When SAP HANA met R – What’s new?

February 18, 2013
By

(This article was first published on Blag's bag of rants, and kindly contributed to R-bloggers)


Since I wrote my blog When SAP HANA met R - First kiss I had received a lot of nice feedback...and one those feedbacks was..."What's new?"...

Well...as you might now SAP HANA works with R by using Rserve, a package that allows communication to an R Server, so really...there can't be too many new features...but...the cool thing is that SAP HANA has been tested with R 2.15 and Rserve 0.6-8 so any new features added on R and Rserve and instantly available on SAP HANA -;)

But hey! I wouldn't write a blog is there wasn't at least one new cool feature, right? You can read more about it here SAP HANA R Integration Guide.

Of course...for this you need SAP HANA rev. 48 (SPS5)

So what's the cool feature? Well...you can store a train model like lm() or ksvm() directly on a table for later use. This is really cool, because if you have a big calculation to be made, you only need to store the model and use it later without having to reprocess everything again.

Let's make an example...and hope all the R fans doesn't kill me for this...because when it comes to statistics...I'm really lost in the woods -:(

Let's say we have two tables from the SFLIGHT package...SPFLI and STICKET, so we want to predict how many times a customer is going to flight to different destinations (CITYFROM-CITYTO) depending on how many times all the customers has flights to those very same locations.

We're going to create one SQLScript file to get the information, transform it, create the model and store it in the database...

Build_Flight_Model.sql
--Create a TYPE T_FLIGHTS to grab the information from the SPFLI and STICKET tables.

DROP TYPE T_FLIGHTS;
CREATE TYPE T_FLIGHTS AS TABLE (
CARRID NVARCHAR(3),
CUSTOMID NVARCHAR(8),
CITYFROM NVARCHAR(20),
CITYTO NVARCHAR(20)
);

--Create a TYPE FLIGHT_MODEL_T and a table FLIGHT_MODEL to get and store the model in the database.

DROP TYPE FLIGHT_MODEL_T;
CREATE TYPE FLIGHT_MODEL_T AS TABLE (
ID INTEGER,
DESCRIPTION VARCHAR(255),
MODEL BLOB
);

DROP TABLE FLIGHT_MODEL;
CREATE COLUMN TABLE FLIGHT_MODEL (
ID INTEGER,
DESCRIPTION VARCHAR(255),
MODEL BLOB
);

--This R procedure will receive the T_FLIGHTS information, create table containing a field call FLIGHT_NAME
--that will contain the concatenation
--of the CARRID, CITYFROM and CITYTO. ie: AA-NEW YORK-SAN FRANCISCO.
--We're going to convert the table into a data.frame, so all the similar values in FLIGHT_NAME are going to
--be summarized.
--Using the subset() function, we're going to get rid of all the FLIGHT_NAME's that has a frequency lower
--or equal than 0.
--We're going to use the nrow() function to count all the FLIGHT_NAME occurrences and multiply that by
--10 (Stored in f_rows)
--We're going to use the sum() function to sum all the frequencies and the divide it by f_rows
--(Stored in f_sum)
--We're going to use the mapply() function to divide each of the frequencies by f_sum
--We're going to use the order() function to sort by FLIGHT_NAME
--We're going to use the colnames() function to assign names to our data.frame
--We're going to use the lm() function to generate a Linear Regression based on the FLIGHT_NAME
--and it's frequency
--Finally, we're going to use the generateRobjColumn() custom created function to store the result of the
--model in the buffer.

DROP PROCEDURE FLIGHT_TRAIN_PROC;
CREATE PROCEDURE FLIGHT_TRAIN_PROC (IN traininput "T_FLIGHTS", OUT modelresult FLIGHT_MODEL_T)
LANGUAGE RLANG AS
BEGIN
generateRobjColumn <- function(...){
result <- as.data.frame(cbind(
lapply(
list(...),
function(x) if (is.null(x)) NULL else serialize(x, NULL)
)
))
names(result) <- NULL
names(result[[1]]) <- NULL
result
}
tab<-table(FLIGHT_NAME=paste(traininput$CARRID,traininput$CITYFROM,traininput$CITYTO,sep="-"))
df<-data.frame(tab)
ss<-subset(df,(df$Freq>0))
freq<-ss$Freq
f_rows<-(nrow(ss)) * 10
fsum<-sum(freq) / f_rows
ss$Freq<-mapply("/",ss$Freq, fsum)
flights<-ss[order(ss$FLIGHT_NAME),]
colnames(flights)<-c("FLIGHT_NAME","FREQUENCY")
lmModel<-lm(FREQUENCY ~ FLIGHT_NAME,data=flights)
modelresult<-data.frame(
ID=c(1),
DESCRIPTION=c("Flight Model"),
MODEL=generateRobjColumn(lmModel)
)
END;

--This SQLSCRIPT procedure will grab all the needed information from the tables SPFLI and STICKET
--and will assign it to flights
--We're going to call the R procedure FLIGHT_TRAIN_PROC
--We're going to do an INSERT to finally store the model from the buffer into the database

DROP PROCEDURE POPULATE_FLIGHTS;
CREATE PROCEDURE POPULATE_FLIGHTS ()
LANGUAGE SQLSCRIPT AS
BEGIN
flights = SELECT SPFLI.CARRID, CUSTOMID, CITYFROM, CITYTO
FROM SFLIGHT.SPFLI INNER JOIN SFLIGHT.STICKET
ON SPFLI.CARRID = STICKET.CARRID
AND SPFLI.CONNID = STICKET.CONNID;
CALL FLIGHT_TRAIN_PROC(:flights, FLIGHT_MODEL_T);
INSERT INTO "FLIGHT_MODEL" SELECT * FROM :FLIGHT_MODEL_T;
END;

CALL POPULATE_FLIGHTS();

When we call POPULATE_FLIGHTS(), our FLIGHT_MODEL table should look like this...



If you are wondering why we have an "X"...it's because the content is serialized and stored in a BLOB field...if you inspect the content, you will receive a bunch of weird hexadecimal numbers...

Anyway...it took 6.165 seconds to SAP HANA to process 1,842,160 records.

Now the we have our model safely stored in the database, we can move to our next SQLScript file...

Get_and_Use_Flight_Model.sql
--We're going to create a TYPE T_PREDICTED_FLIGHTS and a table PREDICTED_FLIGHTS to store the information
--of the current number of flights and
--the estimated (according to our prediction) number of flights

DROP TYPE T_PREDICTED_FLIGHTS;
CREATE TYPE T_PREDICTED_FLIGHTS AS TABLE (
CUSTOMID NVARCHAR(8),
FLIGHT_NAME NVARCHAR(60),
FREQUENCY INTEGER,
PREDICTED INTEGER
);

DROP TABLE PREDICTED_FLIGHTS;
CREATE TABLE PREDICTED_FLIGHTS (
CUSTOMID NVARCHAR(8),
FLIGHT_NAME NVARCHAR(60),
FREQUENCY INTEGER,
PREDICTED INTEGER
);

--In this R procedure, we're going to receive the flight for a given customer, the model stored in the database 
--and we're going to return the result so it can be
--stored in our PREDICTED_FLIGHTS table.
--We're going to use the unserialize() function to extract the model.
--We're going to create a table containing a field call FLIGHT_NAME that will contain the concatenation of the 
--CARRID, CITYFROM and CITYTO.
--ie: AA-NEW YORK-SAN FRANCISCO. and also the CUSTOMID
--We're going to convert the table into a data.frame, so all the similar values in FLIGHT_NAME are going to be 
--summarized.
--We're going to use the colnames() function to assign names to our data.frame
--We're going to use the nrow() function to get the number of records in the data.frame (Stored in dfrows)
--We're going to use the rep() function to repeat the CUSTOMID value of the first record dfrows times
--We're going to use the predict() function to predict the amount of flights based on our model (retrieved 
--from the database) and the new data that we recovered
--Finally, we're going to create a data.frame containing all the information that should be stored in our 
--table PREDICTED_FLIGHTS

DROP PROCEDURE USE_FLIGHT;
CREATE PROCEDURE USE_FLIGHT(IN flights T_FLIGHTS, IN modeltbl FLIGHT_MODEL_T, OUT out_flights T_PREDICTED_FLIGHTS)
LANGUAGE RLANG AS
BEGIN
lmModel<-unserialize(modeltbl$MODEL[[1]])
tab<-table(FLIGHT_NAME=paste(flights$CARRID,flights$CITYFROM,flights$CITYTO,sep="-"),CUSTOMID=flights$CUSTOMID)
df<-data.frame(tab)
colnames(df)<-c("FLIGHT_NAME","CUSTOMID","FREQUENCY")
dfrows<-nrow(df)
customid<-rep(df$CUSTOMID[1],dfrows)
prediction=predict(lmModel,df,interval="none")
out_flights<-data.frame(CUSTOMID=customid,FLIGHT_NAME=df$FLIGHT_NAME,FREQUENCY=df$FREQUENCY,PREDICTED=prediction)
END;

--This SQLSCRIPT procedure will select the information from the FLIGHT_MODEL table and store in the flight_model 
--variable
--We're going to select all the needed information from the table SPFLI and STICKET based on the customer ID number
--We're going to call the R procedure USE_FLIGHT and it will return us the PREDICTED_FLIGHTS that we're going to 
--store in the database

DROP PROCEDURE GET_FLIGHTS;
CREATE PROCEDURE GET_FLIGHTS(IN customId NVARCHAR(8))
LANGUAGE SQLSCRIPT AS
BEGIN
flight_model = SELECT * FROM FLIGHT_MODEL;
out_flights = SELECT SPFLI.CARRID, CUSTOMID, CITYFROM, CITYTO FROM SFLIGHT.SPFLI INNER JOIN SFLIGHT.STICKET
ON SPFLI.CARRID = STICKET.CARRID AND SPFLI.CONNID = STICKET.CONNID
WHERE CUSTOMID = :customId;
CALL USE_FLIGHT(:out_flights, :flight_model, PREDICTED_FLIGHTS);
INSERT INTO "PREDICTED_FLIGHTS" SELECT * FROM :PREDICTED_FLIGHTS;
END;

Now that we have all our Stored Procedures ready...we can create the last SQLScript file to actually fill our PREDICTED_FLIGHTS with some data...

Predict_Flights_for_Customers.sql
CALL GET_FLIGHTS('00000156');
CALL GET_FLIGHTS('00002078');
CALL GET_FLIGHTS('00002463');

As you can see...we only need to call the GET_FLIGHTS procedure, passing the Customer ID's...

This process took only 970ms and generate 122 records for the 3 customers...

Now I'm sure you realize how cool is this...if we haven't stored our model in the database...then we would have to calculate the model for each customer...and it would have took us around 7 seconds for each (get the lm(), plus the predict)...that would have been around 21 seconds for the 3 customers...while we can say that the whole process took us only 7 seconds...if you needed to calculate the prediction for all the more than 1 million customers...you will be in sort of trouble -:)

Let's see the content of our PREDICTED_FLIGHTS table...of course, I'm going to only show a part of it...


We can dump this information to a CSV file from SAP HANA Studio and let's say...use it on SAP Visual Intelligence to generate a nice graphic...


Hope you like it -:)

Greetrings,

Blag.

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