NegativeArraySizeException同时训练预测通用推荐器

时间：2016-10-26 10:02:50

标签： apache-spark predictionio

我正在尝试部署预测系统。 我在训练阶段得到了NegativeArraySizeException。

非常感谢帮助。

我推送的事件将entityType用户和targetEntityType作为项目，并通过

进行验证 
http://localhost:7070/events.json?accessKey=<MyAcccessKey>

[{
"eventId": "AAX2w8B2UFaxUYDlzyigBgAAAVgABV1uhz7ErglAtBA",
"event": "purchase",
"entityType": "user",
"entityId": "b571c84da7104d339a436b40d07ba59c",
"targetEntityType": "item",
"targetEntityId": "00572208a2e742f397f7e082aa40ae2e",
"properties": {},
"eventTime": "2016-10-26T08:05:01.422Z",
"creationTime": "2016-10-26T08:05:01.423Z"
}]

[INFO] [Engine] Extracting datasource params...
[INFO] [WorkflowUtils$] No 'name' is found. Default empty String will be used.
[INFO] [Engine] Datasource params: (,DataSourceParams(JuggernautRecommendor,List(purchase, view)))
[INFO] [Engine] Extracting preparator params...
[INFO] [Engine] Preparator params: (,Empty)
[INFO] [Engine] Extracting serving params...
[INFO] [Engine] Serving params: (,Empty)
[INFO] [Remoting] Starting remoting
[INFO] [Remoting] Remoting started; listening on addresses :[akka.tcp://sparkDriver@172.17.0.2:34162]
[WARN] [MetricsSystem] Using default name DAGScheduler for source because spark.app.id is not set.
[INFO] [Engine$] EngineWorkflow.train
[INFO] [Engine$] DataSource: com.juggernaut.DataSource@5c1b89ac
[INFO] [Engine$] Preparator: com.juggernaut.Preparator@2b79c8ff
[INFO] [Engine$] AlgorithmList: List(com.juggernaut.URAlgorithm@5d14e99e)
[INFO] [Engine$] Data sanity check is on.
[INFO] [Engine$] com.juggernaut.TrainingData does not support data sanity check. Skipping check.
[INFO] [Engine$] com.juggernaut.PreparedData does not support data sanity check. Skipping check.
[INFO] [URAlgorithm] Actions read now creating correlators
[ERROR] [Executor] Exception in task 0.0 in stage 29.0 (TID 20)
[WARN] [TaskSetManager] Lost task 0.0 in stage 29.0 (TID 20, localhost): java.lang.NegativeArraySizeException
    at org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)
    at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:78)
    at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:77)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:88)
    at org.apache.spark.executor.Executor$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)

[ERROR] [TaskSetManager] Task 0 in stage 29.0 failed 1 times; aborting job
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 29.0 failed 1 times, most recent failure: Lost task 0.0 in stage 29.0 (TID 20, localhost): java.lang.NegativeArraySizeException
    at org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)
    at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:78)
    at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:77)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:88)
    at org.apache.spark.executor.Executor$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)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1283)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1271)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1270)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1270)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1496)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1458)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1447)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1822)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1942)
    at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1003)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:306)
    at org.apache.spark.rdd.RDD.reduce(RDD.scala:985)
    at org.apache.mahout.sparkbindings.SparkEngine$.numNonZeroElementsPerColumn(SparkEngine.scala:86)
    at org.apache.mahout.math.drm.CheckpointedOps.numNonZeroElementsPerColumn(CheckpointedOps.scala:37)
    at org.apache.mahout.math.cf.SimilarityAnalysis$.sampleDownAndBinarize(SimilarityAnalysis.scala:286)
    at org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:89)
    at org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:84)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
    at org.apache.mahout.math.cf.SimilarityAnalysis$.cooccurrences(SimilarityAnalysis.scala:84)
    at org.apache.mahout.math.cf.SimilarityAnalysis$.cooccurrencesIDSs(SimilarityAnalysis.scala:141)
    at com.juggernaut.URAlgorithm.calcAll(URAlgorithm.scala:143)
    at com.juggernaut.URAlgorithm.train(URAlgorithm.scala:117)
    at com.juggernaut.URAlgorithm.train(URAlgorithm.scala:102)
    at io.prediction.controller.P2LAlgorithm.trainBase(P2LAlgorithm.scala:46)
    at io.prediction.controller.Engine$$anonfun$18.apply(Engine.scala:689)
    at io.prediction.controller.Engine$$anonfun$18.apply(Engine.scala:689)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.immutable.List.foreach(List.scala:318)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    at io.prediction.controller.Engine$.train(Engine.scala:689)
    at io.prediction.controller.Engine.train(Engine.scala:174)
    at io.prediction.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:65)
    at io.prediction.workflow.CreateWorkflow$.main(CreateWorkflow.scala:247)
    at io.prediction.workflow.CreateWorkflow.main(CreateWorkflow.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:672)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.NegativeArraySizeException
    at org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)
    at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:78)
    at org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:77)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:88)
    at org.apache.spark.executor.Executor$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)

1 个答案:

答案 0 :(得分：0)

engine.json中定义的所有事件必须至少有一个数据集中的事件。

https://groups.google.com/forum/#!topic/predictionio-user/FDGOY4DisCg

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