Table classification method and device, table creation method and device, equipment and medium

Abstract

The invention relates to a table classification method and device, a table creation method and device, equipment and a medium. The method comprises the steps of obtaining to-be-classified tables; wherein the to-be-classified table is a table generated by adopting a query tool based on a query instruction; querying whether the to-be-allocated table carries a source label or not to obtain a query result; wherein the source label is used for representing a query tool category for generating the to-be-classified table; determining a query tool category for generating the to-be-classified table according to the query result; wherein the query tool category is used for representing the category of the query tool for generating the to-be-classified table. By adopting the method, the to-be-classified tables can be accurately classified according to the generated query tool, so that the blood relationship analysis result is more accurate, data tracking is more accurate and convenient, and the database is more convenient to maintain.

Description

technical field [0001] The present application relates to the technical field of computer software, in particular to a table classification method, a table creation method, a device, a device and a medium. Background technique [0002] With the development of computer technology and the interaction of data and information, blood relationship analysis is widely used in many fields, such as the source analysis of fund data in the banking system, big data analysis of users of e-commerce platforms, and so on. By performing blood relationship analysis on the data, it is possible to determine where a table or a database comes from and what changes it has experienced in the middle. At present, with the increasing level of digitalization, the importance of data lineage analysis is also increasing. For example, in the scenario of distributed data sharing, accurate lineage analysis can more accurately determine the credibility, quality, and version information of data. etc., can also...

AI Technical Summary

Problems solved by technology

[0003] For different databases in the same cluster, since the metadata used by these databases is the same, the tables created by these databases often cannot distinguish their sources

For example, the hive database and the impala database, the tables created in the hive database can be queried in the impala database, and the tables created in the impala database can also be queried from the hive database, but it is impossible to distinguish whether the table is created by the impala database or the hive database , so that the results of blood relationship analysis are inaccurate, which is not conducive to determining the credibility, quality, version information, etc. of the data, and is not conducive to tracking various problems, resulting in various problems such as affecting database maintenance.

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