apache spark - How to create DataFrame from Scala's List of Iterables?

Question

I have the following Scala value:

val values: List[Iterable[Any]] = Traces().evaluate(features).toList

and I want to convert it to a DataFrame.

When I try the following:

sqlContext.createDataFrame(values)

I got this error:

error: overloaded method value createDataFrame with alternatives:

[A <: Product](data: Seq[A])(implicit evidence$2: reflect.runtime.universe.TypeTag[A])org.apache.spark.sql.DataFrame 
[A <: Product](rdd: org.apache.spark.rdd.RDD[A])(implicit evidence$1: reflect.runtime.universe.TypeTag[A])org.apache.spark.sql.DataFrame
cannot be applied to (List[Iterable[Any]])
          sqlContext.createDataFrame(values)

Why?

Answer 1

Thats what spark implicits object is for. It allows you to convert your common scala collection types into DataFrame / DataSet / RDD. Here is an example with Spark 2.0 but it exists in older versions too

import org.apache.spark.sql.SparkSession
val values = List(1,2,3,4,5)

val spark = SparkSession.builder().master("local").getOrCreate()
import spark.implicits._
val df = values.toDF()

Edit: Just realised you were after 2d list. Here is something I tried on spark-shell. I converted a 2d List to List of Tuples and used implicit conversion to DataFrame:

val values = List(List("1", "One") ,List("2", "Two") ,List("3", "Three"),List("4","4")).map(x =>(x(0), x(1)))
import spark.implicits._
val df = values.toDF

Edit2: The original question by MTT was How to create spark dataframe from a scala list for a 2d list for which this is a correct answer. The original question is https://stackoverflow.com/revisions/38063195/1 The question was later changed to match an accepted answer. Adding this edit so that if someone else looking for something similar to the original question can find it.