W
Wes Gamble
All,
I am currently using the parseexcel gem to parse an Excel file so that I
can save it's data into a database (in a Rails app.).
When I run it against an Excel file with ~42000 rows and 11 columns (the
spreadsheet is about 10MB in size), it takes the better part of 10
minutes to parse the file and uses about 200 MB of memory. I'm not
actually sure if the parsing completes successfully - the app. stops
right after the parsing appears to finish.
It would seem that I have two issues - memory usage and performance.
Is anyone aware of a much faster way to parse large Excel files?
Is anyone aware of a "windowing" scheme that uses a set amount of memory
to transfer portions of the data to a client requesting the data so as
to keep the memory required at a fixed level?
Should I look into using the Win32OLE libraries with the hope that it
would be more efficient?
I am transferring this data into a SQL Server database, so perhaps I
could look into some M$-native method of moving this data.
Any thoughts or advice is appreciated.
Thanks,
Wes Gamble
I am currently using the parseexcel gem to parse an Excel file so that I
can save it's data into a database (in a Rails app.).
When I run it against an Excel file with ~42000 rows and 11 columns (the
spreadsheet is about 10MB in size), it takes the better part of 10
minutes to parse the file and uses about 200 MB of memory. I'm not
actually sure if the parsing completes successfully - the app. stops
right after the parsing appears to finish.
It would seem that I have two issues - memory usage and performance.
Is anyone aware of a much faster way to parse large Excel files?
Is anyone aware of a "windowing" scheme that uses a set amount of memory
to transfer portions of the data to a client requesting the data so as
to keep the memory required at a fixed level?
Should I look into using the Win32OLE libraries with the hope that it
would be more efficient?
I am transferring this data into a SQL Server database, so perhaps I
could look into some M$-native method of moving this data.
Any thoughts or advice is appreciated.
Thanks,
Wes Gamble