Scala 类型失配；找到：org.apache.spark.sql.DataFrame必需：org.apache.spark.rdd.rdd

我是scala和mllib的新手，我一直遇到以下错误。如果有人能解决类似问题，请告诉我

import org.apache.spark.sql.SparkSession
import org.apache.spark.mllib.clustering.{KMeans, KMeansModel}
import org.apache.spark.mllib.linalg.Vectors
.
.
.
val conf = new SparkConf().setMaster("local").setAppName("SampleApp")
val sContext = new SparkContext(conf)
val sc = SparkSession.builder().master("local").appName("SampleApp").getOrCreate()
val sampleData = sc.read.json("input/sampleData.json")
val clusters = KMeans.train(sampleData, 10, 10)
val WSSSE = clusters.computeCost(sampleData)
clusters.save(sc, "target/org/apache/spark/KMeansExample/KMeansModel")
val sameModel = KMeansModel.load(sContext, "target/org/apache/spark/KMeansExample/KMeansModel")

上面这一行给出了一个错误，如下所示：

type mismatch; found : org.apache.spark.sql.DataFrame (which expands to) org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] required: org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector]

所以我试着：

import org.apache.spark.ml.clustering.KMeans
val kmeans = new KMeans().setK(20)
val model = kmeans.fit(sampleData)
val predictions = model.transform(sampleData)
val evaluator = new ClusteringEvaluator()
val silhouette = evaluator.evaluate(predictions)

这会产生以下错误：

Exception in thread "main" java.lang.IllegalArgumentException: Field "features" does not exist.
Available fields: address, attributes, business_id
at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:267)
at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:267)
at scala.collection.MapLike$class.getOrElse(MapLike.scala:128)
at scala.collection.AbstractMap.getOrElse(Map.scala:59)
at org.apache.spark.sql.types.StructType.apply(StructType.scala:266)
at org.apache.spark.ml.util.SchemaUtils$.checkColumnTypes(SchemaUtils.scala:58)
at org.apache.spark.ml.util.SchemaUtils$.validateVectorCompatibleColumn(SchemaUtils.scala:119)
at org.apache.spark.ml.clustering.KMeansParams$class.validateAndTransformSchema(KMeans.scala:96)
at org.apache.spark.ml.clustering.KMeans.validateAndTransformSchema(KMeans.scala:285)
at org.apache.spark.ml.clustering.KMeans.transformSchema(KMeans.scala:382)
at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74)
at org.apache.spark.ml.clustering.KMeans$$anonfun$fit$1.apply(KMeans.scala:341)
at org.apache.spark.ml.clustering.KMeans$$anonfun$fit$1.apply(KMeans.scala:340)
at org.apache.spark.ml.util.Instrumentation$$anonfun$11.apply(Instrumentation.scala:183)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:183)
at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:340)

我指的是和

编辑

使用setFeaturesCol（）

这仍然会产生不同的错误：

Exception in thread "main" java.lang.NoSuchMethodError: org.apache.spark.util.Utils$.getSimpleName(Ljava/lang/Class;)Ljava/lang/String;
at org.apache.spark.ml.util.Instrumentation.logPipelineStage(Instrumentation.scala:52)
at org.apache.spark.ml.clustering.KMeans$$anonfun$fit$1.apply(KMeans.scala:350)
at org.apache.spark.ml.clustering.KMeans$$anonfun$fit$1.apply(KMeans.scala:340)
at org.apache.spark.ml.util.Instrumentation$$anonfun$11.apply(Instrumentation.scala:183)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:183)
at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:340)

谢谢。

请阅读并选择一个或另一个具有适当分布式数据结构的库。请使用较新的ml库（如第二次尝试）。对于

KMeans

您需要指定要用作输入的列（默认为“features”，因此错误为features不存在）。使用

setFeaturesCol（）

执行此操作。

Exception in thread "main" java.lang.NoSuchMethodError: org.apache.spark.util.Utils$.getSimpleName(Ljava/lang/Class;)Ljava/lang/String;
at org.apache.spark.ml.util.Instrumentation.logPipelineStage(Instrumentation.scala:52)
at org.apache.spark.ml.clustering.KMeans$$anonfun$fit$1.apply(KMeans.scala:350)
at org.apache.spark.ml.clustering.KMeans$$anonfun$fit$1.apply(KMeans.scala:340)
at org.apache.spark.ml.util.Instrumentation$$anonfun$11.apply(Instrumentation.scala:183)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:183)
at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:340)