org.apache.spark.SparkException:作业由于阶段故障而中止:阶段 11.0 中的任务 98

Question

我正在使用Google Cloud Dataproc来做火花工作，我的编辑是Zepplin。我试图将 json 数据写入 gcp 存储桶。当我尝试 10MB 文件时，它之前成功了。但 10GB 文件失败。我的 dataproc 有 1 个主节点，带有 4CPU、26GB 内存、500GB 磁盘。5 个具有相同配置的工人。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 worker在较小的数据集上运行，而不是在较大的数据集上运行，则很可能会遇到内存不足限制。每个工作线程的内存问题将更多地是分区和每个执行程序设置的功能，而不是可用的群集范围的总内存(因此创建更大的群集无助于此类问题)。

您可以尝试以下任意组合：

1. 重新分区为更多数量的分区进行输出，而不是 10
个
2. 使用highmem而不是standard计算机创建群集
3. 使用更改内存与 CPU 比率的 Spark 内存设置创建群集：例如，gcloud dataproc clusters create --properties spark:spark.executor.cores=1会将每个执行程序更改为一次仅以相同的内存量运行一个任务，而 Dataproc 通常每台机器运行 2 个执行程序并相应地划分 CPU。在 4 核计算机上，通常有 2 个执行程序，每个执行程序允许 2 个内核。然后，此设置将仅为这 2 个执行程序中的每一个提供 1 个内核，同时仍使用半台机器的内存。