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I have a SQL DB2 table where the first two fields are the primary keys (not including the third field which is date/time stamp).
The table was designed by another team with the intent to make it generic.
I was brought into the project after the key value for the second field was coded, for when it was inserted onto the table.

This leads me to this: We now have to do a cursor select with a WHERE clause that includes the first primary key – and then for the second primary key it must be for only when it is a specific value in position 21 for 8 bytes (And we will always know what that value will be for the second field.) The second field is a generic 70 byte field (alphanumeric).

My question is: Should we use a LIKE wildcard for the WHERE clause statement for the second primary field condition or instead a SUBSTR, since we know the position of the value?

I ask because I have done an EXPLAIN yet, I do not see a difference between the two (neither does my database analyst).
And this is for a few million records for a 1300 byte long table.

However, my concern is volume of the data on the table will grow on various systems. Therefore performance may become an issue. Just right now it is hard to measure the difference between LIKE and SUBSTR. But I would like to do my due diligence and code this for long term performance.

And if there is a third option, please let me know.

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I assume the primary key fields are immutable, especially the 2nd key. So you can add another column to the table, holding a copy of SUBSTR(secondKeyColumn,21,8). Immutability will assure the redundancy will not lead to inconsistency problems. It may be a good idea to add an AFTER INSERT trigger which makes sure the column will always be filled correctly when a new record is created.

That will make it possible to add an index to the new column, and change your query using it.

Note this solution is in no way special to DB2, it will work on any relational database which supports triggers and indexes (like Oracle, MS SQL, PostgreSQL, Sybase, or MySQL).

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