Apache Spark — MlLib — Collaborative filtering

I'm trying to use MlLib for my colloborative filtering.

I encounter the following error in my Scala program when I run it in Apache Spark 1.0.0.

   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

How can I solve this error?

Spark documentation clearly mentions that MLLib uses native libraries, which need to be present on the nodes. (that is it does not come with spark installation)

MLlib uses the jblas linear algebra library, which itself depends on native Fortran routines. You may need to install the gfortran runtime library if it is not already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries automatically.

You have to make sure that libgfortran library exists on all nodes.

for debian/ubuntu use: sudo apt-get install libgfortran3

for centos use: sudo yum install gcc-gfortran