Question

我正在从存储在HDFS上的文件夹中读取数据流。我有以下一小段代码：

// Convert text into a DataSet of LogEntry rows. Select the two columns we care about
  val df = rawData.flatMap(parseLog).select("ip", "status")
  df .isStreaming


  val kmeans = new KMeans().setK(2).setSeed(1L)
  val model = kmeans.fit(df)

  // Evaluate clustering by computing Within Set Sum of Squared Errors.
  val WSSSE = model.computeCost(df)
  println(s"Within Set Sum of Squared Errors = $WSSSE")

  // Shows the K-means result
  println("Cluster Centers: ")
  model.clusterCenters.foreach(println)

当我运行上述操作时，出现以下错误：

java.lang.IllegalArgumentException: Field "features" does not exist.
  at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:266)
  at org.apache.spark.sql.types.StructType$$anonfun$apply$1.apply(StructType.scala:266)
  at scala.collection.MapLike$class.getOrElse(MapLike.scala:128)
  at scala.collection.AbstractMap.getOrElse(Map.scala:59)
  at org.apache.spark.sql.types.StructType.apply(StructType.scala:265)
  at org.apache.spark.ml.util.SchemaUtils$.checkColumnType(SchemaUtils.scala:40)
  at org.apache.spark.ml.clustering.KMeansParams$class.validateAndTransformSchema(KMeans.scala:93)
  at org.apache.spark.ml.clustering.KMeans.validateAndTransformSchema(KMeans.scala:254)
  at org.apache.spark.ml.clustering.KMeans.transformSchema(KMeans.scala:340)
  at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74)
  at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:305)
  at StructuredStreaming$.main(<console>:189)
  ... 90 elided

我完全被这个

困住了

。任何帮助将不胜感激。

更新

我对EmiCareOfCell44做了以下修改答案：

import org.apache.spark.ml.feature.VectorAssembler import org.apache.spark.ml.linalg.Vectors

val assembler = new VectorAssembler().setInputCols(Array("ip", "status")).setOutputCol("features")
val output = assembler.transform(df).select("features")
val kmeans = new KMeans().setK(2).setSeed(1L)
val model = kmeans.fit(output)

代码现在加载但是当我去运行它时，我收到以下错误：

java.lang.IllegalArgumentException: Data type StringType is not supported.
  at org.apache.spark.ml.feature.VectorAssembler$$anonfun$transformSchema$1.apply(VectorAssembler.scala:121)
  at org.apache.spark.ml.feature.VectorAssembler$$anonfun$transformSchema$1.apply(VectorAssembler.scala:117)
  at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
  at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
  at org.apache.spark.ml.feature.VectorAssembler.transformSchema(VectorAssembler.scala:117)
  at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74)
  at org.apache.spark.ml.feature.VectorAssembler.transform(VectorAssembler.scala:54)
  at StructuredStreaming$.main(<console>:129)
  ... 60 elided

我认为它越来越近了，只需要调整一下。

Answer 1

您必须首先使用VectorAssembler来创建特征向量。类似的东西：

 val assembler = new VectorAssembler().setInputCols(Array("ip", "status")).setOutputCol("features")
  val df2 = assembler.transform(df).select("features")
  val kmeans = new KMeans().setK(2).setSeed(1L)
  val model = kmeans.fit(df2)

Spark结构化流媒体2.2和k-means

1 个答案: