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Alteryx designer Discussions

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Join tool corrupts columns



I'm performing a join between two data sets and the join function is changing the columns. I've successfully performed the same join on the same two files many times, suddenly it doesn't join properly. 


Here are screenshots:

Capture1.PNGLeft inputright input.PNGright inputjoin.PNGJoin results


What is especially confusing to me is this same worklfow  worked fine up until yesterday.


Hope someone can help.


Hi @cugoretz,


Have you verified the integrity of the data in your left and right inputs? If things break all of a sudden and were previously working fine, it is often a feed issue. 


It could also be a parsing issue based on the way things are feeding into the output. 


If you have verified integrity through out both data sources can you please provide some insight into how the join is set up? 


Thank you,




Below is the join configuration.


join config.PNG


I also notice in the Join output above, the tool is taking the original column names and placing them as row 1. 


@cugoretz Have you checked the Mot Code and Appeal code columns? This still leads me to believe you either have a parsing or data integrity issue. Can you identify what has changed between your data sources yesterday and today? 



"I also notice in the Join output above, the tool is taking the original column names and placing them as row 1." for this I would say double check that your data is being brought in correctly. It sounds like the data format has changed. Use some browse tools along your workflow and check to see that everything looks as you would expect. 


This is about as much as I can say without the actual workflow/Data. Maybe someone else in the forum can chime in if they have other ideas...


@cugoretz The join tool will resort your data ascending by the fields selected to join on (in this case Mot Code/ Appear Code) I would check the Mot Code for those first few records and use those to filter the left side of your data before your join. I suspect that you have bad data further upstream and the join is just resorting the data.