我已经设置了一个spark集群，所有节点都可以访问网络共享存储，在那里他们可以访问要读取的文件。我在python jupyter笔记本中运行它。它在几天前工作，现在它停止工作但我不知道为什么，或者我已经改变了。
我尝试重新启动节点并掌握。
我还尝试将csv文件复制到一个新目录并将spark.read指向那里，但它仍然会出现同样的错误。
当我删除csv文件时，它会显示更短的错误，说“找不到文件”
非常感谢任何帮助。
这是我的代码：
from pyspark.sql import SparkSession
from pyspark.conf import SparkConf
spark = SparkSession.builder \
.master("spark://IP:PORT") \
.appName("app_1") \
.config(conf=SparkConf()) \
.getOrCreate()
df = spark.read.csv("/nas/file123.csv")
string1 = df.rdd.map(lambda x: x.column1).collect()
然而，我收到此错误：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-2-12bd938122cd> in <module>()
29
30
---> 31 string1 = df.rdd.map(lambda x: x.column1).collect()
32
33
/home/hjk/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in collect(self)
807 """
808 with SCCallSiteSync(self.context) as css:
--> 809 port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
810 return list(_load_from_socket(port, self._jrdd_deserializer))
811
/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
/home/hjk/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc 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()
/usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 4 in stage 3.0 failed 4 times, most recent failure: Lost task 4.3 in stage 3.0 (TID 37, executor 2): java.io.FileNotFoundException: File file:/nas/file123.csv does not exist
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:157)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.hasNext(SerDeUtil.scala:117)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:112)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1951)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:453)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.FileNotFoundException: File file:/nas/file123.csv does not exist
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:157)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.hasNext(SerDeUtil.scala:117)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:112)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1951)
at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)
答案 0 :(得分：1)
从错误看起来它正在检查本地系统上的文件。只需确保您在指定的路径上存在文件。另请尝试以下建议。
希望这有帮助。
此致
Neeraj