org.apache.spark.SparkException: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.

GitHub | car2008 | 7 months ago
tip
Your exception is missing from the Samebug knowledge base.
Here are the best solutions we found on the Internet.
Click on the to mark the helpful solution and get rewards for you help.
  1. 0

    Fail to connect to master. ERROR SparkDeploySchedulerBackend: Application has been killed. Reason: All masters are unresponsive

    Stack Overflow | 9 months ago | Yu Shi
    org.apache.spark.SparkException: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.
  2. 0

    GitHub comment 572#246278541

    GitHub | 7 months ago | car2008
    org.apache.spark.SparkException: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.
  3. 0

    Is it necessary to submit spark application jar?

    Stack Overflow | 1 year ago | Marcin Lagowski
    org.apache.spark.SparkException: Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.
  4. Speed up your debug routine!

    Automated exception search integrated into your IDE

  5. 0

    RE: Not Serializable exception when integrating SQL and Spark Streaming

    apache.org | 2 years ago
    org.apache.spark.SparkException: Task not serializable at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158) at org.apache.spark.SparkContext.clean(SparkContext.scala:1435) at org.apache.spark.rdd.RDD.map(RDD.scala:271) at org.apache.spark.api.java.JavaRDDLike$class.map(JavaRDDLike.scala:78) at org.apache.spark.sql.api.java.JavaSchemaRDD.map(JavaSchemaRDD.scala:42) at com.basic.spark.NumberCount$2.call(NumberCount.java:79) at com.basic.spark.NumberCount$2.call(NumberCount.java:67) at org.apache.spark.streaming.api.java.JavaDStreamLike$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:274) at org.apache.spark.streaming.api.java.JavaDStreamLike$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:274) at org.apache.spark.streaming.dstream.DStream$anonfun$foreachRDD$1.apply(DStream.scala:529) at org.apache.spark.streaming.dstream.DStream$anonfun$foreachRDD$1.apply(DStream.scala:529) at org.apache.spark.streaming.dstream.ForEachDStream$anonfun$1.apply$mcV$sp(ForEachDStream.scala:42) at org.apache.spark.streaming.dstream.ForEachDStream$anonfun$1.apply(ForEachDStream.scala:40) at org.apache.spark.streaming.dstream.ForEachDStream$anonfun$1.apply(ForEachDStream.scala:40) at scala.util.Try$.apply(Try.scala:161) at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:171) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
  6. 0

    RE: Not Serializable exception when integrating SQL and Spark Streaming

    apache.org | 1 year ago
    org.apache.spark.SparkException: Task not serializable at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158) at org.apache.spark.SparkContext.clean(SparkContext.scala:1435) at org.apache.spark.rdd.RDD.map(RDD.scala:271) at org.apache.spark.api.java.JavaRDDLike$class.map(JavaRDDLike.scala:78) at org.apache.spark.sql.api.java.JavaSchemaRDD.map(JavaSchemaRDD.scala:42) at com.basic.spark.NumberCount$2.call(NumberCount.java:79) at com.basic.spark.NumberCount$2.call(NumberCount.java:67) at org.apache.spark.streaming.api.java.JavaDStreamLike$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:274) at org.apache.spark.streaming.api.java.JavaDStreamLike$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:274) at org.apache.spark.streaming.dstream.DStream$anonfun$foreachRDD$1.apply(DStream.scala:529) at org.apache.spark.streaming.dstream.DStream$anonfun$foreachRDD$1.apply(DStream.scala:529) at org.apache.spark.streaming.dstream.ForEachDStream$anonfun$1.apply$mcV$sp(ForEachDStream.scala:42) at org.apache.spark.streaming.dstream.ForEachDStream$anonfun$1.apply(ForEachDStream.scala:40) at org.apache.spark.streaming.dstream.ForEachDStream$anonfun$1.apply(ForEachDStream.scala:40) at scala.util.Try$.apply(Try.scala:161) at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:171) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)

    Root Cause Analysis

    1. org.apache.spark.SparkException

      Exiting due to error from cluster scheduler: All masters are unresponsive! Giving up.

      at org.apache.spark.scheduler.TaskSchedulerImpl.error()
    2. Spark
      AppClient$ClientEndpoint$$anon$2.run
      1. org.apache.spark.scheduler.TaskSchedulerImpl.error(TaskSchedulerImpl.scala:438)
      2. org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.dead(SparkDeploySchedulerBackend.scala:124)
      3. org.apache.spark.deploy.client.AppClient$ClientEndpoint.markDead(AppClient.scala:264)
      4. org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$anonfun$run$1.apply$mcV$sp(AppClient.scala:134)
      5. org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1163)
      6. org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(AppClient.scala:129)
      6 frames
    3. Java RT
      Thread.run
      1. java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
      2. java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
      3. java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
      4. java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
      5. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
      6. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
      7. java.lang.Thread.run(Thread.java:745)
      7 frames