Interactive data migration method from relational data to graph data

Abstract

The invention relates to an interactive data migration method from relational data to graph data. The method comprises the following steps: a user configures information of a source relational database and a target graph database and stores configuration information; inquiring metadata of the relational database to construct an ER model: automatically extracting each table in the relational database, fields in the tables and primary and external key connections among the tables by inquiring the metadata of the relational database to construct the ER model; performing user-defined adjustment and modification on the ER model to obtain a target ER model; mapping a corresponding target graph model according to the target ER model; and importing the source relation database into the target graph database according to the target graph model to complete data migration. The method is high in expandability, high in flexibility and simple and convenient to operate, and user-defined adjustment from relation data to graph data is achieved. The user can fully adjust and modify the ER model, so that the schema of the finally imported graph data can meet the query and management requirements of the user.

Description

technical field [0001] The invention relates to the technical field of database migration, in particular to an interactive data migration method from relational data to graph data. Background technique [0002] In recent years, more and more users are keen to migrate relational databases to graph databases because graph databases provide powerful and flexible data models and can analyze a large amount of highly complex data, overcoming the limitations of relational databases for unstructured big data management. to manage the analysis. Although graph databases have different interfaces to perform data migration, the import of graph data generally requires defining the structure of the graph in advance, cleaning and sorting the data into a format consistent with the graph structure, and finally executing the import interface provided by the specific graph database. Complete the data migration. At the same time, if you want to change the graph structure, you need to redefine...

AI Technical Summary

Problems solved by technology

The defects of Neo4j ETL tools are in the following three points: First, the modification of the mapping file occurs before the formation of the ER model, which requires the user to know the source relational database very well before knowing which tables and attributes to select for migration. Once the user enters the next step Forming an ER model, if you are not satisfied with the selected tables and attributes, it is troublesome to reconnect to the source relational database to generate a mapping file modification; second, manual modification of the mapping file is somewhat unfriendly to non-professional personnel, and it is easier for professionals to understand the mapping file Mapping content, but for non-professionals, if you want to specifically select the table migration you want, you need to learn and understand the mapping file before you know how to modify it before you can proceed to the next step; third, after the ER model is formed, the Neo4j ETL tool can only change the node name, Relationships, attribute names and types, do not allow users to modify the entity and attribute mapping from the relational model to the graph model on the ER model, nor allow users to define the structure of the graph themselves

The reason for these defects is that the developers did not pay enough attention to the use of users when designing and implementing products, especially the lack of attention to the use of non-professionals.

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