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Extra info for (1,k)-configuration facets for the generalized assignment problem - Elsie Sterbin Gottlieb and M. R. Rao

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Another client found that SQL Server was coming up with some very poor query execution plans on its 8-processor server. Disabling SQL Server’s parallelism feature resulted in the faster execution plans the client had expected, suggesting that SQL Server’s query optimizer was underestimating the cost of using parallelism when it considered different execution plans. Design Issues Database design can kill SQL Server’s performance. For example, I one worked with a real estate company that stored, among other things, street address information.

How you record your performance measurements depends on how much time and effort you want to invest into performance management. At the least, you need to jot down key performance measurements on a piece of graph paper. You may prefer to enter the information into an Excel spreadsheet. If you’re really on the ball, you’ll enter the information into a SQL Server database, from which you can report on the information and analyze it more easily. No matter what you decide to do, you must document and archive your performance measurements for them to be of any use.

However, estimated plans can often vary widely from actual plans. One of the most important steps in SQL Server’s execution plan generation is an estimation of the server’s current workload. A busy server will generate markedly different execution plans than a less-busy server, which will have time to search for more efficient plans. Estimated plans don’t take any current server activity into account. Another Query Analyzer feature lets you see the trace associated with your query. A trace is basically a list of each step SQL Server took to complete the query, including the duration (in milliseconds) for each step.

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(1,k)-configuration facets for the generalized assignment problem - Elsie Sterbin Gottlieb and M. R. Rao by Gottlieb E. S.

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