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Optimizing SQL Server 2005 Performance
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Lesson 3: Optimizing Data Storage
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Estimated lesson time: 25 minutes
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The optimization of data storage is dependent on why you are optimizing as well as what your users requirements are. Sometimes, you might be optimizing data storage to save disk space (which is typical for data warehouses), and other times, you might want to save memory or optimize query performance.
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One of the ways to optimize a database to save disk space is simply to remove seldom-used indexes that allocate a lot of disk space. In Lesson 2, the section titled Analyzing Index Usage covered the details regarding which indexes to remove. Another important task is to optimize the row width of large indexes and tables. SQL Server stores data on 8-kilobyte pages, and it is important to have as little unused space as possible in each data page. Note that this type of optimization is typically only useful on tables that contain wide rows that is, those that are at least over 400 bytes per row. Each data page has a 96-byte header and 8,096 bytes available for rows (data) and row offsets (the pointers stored at the end of each page telling where each row begins). If a row cannot fit into the free space of a data page, it will need to be stored in a new data page. This means that, for some tables and indexes, the size of the rows might cause data pages not to be filled. The less that each data page is filled, the more disk space is wasted. If the table isn t very big (1 GB, for example), you might not care so much about the price of disk space, but consider that everything located on the disk will be placed in memory when it is accessed. If the page space is poorly used, it will use more memory than necessary, and memory is far more expensive than disk space. Data stored using variable-length data types (VARCHAR, NVARCHAR, and VARBINARY) can be moved to overflow pages if they cannot fit into the row s original data page. The following is an example of one table (Test.BigWaste) with a row width that causes a large amount of unused space in each data page and another table (Test.OnlyALittleWaste) that almost fills each data page completely. Each table is populated with 10,000 rows.
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CREATE TABLE Test.BigWaste ( Txt CHAR(3000) NOT NULL ); INSERT Test.BigWaste (Txt) SELECT TOP(10000) text FROM sys.messages; CREATE TABLE Test.OnlyALittleWaste (
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Lesson 3: Optimizing Data Storage
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Txt CHAR(4000) NOT NULL ); INSERT Test.OnlyALittleWaste (Txt) SELECT TOP(10000) text FROM sys.messages;
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Note that, for the purpose of this example, the empty space caused by a column using a large char data type is not considered to be waste. Now, the sys.dm_db_index_physical_stats database management function is used to retrieve information on the storage of these two tables:
SELECT OBJECT_NAME(object_id) AS TableName ,page_count ,ROUND(avg_page_space_used_in_percent, 2) AS avg_page_space_used_in_percent FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('Test.BigWaste'), NULL, NULL, 'DETAILED') UNION ALL SELECT OBJECT_NAME(object_id) AS TableName ,page_count ,ROUND(avg_page_space_used_in_percent, 2) AS avg_page_space_used_in_percent FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('Test.OnlyALittleWaste'), NULL, NULL, 'DETAILED') ORDER BY TableName;
The output of the previous query is shown in the following table.
TableName BigWaste OnlyALittleWaste page_count 5,000 5,000 avg_page_space_used_ in_percent 74.33 99.04
Note that both tables use exactly the same number of pages to store their data, even though each row in the OnlyALittleWaste table is 1,000 bytes wider than in the BigWaste table. As you can see, in this example, almost 26 percent of the BigWaste table s size is wasted on empty page space. If this table was 10 GB in size, 26 percent would amount to 2.6 GB.
De-Normalizing
De-normalizing a database design implies storing data redundantly. The benefit of this is typically less work for the database engine when reading data, while it means more work when writing data because of the need to update more places than one. De-normalizing can also result in bugs when redundant data is not updated accordingly. De-normalizing a database design should typically be used as a last resort. That said, denormalization can increase performance dramatically. In fact, you could say that indexing,
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