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19 Jul 2017 ... MVC iTextSharp Example: Convert HTML to PDF using iTextSharp in ASP. ... The HTML of the Grid sent from the View is extracted from the ...

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As the number of dimensions grows, the number of possible GROUP BY queries increases dramatically For n dimensions, there are 2 n different queries With 3 dimensions, you're looking at 8 possible requests; with 4 dimensions, there are 16 With 10 dimensions (the maximum number of grouping expressions we will be able to use with CUBE), users could request any one of 1024 different GROUP BY queries Simply put, adding the option WITH CUBE to a query with all dimensions specified in the GROUP BY clause generates one unified result set out of the result sets of all the different GROUP BY queries over subsets of the dimensions If you think about it, Analysis Services cubes give you similar functionality, but on a much larger scale and with substantially more sophisticated options.

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Like the T-SQL cursor-based solution, the CLR-based solution also has linear complexity with respect to both the number of partitions and the partition size except that the former is about five times slower . I ran a benchmark to compare the different solutions and to show how they scale . Because all solutions scale linearly with respect to changes in number of partitions, I used a constant

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However, when you don't need to support dynamic analysis on such a scale and at such a level of sophistication, the option WITH CUBE allows you to achieve this within the relational database Because each set of dimensions generates a result set with a different subset of all possible result columns, the designers who implemented CUBE and ROLLUP had to come up with a placeholder for the values in the unneeded columns The designers chose NULL So, for example, all rows from the result set of a GROUP BY empid, custid would have NULL in the orderyear result column This allows all result sets to be unified into one result set with one schema.

number of partitions . However, I did use varying partition sizes to show the different manner in which the solutions scale . The results of the benchmark are shown in Figure 8-2 .

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As an example, the following CUBE query returns all possible aggregations (total quantities) of orders based on the dimensions empid, custid, and orderyear, generating the output shown in Table 6-32: SELECT empid, custid, YEAR(orderdate) AS orderyear, SUM(qty) AS totalqty FROM dboOrders GROUP BY empid, custid, YEAR(orderdate) WITH CUBE;.

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200 300 400 500 600 700 800 # Rows per Partition (with 1,000 partitions) T-SQL Cursor CLR Data Reader

The only structure that keeps track of the data belonging to a heap is a bitmap page (or a series of pages if needed) called the Index Allocation Map (IAM). This bitmap has pointers to the rst eight pages allocated from mixed extents and a representative bit for each extent in a range of 4 GB in the le. The bit is 0 if the extent it represents does not belong to the object owning the IAM page and 1 if it does. If one IAM is not enough to cover all the object s data, SQL Server will maintain a chain of IAM pages. SQL Server uses IAM pages to move through the object s data when the object needs to be scanned. SQL Server loads the object s rst IAM page and then directs the disk arm sequentially to fetch the extents by their le order. As long as there s no le system fragmentation of the data les, the scan is done in a sequential manner on disk. As you can see in Figure 4-15, SQL Server maintains internal pointers to the rst IAM page and the rst data page of a heap. Those pointers can be found in the system view sys.system_internals_allocation_units.

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empid custid orderyear totalqty 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 4 4 4 4 NULL NULL NULL NULL NULL NULL NULL NULL NULL A A B B C C NULL B B C C NULL A A B B C C D D NULL A A A NULL NULL A A A A B B B B 2002 NULL 2002 NULL 2003 NULL NULL 2003 NULL 2004 NULL NULL 2002 NULL 2004 NULL 2002 NULL 2002 NULL NULL 2003 2004 NULL NULL NULL 2002 2003 2004 NULL 2002 2003 2004 NULL 12 12 20 20 14 14 46 12 12 20 20 32 10 10 15 15 22 22 30 30 77 40 10 50 50 205 22 40 10 72 20 12 15 47

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