Method for loading parallel compressed mass data of database cluster

Abstract

The invention relates to a method for loading parallel compressed mass data of a database cluster. The method is mainly technically characterized by comprising the following steps of: 1, establishing an assembly architecture comprising a data loading tool, a driving program and a database cluster for loading parallel compressed mass data; 2, sending a data loading command to the database cluster through the data loading tool and the driving program by using a client, acquiring connection information of each database node, connecting each database and transmitting data required to be loaded; providing connection information of each database node of the cluster and relevant information of each table for the client by using a database cluster server; and receiving data transmitted by the client and saving by using the database nodes. In the method, a mass data communication protocol and parallel optimizing processing and compression optimizing processing methods are adopted, and multiple CPUs (Central Processing Units) and network resources of the server are utilized fully and are merged in a mass data loading process, so that the building speed of the database cluster is increased.

Description

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AI Technical Summary

Benefits of technology

In this new system that improves efficiency on handling large amounts of data quickly without overloading any server(ies). It achieves these goals by integrating both hardware (CPU) and software modules into one machine called an adapter), which allows more efficient access to memory space than just two levels like traditional systems. By doing things efficiently at different stages within it, we can achieve faster build times while still utilising all available computer power effectively.

Problems solved by technology

This patented problem addressed by this patents relates to efficiently preparation large amounts of data from multiple databases on one machine without having any extra steps needed during installation. Additionally, current techniques like inserting rows can be slowed down due to resource limitations caused by these processes.

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