Scala Spark-调用createDataFrame时获取重载方法

我尝试从数组Double（Array[Array[Double]]）创建一个数据帧，如下所示：

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)

但是得到这个错误：

<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)

：113:错误：重载了方法值createDataFrame，并带有可选项：
（数据：java.util.List[\ux]，beanClass:Class[\ux]）org.apache.spark.sql.DataFrame
（rdd:org.apache.spark.api.java.JavaRDD[_]，beanClass:Class[_]）org.apache.spark.sql.DataFrame
（rdd:org.apache.spark.rdd.rdd[\u0]，beanClass:Class[\u0]）org.apache.spark.sql.DataFrame
（行：java.util.List[org.apache.spark.sql.Row]，模式：org.apache.spark.sql.types.StructType）org.apache.spark.sql.DataFrame
（rowRDD:org.apache.spark.api.java.JavaRDD[org.apache.spark.sql.Row]，schema:org.apache.spark.sql.types.StructType）org.apache.spark.sql.DataFrame
（rowRDD:org.apache.spark.rdd.rdd[org.apache.spark.sql.Row]，schema:org.apache.spark.sql.types.StructType）org.apache.spark.sql.DataFrame
无法应用于（scala.collection.mutable.ArrayBuffer[Array[Double]]，org.apache.spark.sql.types.StructType）
val newDF=sqlContext.createDataFrame（点，myschema）

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我在互联网上搜索过，但找不到如何修复它！所以如果有人对此有任何想法，请帮助我

没有方法

createDataFrame

重载，该方法接受

ArrayBuffer[Array[Double]]

的实例。您对

points.map的调用未分配给任何对象，它返回一个新实例，而不是就地操作。尝试：

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)

这对我很有用：

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

请解释一下为什么被否决，我将不胜感激。谢谢