How do SQL Server’s STUFF and FOR XML PATH features combine to aggregate row values? Can anyone explain or suggest a simpler method?
SELECT recordID,
mergedText = STUFF(
(SELECT '|' + itemText
FROM SampleRecords AS sr
WHERE sr.recordID = mainRec.recordID FOR XML PATH('')),1,1,'')
FROM SampleRecords AS mainRec
GROUP BY recordID;
The mechanism essentially leverages the XML capabilities of SQL Server. The FOR XML PATH clause is used to concatenate values from multiple rows, creating a single XML string from them. This process aggregates row values into one column by concatenating each row’s field with a delimiter. The STUFF function then comes into play by removing the unnecessary leading delimiter from the result. In my experience, this technique is widely used to pivot data and generate readable, concatenated lists from multiple rows without resorting to cursors or other iterative methods, making it both efficient and elegant.
i think it basically nets a repeated string of row values: for xml path creates a long string with delimiters and stuff then cleans up by removing the extra one at the beginning. its kinda a clever concat hack.
hey ppl, i find the for xml path + stuff combo a neat way to join rows. it’s like a hack to merge strings. has anyone tried comparing it to string_agg? i wonder which runs smoother on larger datasets, any thoughts?
hey, i see it as a sneeky method: for xml path fuses rows together as one messy string, then stuff wipes the extra delimiter off. not the prettiest code but gets the job done in a pinch.
The use of STUFF combined with FOR XML PATH is a clever device that leverages SQL Server’s XML generation capabilities to perform string aggregation without the need for a cursor or looping construct. I have used this method in projects where multiple rows needed to be consolidated into a single, delimiter-separated string. FOR XML PATH collects the values into an XML format, which then appears as a concatenated string, and STUFF is applied to remove the initial delimiter. This approach has proven both efficient and reliable for dynamic SQL generation.