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Ron Talmage is a mentor and cofounder of Solid Quality Mentors. He s a SQL Server MVP, a PASS Regional Mentor, and current president of the Pacific Northwest SQL Server Users Group. He has been writing white papers and articles on SQL Server since way back when.
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32 Partitioning for manageability (and maybe performance)
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Large tables are a bane to database administrators (DBAs). It takes seemingly forever to rebuild indexes on tables with many millions or billions of rows, not to mention the exorbitant space required to do so. For example, you must maintain more than 1 terabyte (TB) of database free space just to rebuild the clustered index on a 1 TB table, because both the old and new index must coexist until the rebuild is done. Developers are challenged to design applications that can load, purge, or archive vast amounts of data efficiently while providing high data availability. SQL Server s table and index partitioning feature is a great way to address these manageability and load/purge/archive issues. But you must be aware that partitioning is a fundamental structural change that influences execution plans and query performance. Care is needed to implement partitioning wisely to maximize performance and manageability benefits while avoiding potential problems. Before I discuss important partitioned table design considerations and common pitfalls, let s first review core partitioning concepts.
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The table partitioning feature is available in Enterprise and Developer editions of SQL Server 2005 and later versions. Partitioning a table or index divides it into partitions (subsets) based on the partitioning column value. Each partition has welldefined boundaries to ensure that a given partitioning column value is mapped to exactly one partition and a partition exists for all possible values. A table or index can be partitioned based on any persisted column value, with the exception of columns of data type text, ntext, image, xml, timestamp, varchar(max), nvarchar(max), varbinary(max), and FILESTREAM. The timestamp data type is disallowed because it is a synonym for the rowversion data type and not related to date or time. Each partition is a complete heap or b-tree structure, as illustrated in figures 1 and 2. This allows a partition to be managed individually in data definition language (DDL) operations when it is advantageous to do so. For example, a
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Partitioning for manageability (and maybe performance)
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data pages Partition 1 Partition 2
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Figure 1 Partitioned heap with two partitions
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fragmented index partition can be rebuilt or reorganized while avoiding other nonfragmented partitions of the same index. Furthermore, when an individual index partition is rebuilt, the available filegroup free space needed is determined by the partition size rather than the size of entire index. Yet SQL queries use a partitioned table like any other table, so partitioning is entirely transparent to application queries. The query optimizer strives to generate an execution plan with operators and filters to access only the partitions needed by a given query based on the partitioning column value. SQL 2008 includes improvements to better optimize partitioned table queries. Partitioning is flexible so that a table and its indexes can be partitioned similarly, differently, or not at all. In practice, it is often advantageous to partition a table and its indexes similarly, a technique known as alignment. When aligned, corresponding data and index partitions have the same partitioning column and boundaries, and therefore include the same subset of data. Aligning tables and indexes makes it possible to switch an entire partition between objects with identical schema in a single DDL operation, as long as the source and target partitions reside on the same filegroups. Switching data in this manner allows massive amounts of data to be loaded or archived/purged nearly instantaneously, and provides a tremendous performance benefit compared to traditional data manipulation language (DML) methods. No physical data movement is required for the switch operation, and logging is minimal regardless of the database recovery model, again, as long as everything involved resides on the same filegroups. Partitioning allows granular control of data placement within a single table. A database administrator (DBA) can place partitions on separate physical disks/arrays via filegroups in order to place infrequently accessed historical data on less-expensive, slower storage. Controlling intra-table data placement is difficult to accomplish without partitioning. Queries that scan vast amounts of data may experience a performance benefit from partitioning. Depending on the particulars of a query, SQL Server can scan only those partitions needed, scan partitions in parallel using multiple threads, or join similarly partitioned tables more efficiently. Together with thoughtful file placement and multiple processors, performance of these concurrent scan operations can be maximized with partitioning.
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