SQL Substitute Hard-Coding of Search/Replace Strings in Long Case Statement by Using a Lookup Table

Overview

As data grows, so does the complexity of the queries we write to manage it. In this article, we’ll explore an efficient way to substitute hard-coded search and replace strings in long case statements by using a lookup table. This approach can be particularly useful when dealing with large datasets and multiple search patterns.

Introduction to Case Statements

In SQL, a case statement is used to perform different actions based on the value of a condition. In our scenario, we have a pagestring column that needs to match certain strings for updating the corresponding subclass value.

UPDATE Table1
SET subclass = CASE
    WHEN pagestring LIKE ANY ('index%', 'Store/%') THEN 'Home'
    ...

The issue with this approach is that it requires hard-coding multiple search patterns in the case statement. As our dataset grows, so does the number of search patterns.

Introduction to Lookup Tables

A lookup table is a table used to store data that will be used for searching or matching purposes. In our case, we’ll use it to store the search strings, their lengths, and corresponding subclass values.

CREATE TABLE Table2 (
    seq byteint,
    searchstring varchar(100),
    searchlength integer,
    subclass varchar(50)
);

INSERT INTO Table2 (seq, searchstring, searchlength, subclass)
VALUES
    (1, 'index', 5, 'Home'),
    (2, 'Store', 5, 'Home'),
    ...

SQL Substitute Hard-Coding of Search/Replace Strings

Using a lookup table, we can substitute the hard-coded search and replace strings in the long case statement. We’ll use the REGEXP_SIMILAR function to match the search strings.

MERGE INTO Table1 AS tgt 
USING (
    SELECT t2.pagestring, t2.subclass
    FROM Table1 T1
    JOIN Table2 T2 ON T1.pagestring LIKE REGEXP SIMILAR (T2.searchstring)
) AS src 
ON src.pagestring = tgt.pagestring 
WHEN MATCHED THEN 
  UPDATE SET tgt.subclass = src.subclass 

Explanation of the SQL Code

Here’s a breakdown of what the code does:

• We join Table1 with Table2 on the condition that the pagestring column in Table1 matches any string in Table2 using the REGEXP SIMILAR function.
• The REGEXP SIMILAR function performs a regular expression match, which is similar to the LIKE operator but more powerful. It allows us to use patterns that are not limited by simple character matching.

Example with ‘chair XX’ versus ‘chair '

To illustrate this approach, let’s consider an example where we want to check for both ‘chair XX’ and ‘chair ‘. We can create a regular expression pattern that matches any string starting with ‘chair ’ but followed by either ‘XX’ or not.

MERGE INTO Table1 AS tgt 
USING (
    SELECT t2.pagestring, t2.subclass
    FROM Table1 T1
    JOIN Table2 T2 ON T1.pagestring LIKE REGEXP SIMILAR (T2.searchstring)
) AS src 
ON src.pagestring = tgt.pagestring 
WHEN MATCHED THEN 
  UPDATE SET tgt.subclass = src.subclass

To match strings that do not end with ‘XX’, we can use a negative lookahead assertion:

MERGE INTO Table1 AS tgt 
USING (
    SELECT t2.pagestring, t2.subclass
    FROM Table1 T1
    JOIN Table2 T2 ON T1.pagestring LIKE REGEXP SIMILAR (T2.searchstring)
) AS src 
ON src.pagestring = tgt.pagestring 
WHEN MATCHED THEN 
  UPDATE SET tgt.subclass = src.subclass

In the case of the ‘chair XX’ versus ‘chair ’ scenario, we need a special regular expression pattern to identify instances of ‘chair ’ NOT followed by ‘XX’. This can be achieved using a negative lookahead assertion:

REGEXP SIMILAR (T2.searchstring) => REGEXP SIMILAR (T2.searchstring) %(?!XX)

Using this approach, we can eliminate the need for hard-coding multiple search patterns in the case statement.

Conclusion

Substituting hard-coded search and replace strings in long case statements by using a lookup table is an efficient way to manage large datasets and improve maintainability. By leveraging regular expression matching with a lookup table, you can create flexible and scalable solutions that adapt to changing data requirements.

Last modified on 2025-02-12