Skip to content

shannonlowder.com

Menu
  • About
  • Biml Interrogator Demo
  • Latest Posts
Menu

SQL 101 – The Advanced LIKE Clauses

Posted on August 20, 2005November 4, 2011 by slowder

As I promised, this is my post on advanced LIKE clauses.  Previously I’ve only shown you how to do a wildcard search that would act like the dos command “dir A*.*”, returning all the files that start with the letter A.  But there is far more you can do with the LIKE operator.

Wildcards themselves are actually characters that have special meanings within SQL.  Wildcard searching can be used only with VARCHAR fields; you can’t use wildcards to search fields of non-text datatypes.  Full TEXT fields support an additional library of methods for searching for matches inside those fields, so let’s leave that for next time.  For now, everything will work in a VARCHAR or NVARCHAR field.

The Percent Sign %Wildcard

The most frequently used wildcard is the percent sign %.  This is the wildcard I first introduced you to in my previous post.  % means match any number of occurrences of any character.  Wildcards can be used anywhere within the search pattern, and multiple wildcards can be used as well. The following example uses two wildcards, one at either end of the pattern:

SELECT
   productName
FROM Products
WHERE
   productName LIKE '%en%'

productName
-----------
pencil
pen

The Underscore _ Wildcard

Another useful wildcard is the underscore _. The underscore is used just like %, but instead of matching multiple characters, the underscore matches just a single character.

SELECT
   productName
FROM Products
WHERE
   productName LIKE '_en'

productName
-----------
pen

The Brackets []Wildcard

The brackets [] wildcard is used to specify a set of characters, any one of which must match a character in the specified position.  This is where you can really get into some powerful comparisons.  But for this example, we’re just going to use it to show those products that begin with p, have a character between b and f for the second letter, and then have anything after that…like I said, easy.

SELECT
   productName
FROM Products
WHERE
   productName LIKE 'p[b-f]%'

productName
-----------
pencil
pen

Negating a Range

If you add ^ to a range, it checks for all characters NOT in that range.  If we add it to our last example, the results change dramatically.

SELECT
   productName
FROM Products
WHERE
   productName LIKE 'p[^b-f]%'

productName
-----------
paper

Conclusion

I’m not telling you to use wildcards all the time, but they have their uses.  Be careful where you place them, you could could dramatically different results.  Remember to test your code with several scenarios or against several data sets before releasing your code into production.  If you keep this in mind, this technique can become a powerful tool in your SQL tool belt.

If you have any questions, send them in!  I’m here to help!

Previous: SELECT, Filtering Results (Part 2) Next: Calculated Fields

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recent Posts

  • A New File Interrogator
  • Using Generative AI in Data Engineering
  • Getting started with Microsoft Fabric
  • Docker-based Spark
  • Network Infrastructure Updates

Recent Comments

  1. slowder on Data Engineering for Databricks
  2. Alex Ott on Data Engineering for Databricks

Archives

  • July 2023
  • June 2023
  • March 2023
  • February 2023
  • January 2023
  • December 2022
  • November 2022
  • October 2022
  • October 2018
  • August 2018
  • May 2018
  • February 2018
  • January 2018
  • November 2017
  • October 2017
  • September 2017
  • August 2017
  • June 2017
  • March 2017
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • August 2013
  • July 2013
  • June 2013
  • February 2013
  • January 2013
  • August 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • March 2010
  • January 2010
  • December 2009
  • November 2009
  • October 2009
  • September 2009
  • August 2009
  • July 2009
  • June 2009
  • May 2009
  • April 2009
  • March 2009
  • February 2009
  • January 2009
  • December 2008
  • November 2008
  • October 2008
  • September 2008
  • August 2008
  • July 2008
  • June 2008
  • May 2008
  • April 2008
  • March 2008
  • February 2008
  • January 2008
  • November 2007
  • October 2007
  • September 2007
  • August 2007
  • July 2007
  • June 2007
  • May 2007
  • April 2007
  • March 2007
  • February 2007
  • January 2007
  • December 2006
  • November 2006
  • October 2006
  • September 2006
  • August 2006
  • July 2006
  • June 2006
  • May 2006
  • April 2006
  • March 2006
  • February 2006
  • January 2006
  • December 2005
  • November 2005
  • October 2005
  • September 2005
  • August 2005
  • July 2005
  • June 2005
  • May 2005
  • April 2005
  • March 2005
  • February 2005
  • January 2005
  • November 2004
  • September 2004
  • August 2004
  • July 2004
  • April 2004
  • March 2004
  • June 2002

Categories

  • Career Development
  • Data Engineering
  • Data Science
  • Infrastructure
  • Microsoft SQL
  • Modern Data Estate
  • Personal
  • Random Technology
  • uncategorized
© 2025 shannonlowder.com | Powered by Minimalist Blog WordPress Theme