Running Mlib via Spark Job Server - scala

Question

I was practising developing sample model using online resources provided in spark website. I managed to create the model and run it for sample data using Spark-Shell , But how to do actually run the model in production environment ? Is it via Spark Job server ?
import org.apache.spark.mllib.classification.SVMWithSGD
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
val data = sc.textFile("hdfs://mycluster/user/Cancer.csv")
val parsedData = data.map { line =>
val parts = line.split(',')
LabeledPoint(parts.last.toDouble, Vectors.dense(parts.take(9).map(_.toDouble)))
}
var svm = new SVMWithSGD().setIntercept(true)
val model = svm.run(parsedData)
var predictedValue = model.predict(Vectors.dense(5,1,1,1,2,1,3,1,1))
println(predictedValue)
The above code works perfect when i run it in spark-shell , But i have no idea how do we actually run model in production environment. I tried to run it via spark jobserver but i get error ,
curl -d "input.string = 1, 2, 3, 4, 5, 6, 7, 8, 9" 'ptfhadoop01v:8090/jobs?appName=SQL&classPath=spark.jobserver.SparkPredict'
I am sure its because am passing a String value whereas the program expects it be vector elements , Can someone guide me on how to achieve this . And also is this how the data being passed to Model in production environment ? Or is it some other way.

Answer 1

Spark Job-server is used in production use-cases, where you want to design pipelines of Spark jobs, and also (optionally) use the SparkContext across jobs, over a REST API. Sparkplug is an alternative to Spark Job-server, providing similar constructs.
However, to answer your question on how to run a (singular) Spark job in production environments, the answer is you do not need a third-party library to do so. You only need to construct a SparkContext object, and use it to trigger Spark jobs. For instance, for your code snippet, all that is needed is;
package runner
import org.apache.spark.mllib.classification.SVMWithSGD
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import com.typesafe.config.{ConfigFactory, Config}
import org.apache.spark.{SparkConf, SparkContext}
/**
*
*/
object SparkRunner {
def main (args: Array[String]){
val config: Config = ConfigFactory.load("app-default-config") /*Use a library to read a config file*/
val sc: SparkContext = constructSparkContext(config)
val data = sc.textFile("hdfs://mycluster/user/Cancer.csv")
val parsedData = data.map { line =>
val parts = line.split(',')
LabeledPoint(parts.last.toDouble, Vectors.dense(parts.take(9).map(_.toDouble)))
}
var svm = new SVMWithSGD().setIntercept(true)
val model = svm.run(parsedData)
var predictedValue = model.predict(Vectors.dense(5,1,1,1,2,1,3,1,1))
println(predictedValue)
}
def constructSparkContext(config: Config): SparkContext = {
val conf = new SparkConf()
conf
.setMaster(config.getString("spark.master"))
.setAppName(config.getString("app.name"))
/*Set more configuration values here*/
new SparkContext(conf)
}
}
Optionally, you can also use the wrapper for spark-submit script, SparkSubmit, provided in the Spark library itself.