A Big Data Drift Method Based on Structured Database

Abstract

The invention relates to a big data drifting technology based on a structured database. The technology comprises character type array setting, first data drifting processing and the nth data drifting processing; the character type array setting comprises the followings: setting a primary table and a first array, additionally setting a second array in the primary table, and setting the type of the second array as length-variable Unicode character type data; updating the primary table; updating the primary table, and assigning the value of the first array to the second array; the first data drifting processing comprises the followings: setting the expiry date that the first data drifting processing is performed in the primary table, determining the name of an object required to be subjected to data drifting processing, and adding a first drifting processing suffix behind the name of the object subjected to data drifting processing; the nth data drifting processing comprises the followings: setting the starting date and the expiry date of the nth data drifting processing, and adding an nth drifting processing suffix behind the name of the object required to be subjected to data drifting processing in the primary table. The big data drifting technology has the advantages of simple management, rapidness in analysis and visual display, provides a rapid method for processing mass data, and facilitates management of mass data for a government, a company or a private person.

Description

technical field [0001] The invention relates to the technical field of data drift processing, in particular to a large data drift method based on a structured database. Background technique [0002] When analyzing and diagnosing data, we need to monitor and analyze the entire life cycle of a certain product, or conduct trend research on a certain market segment, which requires the information in the database to completely and objectively show the status of the product in each period. real state. Taking the commodity price as an example, the commodity identification code code is a unique identifier, that is, it is the primary key in the database. If a product A has had two price adjustments since it was launched, and because the code is unique, when it is associated with the main table in the database, only one price can be taken, that is, the price in the latest state. If you want to see clearly For each price, data drift processing needs to be performed on the database. ...

AI Technical Summary

Problems solved by technology

[0003] At present, domestic data drift processing only stays at the level of manual processing, that is, manual processing of a small amount of data on simple office software such as excel. Due to the simple application, low efficiency, and time-consuming and labor-intensive use of this data processing method, it can only satisfy simple tasks. Work needs, and in the face of massive data drift processing, people need big data drift processing methods to record the change trajectory of massive data. At present, people have no specific and effective way to apply the idea of ​​big data drift

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