Synchronous processing method, device, server and system for memory data

Abstract

The application provides a synchronous processing method, device, server and system for memory data. The method comprises the following steps: setting current update time of the memory data as current system time when notification messages updated by a backend database are received; determining whether historical update time of the memory data is less than the current update time after receiving a memory synchronization update request sent by a peripheral system server; when the result of the determination is yes, reading the data in the backend database and updating the memory data; and when the memory data is updated successfully, updating the historical update time of the memory data as the current system time. According to the synchronous processing method, device, server and system for the memory data, the situation that the system accesses to read the latest memory data is guaranteed, the backend database IO page view and memory occupancy volume are reduced, synchronous processing efficiency of the memory data is optimized, and the reliability of the system operation is provided.

Description

technical field [0001] The present application belongs to the technical field of computer data processing, and in particular relates to a method, device, server and system for synchronous processing of memory data. Background technique [0002] With the development of computer information technology and the popularization of Internet applications, the amount of data that servers need to process is also increasing. A single computer is already difficult to meet the processing requirements of some large-scale data, so it is necessary to combine multiple computers to form a distributed service system. The distributed service system can use a large number of cheap computers to form a computer cluster, which can replace expensive high-speed computers for high-speed calculation and storage. At present, the distributed service system has also become an increasingly important way of building servers in many application fields. [0003] Generally, some large-scale distributed servi...

AI Technical Summary

Problems solved by technology

In the process of updating the memory of the subsystem server, if the peripheral system server calls the historical memory data of the subsystem server that has not been updated to the latest memory data before all the resources in the memory are loaded or fails to load, it will cause The inconsistency of the memory data used in the business of multi-party application systems may lead to serious consequences such as damage to business system resources, business logic errors, and even service system failures or downtime.

In the prior art distributed service system, if the process of updating the memory data of the subsystem server accessed by the peripheral system server in real time has not been completed, the memory data synchronized by the peripheral system server will not be the latest real-time data, which will affect the distributed service The business system application in the system poses a great risk

[0005] At the same time, if a large number of peripheral servers in the peripheral service system access and call the subsystem server at the same time or in a short period of time, it will often cause the subsystem to read the input and output interfaces of the database at the same time in the case of large concurrent requests from the peripheral system servers If the amount of access is too large and the temporary cache of the business core system increases, if the amount of data read is also large, it is likely to cause memory overflow

[0006] Therefore, in the synchronous processing method of memory data in the prior art, there is still a problem that peripheral system services do not access the latest memory data synchronously, and in the case of large concurrent requests, the number of visits to the input and output interfaces of the background database and temporary cache will also be greatly increased. , which increases the load pressure of subsystem memory data processing, which greatly reduces the reliability of the subsystem server and even the entire distributed service system.

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