Error in using MLlib function ALS in Spark - scala

Question

I have read from a file like this:
val ratingText = sc.textFile("/home/cloudera/rec_data/processed_data/ratings/000000_0")
Used the following function to parse this data:
def parseRating(str: String): Rating= {
val fields = str.split(",")
Rating(fields(0).toInt, fields(1).trim.toInt, fields(2).trim.toDouble)
}
And created a rdd, which is then split into different RDDs
val ratingsRDD = ratingText.map(x=>parseRating(x)).cache()
val splits = ratingsRDD.randomSplit(Array(0.8, 0.2), 0L)
val trainingRatingsRDD = splits(0).cache()
Used the training RDD to create a model as follows:
val model = (new ALS().setRank(20).setIterations(10) .run(trainingRatingsRDD))
I get the following error in the last command
16/10/28 01:03:44 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
16/10/28 01:03:44 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
16/10/28 01:03:46 WARN LAPACK: Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
16/10/28 01:03:46 WARN LAPACK: Failed to load implementation from: com.github.fommil.netlib.NativeRefLAPACK
Edit: T. Gaweda's suggestion helped in removing the errors, but I am still getting the following warning:
16/10/28 01:53:59 WARN Executor: 1 block locks were not released by TID = 60:
[rdd_420_0]
16/10/28 01:54:00 WARN Executor: 1 block locks were not released by TID = 61:
[rdd_421_0]
And I think this has resulted in an empty model , because the next step is resulting in the following error:
val topRecsForUser = model.recommendProducts(4276736,3)
Error is:
java.util.NoSuchElementException: next on empty iterator at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
Please help!

Answer 1

It's only a warning. Spark uses BLAS to perform calculations. BLAS has native implementations and JVM implementation, the native one is more optimized / faster. However, you must install native library individually.
Without this configuration the warning message will appear and Spark will use JVM implementation of BLAS. Results should be the same, maybe calculated quite slower.
Here you've got description what is BLAS and how to configure it, for example on Cent OS is should be only: yum install openblas lapack