mvc display pdf from byte array

mvc display pdf from byte array

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Provided that a covering index is in place defined with the cursor ordering columns as the index keys and the measure you re aggregating as an included column, the cursor solution scans the data only once without the need for sorting . If you have p partitions with an average of r rows per partition, and a relatively uniform distribution of rows per partition, you can express the cost of this solution as p r + p r o, where o, as before, represents the cursor overhead . Therefore, the complexity of this solution is linearly proportional to the number of partitions and linearly proportional to the partition size . Assuming you have the aforementioned index in place, the cost of the set-based solution can be expressed as p r + p (r + r 2)/2. With respect to the number of partitions, the complexity of the set-based solution is linear, but with respect to the partition size, it s quadratic, or O(n2) . If the partition size grows by a factor of f, the cost increases by almost f 2 . Without the aforementioned index in place, of course, things are even worse . When the partition size is small, the set-based solution will typically be faster than the cursor solution . That s when the cursor s overhead exceeds the set-based solution s extra work of

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scanning more data . However, as the partition size increases, the cursor solution s cost grows linearly while the set-based solution s cost grows much more rapidly quadratically, and at some point the cursor solution becomes faster . Tests that I ve done show that up to a partition size of about 500 rows the set-based solution tends to perform better, whereas beyond 500 rows the cursor solution performs better . I ll provide actual benchmark results later in this section . So now you can see that in certain cases a T-SQL cursor-based solution can be faster than a set-based solution . If you are willing to consider an iterative approach based on cursors, you should consider a CLR-based solution . Using a CLR procedure, you can use a data reader to iterate through the records one at a time, then pipe the rows in the result back to the caller . Think of a .NET data reader as a faster cursor than the T-SQL one . You still pay some overhead to manipulate each row, but the overhead is much lower than that of the T-SQL cursor . Here s the definition of a CLR stored procedure called SalesRunningSum that implements our task in C#:

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using using using using using System; System.Data; System.Data.SqlClient; System.Data.SqlTypes; Microsoft.SqlServer.Server;

For each such period, you need to return the starting date, ending date, duration in days, and the peak (maximum) price. Let's start with a solution that does not use row numbers. The first step here is to filter only the rows where the price is greater than or equal to 50. Unlike the traditional problem where you really have gaps in the data, here the gaps appear only after filtering. The whole sequence still appears in the Stocks table. You can use this fact to your advantage. Of course, you could take the long route of calculating the maximum date within the group (the first date that is both later than or equal to the current date and followed by a gap). However, a much simpler and faster technique to calculate the grouping factor would be to return the first date that is greater than the current, on which the stock's price is less than 50. Here, you still get the same grouping factor for all elements of the same target group, yet you need only one nesting level of subqueries instead of two. Here's the query that generates the desired result shown in Table 6-31: SELECT MIN(dt) AS startrange, MAX(dt) AS endrange, DATEDIFF(day, MIN(dt), MAX(dt)) + 1 AS numdays, MAX(price) AS maxprice FROM (SELECT dt, price, (SELECT MIN(dt) FROM dbo.Stocks AS S2 WHERE S2.dt > S1.dt AND price < 50) AS grp FROM dbo.Stocks AS S1 WHERE price >= 50) AS D GROUP BY grp;

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