How to create a sample dataframe in Scala / Spark - scala

Question

I'm trying to create a simple DataFrame as follows:
import sqlContext.implicits._
val lookup = Array("one", "two", "three", "four", "five")
val theRow = Array("1",Array(1,2,3), Array(0.1,0.4,0.5))
val theRdd = sc.makeRDD(theRow)
case class X(id: String, indices: Array[Integer], weights: Array[Float] )
val df = theRdd.map{
case Array(s0,s1,s2) => X(s0.asInstanceOf[String],s1.asInstanceOf[Array[Integer]],s2.asInstanceOf[Array[Float]])
}.toDF()
df.show()
df is defined as
df: org.apache.spark.sql.DataFrame = [id: string, indices: array<int>, weights: array<float>]
which is what I want.
Upon executing, I get
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 13.0 failed 1 times, most recent failure: Lost task 1.0 in stage 13.0 (TID 50, localhost): scala.MatchError: 1 (of class java.lang.String)
Where is this MatchError coming from? And, is there a simpler way to create sample DataFrames programmatically?

Answer 1

First, theRow should be a Row and not an Array. Now, if you modify your types in such a way that the compatibility between Java and Scala is respected, your example will work
val theRow =Row("1",Array[java.lang.Integer](1,2,3), Array[Double](0.1,0.4,0.5))
val theRdd = sc.makeRDD(Array(theRow))
case class X(id: String, indices: Array[Integer], weights: Array[Double] )
val df=theRdd.map{
case Row(s0,s1,s2)=>X(s0.asInstanceOf[String],s1.asInstanceOf[Array[Integer]],s2.asInstanceOf[Array[Double]])
}.toDF()
df.show()
//+---+---------+---------------+
//| id| indices| weights|
//+---+---------+---------------+
//| 1|[1, 2, 3]|[0.1, 0.4, 0.5]|
//+---+---------+---------------+

Answer 2

For another example that you can refer
import spark.implicits._
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val columns=Array("id", "first", "last", "year")
val df1=sc.parallelize(Seq(
(1, "John", "Doe", 1986),
(2, "Ive", "Fish", 1990),
(4, "John", "Wayne", 1995)
)).toDF(columns: _*)
val df2=sc.parallelize(Seq(
(1, "John", "Doe", 1986),
(2, "IveNew", "Fish", 1990),
(3, "San", "Simon", 1974)
)).toDF(columns: _*)