PS: I am using Spark1.6.1, kafka 0.10.0.0
--------------------------------
Thanks&Best regards!
San.Luo
----- 原始邮件 -----
发件人：<luohui20...@sina.com>
收件人："user" <user@spark.apache.org>
主题：How to get recommand result for users in a kafka SparkStreaming Application
日期：2016年08月03日 15点01分
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
