code 128 generator c# Correlating SQL Profiler with PerfMon in C#.NET

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Correlating SQL Profiler with PerfMon
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data sets. Application performance in a Microsoft SQL Server environment is directly related to the efficiency of the SQL statements involved. By reviewing the bottlenecks in SQL Server resources as revealed by PerfMon, and correlating those bottlenecks with Profiler data, you can see exactly which SQL statements have hindered performance. Such statements are obvious tuning targets.
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About the author
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Kevin Kline is the Technical Strategy Manager for SQL Server Solutions at Quest Software. A Microsoft SQL Server MVP, Kevin is a founding board member of PASS (www.sqlpass.org) and the author of several books, including the best selling SQL in a Nutshell (O Reilly). Kevin is a top-rated blogger, writer, and speaker at industry trade shows and has been active in the IT industry since 1986.
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46 Using correlation to improve query performance
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SQL Server doesn t keep statistics on the correlation between nonclustered indexes
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and the clustered index (with the exception of correlation information between
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datetime columns, if the DATE_CORRELATION_OPTIMIZATION setting is turned on).
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Instead, the optimizer assumes it has a low correlation; it assumes that a range of nonclustered index values is scattered all over the clustered index. This assumption affects the optimizer s decision whether or not to use the nonclustered index. If there is a high correlation, the optimizer will overestimate the cost of using the nonclustered index, which can cause it to disqualify the index from the query plan evaluation, resulting in a suboptimal query plan. The performance difference can be big, even by orders of magnitude. This chapter explains what it means to have a high correlation with the clustered index, why the optimizer can misjudge such situations, how to determine the correlation for your situation, and how to optimize your queries accordingly.
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NOTE
In this chapter, I ll assume that you re familiar with indexes, and more specifically with the differences between clustered and nonclustered indexes. I ll also assume that you have some experience with query tuning.
The purpose of the optimizer
It s the optimizer s job to find the best query plan for a query. What is best For the optimizer, it s the one that results in the shortest execution time. In most cases, the performance of a typical query will be determined by the number of physical reads that are needed to satisfy the query. In a worst-case scenario, you have a cold cache, which means that no data or index pages are in cache. In a best-case scenario, you have a hot cache, and all relevant data is in memory.
Using correlation to improve query performance
If the page is cached, the data can be returned immediately. Otherwise, a physical read has to be issued to retrieve the page from disk. The duration of such a physical read depends on your storage subsystem. A physical read is typically several orders of magnitude slower than a read satisfied by the cache. Physical reads come in two flavors: sequential reads and random reads. Although it depends on your storage subsystem and other factors like fragmentation, sequential reads are assumed to be significantly faster than random reads. Because of this, behind the scenes, the optimizer s primary objective is to minimize the number of physical reads and writes, and to favor sequential reads over random reads. This chapter has the same goal: to help you minimize the number of physical reads. If you want to optimize your queries for a hot cache, then this chapter won t help you because the storage engine s performance characteristics are different for cached data.
Correlation with the clustered index
The term correlation is used loosely here. We re trying to determine the chance that two consecutive nonclustered index keys refer to the same data page. And we re only considering tables that have a clustered index, although the same theory would also hold for heaps.
Low correlation
Most nonclustered indexes have a low correlation with the clustered index. Figure 1 shows such a situation for the Product table. It has a nonclustered index on Product Name and a clustered index on Product ID. The top row shows the nonclustered index values (the first 32 values). The letters in a block indicate the range of values. The number above a block indicates the number of values in that range. The bottom row shows the clustered index pages on which the row data is stored. You can see that the first nine values in the nonclustered index refer to seven different pages in the clustered index, most of which are not shown. You can also see that if the storage engine would follow the nonclustered index, it would have to jump back and forth in the clustered index to retrieve the corresponding data.
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