex. The core is to allow data to have inconsistent states for a certain period of time, but ultimately it will reach consistency. This article will focus on its specific application scenarios and data repair mechanisms in inconsistent states.

A typical AP model application scenario is the user registration process. Suppose that user registration involves multiple subsystems, such as user information systems, points systems, and mail systems. If one of the subsystems fails to register and the other subsystems have completed registration, the entire registration process can still be considered successful. In the future, the data inconsistency of other subsystems can be compensated for by mechanisms such as timing tasks or message queues, and ultimately achieve data consistency. For example, if the points system registration fails, the points can be re-entered to the user account through asynchronous tasks.

Compared with the CP model, the AP model focuses more on the availability of the system and partition fault tolerance, and can ensure the continuous operation of the system when dealing with abnormal situations such as network partitioning. The selection of the mode depends on the specific business needs. If the service has extremely high requirements for data consistency and can tolerate certain performance losses, choose the CP model; if the service pays more attention to availability and accepts inconsistencies in data over a period of time, choose the AP model. Even if you choose an AP model, you need to carefully design a data compensation mechanism to ensure the consistency of the final data.

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