Business data query method, device and equipment and storage medium

Abstract

The invention relates to the technical field of big data, and discloses a business data query method, device and equipment and a storage medium, which are used for improving the data query efficiency.The business data query method comprises the steps of obtaining a plurality of business table names and a plurality of pieces of business field information corresponding to each business table; generating a target page based on the label tree data, and setting a corresponding content data box for each piece of service field information in the target page; screening a content data box corresponding to each piece of service field information according to the data box operation instruction to obtain to-be-queried field information; determining a to-be-queried data table name from the plurality of service table names, and sending the to-be-queried data table name and the to-be-queried field information to a target server, so that the target server splices the to-be-queried data table name andthe to-be-queried field information into a structured query language statement; and obtaining target service data according to the structured query language statement. In addition, the invention alsorelates to the blockchain technology, and a plurality of pieces of service field information can be stored in the blockchain node.

Description

technical field [0001] The invention relates to the field of sequential query of big data technology, in particular to a business data query method, device, equipment and storage medium. Background technique [0002] A joint query is a result set that can combine multiple similar selection queries, that is, append one table to another table, so as to combine the queries of the two tables together. General query business needs to perform joint query of multiple fields and multiple tables to obtain corresponding data. [0003] In the prior art, multiple fields are spliced ​​together to determine whether the field value exists and query as a filter condition. It is time-consuming to maintain new fields, delete fields, and modify fields. In addition, the tables used in the query require multi-table association query splicing statements in advance, resulting in slow query speed and many returned fields, resulting in slow data processing speed and low data processing efficiency. ...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, multiple fields are spliced ​​to determine whether the field value exists and query as a filter condition. It is time-consuming to maintain new fields, delete fields, and modify fields.

In addition, the tables used in the query require multi-table association query splicing statements in advance, resulting in slow query speed and many returned fields, resulting in slow data processing speed and low data processing efficiency

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