我正在使用共享的 Apache Zeppelin 服务器。几乎每天，我都尝试运行一个命令并收到此错误:Job 65 cancelled because SparkContext was shut down
我很想了解更多有关导致 SparkContext 关闭的原因。我的理解是 Zeppelin 是一个 kube 应用程序，可以将命令发送到机器进行处理。
当 SparkContext 关闭时，这是否意味着我与 Spark 集群的桥已关闭？而且，如果是这样的话，我怎样才能使通往 Spark 簇的桥折叠？
在这个例子中，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
最佳答案
您的工作在写入步骤中止。 Job aborted. 是异常消息，导致 Spark Context 关闭。
查看优化写入步骤，maxRecordsPerFile 可能是罪魁祸首；也许尝试一个较低的数字..您目前在一个文件中有 100 万条记录!
一般来说，Job ${job.jobId} cancelled because SparkContext was shut down 只是意味着它是一个异常，因为 DAG 无法继续并需要出错。它是 Spark scheduler throwing this error 遇到异常时，它可能是您的代码中未处理的异常或由于任何其他原因导致的作业失败。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)。
对于您的问题——
SparkContext 代表与 Spark 集群的连接，因此如果它死了，则意味着您无法在失去链接时对其运行运行作业!在 Zepplin 上，您只需重新启动 SparkContext(菜单 -> 解释器 -> Spark 解释器 -> 重新启动)
在作业中使用 SparkException/Error 或手动使用 sc.stop()
关于apache-spark - 由于 SparkContext 已关闭，作业 65 被取消，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/61837678/