You can pass the entire df, example:
> data(iris)
> iris$sp = as.character(iris$Species)
> o=.jarray(lapply(iris, .jarray))
> .jcall("C",,"df",o)
df, 6 variables
[0]: double[150]
[1]: double[150]
[2]: double[150]
[3]: double[150]
[4]: int[150]
[5]: String[150]
Java code:
public class C {
static void df(Object df[]) {
int n;
System.out.println("df, " + (n = df.length) + " variables");
int i = 0;
while (i < n) {
if (df[i] instanceof double[]) {
double d[] = (double[]) df[i];
System.out.println("["+i+"]: double["+d.length+"]");
} else if (df[i] instanceof int[]) {
int d[] = (int[]) df[i];
System.out.println("["+i+"]: int["+d.length+"]");
} else if (df[i] instanceof String[]) {
String s[] = (String[]) df[i];
System.out.println("["+i+"]: String["+s.length+"]");
} else {
System.out.println("["+i+"]: some other type...");
}
i++;
}
}
}
Normally, you wouldn't pass the entire df but instead have methods for the
types you care about as the modeling function - that's more Java-like approach,
but either is valid and there is no difference in efficiency.
Cheers,
Simon
> On Dec 15, 2015, at 12:50 PM, Ing. Jaroslav Kuchař
> <jaroslav.kuc...@fit.cvut.cz> wrote:
>
> Dear all,
>
> thank you for your hints. I would prefer to do not use Rserve as Dirk
> mentioned.
>
> @Simon
> I have full control over the Java implementation - I can adapt the code
> that I use for the communication R <-> Java.
>
>> You can natively access structures on each side. The fastest way is to
>> use R representation (column-oriented) in Java - that is much faster
>> than any kind of serialization or anything you mention above since you
>> pass the variables as a whole.
>
> Could you please send any reference to more examples or documentation
> that can help me?
> The main goal is to copy a full dataframe from R to Java.
>
> Best regards,
> Jaroslav
>
> On 2015-12-07 03:19, Simon Urbanek wrote:
>> On Dec 6, 2015, at 12:36 PM, Ing. Jaroslav Kuchař
>> <jaroslav.kuc...@fit.cvut.cz> wrote:
>>
>>> Dear all,
>>>
>>> in our ongoing project we use Java implementations of several
>>> algorithms. We also provide a “wrapper” implemented as an R package
>>> using rJava (https://github.com/jaroslav-kuchar/rCBA). Based on our
>>> recent experiments, the significant portion of time is spent on copying
>>> a dataframe from R to Java. The Java implementation needs access to the
>>> source dataframe.
>>>
>>> I have tested several approaches: calling Java method row-by-row;
>>> serialize the whole data-frame to a temp file and parsing in Java; or
>>> row binding to a single vector and calling a single Java method. Each
>>> approach has its limitations e.g. time-consuming row-by-row copying,
>>> serialization and parsing performance or memory limitations of a single
>>> vector.
>>>
>>> Is there an efficient approach how to copy a dataframe from R to Java
>>> and another one from Java to R?
>>>
>>> Thanks for any help you can provide...
>>>
>>
>> You can natively access structures on each side. The fastest way is to
>> use R representation (column-oriented) in Java - that is much faster
>> than any kind of serialization or anything you mention above since you
>> pass the variables as a whole.
>>
>> Typically, the bottleneck are Java applications which may require very
>> inefficient data structures. If you have control over the algorithms,
>> you can simply use proper data structures and avoid that problem. If
>> you don't have control, you'll have to add Java code that converts to
>> whatever structure is needed by the Java code form the data frame
>> pushed to the Java side. The main point here is that you do NOT want
>> to do any conversion on the R side.
>>
>> Cheers,
>> Šimon
>
______________________________________________
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
