org.apache.spark.SparkException：由于阶段失败而中止作业：阶段 11.0 中的任务 98 失败了 4 次

Question

我正在使用 Google Cloud Dataproc 来做 spark 工作，我的编辑器是 Zepplin。我试图将 json 数据写入 gcp 存储桶。当我尝试 10MB 文件时，它成功了。但失败了 10GB 文件。我的 dataproc 有 1 个带有 4CPU、26GB 内存、500GB 磁盘的主服务器。5 名工人具有相同的配置。我想它应该能够处理 10GB 的数据。

我的命令是 toDatabase.repartition(10).write.json("gs://mypath")

错误是

org.apache.spark.SparkException: Job aborted.
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:224)
  at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
  at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
  at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
  at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)
  at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:225)
  at org.apache.spark.sql.DataFrameWriter.json(DataFrameWriter.scala:528)
  ... 54 elided
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 98 in stage 11.0 failed 4 times, most recent failure: Lost task 98.3 in stage 11.0 (TID 3895, etl-w-2.us-east1-b.c.team-etl-234919.internal, executor 294): ExecutorLostFailure (executor 294 exited caused by one of the running tasks) Reason: Container marked as failed: container_1554684028327_0001_01_000307 on host: etl-w-2.us-east1-b.c.team-etl-234919.internal. Exit status: 143. Diagnostics: [2019-04-08 01:50:14.153]Container killed on request. Exit code is 143
[2019-04-08 01:50:14.153]Container exited with a non-zero exit code 143.
[2019-04-08 01:50:14.154]Killed by external signal

Driver stacktrace:
  at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1651)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1639)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1638)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1638)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
  at scala.Option.foreach(Option.scala:257)
  at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1872)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1821)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1810)
  at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:194)
  ... 74 more

知道为什么吗？

Answer 1

如果 Spark 工作程序在较小的数据集上运行，但在较大的数据集上运行，则很可能会遇到内存不足的限制。每个工作线程的内存问题更多地取决于分区和每个执行程序的设置，而不是整个集群范围内的可用内存（因此创建更大的集群无助于解决此类问题）。

您可以尝试以下任意组合：

1. 重新分区为更多数量的分区以进行输出，而不是 10 个
2. highmem使用而不是standard机器创建集群
3. 使用 Spark 内存设置创建集群，更改内存与 CPU 的比率：gcloud dataproc clusters create --properties spark:spark.executor.cores=1例如，将每个执行器更改为使用相同内存量一次仅运行一个任务，而 Dataproc 通常每台机器运行 2 个执行器，并相应地划分 CPU。在 4 核机器上，通常有 2 个执行器，每个执行器允许 2 个核心。然后，此设置只会为这 2 个执行程序中的每一个提供 1 个核心，同时仍使用半台机器的内存。