Distributed database capacity reduction method and device, equipment and medium

Abstract

The invention discloses a capacity reduction method and device for a distributed database, equipment and a medium. The method comprises the following steps: determining a data node to be subjected to capacity reduction from a plurality of data nodes, and determining a first target migration node to which a first copy data child node on the data node to be subjected to capacity reduction is to be migrated and a second target migration node to which a second copy data child node on the first target migration node is to be migrated; respectively migrating the first copy data sub-node to a first target migration node and the second copy data sub-node to a second target migration node; determining a third target migration node to which the first main data child node on the data node to be subjected to capacity reduction is to be migrated; and migrating the first main data child node to a third target migration node, and deleting the data node to be subjected to capacity reduction from the distributed database. According to the method and the device, the capacity of the distributed database is reduced through an online capacity reduction scheme, so that the distributed database does not need to interrupt service in the capacity reduction process, and the service can be normally provided.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular, to a method, device, device and medium for reducing the capacity of a distributed database. Background technique [0002] Distributed databases (such as GreenPlum database) are high-performance databases based on PostgreSQL and adopt Massively Parallel Processing (MPP) architecture, which has the advantages of high availability, high scalability and high cost performance. [0003] In the process of use, in order to meet the requirements of the scale of data volume and computing processing capacity, it is usually necessary to perform adaptive shrinkage processing on the GreenPlum database. At present, most of them achieve the purpose of shrinking capacity based on backup and recovery. The specific implementation process is: back up all the data stored on each data node (Segment) in the GreenPlum database to other storage devices (such as hard disks o...

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Problems solved by technology

[0004] However, the above shrinking method requires the GreenPlum database to inter

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