Kx Systems was founded in 1993 to address a simple problem: the inability of traditional relational database technology to keep up with escalating volumes of data. This problem is even more acute today, where data records, especially in the financial industry, number in the trillions. To provide users to fast data, the founders of Kx have developed kdb+ and vector-processing programming language 'Q'.
Kdb+ has the ability to quickly process data and for more complex analysis it has access to other programming languages. To call the NAG C Library from kdb+, a wrapper must be placed around the NAG routine, taking care when passing data types. The wrapped functions must then be compiled into an external library. Below is an example for calling a NAG regression routine:
#define KXVER 3 // Using version 3, this must come before #include "k.h"
#include"k.h" // kx header file containing K structures
K regression(K x, K y) //The two arrays we will input from kdb+
K kout; //Array that we will return to kdb+
kout=ktn(KF,7); //Allocate kout - it will have 7 elements. KF indicates
// it is an array of floating point numbers
F*out=kF(kout); //Set pointer to kout - comes after kout is allocated
F*(x_input)=kF(x); //Need to set pointers to input arrays into NAG
nag_simple_linear_regression( Nag_AboutMean, (Integer) x->n, x_input, y_input, wt, &out, &out, &out, &out, &out, &out, &out, &fail );
if(fail.code != NE_NOERROR)
return r0(kout),krr(ss(fail.message)); //Free memory and return any
//NAG fail to Q
In production level code, we should really check array bounds and data types. If you would like, you can add the following checks:
if(x->n != y->n) return krr("data not same length");
if(x->t != -KF || y->t != -KF) return krr("input data wrong type");
We must then compile the above code into a sharable library. On Linux the command is:
gcc -shared -fPIC nag_regression.c -I/[NAG_Directory]/include [NAG_Directory]/lib/libnagc_nag.so -o nag_regression.so
Before we start the Q language, make sure the library can be located:
Finally, we load the library in Q and test the NAG example program:
q)nag_reg[1.0 0.0 4.0 7.5 2.5 0.0 10.0 5.0;20.0 15.5 28.3 45.0 24.5 10.0 99.0 31.2]
7.598166 7.090489 6.685763 1.322359 .8273433 965.2454 6
Outputs from the regression are the regression coefficients, the standard errors, the R-squared, sum of squares, and degrees of freedom (which all agree with the NAG example).
Interested in other NAG routines in kdb+? Email email@example.com and let us know! Many thanks to the support team at Kx Systems.