partitioning

Here is the code, in python: # function for pentagonal numbers def pent (n): return int((0.5*n)*((3*n)-1)) # function for generalized pentagonal numbers def gen_pent (n): return pent(int(((-1)**(n+1))*(round((n+1)/2)))) # array for storing partitions - first ten already stored partitions = [1, 1, 2, 3, 5, 7, 11, 15, 22, 30, 42] # function to generate partitions def partition (k): if (k < len(partitions)): return partitions[k] total, sign, i = 0, 1, 1 while (k - gen_pent(i)) >= 0: sign = (-1)**(int((i-1)/2)) total += sign*(partition(k - gen_pent(i))) i += 1 partitions.insert(k,total) return

I'm trying to Partition a Table by both Year and Month . The Column through which I'll partition is a datetime type column with an ISO Format ('20150110', 20150202', etc). For example, I have sales data for 2010, 2011, 2012. I'd Like the data to be partitioned by year and each year be partitioned by month as well. (2010/01, 2010/02, ... 2010/12, 2011/01, ... 2015/01...) E.X: Sales2010Jan, Sales2010Feb, Sales2011Jan, Sales2011Feb, Sales2012Dec, etc. My Question is: is it even possible? If it is, how an I automate the process using SSIS? Julien Vavasseur SSIS is an ETL (extract, transform, load)

In a recent project the "lead" developer designed a database schema where "larger" tables would be split across two separate databases with a view on the main database which would union the two separate database-tables together. The main database is what the application was driven off of so these tables looked and felt like ordinary tables (except some quirky things around updating). This seemed like a HUGE performance problem. We do see problems with performance around these tables but nothing to make him change his mind about his design. Just wondering what is the best way to do this, or if

This is a question that's been lingering in my mind for some time ... Suppose I have a list of items and an equivalence relation on them, and comparing two items takes constant time. I want to return a partition of the items, e.g. a list of linked lists, each containing all equivalent items. One way of doing this is to extend the equivalence to an ordering on the items and order them (with a sorting algorithm); then all equivalent items will be adjacent. But can it be done more efficiently than with sorting? Is the time complexity of this problem lower than that of sorting? If not, why not?

We have recently updated our logging to use Azure table storage, which owing to its low cost and high performance when querying by row and partition is highly suited to this purpose. We are trying to follow the guidelines given in the document Designing a Scalable Partitioning Strategy for Azure Table Storage . As we are making a great number of inserts to this table (and hopefully an increasing number, as we scale) we need to ensure that we don't hit our limits resulting in logs being lost. We structured our design as follows: We have a Azure storage account per environment (DEV, TEST, PROD).