创建Spark数据集时无法解析给定输入列错误的xyz

Question

我想做一些非常简单的事情,但我不敢相信它不起作用......我可能错过了一些非常明显的事情.请帮忙.

目标:将Iris数据集(csv文件,无标题)读入数据集

码:

case class Iris(sepalWidth: Double, sepalLength: Double, petalWidth: Double, petalLength: Double, irisClass: String)
val ds = spark.read.format("csv").option("inferSchema", true).load("/home/ec2-user/spark-2.0.1-bin-hadoop2.7/tkdata/iris.data").as[Iris]

错误:

org.apache.spark.sql.AnalysisException: cannot resolve '`sepalWidth`' given input columns: [_c1, _c3, _c0, _c4, _c2];
  at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5$$anonfun$apply$11.apply(TreeNode.scala:350)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
  at scala.collection.immutable.List.map(List.scala:285)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:348)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:190)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:200)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$5.apply(QueryPlan.scala:209)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:209)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
  at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolveAndBind(ExpressionEncoder.scala:245)
  at org.apache.spark.sql.Dataset.<init>(Dataset.scala:210)
  at org.apache.spark.sql.Dataset.<init>(Dataset.scala:167)
  at org.apache.spark.sql.Dataset$.apply(Dataset.scala:59)
  at org.apache.spark.sql.Dataset.as(Dataset.scala:359)
  ... 54 elided

这是数据文件的样子:

$ head iris.data 
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa

Answer 1

类型和名称必须匹配.尝试:

spark.read.format("csv").option("inferSchema", true).load(...)
  .toDF("sepalWidth", "sepalLength", "petalWidth", "petalLength", "irisClass")
  .as[Iris]

或更好

import org.apache.spark.sql.Encoders

spark.read
  .schema(Encoders.product[Iris].schema)
  .csv("iris.data")
  .as[Iris]

此外,如果源具有弱保证,则应使用Java类型将字段声明为可空

type JDouble = java.lang.Double

case class Iris(
  sepalWidth: JDouble, sepalLength: JDouble,
  petalWidth: JDouble, petalLength: JDouble, 
  irisClass: String)

或者Options:

case class Iris(
  sepalWidth: Option[Double], sepalLength: Option[Double],
  petalWidth: Option[Double], petalLength: Option[Double], 
  irisClass: String)