Debugging Plan Issues in VB.NET

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Determining when to use a hint requires an understanding of the workings of the Query Optimizer, how it might not be making a correct decision, and then an understanding of how the hint might change the plan generation process to address the problem. For more than half of the problems that Microsoft typically sees in support calls about query plans, issues with incorrect cardinality estimates are the primary cause of a poor plan choice. In other words, a better cardinality estimate would yield a plan choice that is acceptable. In other cases, more complex issues around costing, physical data layout, lock escalation, memory contention, or other issues were factors in the performance degradation. This section explains how to identify cardinality estimation errors and then use hints to correct poor plan choices, as these are usually something that can be xed without buying new hardware or otherwise altering the host machine. The primary tool to identify cardinality estimation errors is the statistics pro le output in SQL Server. When enabled, this mode generates the actual cardinalities for each query operator in the plan. These can be compared against the Query Optimizer s estimated cardinalities to nd any differences. As cardinality estimation is performed from the bottom of the tree upwards (right to left in the showplan s graphical display), errors propagate. Usually, the location of the lowest error indicates where to consider hints. Other tools exist to track down performance issues with query plans. Setting statistics time on is a great way to determine run-time information for a query. SQL Pro ler is a great tool for tracking deadlocks and other system-wide issues that can be captured by tracing. DBCC MEMORYSTATUS is an excellent tool to nd out what components in the system are causing memory pressure within SQL Server. Most of these tools fall outside of the scope of this chapter, though these tools can be helpful for some plan issues. It is recommended that cardinality issues be researched rst when there are concerns about plan quality, as this is probably the most common issue.
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Microsoft SQL Server 2008 Internals
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In Figure 8-77, we run a query against one of the catalog views to see how each operator s estimated and actual cardinalities match up.
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SET STATISTICS PROFILE ON; SELECT * FROM sys.objects;
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FIGURE 8-77 Statistics pro le ouput
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Note The EstimateRows and EstimateExecutions columns have been moved from the actual output order for display in the screenshot. While the estimation for this query is actually perfect, it is common for estimates to vary from the actual cardinalities, especially as queries get more complex. Usually, you d like them to be close enough that the plan choice won t change, which is almost always less than an order of magnitude off by the top of the tree. Also, note that the EstimateRows number is the average per-execution, whereas Rows is merely total rows. You can divide Rows by Executes to get the numbers to be comparable. If an error is found by looking at the statistics pro le output, this can help identify a place where the Query Optimizer has used bad information to make a decision. Usually, updating statistics with fullscan can help isolate whether this is an issue with out-of-date or undersampled statistics. If the Query Optimizer makes a poor decision even with up-to-date statistics, then this might mean that there is an out-of-model condition with the Query Optimizer. For example, if there is a strong data correlation between two columns in a query, this could cause errors in the cardinalities seen in the query. Once an out-of-model condition is identi ed as being the cause of a poor plan choice, hints are the mechanism to correct the plan choice and to use a better query plan. This section describes how most of the query or table hints t within the context of the Query Optimizer s architecture, including situations where it might be appropriate to use hints.
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