ErrorCode=UserErrorInvalidColumnMappingColumnCountMismatch,Message=The column count in column mappings is more than the count in source/sink table.


Currently working on a pipeline which is copying data from a csv file and loading it into a database Table, so for that I configured the CopyData activity 

i. Configured  the source settings and to preview data provided path to a sample csv file 

ii. Configured sink settings - provided the Table name and pre-copy scripts

iii. Once both have defined, next was the mapping between them and for that you have to click on the import schema, so what it does is basically gets the schema from this source file which you have provided and from database table you have mentioned in the sink side.

After it gets the schema from both the side, it presents you with the auto suggested links so if you find those ok, use  that else you can change it as per your requirement .

That's what I did, but then when I started to test, got below error. 

The column count in column mappings is more than the count in source/sink table.

Why it happened

ErrorCode=UserErrorInvalidColumnMappingColumnCountMismatch,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=The column count in column mappings is more than the count in source/sink table.,Source=Microsoft.DataTransfer.ClientLibrary,'

As the error points out, the ADF runtime is telling you that the number of columns coming in the input file is less than the number of columns which are defined in the column mapping. 

column mapping in azure data factory

As can be seen in the above image the number of columns mapped is five but the file which is used for testing has less number of columns (in my case the input file had 3 columns)

What to do

The error can be of due to either reason

i.  Incorrect column mapping 

ii. Incorrect data in the file

In either of the cases you have to make sure that both are in adherence to each other. Here in my case the column mapping was correct and the input data file had issue - so corrected the data  and issue was resolved.

Sometimes the issue can be with column mapping due to incorrect requirement understanding or use of incorrect sample file to generate schemas before mapping. 

If you have questions or suggestions, feel free to do in comments section below !!!

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Learn More about some more Azure Data Factory errors

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