Using checksum function to check multiple columns for a merge

Question

Hi, in a stored procedure I need to implement a merge matching a source table with a destination table. Both tables have more columns and I need to detect a change matching more columns and not few ones. So I think to use a checksum function, if it is a good practice. Any suggests to me, please? Thanks

Answer 1

Here is how your MERGE statement can be :

MERGE INTO destination_table AS dest USING (SELECT *, CHECKSUM(col1, col2, ..., colN) AS checksum_value FROM source_table) AS src ON dest.primary_key = src.primary_key WHEN MATCHED AND dest.checksum_value <> src.checksum_value THEN     UPDATE SET dest.col1 = src.col1, ..., dest.colN = src.colN WHEN NOT MATCHED BY TARGET THEN     INSERT (col1, col2, ..., colN)     VALUES (src.col1, src.col2, ..., src.colN) WHEN NOT MATCHED BY SOURCE THEN     DELETE; 

You can use the CHECKSUM function to calculate a checksum value for each row in both your source and destination tables. This function takes a list of columns and generates a hash value based on their contents.

   SELECT *, CHECKSUM(col1, col2, ..., colN) AS checksum_value
   FROM your_table

In your MERGE statement, you can then use these checksum values to identify rows that have changed. Rows with different checksum values in the source and destination tables are considered to have changed. Just be aware that it can produce collisions (different rows having the same checksum value). Alternative apporach :BINARY_CHECKSUM or HASHBYTES

• BINARY_CHECKSUM: Similar to CHECKSUM, but it includes the order of the columns in its calculation. It might reduce collisions compared to CHECKSUM.
• HASHBYTES: Offers a more robust way to generate a hash value for a row. It supports various algorithms like SHA1, SHA2, etc. It is less likely to produce collisions compared to CHECKSUM but is more computationally intensive.

   SELECT *, HASHBYTES('SHA1', CONCAT(col1, col2, ..., colN)) AS hash_value
   FROM your_table

Answer 2

Hi @pmscorca, There is a better way to implement what you need by using a set based operation INTERSECT. Check it out T-SQL below. For completeness, I am providing both methods.

-- DDL and sample data population, start
DECLARE @Source TABLE (APK INT IDENTITY PRIMARY KEY, ID_NUMBER INT, UpdatedOn DATETIMEOFFSET(3));
DECLARE @Target TABLE (APK INT IDENTITY PRIMARY KEY, ID_NUMBER INT, UpdatedOn DATETIMEOFFSET(3));

INSERT INTO @Source (ID_NUMBER)
  VALUES (null), (null), (7), (7), (5);

INSERT INTO @Target (ID_NUMBER)
  VALUES (null), (7), (null), (7), (4);
-- DDL and sample data population, end

SELECT * FROM @Source;
SELECT * FROM @Target;

-- Method #1
WITH source AS
(
   SELECT sp.*, HASHBYTES('sha2_256', xmlcol) AS [Checksum] 
    FROM @Source AS sp
    CROSS APPLY (SELECT sp.* FOR XML RAW) AS x(xmlcol)
), target AS
(
   SELECT sp.*, HASHBYTES('sha2_256', xmlcol) AS [Checksum] 
    FROM @Target AS sp
    CROSS APPLY (SELECT sp.* FOR XML RAW) AS x(xmlcol)
)
UPDATE T 
SET T.ID_NUMBER = S.ID_NUMBER
   , T.UpdatedOn = SYSDATETIMEOFFSET()
FROM TARGET AS T
    INNER JOIN SOURCE AS S
      ON T.APK = S.APK
WHERE T.[Checksum] <> S.[Checksum];

-- Method #2
UPDATE T 
SET T.ID_NUMBER = S.ID_NUMBER
   , T.UpdatedOn = SYSDATETIMEOFFSET()
FROM @Target AS T
    INNER JOIN @Source AS S
      ON T.APK = S.APK
WHERE NOT EXISTS (SELECT S.* INTERSECT SELECT T.*);

-- test
SELECT * FROM @Target;

Answer 3

So I think to use a checksum function, if it is a good practice.

Using CHECKSUM for this case is far away from good practice. There is no garantuee CHECKSUM will return unique values and in worst case you will mess up your data.