mongodbscalaapache-sparkmahoutmahout-recommender

Run Mahout RowSimilarity recommender on MongoDB data

I have managed to run Mahout rowsimilarity on flat files of below format:

item-id tag1 tag-2 tag3

This has to be run via cli and the output is again flat files. I want to make this such that it reads data from MongoDB (open to using other DBs too) and then dumps the output to DB which can then be picked from our system.

I've researched for past few days and found below things:

What I'm yet to figure out is how do I go about importing data from DB to IndexedDataSet. Also, I've read about RDD format and still can't figure out how to convert json data to RDD which can be used by RowSimilarity code.

tl;dr: How to convert MongoDB data so that it can be processed by mahout/spark rowsimilarity?

Edit1: I have found some code that converts Mongodata to RDD from this link: https://github.com/mongodb/mongo-hadoop/wiki/Spark-Usage#scala-example

Now I need help to convert it to IndexedDataset so that it can be passed to SimilarityAnalysis.rowSimilarityIDS.

tl;dr: How do I convert RDD to IndexedDataset

Solution

  • Below is the answer:

    import org.apache.hadoop.conf.Configuration
import org.apache.mahout.math.cf.SimilarityAnalysis
import org.apache.mahout.math.indexeddataset.Schema
import org.apache.mahout.sparkbindings
import org.apache.mahout.sparkbindings.indexeddataset.IndexedDatasetSpark
import org.apache.spark.rdd.RDD
import org.bson.BSONObject
import com.mongodb.hadoop.MongoInputFormat


object SparkExample extends App {
  implicit val mc = sparkbindings.mahoutSparkContext(masterUrl = "local", appName = "RowSimilarity")
  val mongoConfig = new Configuration()
  mongoConfig.set("mongo.input.uri", "mongodb://hostname:27017/db.collection")

  val documents: RDD[(Object, BSONObject)] = mc.newAPIHadoopRDD(
    mongoConfig,
    classOf[MongoInputFormat],
    classOf[Object],
    classOf[BSONObject]
  )

  val documents_Array: RDD[(String, Array[String])] = documents.map(
    doc1 => (
      doc1._2.get("product_id").toString(),
      doc1._2.get("product_attribute_value").toString().replace("[ \"", "").replace("\"]", "").split("\" , \"").map(value => value.toLowerCase.replace(" ", "-").mkString(" "))
    )
  )

  val new_doc: RDD[(String, String)] = documents_Array.flatMapValues(x => x)
  val myIDs = IndexedDatasetSpark(new_doc)(mc)

  val readWriteSchema = new Schema(
    "rowKeyDelim" -> "\t",
    "columnIdStrengthDelim" -> ":",
    "omitScore" -> false,
    "elementDelim" -> " "
  )
  SimilarityAnalysis.rowSimilarityIDS(myIDs).dfsWrite("hdfs://hadoop:9000/mongo-hadoop-rowsimilarity", readWriteSchema)(mc)

}

    build.sbt:

    name := "scala-mongo"
version := "1.0"
scalaVersion := "2.10.6"
libraryDependencies += "org.mongodb" %% "casbah" % "3.1.1"
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.6.1"
libraryDependencies += "org.mongodb.mongo-hadoop" % "mongo-hadoop-core" % "1.4.2"

libraryDependencies ++= Seq(
  "org.apache.hadoop" % "hadoop-client" % "2.6.0" exclude("javax.servlet", "servlet-api") exclude ("com.sun.jmx", "jmxri") exclude ("com.sun.jdmk", "jmxtools") exclude ("javax.jms", "jms") exclude ("org.slf4j", "slf4j-log4j12") exclude("hsqldb","hsqldb"),
  "org.scalatest" % "scalatest_2.10" % "1.9.2" % "test"
)
libraryDependencies += "org.apache.mahout" % "mahout-math-scala_2.10" % "0.11.2"
libraryDependencies += "org.apache.mahout" % "mahout-spark_2.10" % "0.11.2"
libraryDependencies += "org.apache.mahout" % "mahout-math" % "0.11.2"
libraryDependencies += "org.apache.mahout" % "mahout-hdfs" % "0.11.2"

resolvers += "typesafe repo" at " http://repo.typesafe.com/typesafe/releases/"
resolvers += Resolver.mavenLocal

    I've used mongo-hadoop to get data from Mongo and use it. Since my data had an array, I had to use flatMapValues to flatten it and then pass to IDS for proper output.

    PS: I posted the answer here and not the linked question because this Q&A covers the full scope of getting data and processing it.