I am using R2.6.0 on “Windows Small Business Server 2003”.  I apologize if the answer to my question is available…I have searched but have not found anything that I thought helped me.
   
  I have a dataframe that contains ~4.5 million rows and 5 columns.  (see memory and df details below).  I am trying to save the dataframe to a MS SQL Server database, using the “sqlSave” function.  The code below seems to work, but takes several hours.
   
  “sqlSave(channel, dat=idxdata, varTypes=c(ddates="datetime") )”
   
  Any tips how I can speed things up? Or is my dataframe so large that it is going to take a while? (I have ~20 dataframes that I need to save to SQL, so speed is somewhat important.)  Is there an altogether different approach I should consider taking?
   
  FYI, here is information re: the dataframe and memory on my system.  Please let me know if there is any further information I should provide.
   
  > memory.size(max = F) #reports amount of memory currently in use
  [1] 131.8365
   
  > str(idxdata)
  'data.frame':   4474553 obs. of  5 variables:
   $ idkey   : int  1003 1003 1003 1003 1003 1003 1003 1003 1003 1003 ...
   $ nnd     : Factor w/ 25 levels "01","01C","02",..: 1 1 1 1 1 1 1 1 1 1 ...
   $ curcdd  : Factor w/ 2 levels "CAD","USD": 2 2 2 2 2 2 2 2 2 2 ...
   $ ddates:Class 'Date'  num [1:4474553] 6942 6943 6944 6945 6948 ...
   $ idx     : num  100 100 100 100 100 100 100 100 100 100 ...
   
  > object.size(idxdata)
  [1] 125289688
   
   
   
   
   
   

       
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