memory problem in handling large dataset
Hi, Jim: Thanks for the calculation. I think you won't mind if I cc the reply to r-help too so that I can get more info. I assume you use 4 bytes for integer and 8 bytes for float, so 300x8+50x4=2600 bytes for each observation, right? I wish I could have 500x8 G memory :) just kidding.. definately, sampling will be proceeded as the first step. Some feature selections (filtering, mainly) will be applied. Accepting Berton's suggestion, I will probably use python to do the sampling since whenever I have some "slow" situations like this, python never fails me. (I am not saying R is bad though) I understand "I get what I pay" here. But more information or experience on R's handling large dataset (like using RMySQL) will be appreciated. regards, Weiwei
On 10/27/05, jim holtman <jholtman at gmail.com> wrote:
Based on the numbers that you gave, if you wanted all the data in memory at once, you would need 4.4TB of memory, about 500X what you currently have. Each of you observation will require about 2,600 bytes of memory. You probably don't want to have more than 25% for a single object since many of the algorithms make copies. This would limit you to about 700,000 observations at a time for processing. The real question is what are you trying to do with the data. Can you partition the data and do analysis on the subsets? On 10/27/05, Weiwei Shi <helprhelp at gmail.com> wrote:
Dear Listers: I have a question on handling large dataset. I searched R-Search and I hope I can get more information as to my specific case. First, my dataset has 1.7 billion observations and 350 variables, among which, 300 are float and 50 are integers. My system has 8 G memory, 64bit CPU, linux box. (currently, we don't plan to buy more memory).
R.version
_ platform i686-redhat-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 2 minor 1.1 year 2005 month 06 day 20 language R If I want to do some analysis for example like randomForest on a dataset, how many max observations can I load to get the machine run smoothly? After figuring out that number, I want to do some sampling first, but I did not find read.table or scan can do this. I guess I can load it into mysql and then use RMySQL do the sampling or use python to subset the data first. My question is, is there a way I can subsample directly from file just using R? Thanks, -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III
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-- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III