The Not In node is the In node's opposite, as it will only return records that are in one data set but not in another. This is again best illustrated by way of example.
For instance, suppose you had the following two datasets:
|
id |
measure |
|---|---|
|
01 |
100 |
|
02 |
200 |
|
03 |
300 |
|
04 |
400 |
|
05 |
500 |
Input 1
|
id |
measure |
|---|---|
|
01 |
1000 |
|
03 |
3000 |
|
05 |
5000 |
|
07 |
7000 |
|
09 |
9000 |
Input 2
There are two potential ways these datasets can be fed into a Not In node, and, thus, two potential outcomes. In either case, if id is specified as the Outer Source Field, only non-matching records from the id field will be returned. Which non-matching records are returned will in turn depend upon which input is specified as the "Outer Source."
Note: When using the Not In node, the data types of the Inner source and Outer source field need to be the same.