齐柏林飞艇:任何本地目录中都没有可用空间

Question

我正在使用齐柏林飞艇笔记本将数据帧保存在 s3 中。

df=spark.sql("select * from person")
df.write.mode('overwrite').option("header", "true").csv("s3a://file/location/")

我在齐柏林飞艇输出中遇到错误：

Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-3486998044016857551.py", line 367, in <module>
raise Exception(traceback.format_exc())
Exception: Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-3486998044016857551.py", line 360, in <module>
exec(code, _zcUserQueryNameSpace)
File "<stdin>", line 2, in <module>
File "/usr/lib/spark/python/pyspark/sql/readwriter.py", line 766, in csv
self._jwrite.csv(path)
File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/usr/lib/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o454.csv.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:213)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:145)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:435)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:471)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:50)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:609)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:233)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:217)
at org.apache.spark.sql.DataFrameWriter.csv(DataFrameWriter.scala:597)
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:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.hadoop.util.DiskChecker$DiskErrorException: No space available in any of the local directories.
at org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:399)
at org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.createTmpFileForWrite(LocalDirAllocator.java:455)
at org.apache.hadoop.fs.LocalDirAllocator.createTmpFileForWrite(LocalDirAllocator.java:199)
at org.apache.hadoop.fs.s3a.S3AFileSystem.createTmpFileForWrite(S3AFileSystem.java:412)
at org.apache.hadoop.fs.s3a.S3AOutputStream.<init>(S3AOutputStream.java:67)
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:591)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:932)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:913)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:810)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJobInternal(FileOutputCommitter.java:424)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJob(FileOutputCommitter.java:364)
at org.apache.hadoop.mapreduce.lib.output.DirectFileOutputCommitter.commitJob(DirectFileOutputCommitter.java:119)
at org.apache.spark.internal.io.HadoopMapReduceCommitProtocol.commitJob(HadoopMapReduceCommitProtocol.scala:142)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:207)
... 45 more

但是当我检查Spark UI时，工作成功完成了。然后我检查了S3 console，数据写在那里。

当我使用pyspark console运行相同的代码时，它运行成功。

请帮助我解决齐柏林飞艇的这个问题。

我也检查了其他链接，但没有帮助

编辑：

解决方案：将网址从 s3a 更改为 s3 时，它工作正常。请 帮我找出原因。

Answer 1

看起来它在创建零字节_SUCCESS标记时失败了。

1. 如果作业没有该标记，则永远无法确定作业是否成功完成;可能出了问题。
2. 如果在临时创建期间创建的临时文件 (256K( 没有空间，则该特定计算机将遇到麻烦。

无论如何：这是没有意义的。

由于 S3

的最终一致性，如果没有一致性层，您无法安全地将 S3 用作通过FileOutputCommitter提交的工作的直接目标。

对于AWS EMR，这是"一致的EMR"，对于S3A，这是S3Guard，甚至更好的是，使用Hadoop 3.1中的S3A提交程序。

如果没有这些，一切可能看起来都正常，但每隔一段时间，S3 中的不一致列表会导致其中一个工作线程创建的数据丢失，导致最终结果中的数据少于预期，并且不会报告任何内容，因为没有人注意到这一点

我不是在编造这个。如果您想了解详细信息，请查看HADOOP-13345 HADOOP-13786和零重命名提交者。