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There is not an enormous amount of metadata information relating to data compression. The catalog view sys.partitions has a data_compression column and a data_compression_desc column. The data_compression column has possible values of 0, 1, and 2, corresponding to data_compression_desc values of NONE, ROW, and PAGE. Keep in mind that although row compression is always performed, page compression is not. Even if sys.partitions indicates that a table or partition is page-compressed, that just means that page compression is enabled. Each page is analyzed individually, and if a page is not full, or if compression would not save enough space, the page is not compressed. You can also inspect the dynamic management function sys.dm_db_index_operational_stats. This table-valued function returns the following compression-related columns:
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page_compression_attempt_count The number of pages that were evaluated for PAGE-level compression for speci c partitions of a table, index, or indexed view. Includes pages that were not compressed because signi cant savings could not be achieved. page_compression_success_count The number of data pages that were compressed by using PAGE compression for speci c partitions of a table, index, or indexed view.
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SQL Server 2008 also provides a stored procedure called sp_estimate_data_compression_savings, which can give you some idea of whether compression provides a large space savings or not.
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This procedure samples up to 5,000 pages of the table and creates an equivalent table with the sampled pages in tempdb. Using this temporary table, SQL Server can estimate the new table size for the requested compression state (NONE, ROW, or PAGE). Compression can be evaluated for whole tables or parts of tables. This includes heaps, clustered indexes, nonclustered indexes, indexed views, and table and index partitions. Keep in mind that the result is only an estimate and your actual savings can vary widely based on the llfactor and the size of the rows. If the procedure indicates that you can reduce your row size by 40 percent, you might not actually get a 40-percent space savings for the whole table. For example, if you have a row that is 8,000 bytes long and you reduce its size by 40 percent, you still can t only one row on a data page and your table still needs the same number of pages. You may get results from running sp_estimate_data_compression_savings that indicate that the table will grow. This can happen when many rows in the table use almost the whole maximum size of the data types, and the addition of the overhead needed for the compression information is more than the savings from compression. If the table is already compressed, you can use this procedure to estimate the size of the table (or index) if it were to be uncompressed.
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The main motivation for compressing your data is to save space with extremely large tables, such as data warehouse fact tables. A second goal is to increase performance when scanning a table for reporting purposes, because far fewer pages need to be read. You need to keep in mind that compression comes at a cost: there is a tradeoff between the space savings and the extra CPU overhead to compress the data for storage and then uncompress the data when it needs to be used. On a CPU-bound system, you may nd that compressing your data can actually slow down your system considerably. Page compression provides the most bene t for systems that are I/O-bound, with tables for which the data is written once and then read repeatedly, as in the situations I mentioned in the previous paragraph: data warehousing and reporting. For environments with heavy read and write activity, such as OLTP applications, you might want to consider enabling row compression only and avoid the costs of analyzing the pages and rebuilding the CI record. In this case, the CPU overhead is minimal. In fact, row compression is highly optimized so that it is visible only at the storage engine layer. The relational engine (query processor) doesn t need to deal with compressed rows at all. The relational engine sends uncompressed rows to the storage engine, which compresses them if required. When returning rows to the relational engine, the storage engine waits as long as it can before uncompressing them. In the storage engine, comparisons can be done on compressed data, as internal conversions can convert a data type to its compressed form before comparing to data in the table. In addition, only columns requested by the relational engine need to be uncompressed, as opposed to uncompressing an entire row.
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