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The cost of the plan reflects the estimated time to complete the query For each query, the optimizer must analyze the possible plans and choose the one with the lowest estimated cost Some complex statements have millions of possible execution plans In these cases, the query optimizer does not analyze all possible combinations Instead, it tries to find an execution plan that has a cost reasonably close to the theoretical minimum Later in this section, I'll explain some ways the optimizer can reduce the amount of time it spends on optimization The lowest estimated cost is not necessarily the lowest resource cost; the query optimizer chooses the plan that most quickly returns results to the user with a reasonable cost in resources.

This is a very ef cient plan that performs a seek within the index to reach the low boundary row (row number 6 in this case), followed by a partial scan (not visible in the plan), until it reaches the high boundary row (row number 10) Only the rows of the requested page of results are scanned within the index If your application design is such that it disconnects after each request, obviously the temporary table will be gone as soon as the creating session disconnects In such a case, you might want to create a permanent table that is logically temporary You can achieve this by naming the table some_name<some_identi er> for example, T<guid> (Global Unique Identi er).

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20090212 12:30:15 .123 2009-02-12T12:30:15 .123 20090212 20090212 12:30 2009-02-12T12:30 20090212 20090212 2009-02-12 20090212 12:30:15 .1234567 2009-02-12 12:30:15 .1234567 2009-02-12T12:30:15 .1234567 20090212 2009-02-12 20090212 12:30:15 .1234567 +02:00 2009-02-12 12:30:15 .1234567 +02:00 2009-02-12T12:30:15 .1234567 20090212 2009-02-12

You also need to develop a garbage-collection (cleanup) process that gets rid of tables that the application didn t have a chance to drop explicitly in cases where it terminated in a disorderly way In cases where you need to support large result sets or a high level of concurrency, you will have scalability issues related to tempdb resources You can develop a partitioned solution that materializes only a certain number of pages and not all of them for example, 1,000 rows at a time Typically, users don t request more than the rst few pages anyway If a user ends up requesting pages beyond the rst batch, you can materialize the next partition (that is, the next 1,000 rows).

YYYYMMDD YYYY-MM-DD YYYYMMDD hh:mm:ss .nnnnnnn YYYY-MM-DD hh:mm:ss .nnnnnnn YYYY-MM-DDThh:mm:ss .nnnnnnn YYYYMMDD YYYY-MM-DD

For example, processing a query in parallel (using multiple CPUs simultaneously for the same query) typically uses more resources than processing it serially using a single CPU, but the query completes much faster in parallel The optimizer will propose a parallel execution plan to return results, and SQL Server will use such a parallel plan for execution if the load on the server is not adversely affected Optimization itself involves several steps The trivial plan optimization is the first step The idea behind trivial plan optimization is that cost-based optimization is expensive to initialize and run The optimizer can try many possible variations in looking for the cheapest plan If SQL Server knows by investigating the query and the relevant metadata that there is only one viable plan for a query, it can avoid a lot of the work required to initialize and perform cost-based optimization.

When you don t care about materializing the result set in a temporary table for multipage access, you might want to consider using a table variable to hold the rst batch of pages (for example, 1,000 rows) Table variables don t involve recompilations, and they suffer less from logging and locking issues The optimizer doesn t maintain distribution statistics for table variables, so you should be very cautious and selective in choosing the cases to use them for But when all you need to do is store a small result set and scan it entirely anyway, this technique is ne Once you re done using this table, you can drop it:.

YYYYMMDD hh:mm:ss .nnnnnnn [+|-]hh:mm YYYY-MM-DD hh:mm:ss .nnnnnnn [+|-]hh:mm YYYY-MM-DDThh:mm:ss .nnnnnnn YYYYMMDD YYYY-MM-DD

DROP TABLE #SalesRN;

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