adobe spark demo

tasksetmanager:starting Task 1.0 in Stage 0.0 (TID 1, localhost, process_local, 1260bytes)15/07/27 19:50:23 INFO executor:running Task 1.0 in Stage 0.0 (TID 1)15/07/27 19:50:23 INFO tasksetmanager:finished task 0.0 in stage 0.0 (TID 0) in the MS on localhost (1/2)15/07/27 19:50:23 INFO executor:finished Task 1.0 in Stage 0.0 (TID 1). 727bytes result sent to driver15/07/27 19:50:23 INFO tasksetmanager:finished Task 1.0 in Stage 0.0 (TID 1) in the MS on localhost (2/2)15/07/27 19:50:23 INFO dagsc

Spark runs a simple demo programUsing Spark, you can start Spark-shell directly on the command line and then use Scala for data processing in Spark-shell. Now we're going to show you how to write handlers using the IDE. Prerequisite: 1, already installed

Https://files.cnblogs.com/files/wifi0/spark2.1.1example_api_sql_streaming_eclipseProject.zipHttps://files.cnblogs.com/files/wifi0/runconfig.zipBuilding Code Reading EnvironmentDownload spark-2.1.1-bin-hadoop2.7.tgzHttp://spark.apache.org/downloads.htmlDecompression spark-2.1.1-bin-hadoop2.7.tgzNote: The jar package under the examples directory and all the jar packages under Examples\jars will be used to cre

)).Map(_.split ("::") match { case Array (user, item, rate) = Rating (User.toint, Item.toint, rate.todouble)})Set number of stealth factors, number of iterationsVal Rank= 10Val numiterations= 5//CallALSClass ofTrainMethods, passing in the data to be trained and so on model trainingVal Model=ALS.Train(ratings, rank, numiterations, 0.01)Convert the training data into(User,item)Format to be used as a test model for predicting data (collaborative filtering of model predictions when the incoming(Use

={}, Numtrees=3, featuresubsetstrategy="Auto", impurity='Gini', maxdepth=4, maxbins=32) #Evaluate model on test instances and compute test errorpredictions = Model.predict (Testdata.map (Lambdax:x.features)) Labelsandpredictions= Testdata.map (LambdaLp:lp.label). zip (predictions) Testerr= Labelsandpredictions.filter (Lambda(V, p): V! = p). Count ()/Float (testdata.count ())Print('Test Error ='+str (testerr))Print('learned classification forest model:') Print(Model.todebugstring ())#Save a