Data operating method, device, equipment and storage medium

Abstract

The invention provides a data operating method, a device, equipment and a storage medium. The method comprises the following steps: obtaining a first data operation instruction, according to the dataattribute of the to-be-operated data included in the first data operation instruction, the to-be-operated data is acquired; determining a database corresponding thereto, if the data attribute indicates that the to-be-operated data is the data of the non-extensible field; if so, determining that the corresponding database is a relational database; If the data attribute indicates that the to-be-operated data is the data of the extensible field, determining that the database corresponding to the to-be-operated data is a non-relational database, and executing corresponding operation on the to-be-operated data in the determined database according to an operation mode included in the first data operation instruction, thereby finishing a data operation process. Compared with the prior art, the main body data which do not need to be expanded and are stored in the relational database is reserved, Meanwhile, the objects which need field expansion and are stored in the non-relational database areadded, so that field expansion can be realized in the data operation process, and the field expansion efficiency is improved.

Description

technical field [0001] The present application relates to database technology, and in particular to a data operation method, device, equipment and storage medium. Background technique [0002] Currently, the commonly used database types in the database field include relational databases and non-relational databases. Among them, relational databases are widely used in the Internet industry because of their diverse query syntax, support for multi-table joint queries, and support for transactions. However, with the development of cloud computing and the advent of the era of big data, the amount of data has increased rapidly, requiring higher scalability of the database, and the shortcomings of relational databases have become more and more obvious. In the expansion of table fields and the increase of indexes On the other hand, it is not flexible and efficient enough. [0003] In related technologies, it is generally chosen to reserve some columns in the table for field expans...

AI Technical Summary

Problems solved by technology

However, with the development of cloud computing and the advent of the era of big data, the amount of data has increased rapidly, requiring higher scalability of the database, and the shortcomings of relational databases have become more and more obvious. In the expansion of table fields and the increase of indexes On, not flexible and efficient enough

[0003] In related technologies, it is generally chosen to reserve some columns in the table for field expansion when building a database. However, in the face of unpredictable data volume, the number of reserved fields cannot be estimated at all, so that When the reserved amount is large, space is wasted or secondary expansion is required when the reserved amount is small

Therefore, this static expansion method has poor applicability and low efficiency

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