Hi Ralf,
Ralf B wrote:
I am running a simple SQL SELECT statement that involvs 50k + data
points using R and the RJDBC interface. I am facing very slow response
times in both the RGUI and the R console. When running this SQL
statement directly in a SQL client I have processing times that are a
lot lot faster (which means that the SQL statement itself is not the
problem).
Did any of you compare RJDBC vs RMySQL or is there a better, more
efficient way to extract large data from databases using R? Would you
recommend dumping data out completely into flat files and working with
flat files instead? I expected that this would not be such a problem
given that businesses maintain their data in DBs and R is supposed to
be good in shifting around data. Am I doing something wrong?
Well, if you don't show people what you have done, how can anybody tell
if you are doing something wrong or not?
I have no experience with RJDBC, so cannot say anything about that.
However, I have always found RMySQL to be speedy enough. As an example:
> library(RMySQL)
Loading required package: DBI
> con <- dbConnect("MySQL", host="genome-mysql.cse.ucsc.edu", user =
"genome", dbname = "hg18")
> system.time(a <- dbGetQuery(con, "select name, chromEnd from snp129
where chrom='chr1' and chromStart between 1 and 1e8;")
+ )
user system elapsed
7.95 0.06 38.59
> dim(a)
[1] 508676 2
So 40 seconds to get half a million records. Since this is via the
internet, I have to imagine things would be much faster querying a local DB.
But then you never say what constitutes 'slow' for you, so maybe this is
slow as well?
Best,
Jim
Ralf
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James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
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