Data model

How to avoid data bloat in the data model?

1. Overview

The data model is designed to prevent data bloat caused by table join in a one-to-many (many-to-one) or many-to-many scenario in a detail table, so the processing logic when acquiring the data is to aggregate the fields involved in the join with the fields bound to the component before join them.

2. Data inflation

Suppose the following data table exists

Dataset 1:

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic1.png

Dataset 2:

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic2.png

Dataset 1 left join dataset 2:

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic4.png

Aggregating sales at this point would result in Shanghai's sales being counted twice, which would lead to incorrect results from the inflated data.

➢Note:

Inner join, outer join, and right join will have the same effect.

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic3.png

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic5.png

2. Data model processing effects

The data model aggregates and then join the fields involved in the join with the fields bound to the component.

The example dataset above produces the data model setup as follows:

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic6.png

Data results presentation:

https://www.vividime.com/help/10.2/en/query_datamodelassociativeprocessinglogic7.png