由于 SparkContext 关闭，作业 65 被取消

Question

我正在开发一个共享的 Apache Zeppelin 服务器。几乎每天，我尝试运行命令并收到此错误：Job 65 cancelled because SparkContext was shut down

我很想了解有关 SparkContext 关闭原因的更多信息。我的理解是 Zeppelin 是一个 kube 应用程序，它将命令发送到机器进行处理。

当 SparkContext 关闭时，是否意味着我与 Spark 集群的桥接已关闭？而且，如果是这样的话，我怎样才能使火花簇的桥断掉呢？

在此示例中，它发生在我尝试将数据上传到 S3 时。

这是代码

val myfiles = readParquet(
    startDate=ew LocalDate(2020, 4, 1),
    endDate=ew LocalDate(2020, 4, 7)
)

log_events.createOrReplaceTempView("log_events")

val mySQLDF = spark.sql(s"""
    select [6 columns]
    from myfiles 
    join [other table]
    on [join_condition]
"""
)

mySQLDF.write.option("maxRecordsPerFile", 1000000).parquet(path)
// mySQLDF has 3M rows and they're all strings or dates

这是堆栈跟踪错误

org.apache.spark.SparkException: Job aborted.
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)
  at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$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$$anonfun$executeQuery$1.apply(SparkPlan.scala:156)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
  at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
  at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
  at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
  at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
  at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
  at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
  at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:566)
  ... 48 elided
Caused by: org.apache.spark.SparkException: Job 44 cancelled because SparkContext was shut down
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:972)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:970)
  at scala.collection.mutable.HashSet.foreach(HashSet.scala:78)
  at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:970)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:2286)
  at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
  at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:2193)
  at org.apache.spark.SparkContext$$anonfun$stop$6.apply$mcV$sp(SparkContext.scala:1949)
  at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1340)
  at org.apache.spark.SparkContext.stop(SparkContext.scala:1948)
  at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$MonitorThread.run(YarnClientSchedulerBackend.scala:121)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
  ... 70 more

Answer 1

您的工作在写入步骤中被中止。Job aborted.是导致 Spark 上下文关闭的异常消息。

考虑优化写入步骤，maxRecordsPerFile可能是罪魁祸首；也许尝试一个更低的数字..您当前在一个文件中有 1M 条记录！

一般来说，Job ${job.jobId} cancelled because SparkContext was shut down仅意味着这是一个异常，导致 DAG 无法继续，需要 Error out。Spark 调度程序在遇到异常时会抛出此错误，这可能是代码中未处理的异常或由于任何其他原因导致作业失败。当 DAG 调度程序停止时，整个应用程序将停止（此消息是清理的一部分）。

对于你的问题——

当 SparkContext 关闭时，是否意味着我与 Spark 集群的桥接已关闭？

SparkContext 代表与 Spark 集群的连接，因此如果它死了，则意味着您无法在其上运行作业，因为您丢失了链接！在 Zepplin 上，您只需重新启动 SparkContext（菜单 -> Interpreter -> Spark Interpreter -> restart）

而且，如果是这样的话，我怎样才能使火花簇的桥断掉呢？

在作业中出现 SparkException/Error 或手动使用sc.stop()