Cannot resolve xyz specified input columns when creating a Spark dataset

Question

I'm trying to make something very simple, but I can’t believe that it doesn’t work ... I probably missed something very obvious. Please, help.

Purpose: Read the Iris dataset (csv file, no header) into the dataset

the code:

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] 

Error:

 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 

Here's what the data file looks like:

 $ 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

Types and names must match. Try:

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

Answer 2

You can read it as a text file and map each input and convert it to a data set.

how

 case class Iris(sepalWidth: Double, sepalLength: Double, petalWidth: Double,petalLength: Double, irisClass: String) val ds = spark.textFile("/home/ec2-user/spark-2.0.1-bin-hadoop2.7/tkdata/iris.data") .map(_.split(",")) .map(t =>Iris(t(0).toDouble,t(1).toDouble,t(2).toDouble,t(3).toDouble,t(4))).toDS()