Zeppelin：任何本地目录中都没有可用空间

问题描述 投票：0回答：1

我正在使用zeppelin笔记本在s3中保存数据帧。

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

我在zeppelin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

但当我检查Spark UI时，工作顺利完成。然后我检查了S3 console，数据写在那里。

当我与pyspark console运行相同的代码时，它运行成功。

请帮我解决zeppelin的这个问题。

我检查了其他没有帮助的qazxsw poi

编辑：

解决方案：在将URL从s3a更改为s3时，其工作正常。请帮我解释一下原因。

apache-spark amazon-s3 yarn amazon-emr apache-zeppelin
1个回答
2
投票

看起来它在创建零字节links标记时失败了。

  1. 如果作业没有该标记，您永远无法确定作业是否成功完成;事情可能出错了。
  2. 如果临时创建期间创建的临时文件没有空间（256K），则该特定计算机出现问题。

无论如何：这没有实际意义。

Due to the eventual consistency of S3 you cannot safely use S3 as a direct destination of work committed through _SUCCESS without a consistency layer.

对于AWS EMR，这是“一致的EMR”，对于S3Aard的S3A，甚至更好，使用Hadoop 3.1中的S3A提交者。

如果没有这些，一切看起来都可行，但S3中不一致的列表会导致其中一个工作人员创建的数据被错过，导致最终结果中的数据少于预期，没有任何报告，因为没有注意到这一点

我不是这样做的。如果你想了解详细信息，请查看FileOutputCommitter HADOOP-13345和HADOOP-13786。

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