Py4JJavaError：在尝试将 rdd 数据帧写入本地目录上的镶木地板文件时调用 o389.parquet 时发生错误
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【中文标题】Py4JJavaError：在尝试将 rdd 数据帧写入本地目录上的镶木地板文件时调用 o389.parquet 时发生错误【英文标题】：Py4JJavaError: An error occurred while calling o389.parquet when trying to write rdd dataframe as parquet files on local directory 【发布时间】：2021-12-31 02:44:39 【问题描述】：我正在尝试使用 Jupyter 笔记本中的以下代码将数据框写入本地目录中的镶木地板文件：
rdd1 = rdd.coalesce(partitions)
schema1 = StructType([StructField('date', DateType()), StructField('open', FloatType()), StructField('high', FloatType()),
StructField('low', FloatType()),StructField('close', FloatType()),StructField('adj_close', FloatType()),
StructField('volume', FloatType()), StructField('stock', StringType())])
rddDF = spark.createDataFrame(rdd1,schema=schema1)
spark.conf.set("spark.sql.parquet.compression.codec", "gzip")
rddDF.write.parquet("C:/Users/"User"/Documents/File/Output/rddDF")
我收到以下错误：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-11-7b2aeb627267> in <module>
16
17 #rddDF.to_parquet("C:/Users/Sabihah/Documents/6. Processing Big Data/Output/rddDF")
---> 18 rddDF.write.parquet("C:/Users/Sabihah/Documents/6. Processing Big Data/Output/rddDF")
19 #rddDF.write.format("parquet").save("C:/Users/Sabihah/Documents/6. Processing Big Data/Output/rddDF")
~\anaconda3\lib\site-packages\pyspark\sql\readwriter.py in parquet(self, path, mode, partitionBy, compression)
883 self.partitionBy(partitionBy)
884 self._set_opts(compression=compression)
--> 885 self._jwrite.parquet(path)
886
887 def text(self, path, compression=None, lineSep=None):
~\anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
1307
1308 answer = self.gateway_client.send_command(command)
-> 1309 return_value = get_return_value(
1310 answer, self.gateway_client, self.target_id, self.name)
1311
~\anaconda3\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw)
109 def deco(*a, **kw):
110 try:
--> 111 return f(*a, **kw)
112 except py4j.protocol.Py4JJavaError as e:
113 converted = convert_exception(e.java_exception)
~\anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
325 if answer[1] == REFERENCE_TYPE:
--> 326 raise Py4JJavaError(
327 "An error occurred while calling 012.\n".
328 format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o48.parquet.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.errors.QueryExecutionErrors$.jobAbortedError(QueryExecutionErrors.scala:496)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:251)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:186)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:113)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:111)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.executeCollect(commands.scala:125)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:110)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:110)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:106)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:481)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:481)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:457)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:106)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:93)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:91)
at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:128)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:848)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:382)
at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:355)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:239)
at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:781)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Unknown Source)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 0.0 failed 1 times, most recent failure: Lost task 1.0 in stage 0.0 (TID 1) (DESKTOP-JBUENQG executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\worker.py", line 619, in main
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\worker.py", line 611, in process
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\serializers.py", line 259, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\util.py", line 74, in wrapper
return f(*args, **kwargs)
File "C:\Users\Sabihah\anaconda3\lib\site-packages\pyspark\sql\session.py", line 682, in prepare
verify_func(obj)
File "C:\Users\Sabihah\anaconda3\lib\site-packages\pyspark\sql\types.py", line 1411, in verify
verify_value(obj)
File "C:\Users\Sabihah\anaconda3\lib\site-packages\pyspark\sql\types.py", line 1398, in verify_struct
raise TypeError(new_msg("StructType can not accept object %r in type %s"
TypeError: StructType can not accept object 'close' in type <class 'str'>
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:545)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:703)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:685)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:498)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:491)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:286)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$16(FileFormatWriter.scala:229)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2403)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2352)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2351)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2351)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1109)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2591)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2533)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2522)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:898)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:218)
... 42 more
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\worker.py", line 619, in main
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\worker.py", line 611, in process
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\serializers.py", line 259, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "C:\Spark\Spark\python\lib\pyspark.zip\pyspark\util.py", line 74, in wrapper
return f(*args, **kwargs)
File "C:\Users\Sabihah\anaconda3\lib\site-packages\pyspark\sql\session.py", line 682, in prepare
verify_func(obj)
File "C:\Users\Sabihah\anaconda3\lib\site-packages\pyspark\sql\types.py", line 1411, in verify
verify_value(obj)
File "C:\Users\Sabihah\anaconda3\lib\site-packages\pyspark\sql\types.py", line 1398, in verify_struct
raise TypeError(new_msg("StructType can not accept object %r in type %s"
TypeError: StructType can not accept object 'close' in type <class 'str'>
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:545)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:703)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:685)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:498)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:491)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:286)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$16(FileFormatWriter.scala:229)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
... 1 more
我检查了所有系统变量： Hadoop_home、Java_home、Spark_home、Scala_home、Pyspark_python、Pyspark_driver_python。
我已经安装了 Spark v3.2 和 Hadoop v2.7 和 Scala 2.12.4，更新到 v2.12.10。 我在笔记本中使用 Python 3.8。
我尝试降级到 Python 3.7，但这并没有解决问题。
我不确定还可以尝试什么来修复此错误。 任何帮助将不胜感激。
更新： 我尝试修复数据类型，但错误仍然存在。
然后我对创建数据框的方式进行了以下更改：
rddDF = spark.createDataFrame([rdd1],schema=schema1)
删除了 TypeError：
StructType can not accept object 'close' in type <class 'str'>
我的错误现在显示：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-23-ec6844f98c97> in <module>
16
17 #rddDF.to_parquet("C:/Users/Sabihah/Documents/6. Processing Big Data/Output/rddDF")
---> 18 rddDF.write.parquet("C:/Users/Sabihah/Documents/6. Processing Big Data/Output/rddDF")
19 #rddDF.write.format("parquet").save("C:/Users/Sabihah/Documents/6. Processing Big Data/Output/rddDF")
~\anaconda3\lib\site-packages\pyspark\sql\readwriter.py in parquet(self, path, mode, partitionBy, compression)
883 self.partitionBy(partitionBy)
884 self._set_opts(compression=compression)
--> 885 self._jwrite.parquet(path)
886
887 def text(self, path, compression=None, lineSep=None):
~\anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args)
1307
1308 answer = self.gateway_client.send_command(command)
-> 1309 return_value = get_return_value(
1310 answer, self.gateway_client, self.target_id, self.name)
1311
~\anaconda3\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw)
109 def deco(*a, **kw):
110 try:
--> 111 return f(*a, **kw)
112 except py4j.protocol.Py4JJavaError as e:
113 converted = convert_exception(e.java_exception)
~\anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
325 if answer[1] == REFERENCE_TYPE:
--> 326 raise Py4JJavaError(
327 "An error occurred while calling 012.\n".
328 format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o389.parquet.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.errors.QueryExecutionErrors$.jobAbortedError(QueryExecutionErrors.scala:496)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:251)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:186)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:113)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:111)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.executeCollect(commands.scala:125)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:110)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:110)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:106)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:481)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:481)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:457)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:106)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:93)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:91)
at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:128)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:848)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:382)
at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:355)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:239)
at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:781)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
at org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Native Method)
at org.apache.hadoop.io.nativeio.NativeIO$Windows.access(NativeIO.java:793)
at org.apache.hadoop.fs.FileUtil.canRead(FileUtil.java:1215)
at org.apache.hadoop.fs.FileUtil.list(FileUtil.java:1420)
at org.apache.hadoop.fs.RawLocalFileSystem.listStatus(RawLocalFileSystem.java:601)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1972)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:2014)
at org.apache.hadoop.fs.ChecksumFileSystem.listStatus(ChecksumFileSystem.java:761)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1972)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:2014)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.getAllCommittedTaskPaths(FileOutputCommitter.java:334)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJobInternal(FileOutputCommitter.java:404)
at org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter.commitJob(FileOutputCommitter.java:377)
at org.apache.parquet.hadoop.ParquetOutputCommitter.commitJob(ParquetOutputCommitter.java:48)
at org.apache.spark.internal.io.HadoopMapReduceCommitProtocol.commitJob(HadoopMapReduceCommitProtocol.scala:182)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$20(FileFormatWriter.scala:240)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.timeTakenMs(Utils.scala:605)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:240)
... 42 more
【问题讨论】：
StructType can not accept object 'close' in type <class 'str'>
谢谢，@GaëlJ。试图解决这个问题，但没有成功。
【参考方案1】：
根据错误：
TypeError: StructType can not accept object 'close' in type <class 'str'>
看来close 列应该定义为StringType 而不是FloatType。
【讨论】：
谢谢，@Gabip。尝试更改数据类型，但错误仍然存在。 你得到同样的错误吗？也许其他字段也有另一个 TypeError？ 这不是完全相同的错误。完整的错误在更新的问题中。 如果你得到一个不同的错误，你不能说error persists。人们不应该猜测你的本地环境会发生什么。
我的意思是 hadoop 版本（目前您使用的是 2.7）而不是 spark。 Spark 3+ 与如此旧的 hadoop 版本不兼容。【参考方案2】：
关于新的错误：
Py4JJavaError: An error occurred while calling o389.parquet.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.errors.QueryExecutionErrors$.jobAbortedError(QueryExecutionErrors.scala:496)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:251)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:186)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:113)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:111)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.executeCollect(commands.scala:125)
我在 Windows 机器上解决问题的方法是将 Hadoop 的“bin”目录（在 HADOOP_HOME 指定的目录中）添加到 PATH 变量中。
当我的 HADOOP_HOME 不存在，或者没有指向正确的位置时，我遇到了一个错误（现在我无法理解）。但是在我修好之后，我遇到了你遇到的那个。在尝试使用 7 个不同版本的 Hadoop 文件（winutils.exe 和 hadoop.dll）后，我出现了“路径”问题。当一个 OSS 项目依赖于一堆其他项目时，这就是一种混乱，它们都有自己的解决依赖关系的方法。
我从提升的 Powershell 提示符运行此程序，重新启动了我的 VsCode 实例（我在其中运行 Jupyter Notebooks），并且能够保存镶木地板文件。
[Environment]::SetEnvironmentVariable("PATH", "$env:PATH;$env:HADOOP_HOME\bin", 'Machine')
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