今天在跑一个Job的时候，遇到了如下异常：
15/01/05 16:54:06 INFO mapreduce.TableInputFormatBase: Input split length: 889 M bytes.
15/01/05 16:55:07 WARN scheduler.TaskSetManager: Lost TID 339 (task 74.0:5)
15/01/05 16:55:07 WARN scheduler.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:724)
经查证后发现，Spark MLib是依赖于jblas这个库的，需要手动在每个节点上安装。
debian/ubuntu use:
for centos use:
debian/ubuntu use:
sudo apt-get install libgfortran3for centos use:
sudo yum install gcc-gfortran参考以下资料：
1、Spark官网：
Dependencies
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.
To use MLlib in Python, you will need NumPy version 1.7 or newer and Python 2.7.
(责任编辑：)
