Can it works in load the MatrixFactorizationModel and predict product with Spark Streaming?

I have finish training MatrixFactorizationModel, I want to load this model in spark-streaming. I think it can be works, but actually not. I don't know why, who can help me? I wrote code like this: val ssc = new StreamingContext ... val bestModel = MatrixFactorizationModel.load(ssc.sparkC

"recommendProductsForUsers" makes worker node crash

I have two workers to run the recommendation job. After spark v1.4.0, I want to try the method "recommendProductsForUsers". This method makes my workers node crash, and timeout to connect. If don't add new worker node. What should I do? -- View this message in context: http://apache-spark-user

DoNotRetryIOException: IllegalAccessError

I use spark to access hbase data. The code below: try { SparkConf conf = new SparkConf().setAppName("WordCountTest"); JavaSparkContext sc = new JavaSparkContext(conf); String tableName = "test"; Configuration hbConf = HBaseConfiguration.create();

Re: MatrixFactorizationModel serialization

I save and reload model like this: val bestModel = ALS.train(training, rank, numIter, lambda) bestModel.get.userFeatures.saveAsObjectFile("hdfs://***:9000/spark/results/userfeatures") bestModel.get.productFeatures.saveAsObjectFile("hdfs://***:9000/spark/results/productfeatures") val bestModel = o