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Data compression, available only in the Enterprise edition of SQL Server 2008, allows you to compress individual tables and indexes using either page compression or row compression, both of which we ll cover shortly. Due to its potentially adverse impact on performance, there s no option to compress the entire database in one action. As you can see in figure 9.10, you can manage a table s compression by right-clicking it and choosing Storage > Manage Compression. When considering compression in a broad sense, lossy and lossless are terms used to categorize the compression method used. Lossy compression techniques are used in situations where a certain level of data loss between the compressed and uncompressed file is accepted as a consequence of gaining higher and faster compression
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Individual tables can be selected for compression using SQL Server Management Studio.
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rates. JPEG images are a good example of lossy compression, where a reduction in data quality between the original image and the compressed JPEG is acceptable. Video and audio streaming are other common applications for lossy compression. It goes without saying that lossy compression is unacceptable in a database environment. SQL Server implements its own custom lossless compression algorithm and attempts to strike a balance between high compression rates and low CPU overhead. Compression rates and overhead will vary, and are dependent on a number of factors that we ll discuss, including fragmentation levels, the compression method chosen, and the nature of the data being compressed. Arguably the most powerful aspect of SQL Server s implementation of data compression is the fact that the compressed data remains compressed, on disk and in the buffer cache, until it s actually required, at which point only the individual columns that are required are uncompressed. Compared to a file system based compression solution, this results in the lowest CPU overhead while maximizing cache efficiency, and is clearly tailored toward the unique needs of a database management system. Let s consider some of the benefits of data compression: Lower storage costs Despite the rapidly decreasing cost of retail storage, storage found in high-end SANs, typically used by SQL Server implementations, is certainly not cheap, particularly when considering actual:usable RAID ratios and duplication of data for various purposes, as figure 9.11 shows. Lower administration costs As databases grow in size, disk I/O bound administration tasks such as backups, DBCC checks, and index maintenance take longer and longer. By compressing certain parts of the database, we re able to reduce the administration impact. For example, a database that s compressed to half its size will take roughly half the time to back up.2
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In addition to data compression, SQL Server 2008 introduces backup compression, covered in detail in the next chapter.
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Figure 9.11 Compared to retail disk, enterprise SAN storage is expensive, a cost magnified by RAID protection and data duplication such as that shown here.
RAM and disk efficiency As mentioned earlier, compressed data read into the buffer cache will remain compressed until required, effectively boosting the buffer size. Further, as the data is compressed on disk, the same quantity of disk time will effectively read more data, thus boosting disk performance as well.
SQL Server 2008 implements two different methods of data compression: page compression and row compression. The makeup of the data in the table or index determines which of these two will yield the best outcome. As we ll see shortly, we can use supplied tools to estimate the effectiveness of each method before proceeding.
Row compression
Row compression extends the variable-length storage format found in previous versions of SQL Server to all fixed-length data types. For example, in the same manner that the varchar data type is used to reduce the storage footprint of variable length strings, SQL Server 2008 can compress integer, char, and float data in the same manner. Crucially, the compression of fixed-length data doesn t expose the data type any differently to applications, so the benefits of compression are gained without requiring any application changes. As an example, consider a table with millions of rows containing an integer column with a maximum value of 19. We could convert the column to tinyint, but not if we need to support the possibility of much larger values. In this example, significant disk savings could be derived through row compression, without requiring any application changes. An alternative to row compression is page compression, our next topic.
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