Load scheduling method and device, electronic equipment and computer readable storage medium

Abstract

The embodiment of the invention discloses a load scheduling method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining load evaluation data which comprises table storage cluster data partition access evaluation data and data channel consumption evaluation data; determining whether the load evaluation data meets a preset condition or not; and when the load evaluation data meets a preset condition, scheduling table storage cluster data partitions and data channels. According to the technical scheme, the data in-out balance of the table storage cluster and the data channel can be effectively maintained, data fluctuation can be quickly coped, the phenomena of unstable data output, data blockage and the like are avoided, the data storage and access quality is greatly improved, meanwhile, the labor cost input can be greatly reduced, and the data storage and access efficiency is improved. The operation is simple, and the real-time balance service of the data can be effectively realized.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of load control, and in particular to a load scheduling method, device, electronic equipment, and computer-readable storage medium. Background technique [0002] With the development of data technology, the application of cloud storage is becoming more and more extensive. In order to cope with the storage and access of massive data, Table Store (TableStore) emerged as the times require. Table Store is a distributed NoSQL database that uses data sharding technology to provide storage and real-time access to massive structured data. However, in the actual data consumption, the mismatch between the data consumption time point and the current time point, and sudden changes in the amount of business data will make the consumption data unbalanced, and even cause data output instability, data congestion and other phenomena, and then Greatly affects the quality of data storage and access. ...

AI Technical Summary

Problems solved by technology

However, in the actual data consumption, the mismatch between the data consumption time point and the current time point, and sudden changes in the amount of business data will make the consumption data unbalanced, and even cause data output instability, data congestion and other phenomena, and then Greatly affects the quality of data storage and access

In the existing technology, the time point of consumption is usually monitored. When an abnormal situation of data consumption is found, the data partition is manually split according to the ratio of data read and write. The need for real-time data services

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