Искра scala: java .lang.ClassCastException: java .lang.Integer не может быть приведена к scala .collection.Seq - PullRequest
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Искра scala: java .lang.ClassCastException: java .lang.Integer не может быть приведена к scala .collection.Seq

0 голосов
/ 29 апреля 2020

В следующем коде: 

def mapAppsToSparseVector(appFeatures: List[String], row: Row): SparseVector = {
    val vectorSize = appFeatures.length
    val indices = new ArrayBuffer[Int]()
    val values = new ArrayBuffer[Double]()
    val apps = row.getList[Tuple4[Int, String, String, String]](0).get(0)._4
    apps.split(":").foreach(m => if(appFeatures.indexOf(m) != -1) {indices += appFeatures.indexOf(m); values += 1.0})
    new SparseVector(vectorSize, indices.toArray, values.toArray)
}

val marketsToAdd = List("m1", "m3", "m5")
val columns = Array("id", "category", "color", "markets")
val df3 = spark.sqlContext.createDataFrame(
  Seq((0, "apples", "red", "m0:m1:m2"),
      (1, "oranges", "orange", "m0:m3"),
      (2, "bananas", "yellow", "m4:m5"),
      (3, "apples", "red", "m0"),
      (4, "bananas", "yellow", "m6:m7"),
      (5, "oranges", "orange", "m5:m7"),
      (6, "oranges", "orange", "m7:m0")
    )).toDF(columns: _*)
import spark.implicits._
val df5 = df3.map(r => (r.getInt(0), mapAppsToSparseVector(marketsToAdd, r))).toDF("id", "features")
df5.printSchema

Пока все хорошо, схема выглядит следующим образом: 

root
 |-- id: integer (nullable = false)
 |-- features: vector (nullable = true)

, как и ожидалось. Однако при попытке df5.show () я получаю ошибку ниже. Я новичок в спринге, я пробовал несколько вещей, таких как попытка добавить неявную функцию на карту с Seq, но я продолжаю получать ту же ошибку. Кто-нибудь имеет представление о том, что происходит? 

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 64.0 failed 1 times, most recent failure: Lost task 0.0 in stage 64.0 (TID 176, localhost, executor driver): java.lang.ClassCastException: java.lang.Integer cannot be cast to scala.collection.Seq
    at org.apache.spark.sql.Row$class.getSeq(Row.scala:283)
    at org.apache.spark.sql.catalyst.expressions.GenericRow.getSeq(rows.scala:166)
    at org.apache.spark.sql.Row$class.getList(Row.scala:291)
    at org.apache.spark.sql.catalyst.expressions.GenericRow.getList(rows.scala:166)
    at mapAppsToSparseVector(<console>:36)
    at $anonfun$1.apply(<console>:82)
    at $anonfun$1.apply(<console>:82)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown Source)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
  at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
  at scala.Option.foreach(Option.scala:257)
  at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
  at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
  at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
  at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
  at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550)
  at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370)
  at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
  at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
  at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3369)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:2550)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2764)
  at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:291)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:751)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:710)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:719)
  ... 46 elided
Caused by: java.lang.ClassCastException: java.lang.Integer cannot be cast to scala.collection.Seq
  at org.apache.spark.sql.Row$class.getSeq(Row.scala:283)
  at org.apache.spark.sql.catalyst.expressions.GenericRow.getSeq(rows.scala:166)
  at org.apache.spark.sql.Row$class.getList(Row.scala:291)
  at org.apache.spark.sql.catalyst.expressions.GenericRow.getList(rows.scala:166)
  at mapAppsToSparseVector(<console>:36)
  at $anonfun$1.apply(<console>:82)
  at $anonfun$1.apply(<console>:82)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown Source)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown Source)
  at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
  at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
  at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
  at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
  at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
  at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
  at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
  at org.apache.spark.scheduler.Task.run(Task.scala:123)
  at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
  at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
  ... 1 more

1 Ответ

0 голосов
/ 09 мая 2020

Обычно я не имею дело с Row напрямую, но, похоже, проблема в следующей строке: 

val apps = row.getList[Tuple4[Int, String, String, String]](0).get(0)._4

Вы пытаетесь получить 0-е поле как java.util.List[Tuple4[Int, String, String, String]], но 0-е поле в вашем фрейме данных на самом деле является Int. Я думаю, что, возможно, вы смотрите на Row.getType методы как более сложные, чем они есть на самом деле.

Любое из следующего должно решить проблему (если я знаю, о чем говорю): 

// by field name
row.getString(row.fieldIndex("markets"))
// by index
row.getString(4)

Хотя, как отмечают некоторые, реализация вашей функции в формате udf облегчит работу.

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