从查询异常中恢复火花结构化流

Question

是否可以从查询执行期间引发的异常中自动恢复？

Context：我正在开发一个Spark应用程序，该应用程序从Kafka主题读取数据，处理数据，然后输出到S3。但是，在生产中运行了几天后，Spark应用程序面临来自S3的一些网络故障，这会引发异常并停止该应用程序。还值得一提的是，此应用程序使用GCP's Spark k8s Operator在Kubernetes上运行。

从目前为止我所看到的，这些异常是次要的，只需重新启动应用程序即可解决此问题。我们可以处理这些异常并自动重新启动结构化流查询吗？

这里是抛出异常的示例：

    Exception in thread "main" org.apache.spark.sql.streaming.StreamingQueryException: Job aborted.
    === Streaming Query ===
    Identifier: ...
    Current Committed Offsets: ...
    Current Available Offsets: ...

    Current State: ACTIVE
    Thread State: RUNNABLE

    Logical Plan: ...

        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:297)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:193)
    Caused by: org.apache.spark.SparkException: Job aborted.
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.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$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
        at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
        at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
        at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
        at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala: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 io.blahblahView$$anonfun$11$$anonfun$apply$2.apply(View.scala:90)
        at io.blahblahView $$anonfun$11$$anonfun$apply$2.apply(View.scala:82)
        at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
        at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
        at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
        at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
        at io.blahblahView$$anonfun$11.apply(View.scala:82)
        at io.blahblahView$$anonfun$11.apply(View.scala:79)
        at org.apache.spark.sql.execution.streaming.sources.ForeachBatchSink.addBatch(ForeachBatchSink.scala:35)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$5$$anonfun$apply$17.apply(MicroBatchExecution.scala:537)
        at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
        at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$5.apply(MicroBatchExecution.scala:535)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:351)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:534)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:198)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:166)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:166)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:351)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:166)
        at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:160)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:281)
        ... 1 more
    Caused by: java.io.FileNotFoundException: No such file or directory: s3a://.../view/v1/_temporary/0
        at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:993)
        at org.apache.hadoop.fs.s3a.S3AFileSystem.listStatus(S3AFileSystem.java:734)
        at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1517)
        at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1557)
        at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.getAllCommittedTaskPaths(FileOutputCommitter.java:291)
        at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJobInternal(FileOutputCommitter.java:361)
        at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJob(FileOutputCommitter.java:334)
        at org.apache.parquet.hadoop.ParquetOutputCommitter.commitJob(ParquetOutputCommitter.java:48)
        at org.apache.spark.internal.io.HadoopMapReduceCommitProtocol.commitJob(HadoopMapReduceCommitProtocol.scala:166)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:187)
        ... 47 more
自动解决此类问题的最简单方法是什么？

是否可以从查询执行过程中引发的异常中自动恢复？上下文：我正在开发一个Spark应用程序，该应用程序从Kafka主题读取数据，处理数据，然后...

Answer 1

不，没有可靠的方法可以做到这一点。顺便说一句，否也是答案。

Answer 2

[花了很多时间试图找到解决该问题的方法，却一无所获，这就是我的想法。