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出错：
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，作业成功完成。然后，我检查了S3 console，数据被写入那里。
当我使用pyspark console运行相同的代码时，其运行成功。
请帮助我解决齐柏林飞艇的问题。
我也检查了其他links，但没有帮助
编辑：
解决方案：将URL从s3a更改为s3时，它可以正常工作。请 帮助我。
答案 0 :(得分：2)
似乎在创建零字节_SUCCESS标记时失败。
无论如何：这是有争议的。
FileOutputCommitter进行的没有一致性层的直接目标。对于AWS EMR，它是“一致的EMR”；对于S3A，它是S3Guard，甚至更好，请在Hadoop 3.1中使用S3A提交程序。
没有这些，一切似乎都可以正常工作，但是每隔3次，S3中的清单不一致都会导致丢失其中一个工作人员创建的数据，从而导致最终结果中的数据少于预期的，并且没有任何结果被报告，因为没有人注意到这一点
我没有弥补。如果您想了解详细信息，请查看HADOOP-13345 HADOOP-13786和A Zero-Rename Committer。