org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 744.0 failed 1 times, most recent failure: Lost task 1.0 in stage 744.0 (TID 1237, localhost): java.lang.Exception: Partition[1]: FATAL ERROR for job S2V_job5364016263210767025. Job status information is available in the Vertica table public.S2V_JOB_STATUS. . Failed rows summary: FailedRowsPercent=1.0; failedRowsPercentTolerance=0.0: FAILED. NOT OK to commit rows to database. Too many rows were rejected. . Unable to create/insert into target table public.sometable

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  1. 0

    Vertica Spark Connector - S2V - FAILED. NOT OK to commit rows to database. Too many rows were rejected

    Stack Overflow | 7 months ago | Syed Muhammad Humayun
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 744.0 failed 1 times, most recent failure: Lost task 1.0 in stage 744.0 (TID 1237, localhost): java.lang.Exception: Partition[1]: FATAL ERROR for job S2V_job5364016263210767025. Job status information is available in the Vertica table public.S2V_JOB_STATUS. . Failed rows summary: FailedRowsPercent=1.0; failedRowsPercentTolerance=0.0: FAILED. NOT OK to commit rows to database. Too many rows were rejected. . Unable to create/insert into target table public.sometable
  2. 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)
  3. 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)
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    Root Cause Analysis

    1. org.apache.spark.SparkException

      Job aborted due to stage failure: Task 1 in stage 744.0 failed 1 times, most recent failure: Lost task 1.0 in stage 744.0 (TID 1237, localhost): java.lang.Exception: Partition[1]: FATAL ERROR for job S2V_job5364016263210767025. Job status information is available in the Vertica table public.S2V_JOB_STATUS. . Failed rows summary: FailedRowsPercent=1.0; failedRowsPercentTolerance=0.0: FAILED. NOT OK to commit rows to database. Too many rows were rejected. . Unable to create/insert into target table public.sometable

      at com.vertica.spark.s2v.S2V.tryTofinalizeSaveToVertica()
    2. com.vertica.spark
      S2V$$anonfun$2.apply
      1. com.vertica.spark.s2v.S2V.tryTofinalizeSaveToVertica(S2V.scala:746)
      2. com.vertica.spark.s2v.S2V$$anonfun$2.apply(S2V.scala:226)
      3. com.vertica.spark.s2v.S2V$$anonfun$2.apply(S2V.scala:128)
      3 frames
    3. Spark
      Executor$TaskRunner.run
      1. org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$22.apply(RDD.scala:745)
      2. org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$22.apply(RDD.scala:745)
      3. org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
      4. org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
      5. org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
      6. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
      7. org.apache.spark.scheduler.Task.run(Task.scala:89)
      8. org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
      8 frames
    4. Java RT
      Thread.run
      1. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
      2. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
      3. java.lang.Thread.run(Thread.java:745)
      3 frames