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FIGURE 8-44 Compute Scalar, only needed when computing new values
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Like all options considered by the Query Optimizer, indexed view alternatives are generated and stored in the Memo and are compared using costing equations against other possible plans. Alternatives including partial matches cost the residual operations as well, and this means that an indexed-view plan can be generated but not picked when the Query Optimizer considers other plans to have lower costs. Indexed views are maintained as part of the update processing for tables on which the view is based. This makes sure that the view provides a consistent result if it is selected by the Query Optimizer for any query plan. Some query operations are incompatible with this design guarantee. As a result, SQL Server places some restrictions on the set of supported constructs in indexed views to make sure that the view can be created, matched, and updated as ef ciently as possible. The description of the restrictions in SQL Server Books Online is very long and detailed, and this can make it very dif cult to understand the higher-level rules. For updating indexed views, the core question behind the restrictions is Can the query processor compute the necessary changes to the Indexed View clustered and nonclustered indexes without having to recompute the whole indexed view If so, the query processor can perform these changes ef ciently as part of the maintenance of the base tables that are referenced in the view. This property is relatively easy for lters, projections (compute scalar), and inner joins on keys. Operators that destroy or create data are more dif cult to maintain, so often these are restricted from use in indexed views. How indexed views are represented in update plans is discussed in the section entitled Updates, later in this chapter.
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As SQL Server is used to store more and more data, management of very large databases becomes a bigger concern for DBAs. First, the time to perform operations like an index rebuild grows with the data size, and eventually this can affect system availability. Second, the size of large tables makes performing operations dif cult because the system is often strained for resources, such as temp space, log space, and physical memory. Table and index partitioning can help you manage large databases better and minimize downtime. Physically, partitioned tables and indexes are really N tables or indexes that store a fraction of the rows. When comparing this to their nonpartitioned equivalents, the difference in the plan is often that the partitioned case requires iterating over a list of tables or a list of indexes to
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The Query Optimizer
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return all the rows. In SQL Server 2005, this was represented using an APPLY operator, which is essentially a nested loops join. In the 2005 representation, a special table of partition IDs was passed in as parameters to the query execution component in a join to iterate over each partition. While this works well in most cases, there are some important scenarios that didn t work well with this model. For example, there is a restriction in parallel query plans that requires that the parallel table or index scan feature (where multiple threads read rows from a table at once to improve performance) did not work on the inner side of a nested loops join, and this was not possible to x before SQL Server 2005 shipped. Unfortunately, this is the majority case for table partitioning. In addition, the APPLY representation enabled join collocation, where two tables partitioned in the same way can be joined very ef ciently. Unfortunately, this turned out to be less common in practice than was foreseen when the feature was originally designed. For reasons like this, the representation was re ned further in the 2008 version of the product. SQL Server 2008 represents partitioning in most cases by storing the partitions within the operator that accesses the partitioned table or index. This provides a number of bene ts, like enabling parallel scans to work properly. It also removed a number of other differences between partitioned and nonpartitioned cases in the Query Optimizer that manifested themselves as missed performance optimizations. Hopefully this makes it easier to deploy partitioning in applications that started out nonpartitioned. Listing 8-13 contains the example to show this new design. Figure 8-45 contains the resulting query plan for SQL Server 2008 over partitioned tables.
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