company

SAP HANA supports this type of sidecar deployment
to offer data scientists the ultimate flexibility
to use any algorithm they want or even create
their own in R. They call R scripts through
SQLScript in SAP HANA and then pass the scripts
to an external R server, along with all of the
required input data. The results are sent back to
SAP HANA and combined with any local data
required to complete the query (see Figure 12).
However, integrating the results from arbitrary
scripting code using an external server results in
significant overhead required to extract and
transfer data to the R server for processing. Performing
all calculations with a local in-memory
predictive engine, such as the PAL for SAP HANA,
avoids this overhead.
PREDICTIVE ANALYSIS LIBRARY: PAL
The predictive analysis library, or PAL, is designed
to take advantage of the ability of SAP HANA to
host execution engines and perform local calculations
in memory. Unlike the previous option that
uses an external predictive server for processing,
this SAP HANA–native library enables users to
perform in-database data mining and statistical
calculations with excellent performance on large
data sets.
Figure 12: Sample Code in SQLScript
/*Create a function using input and output
table types in HANA*/
CREATE FUNCTION LR( IN input1 SUCC_PREC_TYPE,
OUT
output0 R_COEF_TYPE)
LANGUAGE RLANG AS’’’
CHANGE_FREQ