Scala/Spark - Create Dataset with one column from another Dataset - scala

Question

I am trying to create a Dataset with only one column from Case Class.
Below is the code:
case class vectorData(value: Array[String], vectors: Vector)
def main(args: Array[String]) {
val spark = SparkSession.builder
.appName("Hello world!")
.master("local[*]")
.getOrCreate()
import spark.implicits._
//blah blah and read data etc.
val word2vec = new Word2Vec()
.setInputCol("value").setOutputCol("vectors")
.setVectorSize(5).setMinCount(0).setWindowSize(5)
val dataset = spark.createDataset(data)
val model = word2vec.fit(dataset)
val encoder = org.apache.spark.sql.Encoders.product[vectorData]
val result = model.transform(dataset).as(encoder)
//val output: Dataset[Vector] = ???
}
As shown in last line of the code, I want the output to be only the 2nd column which has Vector type with vectors data.
I tried with:
val output = result.map(o => o.vectors)
But this line highlighted error No implicit arguments of type: Encoder[Vector]
How to resolve this?

Answer 1

I think line:
implicit val vectorEncoder: Encoder[Vector] = org.apache.spark.sql.Encoders.product[Vector]
should make
val output = result.map(o => o.vectors)
correct