Data synchronization method and system based on multi-cloud management, terminal and storage medium

Abstract

The invention provides a data synchronization method and system based on multi-cloud management, a terminal and a storage medium. The method comprises: collecting an incremental log of a target cloudplatform by utilizing a database incremental log monitoring tool; capturing the incremental log by utilizing a log analysis service, analyzing the incremental log and sending the incremental log to anintermediate message queue; and creating a monitoring thread of the message queue, and if the monitoring thread monitors that the updated incremental log exists in the message queue, synchronizing the update content of the incremental log to a cache library of a local cloud platform. The defect of overhigh load on the database when the opposite-end platform database is continuously queried is avoided, and the real-time performance of data information updating is improved.

Description

technical field [0001] The present invention relates to the technical field of multi-cloud management, in particular to a hair style recommendation method, system, terminal and storage medium based on big data. Background technique [0002] The IT architecture of enterprises is becoming more and more complex, and the multi-cloud strategy has become the choice of most cloud enterprises. The 2020 Flexera Cloud State Report shows that 93% of enterprises choose a multi-cloud strategy in terms of IT architecture. This diverse environment makes it imperative to connect multiple cloud environments and provide enterprises with query, migration and management functions. In order to facilitate customers and build differentiated competitiveness, Inspur Cloud Computing Basic Platform (ICS for short) has developed a multi-cloud management function, and multiple cloud computing basic platform sites can be added to the platform. Customers only need to select the cloud computing product n...

AI Technical Summary

Problems solved by technology

The management mode here is one-way management through the interface, so there will be the following pain points: after the peer platform is upgraded, the version information cannot be automatically updated; data information such as the peer platform host and virtual machine needs to be frequently loaded, which virtually increases the The server and database load of the end platform

[0003] At present, in order to reduce the query frequency, the industry will introduce a data caching mechanism. Usually, updating the cached data depends on the set timing task, and continuously querying and updating the cached data through the timing task. However, if the query frequency is too high in this way, it will trigger the load on the peer platform. Problem, if the query frequency is too low, the accuracy of the data in the cache may decrease due to the delay in reading and updating data

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