in reply to Re: Best way to store/access large dataset?
in thread Best way to store/access large dataset?
Thanks for the response! I'm currently playing with your code trying to get it to work on my dataset. Currently it's just returning to the prompt after about 2.5 minutes without displaying anything.(I vaguely remember something about there being an issue creating a text file in windows and then trying to read it in while working in linux?)
Once I figure out what I'm doing wrong, I'm going to attempt modifying it to create individual totals for each attribute by category. So the end output would be a list of each attribute in the first column, then each category would be listed across the top, then the totals for each attribute in each category would fill in the table.
Like so:
#Table Square Circle Triangle Rectangle 1 4 4 0 4 2 4 4 0 0 3 0 0 4 4 4 0 4 4 4 5 0 0 4 0 6 0 0 0 4 7 0 4 0 4 8 4 4 0 4 9 0 0 0 4 10 0 0 4 0 11 0 0 4 4 12 4 4 4 0 13 0 4 0 0 14 0 4 0 4 15 0 4 0 0 16 4 0 0 0 17 4 0 0 0 18 0 4 0 0 19 4 4 4 4 20 0 4 4 4 21 0 0 0 4 22 4 4 4 4 23 4 4 4 4 24 0 0 0 0 25 0 0 0 0 26 4 4 4 0 27 0 0 4 0 28 3 0 0 4 29 0 0 0 4 30 3 0 0 4
The ultimate goal of this will to be pulling data from a database and creating the binaries on the fly through a series of calculations, and then using this script to determine the next series of data points to pull from the database. (This serves as a filter.) But with the database connections in mind, it seems like using threads to speed this up would not be recommended. So do you see a way to fork this? Or would forking not help in this case? I think I read that forking will chew up some more memory, but I think I can handle that overhead. (I have 20 cores/40 threads and 192GB ram to work with.)
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Re^3: Best way to store/access large dataset?
by erix (Prior) on Jun 22, 2018 at 19:53 UTC | |
by Speed_Freak (Sexton) on Jun 28, 2018 at 19:09 UTC | |
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Re^3: Best way to store/access large dataset?
by Speed_Freak (Sexton) on Jun 22, 2018 at 15:29 UTC | |
by Speed_Freak (Sexton) on Jun 22, 2018 at 16:11 UTC |