Scala Spark - 调用 createDataFrame 时获取重载方法答案

【问题标题】：Scala Spark - Get Overloaded method when calling createDataFrameScala Spark - 调用 createDataFrame 时获取重载方法
【发布时间】：2017-02-13 14:38:26
【问题描述】：

我尝试从一个双精度数组 (Array[Array[Double]]) 创建一个 DataFrame，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)

我在互联网上进行了搜索，但找不到解决方法！所以如果有人对此有任何想法，请帮助我！

【问题讨论】：

标签： scala apache-spark bigdata

【解决方案1】：

这对我有用：

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

【讨论】：

我会感谢您解释为什么不赞成。谢谢

【解决方案2】：

没有接受ArrayBuffer[Array[Double]] 实例的方法createDataFrame 的重载。您对points.map 的调用没有被分配给任何东西，它返回一个新实例而不是就地操作。试试：

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)

【讨论】：