How to convert RDD[Row] to RDD[Vector]

问题:

I'm trying to implement k-means method using scala. I created a RDD something like that

val df = sc.parallelize(data).groupByKey().collect().map((chunk)=> {
  sc.parallelize(chunk._2.toSeq).toDF()
})

val examples = df.map(dataframe =>{
  dataframe.selectExpr(
    "avg(time) as avg_time",
    "variance(size) as var_size",
    "variance(time) as var_time",
    "count(size) as examples"
  ).rdd
})

val rdd_final=examples.reduce(_ union _)

val kmeans= new KMeans()
val model = kmeans.run(rdd_final)

With this code I obtain an error

type mismatch;
[error]  found   : org.apache.spark.rdd.RDD[org.apache.spark.sql.Row]
[error]  required:org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector]

So I tried to cast doing:

val rdd_final_Vector = rdd_final.map{x:Row => x.getAs[org.apache.spark.mllib.linalg.Vector](0)}

val model = kmeans.run(rdd_final_Vector)

But then I obtain an error:

java.lang.ClassCastException: java.lang.Double cannot be cast to org.apache.spark.mllib.linalg.Vector

So I'm looking for a way to do that cast, but I can't find any method.

Any idea?

Best regards

回答1:

At least a couple of issues here:

1. No you really can not cast a Row to a Vector: a Row is a collection of potentially disparate types understood by Spark SQL. A Vector is not a native spark sql type
2. There seems to be a mismatch between the content of your SQL statement and what you are attempting to achieve with KMeans: the SQL is performing aggregations. But KMeans expects a series of individual data points in the form a Vector (which encapsulates an Array[Double]) . So then - why are you supplying sum's and average's to a KMeans operation?

Addressing just #1 here: you will need to do something along the lines of:

val doubVals = <rows rdd>.map{ row =>   row.getDouble("colname") }
val vector = Vectors.toDense{ doubVals.collect}

Then you have a properly encapsulated Array[Double] (within a Vector) that can be supplied to Kmeans.