X-Ref Joiner

I was looking to see if there was a pre-built node that I could download which extends the current joiner so that it produces 3 outputs with, the top table containing the orphans of the left data set (that is data that didn’t join and only exist in left), the middle table representing all data that joined (essentially the existing Inner outcome) and finally, the bottom table being the orphans from the right data set. I was able to build a wrapped metanode, save it the use it multiple time, however, I can only join on 1 key at a time when I have need for multiple joining criteria. I would think a node would be available in nodepit

KNIMEs philosophy is to only supply basic nodes and not make nodes which you can easily achieve with the existing nodes. I do don’t see why you even need a metanode for this. All you need is a full out join and 2 row splitters.
If that is a problem just copy the metanode and make a version for 2 and 3 joining columns. Not really a big deal and hence no node available for this.


Hi Beginner, because I often need to understand left and right orphans of 2 data sets, repeating the outer join and adding a 2 row split becomes tedious and excessive. The reason I use a metanode is for re-usability.
Any idea how I can do what you propose, that is, extend the existing one that I’ve created to accept more than 1 joining columns?

You don’t need to repeat a join, you just 1 1 full outer join and the split rows accordingly.

And now what you want is simply not possible because joining columns are defined as array and when setting arrays with flow variables you need to set each element separately and the number of elements must exactly match the number of join columns configured.it’s a limitation any yeah I would for KNIME 5 which to be able to be for flexible in that regard eg. setting array variables with arrays.

Only option to achieve what you want is either some complicated switch depending on how many joins columns you configure or doing the join in a python snippet using pandas.

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