I’m comparing two different ways to search for a term within a column in SQL Server:
SELECT * FROM table WHERE Column LIKE '%test%';
Vs.
SELECT * FROM table WHERE CONTAINS(Column, 'test');
From a performance perspective, especially on large datasets, which approach is more efficient?
I understand that LIKE does a pattern match, while CONTAINS is tied to full-text indexing but I’m not sure when one is better than the other.
Are there specific scenarios where SQL CONTAINS is significantly faster or more accurate?
Also, are there any prerequisites (like enabling full-text search) for using CONTAINS effectively?
From experience working on a large e-commerce platform, I can confirm that SQL contains is much faster when full-text search is properly set up.
It uses an inverted index behind the scenes, so queries like:
SELECT * FROM Products WHERE CONTAINS(Description, 'bike');
Run significantly faster than LIKE ‘%bike%’, especially on text-heavy columns.
But keep in mind, you must enable full-text indexing on the table and column to use CONTAINS.
Without it, SQL Server will throw an error. So if you’re dealing with lots of text and want speed, definitely go with CONTAINS.
I’ve found LIKE '%term%' works well for quick-and-dirty searches, especially when you’re dealing with ad-hoc queries or haven’t configured full-text search yet.
It doesn’t require setup, but it scans every row unless there’s an index that supports a leading wildcard (which most don’t).
So if performance matters, and you’re doing this frequently, then switching to SQL contains is worth the investment.
In our BI reporting stack, we switched from LIKE to SQL contains for keyword matching across product reviews and saw huge improvements.
Besides speed, CONTAINS also allows richer querying like:
SELECT * FROM Reviews WHERE CONTAINS(Comment, '"battery life" AND NOT "charger"')
This kind of semantic querying isn’t even possible with LIKE. But yes, you need to set up full-text catalogs and make sure the right columns are indexed.
Once configured, CONTAINS blows LIKE out of the water in both accuracy and speed for search-heavy use cases.