AW: [R] Reading huge chunks of data from MySQL into Windows R

[Dubravko Dolic]

In my (limited) experience R is more powerful concerning data manipulation. An example: I have a vector holding a user id. Some user ids can appear more than once. Doing SELECT COUNT(DISTINCT userid) on MySQL will take approx. 15 min. Doing length(unique(userid)) will take (almost) no time...

So I think the other way round will serve best: Do everything in R and avoid using SQL on the database...




-----Ursprüngliche Nachricht-----
Von: bogdan romocea [mailto:br44114 at yahoo.com] 
Gesendet: Montag, 6. Juni 2005 16:27
An: Dubravko Dolic
Cc: r-help at stat.math.ethz.ch
Betreff: RE: [R] Reading huge chunks of data from MySQL into Windows R

You don't say what you want to do with the data, how many columns you
have etc. However, I would suggest proceeding in this order:
1. Avoid R; do everything in MySQL.
2. Use random samples.
3. If for some reason you need to process all 160 million rows in R, do
it in a loop. Pull no more than, say, 50-100k rows at a time. This
approach would allow you to process billions of rows without the memory
and disk requirements going through the roof.

hth,
b.


-----Original Message-----
From: Dubravko Dolic [mailto:Dubravko.Dolic at komdat.com]
Sent: Monday, June 06, 2005 9:31 AM
To: r-help at stat.math.ethz.ch
Subject: [R] Reading huge chunks of data from MySQL into Windows R


Dear List,

 

I'm trying to use R under Windows on a huge database in MySQL via ODBC
(technical reasons for this...). Now I want to read tables with some
160.000.000 entries into R. I would be lucky if anyone out there has
some good hints what to consider concerning memory management. I'm not
sure about the best methods reading such huge files into R. for the
moment I spilt the whole table into readable parts stick them together
in R again. 

 

Any hints welcome.

 

 

 

Dubravko Dolic

Statistical Analyst

 

Email: dubravko.dolic at komdat.com


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