scala – Apache Spark – MlLib – 协同过滤
发布时间:2020-12-16 08:54:50 所属栏目:安全 来源:网络整理
导读:我正在尝试使用MlLib进行我的colloborative过滤. 当我在Apache Spark 1.0.0中运行它时,我在Scala程序中遇到以下错误. 14/07/15 16:16:31 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes whe
我正在尝试使用MlLib进行我的colloborative过滤.
当我在Apache Spark 1.0.0中运行它时,我在Scala程序中遇到以下错误. 14/07/15 16:16:31 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/07/15 16:16:31 WARN LoadSnappy: Snappy native library not loaded 14/07/15 16:16:31 INFO FileInputFormat: Total input paths to process : 1 14/07/15 16:16:38 WARN TaskSetManager: Lost TID 10 (task 80.0:0) 14/07/15 16:16:38 WARN TaskSetManager: Loss was due to java.lang.UnsatisfiedLinkError java.lang.UnsatisfiedLinkError: org.jblas.NativeBlas.dposv(CII[DII[DII)I at org.jblas.NativeBlas.dposv(Native Method) at org.jblas.SimpleBlas.posv(SimpleBlas.java:369) at org.jblas.Solve.solvePositive(Solve.java:68) at org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateBlock$2.apply(ALS.scala:522) at org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateBlock$2.apply(ALS.scala:509) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) at scala.collection.mutable.ArrayOps$ofInt.foreach(ArrayOps.scala:156) at scala.collection.TraversableLike$class.map(TraversableLike.scala:244) at scala.collection.mutable.ArrayOps$ofInt.map(ArrayOps.scala:156) at org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$updateBlock(ALS.scala:509) at org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateFeatures$2.apply(ALS.scala:445) at org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateFeatures$2.apply(ALS.scala:444) at org.apache.spark.rdd.MappedValuesRDD$$anonfun$compute$1.apply(MappedValuesRDD.scala:31) at org.apache.spark.rdd.MappedValuesRDD$$anonfun$compute$1.apply(MappedValuesRDD.scala:31) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$4.apply(CoGroupedRDD.scala:156) at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$4.apply(CoGroupedRDD.scala:154) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:154) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.rdd.FlatMappedRDD.compute(FlatMappedRDD.scala:33) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) at org.apache.spark.rdd.RDD.iterator(RDD.scala:229) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:158) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) at org.apache.spark.scheduler.Task.run(Task.scala:51) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:744) 14/07/15 16:16:38 ERROR TaskSchedulerImpl: Lost executor 0 on maroki.office.mkechinov.ru: Uncaught exception 14/07/15 16:16:38 WARN TaskSetManager: Lost TID 12 (task 80.0:0) 14/07/15 16:16:42 WARN TaskSetManager: Lost TID 18 (task 80.0:1) 14/07/15 16:16:42 WARN TaskSetManager: Loss was due to fetch failure from null 14/07/15 16:16:42 WARN TaskSetManager: Loss was due to fetch failure from null 14/07/15 16:16:43 WARN TaskSetManager: Lost TID 25 (task 80.1:0) 14/07/15 16:16:43 WARN TaskSetManager: Loss was due to java.lang.UnsatisfiedLinkError 我该如何解决这个错误? 解决方法
Spark documentation清楚地提到MLLib使用本地库,这些库需要存在于节点上. (也就是它没有安装火花)
您必须确保所有节点上都存在libgfortran库. 对于debian / ubuntu使用: 对于centos使用:????sudo yum安装gcc-gfortran (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |