Scala - 创建索引数据集 Spark 对象

Question

我想对从 mongodb 获得的数据运行 Spark RowSimilarity 推荐器。为此，我在下面编写了从 mongo 获取输入的代码，将其转换为对象的 RDD。这需要传递给 IndexedDataSetSpark，然后再传递给 SimilarityAnalysis.rowSimilarityIDS

import org.apache.hadoop.conf.Configuration
import org.apache.mahout.math.cf.SimilarityAnalysis
import org.apache.mahout.sparkbindings.indexeddataset.IndexedDatasetSpark
import org.apache.spark.rdd.{NewHadoopRDD, RDD}
import org.apache.spark.{SparkConf, SparkContext}
import org.bson.BSONObject
import com.mongodb.hadoop.MongoInputFormat

object SparkExample extends App {
  val mongoConfig = new Configuration()
  mongoConfig.set("mongo.input.uri", "mongodb://my_mongo_ip:27017/db.collection")

  val sparkConf = new SparkConf()
  val sc = new SparkContext("local", "SparkExample", sparkConf)

  val documents: RDD[(Object, BSONObject)] = sc.newAPIHadoopRDD(
    mongoConfig,
    classOf[MongoInputFormat],
    classOf[Object],
    classOf[BSONObject]
  )
  val new_doc: RDD[(String, 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(" ")
    )
  )
  var myIDs = IndexedDatasetSpark(new_doc)(sc) 

  SimilarityAnalysis.rowSimilarityIDS(myIDs).dfsWrite("hdfs://myhadoop:9000/myfile", readWriteSchema)

我无法创建可以传递给 SimilarityAnalysis.rowSimilarityIDS 的 IndexedDatasetSpark。请在这件事上帮助我。

编辑1：

我设法创建了 IndexedDatasetSpark 对象，并且代码现在可以正确编译。我必须将(sc) 作为隐式参数添加到IndexedDatasetSpark 才能运行代码：

Error: could not find implicit value for parameter sc: org.apache.spark.SparkContext

现在，当我运行它时，它会给出以下错误：

Error: could not find implicit value for parameter sc: org.apache.mahout.math.drm.DistributedContext

我不知道如何提供 DistributedContext。

这是创建 RDD 并将其转换为 IDS 以便它可以由 rowSimilarityIDS 处理的正确方法吗？

更多上下文：我是从这种情况开始的：Run Mahout RowSimilarity recommender on MongoDB data

我的 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

Edit2：我暂时删除了 dfsWrite 以让代码执行并偶然发现以下错误：

java.io.NotSerializableException: org.apache.mahout.math.DenseVector
Serialization stack:
- object not serializable (class: org.apache.mahout.math.DenseVector, value: {3:1.0,8:1.0,10:1.0})
- field (class: scala.Some, name: x, type: class java.lang.Object)
- object (class scala.Some, Some({3:1.0,8:1.0,10:1.0}))
at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)

是否有一些我可能跳过的序列化？

Answer 1

我会把你删除的东西放回去，次要错误是自己造成的。

原来的错误是因为你还没有创建SparkContext，可以这样做：

implicit val mc = mahoutSparkContext()

此后我认为 mc (a SparkDistributedContext) 到 sc (a SparkContext) 的隐式转换将由包帮助函数处理。如果 sc 仍然丢失，请尝试：

implicit val sc = sdc2sc(mc)