ERROR Executor: Exception in task 0.0 in stage 6.0

2019-07-08 02:25发布

站内文章 / Spark
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问题:

I have a json file like below.

{"name":"method2","name1":"test","parameter1":"C:/Users/test/Desktop/Online.csv","parameter2": 1.0}

I am loading my json file.

val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val df = sqlContext.read.json("C:/Users/test/Desktop/data.json")
val df1=df.select($"name",$"parameter1",$"parameter2").toDF()
df1.show()

I have 3 function like below:

def method1(P1:String, P2:Double) {
val data = spark.read.option("header", true).csv(P1).toDF()
val rs= data.select("CID", "Sc").dropDuplicates("CID", "Sc").withColumn("Rat", lit(P2))
val outPutPath="C:/Users/test/Desktop/output"
rs.coalesce(1).write.format("com.databricks.spark.csv").option("header", "true").save(outPutPath)
}
def method2(P1:String, P2:Double){
val data = spark.read.option("header", true).csv(P1).toDF()
val rs= data.select("CID", "Sc").withColumn("r", lit(P2))
val rs1= rs.filter($"CID" =!= "").groupBy("CID","Sc").agg(sum(rs("r")).alias("R"))
val outPutPath="C:/Users/test/Desktop/output"
rs1.coalesce(1).write.format("com.databricks.spark.csv").option("header", "true").save(outPutPath)
}
def methodn(P1:String, P2:Double) {
println("method 2 printhing")
println(P2)
}

i am trying to call the above functions using below code

df1.map( row => (row.getString(0), row.getString(1), row.getDouble(2) ) ).foreach { x =>
      x._1.trim.toLowerCase match {
          case "method1" => method1(x._2, x._3) 
          case "method2" => method2(x._2, x._3)
          case _ => methodn(x._2, x._3)
      }
   }

based on my json object it should call method2 but when i am trying to execute above code i am getting below error.

17/11/22 16:15:44 ERROR Executor: Exception in task 0.0 in stage 6.0 (TID 6)
java.lang.NullPointerException
        at $line36.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.method2(<console>:24)
        at $line38.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$2.apply(<console>:40)
        at $line38.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$2.apply(<console>:37)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
        at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
        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)
17/11/22 16:15:44 WARN TaskSetManager: Lost task 0.0 in stage 6.0 (TID 6, localhost, executor driver): java.lang.NullPointerException
        at $line36.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw.method2(<console>:24)
        at $line38.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$2.apply(<console>:40)
        at $line38.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$2.apply(<console>:37)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
        at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
        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)

17/11/22 16:15:44 ERROR TaskSetManager: Task 0 in stage 6.0 failed 1 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 6.0 failed 1 times, most recent failure: Lost task 0.0 in stage 6.0 (TID 6, localhost, executor driver): java.lang.NullPointerException
        at method2(<console>:24)
        at $anonfun$2.apply(<console>:40)
        at $anonfun$2.apply(<console>:37)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
        at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
        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)

Driver stacktrace:
  at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
  at scala.Option.foreach(Option.scala:257)
  at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
  at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
  at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:918)
  at org.apache.spark.rdd.RDD$$anonfun$foreach$1.apply(RDD.scala:916)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
  at org.apache.spark.rdd.RDD.foreach(RDD.scala:916)
  at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply$mcV$sp(Dataset.scala:2325)
  at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
  at org.apache.spark.sql.Dataset$$anonfun$foreach$1.apply(Dataset.scala:2325)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
  at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2823)
  at org.apache.spark.sql.Dataset.foreach(Dataset.scala:2324)
  ... 54 elided
Caused by: java.lang.NullPointerException
  at method2(<console>:24)
  at $anonfun$2.apply(<console>:40)
  at $anonfun$2.apply(<console>:37)
  at scala.collection.Iterator$class.foreach(Iterator.scala:893)
  at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
  at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
  at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:918)
  at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
  at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
  at org.apache.spark.scheduler.Task.run(Task.scala:108)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
  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)

please help me on this how to resolve this issue.

回答1:

You are getting NullPointerException because you are trying to access sparkSession(spark) inside the functions(method1, method2). Thats not an actual issue though. The main issue is that you are calling those functions from inside map function of dataframe. Thats the main issue.

You cannot access variables defined outside transformations from within transformations. All the functions are being called inside transformations and Spark could not find any definition for spark variable being used inside those functions. Thats the main reason for getting nullPointerException.

The solution to this would be to call the functions from where spark variable can be accessed and not from within a transformation. So changing your last transformation into an action would do the trick

val process = df1.map( row => (row.getString(0), row.getString(1), row.getDouble(2) ) ).collect

process.foreach { x =>
  x._1.trim.toLowerCase match {
    case "method1" => method1(x._2, x._3)
    case "method2" => method2(x._2, x._3)
    case _ => methodn(x._2, x._3)
  }
}

I hope the answer is helpful



标签： scala apache-spark apache-spark-sql spark-dataframe user-defined-functions
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