spark读取不了本地文件是怎么回事

Question

 textFile=sc.textFile("file:///home/hduser/pythonwork/ipynotebook/data/test.txt")
stringRDD=textFile.flatMap(lambda line:line.split(' '))
stringRDD.collect()

我此路径下是有test文件的：

错误是：

 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 1 in stage 8.0 failed 4 times, most recent failure: Lost task 1.3 in stage 8.0 (TID 58, 192.168.56.103, executor 1): java.io.FileNotFoundException: File file:/home/hduser/pythonwork/ipynotebook/data/test.txt does not exist

。
。
。
Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1599)
    。
    。
    。
    Caused by: java.io.FileNotFoundException: File file:/home/hduser/pythonwork/ipynotebook/data/test.txt does not exist

而且发现若我把代码中test.txt随便改一个名字，比如ttest.txt（肯定是没有的文件）
错误竟然发生了变化：

 Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/hduser/pythonwork/ipynotebook/data/tesst.txt
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:287)
    at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
    at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:200)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
    at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:53)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:153)
    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:483)
    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.GatewayConnection.run(GatewayConnection.java:214)
    at java.lang.Thread.run(Thread.java:745)

注意：
此时我是以spark集群跑的：'spark://emaster:7077'
若是以本地跑就可以找到本地的那个test.txt文件
找hdfs文件系统的文件可以找到（在spark集群跑情况下）
。。。处由于字数显示省略了些不重要的错误提示，若想知道的话可以回复我
跪求大神帮助~感激不尽！！！

Answer 1

如果程序中读取的是本地文件，那么，要在所有的节点都有这个数据文件，只在master中有这个数据文件时执行程序时一直报找不到文件

解决方式1：让每个Worker节点的相应位置都有要读取的数据文件。

解决方式2：直接将数据文件上传到hdfs，达到数据共享。