November 18th 2015 Meeting

November Meeting Invitation

Please join us for the monthly Charlotte SQL Server User Group evening meeting.

  • What ~ Charlotte SQL Server User Group Meeting

  • When ~ Wednesday, November 18th, 2015

    • 5:30 PM Networking with Food & Refreshments
    • 6:00 PM Technical Presentation
    • 7:30 PM Meeting End
  • Where @ 8055 Microsoft Way, Charlotte, NC 28273 Map it

  • Presenter – Dave Turpin

  • Topic – De-Mystifying SQL Server Statistics
    The Ascending Key Problem

  • Registration – Free RSVP required at EventBrite

Register for the Meeting
DaveTurpin Ascending Key
Dave Turpin  Lead DBA, Quaero
Dave has been working with SQL Server since 1996 as a developer, IT manager, and most recently DBA. He is employed by Quaero, a first party audience data and analytics company. He also manages two Netezza massive parallel data warehouse appliances. His interests include the SQL Optimizer, query engine, performance tuning and event alerting. Although he has BS and MS degrees in computer science, most of what he knows that is worth sharing has been learned at the School of Hard Knocks. He is easily excited when he learns something new about SQL Server. He has presented some of those topics at several SQL Server focused training events.
Visit Dave’s Blog at http://www.daveturpin.com
Follow Dave on Twitter: www.twitter.com/sobrietytesting
De-Mystifying SQL Server Statistics: The Ascending Key Problem  In order to build optimal query plans the SQL Server optimizer relies on statistics to estimate the size of the query workload. This session will demonstrate how to determine if your statistics are current, how to make them current, and how bad things can happen when they are not current. After covering some general topics to understand the state of statistics on an index or column statistic, this session will demonstrate how the optimizer in SQL Server 2014 was improved such that queries that will not finish in SQL 2008R2 and SQL 2012 suddenly do in SQL 2014. Although the ascending key problem has broad implications, it is particularly relevant to data professionals responsible for developing and managing data warehouse loads.

 

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