*Thank so much Aljoscha* :)
I was stucked in this point. I didn't know that the print or collect method
collecting all the data in one place.
The execution time has dropped a lot.
However, I still get that Flink is slower (just for 7 seconds).
I really think I'm not getting all the performance out of Flink.
Because Flink draws the execution in a cyclic dependency graph meanwhile
Spark uses a DAG,
so it's clear that the Flin's way results in superior scalability and
performance compared to DAG approach.
So... Which is the problem with my code?
//Read data
val data: DataSet[org.apache.flink.ml.common.LabeledVector] =
MLUtils.readLibSVM(benv, "/inputPath/_.libsvm")
// Create multiple linear regression learner
val mlr = MultipleLinearRegression()
val model = mlr.fit(data)
data.writeAsText("file:///outputPath")
benv.execute()
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