How to Efficiently Join Comma-Separated Data Columns in Databases?

Joining Comma-Separated Data Column: In-Depth Solutions

In database systems, it is often necessary to manipulate data stored in comma-separated columns. Normalizing data into multiple tables is an ideal solution, but there are cases where that may not be feasible. Here we present various methods to join comma-delimited columns efficiently.

Normalization and Table Joins

Normalizing the data into separate tables is the most efficient approach. This involves creating a new table with a row for each unique value in the comma-separated column. The tables can then be joined using a foreign key relationship.

-- T1 Table
CREATE TABLE T1
(
  col1 varchar(2), 
  col2 varchar(5),
  constraint pk1_t1 primary key (col1)
);

-- T2 Table
CREATE TABLE T2
(
  col1 varchar(2), 
  col2 varchar(2),
  constraint pk1_t2 primary key (col1, col2),
  constraint fk1_col2 foreign key (col2) references t1 (col1)
);

Once normalized, the data can be easily queried using a join:

SELECT t2.col1, t1.col2
FROM t2
INNER JOIN t1
  ON t2.col2 = t1.col1

Custom Split Function for Non-Normalized Data

If normalization is not possible, we can create a custom split function to convert the comma-separated data into individual rows.

CREATE FUNCTION [dbo].[Split](@String varchar(MAX), @Delimiter char(1))       
RETURNS @temptable TABLE (items varchar(MAX))       
AS       
BEGIN      
    DECLARE @idx int       
    DECLARE @slice varchar(8000)       

    SELECT @idx = 1       
    IF LEN(@String)<1 OR @String IS NULL RETURN       

    WHILE @idx!= 0       
    BEGIN       
        SET @idx = CHARINDEX(@Delimiter,@String)       
        IF @idx!=0       
            SET @slice = LEFT(@String,@idx - 1)       
        ELSE       
            SET @slice = @String       

        IF(LEN(@slice)>0)  
            INSERT INTO @temptable(Items) VALUES(@slice)       

        SET @String = RIGHT(@String,LEN(@String) - @idx)       
        IF LEN(@String) = 0 BREAK       
    END   
RETURN 
END;

Using this function, we can join the original table with the split data:

;WITH cte AS
(
  SELECT c.col1, t1.col2
  FROM t1
  INNER JOIN 
  (
    SELECT t2.col1, i.items col2
    FROM t2
    CROSS APPLY dbo.split(t2.col2, ',') i
  ) c
    ON t1.col1 = c.col2
)
SELECT DISTINCT c.col1, 
  STUFF(
         (SELECT DISTINCT ', ' + c1.col2
          FROM cte c1
          WHERE c.col1 = c1.col1
          FOR XML PATH('')), 1, 1, '') col2
FROM cte c

FOR XML PATH Direct Application

Another method involves direct application of the FOR XML PATH feature:

SELECT col1, 
(
  SELECT ', '+t1.col2
  FROM t1
  WHERE ','+t2.col2+',' LIKE '%,'+CAST(t1.col1 AS VARCHAR(10))+',%'
  FOR XML PATH(''), TYPE
).value('SUBSTRING(TEXT()[1], 3)', 'VARCHAR(MAX)') AS col2
FROM t2;

Conclusion

The optimal solution depends on the specific scenario. Normalizing the data is the most efficient option, but if that is not feasible, using a custom split function or direct application of FOR XML PATH can provide efficient results.

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