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The larger (more data pages) the table, the smaller the chance of seeing no rows when at least a couple of pages worth are requested For example, if you request 300 rows from a 1,000,000-row table that ts on 10,000 data pages, only in 5% of trials would you see no rows, even though the request is for far less than 1% of the rows By choosing the REPEATABLE option, you will get the same sample each time For most seeds, this will be an empty sample in your case With other seeds, it will contain 37, 179, 216, 358, or 395 rows, depending on which pages were selected, with the larger numbers of rows returned for very few choices of seed.

production system adequately. The performance of ordered scans might vary significantly based on the level of fragmentation of your indexes. Similarly, you also need to examine the average page densities in your production system, and introduce similar page densities in the test system.

DECLARE @g GEOMETRY SET @g = 'POLYGON((0 0, 5 0, 5 5, 0 5, 0 0))' SELECT dbo.LinearTransformation(@g, 2, 3, 4, 5).ToString() AS TransformedWKT GO

That said, I agree that the consequences of returning only full data pages results in very confusing behavior! With small tables, you might want to consider other sampling methods You don t care too much about scanning the whole table because you consider these techniques against small tables anyway For example, the following query will scan the whole table, but it guarantees that you get a single random row:.

SELECT TOP(1) * FROM AdventureWorks2008.Production.ProductCostHistory ORDER BY CHECKSUM(NEWID());

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This results in the following output:

Besides having the ability to design good indexes, it is also important to be able to identify which indexes are used more heavily and which are rarely or never used. You don't want to keep indexes that are rarely used, as they do have negative performance effects on modifications. In SQL Server 2000, the only available techniques to determine index usage were very awkward, to say the least. For example, you could take a script that contains a representative sampling of the queries that run in the system and produce textual SHOWPLAN information for the queries in the script. You could write an application that parses the output, extracts the names of the indexes that are used, and calculates index usage statistics. Obviously, it's a problematic technique that also requires a fair amount of effort. Another option you could use in SQL Server 2000 is to run the Index Tuning Wizard (ITW) based on a sample workload. Among the reports that ITW generates is a report that shows you usage statistics of the existing indexes for the input queries. In SQL Server 2005, you have much more powerful and convenient tools to work with. You get a DMF called dm_db_index_operational_stats and a DMV called dm_db_index_usage_stats . The dm_db_index_operational_stats DMF gives you low-level I/O, locking, latching, and access method activity information. You provide the function a database ID, object ID, index ID (or 0 for a heap), and partition ID. You can also request information about multiple entities by specifying a NULL in the relevant argument. For example, to get information about all objects, indexes, and partitions in the Performance database, you would invoke the function as follows: SELECT * FROM sys.dm_db_index_operational_stats( DB_ID('Performance'), null, null, null);

Note that other database platforms, such as DB2, implement additional algorithms for example, the Bernoulli sampling algorithm. You can implement it in SQL Server by using the following query, provided by Steve Kass:

Transformed WKT --------------POLYGON ((0 0, 10 20, 25 45, 15 25, 0 0))

SELECT * FROM AdventureWorks2008.Production.ProductCostHistory WHERE ABS((ProductID%ProductID)+CHECKSUM(NEWID()))/POWER(2.,31) < 0.01

The dm_db_index_usage_stats DMV gives you usage counts of the different index operations: SELECT * FROM sys.dm_db_index_usage_stats;

Figure 14-30 illustrates the original object, @g (square figure), and the transformed object from the query:

The constant 0.01 is the desired probability (in this case, 1 percent) of choosing a row. The expression ProductID%ProductID was included to make the WHERE clause correlated and force its evaluation on each row of ProductCostHistory. Without it, the value of the WHERE condition would be calculated just once, and either the entire table would be returned or no rows would be returned. Note that this technique requires a full table scan and can take a while with large tables. You can test it against our Orders table and see for yourself.

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DECLARE @g GEOMETRY; SET @g = 'POLYGON((0 0, 5 0, 5 5, 0 5, 0 0))' SELECT dbo.LinearTransformation(@g, 2, 3, 4, 5) UNION ALL SELECT @g;

These dynamic management objects make the analysis of index usage much simpler and more accurate than before.

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