I have a fairly busy processing workflow for a large data set which includes a few pivots/unpivots in metanodes etc. If I add a column further up the workflow, these are automatically included and really mess with my output. Whilst I can manually go in and exclude them, it takes some digging to find them all. Is there a setting anywhere which would automatically exclude them rather than automatically including them? It seems like the more logical choice to me. Thanks all.
When I want to be sure that only the desired columns are propagated further in the flow of a workflow, I definitely use the -Reference Column Filter- node:
Or its equivalent splitter if you need to keep aside the other columns for other treatment:
You would need before hand to hard code the list of columns that you want to be filtering in as shown here below:
Hope it helps.
Interesting! I have not used that before, so it’s good to learn how a new node works. I will have to investigate how it behaves and let you know
Beside @aworker great solution, have you tried to use column filter and force inclusion of your desired columns?
Alternatively Table Validator Nodes?
Yes, this is what I do currently, but there are many steps and metanodes in the workflow where new columns could slip through retained, so the question was more: can Knime ignore new columns by default rather than including them by default?
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