How do I add multiple "NOT LIKE '%?%' in the WHERE clause of sqlite3?

The NOT LIKE operator and strategies for refinement

Casting the wildcard net with NOT LIKE

Wildcards (%) are invaluable when refining exclusions in NOT LIKE. A single wildcard can often suffice to exclude a range of characters:

SELECT * FROM your_table
WHERE your_field NOT LIKE '%[a-c]%' -- a, b, c? Not on my watch!
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This command excludes all rows with 'a', 'b', or 'c' located anywhere in your_field.

Broadening horizons with REGEXP (Regular Expressions)

SQLite doesn't naturally play nice with REGEXP, but fret not. You can supplement this functionality by applying user-defined functions:

SELECT * FROM your_table
WHERE your_field NOT REGEXP '[abc]'    -- Because sometimes, you just can't stand the alphabet!


Poke around the "REGEXP implementation in SQLite" link in the References section to get REGEXP up and running.

Employing subqueries for complex conditions

Subqueries are your best friends when dealing with intricate conditions exceeding the comfort zone of a single-layer WHERE clause. See the magic unfold:

SELECT * FROM your_table
WHERE your_field NOT IN (
  SELECT your_field FROM your_table WHERE your_field LIKE '%foo%' OR your_field LIKE '%bar%'
);  -- Double the queries, double the fun!
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Handling NULL values responsibly to retain data integrity

NULL values are the fox in the SQL henhouse: they dodge both LIKE and NOT LIKE. Adhere to the best practice of always accounting for potential NULLs:

SELECT * FROM your_table
WHERE (your_field NOT LIKE '%foo%' OR your_field IS NULL)
  AND (your_field NOT LIKE '%bar%' OR your_field IS NULL);  -- What's NULL got to do with it?


Integrating NOT LIKE with NOT IN and INTERSECT

• It's significant to know that NOT IN is not a body double for NOT LIKE; they serve different purposes like apples and oranges.
• On the flip side, INTERSECT has its own niche. It's a perfect fit when your objective is comparing the results originating from distinct queries.

Simplifying a complex NOT LIKE maze

Optimal strategies for working with overwhelming NOT LIKE conditions

• Investigating if a db design change can lift the burdensome NOT LIKE conditions.
• Creating a lookup table featuring exclusion patterns and establishing joins predicated on conditions that aren't quite matching up.
• Bringing stored procedures or functions into the game, especially when bundling complex logic under a simple name seems more sensible.

Pitfall prevention: staying accurate and performance-optimized

• Testing is crucial. Ensure the accuracy of your NOT LIKE filters against diverse datasets.
• Be mindful of performance implications. Index columns where feasible and cutback on pattern complexity if it's taking a toll.
• Ascertain your SQLite version’s compatibility with the techniques suggested here.