我正在从多个文件中读取一些数据（8 GB）的数据，对数据进行一些空检查并对该列进行一些提升（操作），例如清理列值，对此我有6到7个功能（自定义功能，不能使用Spark函数）注册为UDF。然后，我将最终结果写入表和CSV文件，现在在“ dataframe.write.saveAsTable（）”上，在写入“ CSV”时，出现EOF异常，请查找文件末尾。不会每次都发生此异常，例如，如果我运行20次，则可能会发生一次。我无法找到其原因和原因，因为它不可复制（在scala和pyspark中都可以找到它），将不胜感激。期待。谢谢
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Py4JJavaError Traceback (most recent call last)
<command-4146672194126555> in <module>()
331 saveMergedLogs(
332 dataframeLogs
333 );
<command-3860558353011740> in saveMergedLogs(dataframeLogs)
45 # ---------------------------------------------------------------------------------------------------------------------------------
46 spark.sql("DROP TABLE IF EXISTS dbo.UsageLogs");
---> 47 dataframeLogs.write.saveAsTable("dbo.UsageLogs")
/databricks/spark/python/pyspark/sql/readwriter.py in saveAsTable(self, name, format, mode, partitionBy, **options)
773 if format is not None:
774 self.format(format)
--> 775 self._jwrite.saveAsTable(name)
776
777 @since(1.4)
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/databricks/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling o3615.saveAsTable.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:196)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:192)
at org.apache.spark.sql.execution.datasources.DataSource.writeAndRead(DataSource.scala:553)
at org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.saveDataIntoTable(createDataSourceTables.scala:216)
at org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:175)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:110)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:108)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:128)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:143)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$5.apply(SparkPlan.scala:183)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:180)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:131)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:114)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:114)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:690)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:690)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withCustomExecutionEnv$1.apply(SQLExecution.scala:99)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:228)
at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:85)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:158)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:690)
at org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:487)
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:466)
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:414)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
at py4j.Gateway.invoke(Gateway.java:295)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:251)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 9 in stage 58.0 failed 4 times, most recent failure: Lost task 9.3 in stage 58.0 (TID 8630, 10.139.64.7, executor 0): java.io.EOFException: Cannot seek past end of file
at com.microsoft.azure.datalake.store.ADLFileInputStream.seek(ADLFileInputStream.java:262)
at com.databricks.adl.AdlFsInputStream.seek(AdlFsInputStream.java:64)
at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:62)
at com.databricks.spark.metrics.FSInputStreamWithMetrics.seek(FileSystemWithMetrics.scala:207)
at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:62)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.initialize(LineRecordReader.java:107)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.<init>(HadoopFileLinesReader.scala:65)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.<init>(HadoopFileLinesReader.scala:47)
at org.apache.spark.sql.execution.datasources.csv.TextInputCSVDataSource$.readFile(CSVDataSource.scala:201)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anonfun$buildReader$2.apply(CSVFileFormat.scala:147)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anonfun$buildReader$2.apply(CSVFileFormat.scala:140)
at org.apache.spark.sql.execution.datasources.FileFormat$$anon$1.apply(FileFormat.scala:147)
at org.apache.spark.sql.execution.datasources.FileFormat$$anon$1.apply(FileFormat.scala:134)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.getNext(FileScanRDD.scala:226)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:196)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:338)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:196)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage443.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:622)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:139)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1432)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:503)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2100)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2088)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2087)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2087)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1076)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:1076)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1076)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2319)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2267)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2255)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:873)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2252)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)
... 36 more
Caused by: java.io.EOFException: Cannot seek past end of file
at com.microsoft.azure.datalake.store.ADLFileInputStream.seek(ADLFileInputStream.java:262)
at com.databricks.adl.AdlFsInputStream.seek(AdlFsInputStream.java:64)
at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:62)
at com.databricks.spark.metrics.FSInputStreamWithMetrics.seek(FileSystemWithMetrics.scala:207)
at org.apache.hadoop.fs.FSDataInputStream.seek(FSDataInputStream.java:62)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.initialize(LineRecordReader.java:107)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.<init>(HadoopFileLinesReader.scala:65)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.<init>(HadoopFileLinesReader.scala:47)
at org.apache.spark.sql.execution.datasources.csv.TextInputCSVDataSource$.readFile(CSVDataSource.scala:201)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anonfun$buildReader$2.apply(CSVFileFormat.scala:147)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anonfun$buildReader$2.apply(CSVFileFormat.scala:140)
at org.apache.spark.sql.execution.datasources.FileFormat$$anon$1.apply(FileFormat.scala:147)
at org.apache.spark.sql.execution.datasources.FileFormat$$anon$1.apply(FileFormat.scala:134)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1$$anon$2.getNext(FileScanRDD.scala:226)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:196)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:338)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:196)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage443.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:622)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:139)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1432)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:503)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
答案 0 :(得分：1)
因此我做了很多工作，但是没有解决方案。我管理的方式是在ADF databricks活动中指定了重试。因此，无论何时发生此问题，它都可以在数据块中重新运行笔记本计算机并通过。但这仍然不是一个完美的解决方案，但是可以。
答案 1 :(得分：0)
从多个文件读取数据时遇到了相同的问题。文件具有相同的列，但列的顺序不完全相同。由于在熊猫中，我们可以在追加DataFrame时设置sort = True，以便不会混淆位于不同位置的相同列，因此我怀疑是导致问题的列顺序。我不确定100％，但是在我用相同顺序的列重新写入文件后，它可以工作。