Multi Window Ranking

Type

transform

Class

fire.nodes.etl.NodeMultiWindowRanking

Fields

Name	Title	Description
windowFunctions	WindowFunction	Window Function Name
partitionByCols	PartitionBy	partition column names separated by comma(,)
orderByCols	OrderBy	order by column names separated by comma(,)
outPutColumns	OutputColumn	Enter output field(column) name

Details

This node Generates a new Dataframe with Rank Columns appended to the incoming Dataframe.

Rank Columns are populated with value based on the Window Function selected.

This node facilitates computation of multiple Rank Columns.

Examples

Incoming Dataframe has following rows:

EMP_CD    |    EMP_NAME    |    DEPT    |    SALARY    |    AGE
------------------------------------------------------------------------
E01       |    ANTHONY     |    HR      |    50000     |    40
E02       |    LISA        |    HR      |    45000     |    35
E03       |    MARTIN      |    HR      |    20000     |    25
E04       |    DAVID       |    SALES   |    55000     |    40
E05       |    MARK        |    SALES   |    60000     |    45
E06       |    JOE         |    SALES   |    40000     |    25
E07       |    BELLA       |    HR      |    60000     |    24

If MultiWindowRanking node is configured as below:

WINDOW FUNCTION    |    PARTITIONBY    |    ORDERBY                |    OUTPUTCOLUMN
----------------------------------------------------------------------------------------------
RANK               |    DEPT           |    AGE DESC, EMP_NAME     |    ORDERED_BY_AGEDESC
ROW_NUMBER         |                   |    EMP_CD                 |    ROW_NUM_EMPCD

then outgoing Dataframe would be created as below

where incoming Dataframe is partitioned by [DEPT] and data is sorted by [AGE DESC, EMP_NAME] and [RANK] value or the order value

within a partition is appended as new column [ORDERED_BY_AGEDESC]

for second Ranking Column, entire incoming Dataframe is sorted by [EMP_CD] and [ROW_NUM_EMPCD] is populated with the

[ROW_NUMBER] value or the order value after sorting of the Dataframe:

EMP_CD    |    EMP_NAME    |    DEPT    |    SALARY    |    AGE    |    ORDERED_BY_AGEDESC    |    ROW_NUM_EMPCD
------------------------------------------------------------------------------------------------------------------------------
E01       |    ANTHONY     |    HR      |    50000     |    40     |    1                     |    1
E02       |    LISA        |    HR      |    45000     |    35     |    2                     |    2
E03       |    MARTIN      |    HR      |    20000     |    25     |    3                     |    3
E07       |    BELLA       |    HR      |    60000     |    24     |    4                     |    7
E04       |    DAVID       |    SALES   |    55000     |    40     |    1                     |    4
E05       |    MARK        |    SALES   |    60000     |    45     |    2                     |    5
E06       |    JOE         |    SALES   |    40000     |    25     |    3                     |    6