Skip to content

shannonlowder.com

Menu
  • About
  • Biml Interrogator Demo
  • Latest Posts
Menu

SQL 202 – FILLFACTOR and Indexes

Posted on April 6, 2009February 9, 2011 by slowder

FILLFACTOR specifies the percentage for how full the Database Engine should make the leaf level of each index page during index creation or rebuild. FILLFACTOR must be an integer value from 1 to 100. The default is 0.

If FILLFACTOR is 100 or 0 (MS SQL treats these the same), the Database Engine creates indexes with leaf pages filled to capacity.

The number you set only applies when the index is created or rebuilt…otherwise, the Database Engine will continue to fill in spaces in the index left available during the last create or rebuild statement. If you need to see the current FILLFACTOR setting for an index, check out sys.indexes.

The reason you have the FILLFACTOR option is to fine tune storage and performance in your database. If you set the FILLFACTOR low (below 50), then you’ve optimized the index for inserts, but you rarely need to do that. When you set the FILLFACTOR high (above 50) you’re optimizing for selects and searches.

The reason this is so is due to page splits. Let’s say you set the fill factor to 0 (or 100). When you insert a new record, the data page (basic unit of storage in the database) must be split. Half the rows to a new page to make room for the new row. This page split makes room for new records, but takes time to perform.

Page splits also cause fragmentation. That causes increased I/O operations. This too can slow down reads across the long haul. If you’re getting frequent page splits, rebuild the index. This is particularly useful after importing a large file into the database. A large file would be something that increases the table’s size by a noticeable percentage (my threshold is usually 25% growth in a table under a gig, and 10% growth for tables over a gig). I’m kind of paranoid about performance that way.

Now, when you choose a low FILLFACTOR below 50%, but non-zero, you may have fewer page splits, but the amount of space used to store the data will be higher. Most of the time reads are more important than writes or updates by a factor of 5 to 10 (per Microsoft’s Books On Line) so optimizing for inserts or updates is a losing cause.

I would like to point out one final note from Microsoft: “specifying a fill factor other than the default can decrease database read performance by an amount inversely proportional to the fill-factor setting. For example, a fill-factor value of 50 can cause database read performance to decrease by two times. Read performance is decreased because the index contains more pages, therefore increasing the disk IO operations required to retrieve the data.”

This is something you’ll have to consider if you have users complaining about slow access times, even when your tables and views are highly indexed. It’s all a balancing act. And you are the clown that has to learn to juggle while standing on a large inflated ball! Need help? Get in touch with me, I can help! I do offer consultation services. Email me today for more information.

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