Scala Spark - 调用createDataFrame时获取重载方法

Question

I try to create a DataFrame from an Array of Array of Double (Array[Array[Double]]) like below:

val points : ArrayBuffer[Array[Double]] = ArrayBuffer(
Array(0.19238990024216676, 1.0, 0.0, 0.0),
Array(0.2864319929878242, 0.0, 1.0, 0.0),
Array(0.11160349352921925, 0.0, 2.0, 1.0),
Array(0.3659220026496052, 2.0, 2.0, 0.0),
Array(0.31809629470827383, 1.0, 1.0, 1.0))

val x = Array("__1", "__2", "__3", "__4")
val myschema = StructType(x.map(fieldName ⇒ StructField(fieldName, DoubleType, true)))

points.map(e => Row(e(0), e(1), e(2), e(3)))
val newDF = sqlContext.createDataFrame(points, myschema)

But get this error:

<console>:113: error: overloaded method value createDataFrame with alternatives:
(data: java.util.List[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
(rdd: org.apache.spark.api.java.JavaRDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
(rdd: org.apache.spark.rdd.RDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
(rows: java.util.List[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
(rowRDD: org.apache.spark.api.java.JavaRDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
(rowRDD: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame
cannot be applied to (scala.collection.mutable.ArrayBuffer[Array[Double]], org.apache.spark.sql.types.StructType)
val newDF = sqlContext.createDataFrame(points, myschema)

I searched over the internet but can't find out how to fix it! So if anyone has any idea about this, please help me!

Answer 1

There is no overload of method createDataFrame that accepts an instance of ArrayBuffer[Array[Double]] . Your call to points.map wasn't being assigned to anything, it returns a new instance rather than operating in-place. Try:

val points : List[Array[Double]] = List(
    Seq(0.19238990024216676, 1.0, 0.0, 0.0),
    Seq(0.2864319929878242, 0.0, 1.0, 0.0),
    Seq(0.11160349352921925, 0.0, 2.0, 1.0),
    Seq(0.3659220026496052, 2.0, 2.0, 0.0),
    Seq(0.31809629470827383, 1.0, 1.0, 1.0))

val x = Array("__1", "__2", "__3", "__4")
val myschema = StructType(x.map(fieldName ⇒ StructField(fieldName, DoubleType, true)))

val newDF = sqlContext.createDataFrame(
    points.map(Row.fromSeq(_), myschema)

Answer 2

This works for me:

import org.apache.spark.sql._
import org.apache.spark.sql.types._
import scala.collection.mutable.ArrayBuffer

val sqlContext = new org.apache.spark.sql.SQLContext(sc)

val points : ArrayBuffer[Array[Double]] = ArrayBuffer(
  Array(0.19238990024216676, 1.0, 0.0, 0.0),
  Array(0.2864319929878242, 0.0, 1.0, 0.0),
  Array(0.11160349352921925, 0.0, 2.0, 1.0),
  Array(0.3659220026496052, 2.0, 2.0, 0.0),
  Array(0.31809629470827383, 1.0, 1.0, 1.0))

val x = Array("__1", "__2", "__3", "__4")
val myschema = StructType(x.map(fieldName ⇒ StructField(fieldName, DoubleType, true)))

val rdd = sc.parallelize(points.map(e => Row(e(0), e(1), e(2), e(3))))
val newDF = sqlContext.createDataFrame(rdd, myschema)

newDF.show