MatrixFactorizationModel is serializable. Instantiate it on the
driver, not on the executors.
On Wed, Aug 3, 2016 at 2:01 AM, <luohui20...@sina.com> wrote:
> hello guys:
> I have an app which consumes json messages from kafka and recommend
> movies for the users in those messages ,the code like this :
>
>
> conf.setAppName("KafkaStreaming")
> val storageLevel = StorageLevel.DISK_ONLY
> val ssc = new StreamingContext(conf, Seconds(batchInterval.toInt /
> 1000))
>
> val kafkaStream = KafkaUtils.createStream(ssc, zkQuorum, group,
> topicMap, storageLevel) val topicMap = topics.split(",").map((_,
> numThreads.toInt)).toMap
> kafkaStream.foreachRDD { rdd =>
> val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)
> val ALSModel = MatrixFactorizationModel.load(rdd.sparkContext,
> "/user/hadoop/model/myCollaborativeFilter20160802/")
> val recRdd = sqlContext.read.json(rdd.values)
> val getRecResult = org.apache.spark.sql.functions.udf((x: Long) =>
> ALSModel.recommendProducts(x.toInt, 10).mkString
> )
> val resultDF = recRdd.withColumn("recommandresult",
> getRecResult(recRdd.col("userid")))
> // val resultDF2 = recRdd.map { x =>
> // ALSModel.recommendProducts(x.getLong(3).toInt, 10)
> // }
>
> println("output result:")
> resultDF.collect.foreach(println)
> }
> ssc.start()
> ssc.awaitTermination()
>
> here are the logs of my app:
> 16/08/03 14:40:49 WARN TaskSetManager: Lost task 0.0 in stage 20.0 (TID 133,
> slave62): org.apache.spark.SparkException: RDD transformations and actions
> can only be invoked by the driver, not inside of other transformations; for
> example, rdd1.map(x => rdd2.values.count() * x) is invalid because the
> values transformation and count action cannot be performed inside of the
> rdd1.map transformation. For more information, see SPARK-5063.
> at
> org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$sc(RDD.scala:87)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> at
> org.apache.spark.rdd.PairRDDFunctions.lookup(PairRDDFunctions.scala:928)
> at
> org.apache.spark.mllib.recommendation.MatrixFactorizationModel.recommendProducts(MatrixFactorizationModel.scala:168)
> at
> org.brave.spark.streaming.KafkaSparkStreaming2$$anonfun$main$1$$anonfun$2.apply(KafkaSparkStreaming2.scala:51)
> at
> org.brave.spark.streaming.KafkaSparkStreaming2$$anonfun$main$1$$anonfun$2.apply(KafkaSparkStreaming2.scala:50)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown
> Source)
> at
> org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:51)
> at
> org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:49)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at
> org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
> at
> org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
> at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 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)
>
> It seems that method MatrixFactorizationModel.recommendProducts is also a
> transformation.
> Is there another way to get the recommended movies for userids from a kafka
> streaming messages?
>
> my data is like this:
> ProducerRecord(topic=test, partition=null, key=null,
> value={"userid":29694,"movieid":6503,"rating":2.5,"timestamp":1.088441729E9},
> timestamp=null)
> ProducerRecord(topic=test, partition=null, key=null,
> value={"userid":33063,"movieid":36,"rating":3.0,"timestamp":9.02034829E8},
> timestamp=null)
> .........
>
>
> --------------------------------
>
> Thanks&Best regards!
> San.Luo
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