Service data updating method and equipment

Abstract

The invention aims to provide a service data updating method and equipment. The method comprises the following steps: periodically requesting update data corresponding to service data from network equipment in a long polling manner at a client to update the service data locally cached by the client, so that a request for caching data with a large data volume to the network equipment is avoided, the resource occupation of the network equipment for different service services is reduced, the occupation of link resources can be avoided, and the frequent degree of network access can also be avoided; meanwhile, in the updating request process of the service data, interaction is carried out in a version information mode, the data structure is simple, data needing to be transmitted is very small,the requirement for the network is not high, network resources of a client side are saved, and network resources of a network equipment side are also saved; and the local Redis database of the networkequipment end is updated in time in an asynchronous refreshing mode at the network equipment end, so that the final consistency of the service data is ensured.

Description

technical field [0001] The present application relates to the field of computers, in particular to a method and device for updating service data. Background technique [0002] With the caching requirements of different application scenarios, distributed caching technology and local caching technology have emerged. Among them, with the development of the Internet, the scale of users, the scale of data, and the diversity of scenarios are also increasing. There are further requirements for system performance, and the requirements for throughput and response timeout are also getting higher and higher. , in which data caching is one of the key components. From simple commodity flash sales to shopping festivals such as Double Eleven and 618, which are invested by the whole people, distributed caching technology can be seen; however, because distributed caching technology is a data The unified management of the data required by the downstream needs to be uniformly obtained from th...

AI Technical Summary

Problems solved by technology

Among them, with the development of the Internet, the scale of users, the scale of data, and the diversity of scenarios are also increasing. There are further requirements for system performance, and the requirements for throughput and response timeout are also getting higher and higher. , in which data caching is one of the key components. From simple commodity flash sales to shopping festivals such as Double Eleven and 618, which are invested by the whole people, distributed caching technology can be seen; however, because distributed caching technology is a data The unified management of the data required by the downstream needs to be uniformly obtained from the distributed cache center. When a large amount of data is obtained from the center (a large number of black and white list information, etc.), the network IO will be blocked, causing the distributed center The processed information cannot be returned normally, resulting in information congestion

With the development of technology, more and more data needs to be managed in a unified manner, but some of them are not large in number, but they are accessed very frequently (such as address codes, commodity classification information, etc.). For these scenarios, local Cache technology, put data in the local cache of the application to improve the access efficiency of the system and reduce unnecessary data access (DB or IO consumption), but the resources of the application system are very precious, especially the CPU and memory, and then need Pay attention to the space occupied by the cache and the invalidation strategy; because the cache of the local cache technology exists locally, although the overhead of disk and network IO is avoided, the local business data cannot be well connected with the cache data of other related application servers Interaction, it is easy to produce data inconsistency. In addition, once the application server restarts, the locally cached data will no longer exist and needs to be reloaded

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