Bin packing part 1: Setting a baseline

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Some problems can only be solved by brute-forcing every possible combination. The problem with such an approach, is that execution time grows exponentially as the amount of input data grows – so that even on the best possible hardware, you will get inacceptable performance once the input data goes beyond the size of a small test set. These problems are called “NP-complete” or “NP-hard”, and the most viable way to deal with them is to use an algorithm that finds a solution that, while not perfect, is at least good enough – with a performance that, while not perfect, is…

Poor men see sharp – more cursor optimization

After making my post on cursor optimization I received some comments that triggered me to do some further investigation. Adam Machanic wrote in my blog’s comments that using SQLCLR to loop over a SqlDataReader would be much faster than any T-SQL based cursor. And Erland Sommarskog wrote in a usenet thread that he has some colleagues who think that a “poor man’s cursor” is always better than a real cursor. So I decided to give these options a try and see what comes out in a real test. I simply reused the test cases I had already used for testing…

Curious cursor optimization options

The best way to optimize performance of a cursor is, of course, to rip it out and replace it with set-based logic. But there is still a small category of problems where a cursor will outperform a set-based solution. The introduction of ranking functions in SQL Server 2005 has taken a large chunk out of that category – but some remain. For those problems, it makes sense to investigate the performance effects of the various cursor options. I am currently preparing a series of blog posts on a neat set-based solution I found for a problem that screams “cursor” from…

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