Filter Combination Processor

Modified on Tue, 30 Nov 2021 at 02:11 PM

Overview

The Filter Combination processor uses a set of filter combinations defined in the right input and applies them on the input data of the left node.


Processor Input

This processor has two input nodes:

  • Right: contains the set of filtering operations to be executed
  • Left: input data on which filtering will be applied


Configuration

The processor can be configured as follows:

Processor Output

According to the defined number of permutations (second field), the processor will generate all possible filtering combinations using entries from the right input node. The higher the number of permutations, the more possible filter combinations will be presented in the results.

These combinations are listed in the "filterQuery" column in the result table.


Example

In the following example, the Filter Combination processor will be applied on a dataset that is loaded via a Data Table Load processor (or a Dataset Load Processor)

Input Data

The input data can be visualized via the Result Table:


and the filtering combinations are:

The two columns "Category" and "Age_Category" are used within the Filter Combination processor, and the number of combinations is kept as default.


Workflow Result

After running the Workflow and opening the Result Table linked to Filter Combination processor, the following result table should be displayed:


It is important to mention that some entries from other columns except "filterQuery" might be redundant, but the condition within the "filterQuery" cell differs.

For instance, the rows four and five include the same entries but the condition differs: for the first row entries from the  column "Category" take the value "Sport" and entries from column "Age_Category" have the value "Old" whereas in the second row the entries from "Category_Age" are in the ranges "New" and "Old" (can take any value from this pair).

Related Articles

Double Input Query

Data Filter

Query Helper Processor


Was this article helpful?

That’s Great!

Thank you for your feedback

Sorry! We couldn't be helpful

Thank you for your feedback

Let us know how can we improve this article!

Select atleast one of the reasons

Feedback sent

We appreciate your effort and will try to fix the article