数据帧筛选器提供 NullPointerException



在Spark 1.6.0中,我有一个数据框,其中有一列保存工作描述,例如:

Description
bartender
bartender
employee
taxi-driver
...

我从该列中检索唯一值的列表:

val jobs = people.select("Description").distinct().rdd.map(r => r(0).asInstanceOf[String]).repartition(4)

然后,对于每个工作描述,我尝试检索具有该工作的人员并执行某些操作,但是我得到了一个NullPointerException:

jobs.foreach { 
  ajob => 
   var peoplewithjob = people.filter($"Description" === ajob)
   // ... do stuff
}

我不明白为什么会这样,因为每个工作都是从人员数据框中提取的,所以至少应该有一个工作......任何提示,欢迎!下面是堆栈跟踪:

Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 4.0 failed 1 times, most recent failure: Lost task 3.0 in stage 4.0 (TID 206, localhost): java.lang.NullPointerException
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:131)
at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$withPlan(DataFrame.scala:2165)
at org.apache.spark.sql.DataFrame.filter(DataFrame.scala:799)
at jago.Run$$anonfun$main$1.apply(Run.scala:89)
at jago.Run$$anonfun$main$1.apply(Run.scala:82)
at scala.collection.Iterator$class.foreach(Iterator.scala:742)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$32.apply(RDD.scala:912)
at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$32.apply(RDD.scala:912)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)

发生这种情况是因为 Spark 不支持嵌套操作或转换。如果要对从DataFrame中提取的不同值进行操作,则必须将结果提取到驱动程序并在本地迭代:

// or toLocalIterator
jobs.collect.foreach { 
  ajob => 
    var peoplewithjob = people.filter($"Description" === ajob)
}

根据您作为"执行操作"应用的转换类型,最好简单地grouBy和聚合:

people.groupBy($"Description").agg(...)    

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