java - 线程 "main"org.apache.spark.SparkException : Task not serializable 中出现异常

Question

已关闭。此问题需要 debugging details 。目前不接受答案。

---

编辑问题以包含 desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem 。这将有助于其他人回答问题。

已关闭 7 年前。

Improve this question

你好

我正在使用Scala 2.11.8 和 Spark 1.6.1。每当我在 ma​​p 中调用函数时，它都会抛出以下异常:

"Exception in thread "main" org.apache.spark.SparkException: Task not serializable"

您可以在下面找到我的完整代码

package moviestream.recommender
import java.io
import java.io.Serializable

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.recommendation.ALS
import org.apache.spark.mllib.recommendation.Rating
import org.jblas.DoubleMatrix


class FeatureExtraction{

  val conf = new SparkConf().setMaster("local[2]").setAppName("Recommendation")
  val sc = new SparkContext(conf)
  val rawData = sc.textFile("data/u.data")
  val rawRatings = rawData.map(_.split("\t").take(3))

  //create rating object from rawratings
  val ratings = rawRatings.map{case Array(user,movie,rating) => Rating(user.toInt,movie.toInt,rating.toDouble)}
  //user Spark ALS library to train our model
  // Build the recommendation model using ALS

  val model = ALS.train(ratings,50,10,0.01)
  //val model = ALS.trainImplicit(ratings,50,10,0.01,0.1) //last parameter is alpha
  val predictedRating = model.predict(789,123)
  //top ten recommended movies for user id 789, where k= number of recommended(10) 789=userid
  val topKRecs = model.recommendProducts(789,10)
  val movies = sc.textFile("data/u.item")
  val titles = movies.map(line=>line.split("\\|").take(2)).map(array=>(array(0).toInt,array(1))).collectAsMap()
  //how many    movies  this    user    has rated
  val moviesForUser = ratings.keyBy(_.user).lookup(789)
  //we  will    take    the 10  movies  with    the highest ratings ction   using   the     field   of  the     object.
  //moviesForUser.sortBy(-_.rating).take(10).map(rating=>(titles(rating.product),rating.rating)).foreach(println)
  //let’s   take    a   look    at  the top 10  recommendations for this    user    and see what    the titles
  //topKRecs.map(rating=>(titles(rating.product),rating.rating)).foreach(println)
  //    we  will    then    need    to  create  a   DoubleMatrix    object

  val itemId = 567
  val itemFactor = model.productFeatures.lookup(itemId).head
  val itemVector = new  DoubleMatrix(itemFactor)


  //we  are ready   to  apply   our similarity  metric  to  each    item
  /*val sims = model.productFeatures.map{ case (id, factor) =>
    val factorVector = new DoubleMatrix(factor)
    val sim = cosineSimilarity(factorVector, itemVector)
    (id, sim)
  }*/

  //we can  compute the top 10  most    similar items   by  sorting out the similarity  score   for each    item
  //val sortedSims = sims.top(10)(Ordering.by[(Int,Double),Double]{case(id,similarity)=>similarity})

  //we  can sense   check   our item-to-item    similarity
  //val sortedSims2 = sims.top(11)(Ordering.by[(Int,Double),Double]{case(id,similarity)=>simintellij idea debugilarity})
  //sortedSims2.slice(1,11).map{case (id,sim)=>(titles(id),sim)}.foreach(println)
  //Finally,we  can print   the 10  items   with    the highest computed    similarity  metric  to  our given item:
  //println("Result = "+titles(123))

  def cosineSimilarity(vect1:DoubleMatrix,vect2:DoubleMatrix): Double = {
    vect1.dot(vect2)/(vect1.norm1()*vect2.norm2())
  }

  val actualRating = moviesForUser.take(1)(0)
  val predictedRatings = model.predict(789,actualRating.product)
  //println(predictedRatings)
  val squaredError = math.pow(predictedRatings - actualRating.rating,2.0)

  val usersProducts = ratings.map{case Rating(user,product,rating) => (user,product)}
  val predictions = model.predict(usersProducts).map{case Rating(user,product,rating)
                          =>((user,product),rating)}
  val ratingsAndPredictions = ratings.map{case Rating(user,product,rating)=>((user,product),rating)}
                                     .join(predictions)
  val MSE = ratingsAndPredictions.map{case ((user,product),(actual,predicted))
          => math.pow((actual-predicted),2)}.reduce(_ + _)/ratingsAndPredictions.count()
  //println("Mean Squared Error = " + MSE)
  val RMSE = math.sqrt(MSE)
  println("Root Mean Squared Error = "+ RMSE)
  def avgPrecisionK(actual:Seq[Int],predicted:Seq[Int],k:Int):Double = {
    val predk = predicted.take(k)
    var score = 0.0
    var numHits = 0.0
    for((p,i)<- predk.zipWithIndex){
      if(actual.contains(p)){
        numHits += 1.0
        score += numHits/(i.toDouble+1.0)
      }
    }
    if(actual.isEmpty) {
      1.0
    }
    else{
      score/scala.math.min(actual.size,k).toDouble
    }


  }

  val actualMovies = moviesForUser.map(_.product)
  val predictedMovies = topKRecs.map(_.product)
 //predictedMovies.foreach(println)
  val apk10 = avgPrecisionK(actualMovies,predictedMovies,10)
  //println(apk10)
  //Locality Sensitive Hashing
  val itemFactors = model.productFeatures.map{case (id,factor)=>factor}.collect()
  val itemMatrix = new  DoubleMatrix(itemFactors)
  //println(itemMatrix.rows,itemMatrix.columns)
  val imBroadcast = sc.broadcast(itemMatrix)
  //println(imBroadcast)
  val allRecs = model.userFeatures.map{case (userId,array)=>
                val userVector = new  DoubleMatrix(array)
                val scores = imBroadcast.value.mmul(userVector)
                val sortedWithId = scores.data.zipWithIndex.sortBy(- _._1)
                val recommendedIds = sortedWithId.map(_._2 +1).toSeq
                (userId,recommendedIds)
  }
  println(allRecs)


}

Answer 1

正如上面评论中提到的，这个问题太宽泛了。但一个猜测可能会有所帮助。您在map内部使用广播值imBroadcast。我猜它包含在与 SparkContext 相同作用域中声明的函数，对吗？然后将它们移动到单独的对象。