1. with Michelle Ufford

2.  Sr. DBA for GoDaddy.com  Working with SQL for 6 years, focused on performance tuning and VLDB’s  Member of the PASS Performance SIG  President, 380PASS SQL User Group - http://380pass.orghttp://380pass.org  Active blogger at http://sqlfool.comhttp://sqlfool.com  Twitters at http://twitter.com/sqlfoolhttp://twitter.com/sqlfool

3.  The general syntax for creating a table is: CREATE TABLE [schema].[tableName] ( ); i.e. CREATE TABLE dbo.myTable ( id int NOT NULL, lastName varchar(20) NOT NULL, firstName varchar(20) NULL );  In general, narrow tables perform better.

4. Heap Table LastNameFirstName CarsonAmanda SmithJohn AdamsMike ThomsonLaura JohnsonBrian Heap Table LastNameFirstName CarsonAmanda SmithJohn AdamsMike ThomsonLaura JohnsonBrian Ordered Table (Clustered Index) LastNameFirstName AdamsMike CarsonAmanda JohnsonBrian SmithJohn ThomsonLaura Ordered Table (Clustered Index) LastNameFirstName AdamsMike CarsonAmanda JohnsonBrian SmithJohn ThomsonLaura un-ordered data fast writes slow reads not typically recommended ordered data can have fast writes (depends on if clustering key is sequential) fast reads (fastest reads when searching on the clustering key) recommended table structure!

5.  General syntax for creating a clustered index: CREATE [UNIQUE] CLUSTERED INDEX ON ( ); i.e. CREATE UNIQUE CLUSTERED INDEX CIX_myTable ON dbo.myTable(id);  Best practice is to create a clustered index on a narrow column with static values  Try to create a unique clustered index for a little better performance

6.  A primary key enforces uniqueness within a table  General syntax for creating a primary key: CREATE TABLE [schema].[tableName] ( CONSTRAINT PRIMARY KEY [NONCLUSTERED|CLUSTERED]( )); i.e. CREATE TABLE dbo.myTable ( idintNOT NULL, lastName varchar(20) NOT NULL, firstName varchar(20) NOT NULL CONSTRAINT PK_myTable PRIMARY KEY NONCLUSTERED(id));

7.  Pay attention to where you’re placing the primary key! CREATE TABLE dbo.employee ( idintNOT NULL, ssnintNOT NULL PRIMARY KEY, lastName varchar(20) NOT NULL, firstName varchar(20) NOT NULL ); SSNID LastNameFirstName 123-45-67898 CarsonAmanda 212-12-1212 3 SmithJohn 323-23-2323 5 AdamsMike 424-24-2424 1 ThomsonLaura 505-05-0505 4 JohnsonBrian SSNID LastNameFirstName 123-45-67898 CarsonAmanda 212-12-1212 3 SmithJohn 323-23-2323 5 AdamsMike 424-24-2424 1 ThomsonLaura 505-05-0505 4 JohnsonBrian SSNID LastNameFirstName 123-45-67898 CarsonAmanda 200-98-76539 EdwardsFrank 212-12-1212 3 SmithJohn 323-23-2323 5 AdamsMike 424-24-2424 1 ThomsonLaura 505-05-0505 4 JohnsonBrian SSNID LastNameFirstName 123-45-67898 CarsonAmanda 200-98-76539 EdwardsFrank 212-12-1212 3 SmithJohn 323-23-2323 5 AdamsMike 424-24-2424 1 ThomsonLaura 505-05-0505 4 JohnsonBrian New Hire

8.  Creating a table with a non-clustered primary key and separate clustered index: CREATE TABLE dbo.employee ( idint Identity(1,1) NOT NULL, ssnint NOT NULL, lastName varchar(20) NOT NULL, firstName varchar(20) NOT NULL CONSTRAINT PK_employee PRIMARY KEY NONCLUSTERED(ssn)); CREATE UNIQUE CLUSTERED INDEX CIX_employee ON dbo.employee(id);

9.  Use the most appropriate data type to meet the requirements of the column  Use the smallest data type while allowing a little room for growth  If the data type is too big, you’re…  wasting space  wasting IO  wasting time

10.  Assume you have 1 million records to store: a unique id, a number 1 to 100, and a timestamp CREATE TABLE dbo.myBigTable ( myID INT IDENTITY(1,1), myNumber INT, myDateDATETIME );  Bytes per row: 4 + 4 + 8 + 10 = 26 bytes (myID + myNumber + myDate + overhead)  Rows per page: 8,060 / 26 = 310 rows  Pages per 1mm rows: 1,000,000 / 310 = 3225 pages

11.  We’re still storing 1 million records: CREATE TABLE dbo.mySmallTable ( myID INT IDENTITY(1,1), myNumber TINYINT, myDate SMALLDATETIME );  Bytes per row: 4 + 1 + 4 + 10 = 19 bytes (myID + myNumber + myDate + overhead)  Rows per page: 8,060 / 19 = 424 rows  Pages per 1mm rows: 1,000,000 / 424 = 2357 pages

12.  myBigTable consumed 3,225 pages  mySmallTable consumed 2,357 pages  mySmallTable used 868 less pages!  Space savings of 27%  A single IO can now return 114 more rows  Performance improvement of 37%

13.  Text Book example: Clustered IndexNon-Clustered Index Book Page Appendix Adams, Mike Carson, Amanda Edwards, Frank Johnson, Brian Adams, Mike Carson, Amanda Edwards, Frank Johnson, Brian Roberts, Amanda Smith, John Thomson, Laura Roberts, Amanda Smith, John Thomson, Laura Amanda  2, 5 Brian  4 Frank  3 Amanda  2, 5 Brian  4 Frank  3 John  6 Laura  7 Mike  1 John  6 Laura  7 Mike  1

14.  General syntax for creating a non-clustered index: CREATE [UNIQUE] NONCLUSTERED INDEX ON ( ) INCLUDE ( ); i.e. CREATE NONCLUSTERED INDEX IX_employee_ssn ON dbo.employee(ssn) INCLUDE (lastName, firstName);  Too may indexes can make writes expensive

15. Included Columns  Non-key columns on the leaf level of an index  Not used as search criteria in an index  SELECT employeeID, lastName, firstName FROM dbo.employee WHERE hireDate < ‘2009-01-01’  CREATE NONCLUSTERED INDEX IX_employee_hireDate ON dbo.employee(hireDate) INCLUDE (lastName, firstName);  hireDate = index key employeeID = clustered key pointer lastName, firstName = included column

16. Want free hosting? First person to tweet… "@GoDaddyGuy I want GoDaddy.com hosting!” …will win free hosting for a year!

17.  When the logical ordering of pages does not match the physical ordering of pages  Both clustered and non-clustered indexes can become fragmented  Fragmentation is caused by insert/update/delete operations

18.  Page 3 Page 4 Page 5   Page 3 Page 4 Page 5   Page 4 Page 5 Page 6   Page 4 Page 5 Page 6   Page 3 Page 4 Page 742   Page 3 Page 4 Page 742   Page 4 Page 742 Page 5   Page 4 Page 742 Page 5   Page 742 Page 5 Page 6   Page 742 Page 5 Page 6  Insert  Page 3 Record 1 Record 2 Record 3 Record 4 Page 5   Page 3 Record 1 Record 2 Record 3 Record 4 Page 5   Page 3 Record 1 Record 2 Page 742   Page 3 Record 1 Record 2 Page 742   Page 4 Record 5 Record 3 Record 4 Page 5   Page 4 Record 5 Record 3 Record 4 Page 5  Insert Page 4 Page 742

19.  SQL Server 2000  DBCC ShowContig  SQL Server 2005 / 2008  Dynamic Management Views (DMV)  sys.dm_db_index_physical_stats  Execute after normal business hours or use “Limited” to avoid impacting the environment

20.  SQL Server 2000  DBCC IndexDefrag - quicker  DBCC dbReindex - more thorough  SQL Server 2005  Alter Index {Reorganize | Rebuild}  My Index Defrag Script: http://sqlfool.com/2009/03/automated-index-defrag-script/

21.  Stored procedures offer:  enhanced security  better performance  easier maintenance  less network traffic  Best practice for accessing data in SQL Server

22.  Select only the columns you actually need  Try to use an existing covering index whenever possible, but…  Don’t create an index for every query!  Place Data Definition (DDL) statements first, then Data Modification (DML)  Specify the schema owner (i.e. “dbo”, “sys”) for every referenced object

23.  Consider MAXDOP restrictions  Consider NOLOCK hints (only if dirty reads are acceptable)  Use “SET NOCOUNT ON;”  Avoid nested proc calls  Keep transactions small

24.  Minimize database calls whenever possible  When working with static data, retrieve data once and store it in the app (i.e. hash table)  When developing high-volume applications:  Batches are key! More manageable updates  Use table-valued parameters (TVP) or XML to handle bulk operations  Staging tables vs single row updates

25.  Books Online, http://msdn.microsoft.com/en-us/library/ms130214.aspx http://msdn.microsoft.com/en-us/library/ms130214.aspx  SQLServerPedia, http://sqlserverpedia.comhttp://sqlserverpedia.com  SQL Server Performance, http://www.sql-server-performance.com http://www.sql-server-performance.com  SQL Server Central, http://www.sqlservercentral.com/ http://www.sqlservercentral.com/  My blog! http://sqlfool.com http://sqlfool.com

26.  Batch Compiliation, Recompliation, and Plan Caching Issues in SQL Server 2005 http://technet.microsoft.com/en-us/library/cc966425.aspx http://technet.microsoft.com/en-us/library/cc966425.aspx  SQL Server 2005 Waits & Queues (Google)  Grant Fritchey’s SQL Server Execution Plans  Kalen Delaney’s SQL Server 2008 Internals  Itzik Ben-Gan’s SQL Server 2008 T-SQL Fundamentals

27. Thank you for attending my presentation! I can be reached at: Michelle Ufford michelle@sqlfool.com http://sqlfool.com