-- Before running script, the model needs to be stored in the database. Run SaveLMModel test using runtests.R

-- Also, the table airlineWithIndex should exist with 10 M rows of data.

-- This code uses trivial parallelism to speed up prediction of 1M rows.

use PerfTuning;

declare @just_model varbinary(max);

select @just_model = [value] from [rdata] where [key] = 'lm.model.1';

declare @pred float;

exec sp_execute_external_script

@language = N'R',

@script = N'

# Prepare the data for single row scoring

InputDataSet[,"DayOfWeek"] <- factor(InputDataSet[,"DayOfWeek"], levels=as.character(1:7))

mm <- unserialize(as.raw(model_param))

# Predict

OutputDataSet <- data.frame(pred=predict(mm, InputDataSet))

',

@input_data_1 = N'SELECT [ArrDelay],[DayOfWeek], [CRSDepTime] FROM airlineWithIndex WHERE rowNum > 9000000',

@parallel = 1,

@params = N'@model_param varbinary(max)',

@model_param = @just_model

with result sets ((pred float));