PySpark Error: Input path does not exist

Question

I am trying to perform data transformations using PySpark.

I have a text file with data(CHANGES.txt)

I am able to execute the command:

RDDread = sc.textFile("file:///home/test/desktop/CHANGES.txt")

but when I run: RDDread.first()

then I get the error:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/srv/spark/python/pyspark/rdd.py", line 1328, in first
    rs = self.take(1)
  File "/srv/spark/python/pyspark/rdd.py", line 1280, in take
    totalParts = self.getNumPartitions()
  File "/srv/spark/python/pyspark/rdd.py", line 356, in getNumPartitions
    return self._jrdd.partitions().size()
  File "/srv/spark/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/srv/spark/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/srv/spark/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o256.partitions.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/test/desktop/CHANGES.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:199)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
    at org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.scala:60)
    at org.apache.spark.api.java.AbstractJavaRDDLike.partitions(JavaRDDLike.scala:45)
    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:237)
    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:745)

It seems that is mentions the file path does not exist. How can I resolve this. I have python, Java, and spark installed on my linux machine.

just check if that path exists on the your local system because you trying to read from a local file system — Rajat Mishra
– Rajat Mishra, Commented Jan 4, 2017 at 4:43
@RajatMishra, It exists within the path. but I still get this error. I think if it did not exist the path I would not have been able to execute the RDDread = sc.textFile("file:///home/test/desktop/CHANGES.txt") command — Techno04335
– Techno04335, Commented Jan 4, 2017 at 6:48
No actually , in spark sc.textFile is lazy evaluated i.e. when an action is called at that time RDDread = sc.textFile("file:///home/test/desktop/CHANGES.txt") will be executed. Due to that when your executing RDDread.first its giving you an error. — Rajat Mishra
– Rajat Mishra, Commented Jan 4, 2017 at 7:38

Raghav · Accepted Answer · 2017-01-04 09:35:13Z

3

If your running in a clustered mode you need to copy the file across all the nodes of same shared file system. Then spark reads that file otherwise you should use HDFS

I copied txt file into HDFS and spark takes file from HDFS.

I copied txt file on the shared filesystem of all nodes then spark read that file.

Both worked for me

edited Jan 4, 2017 at 9:35

answered Jan 4, 2017 at 6:14

Raghav

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2 Comments

Techno04335 Over a year ago

Does this mean I have to download Hadoop HDFS local to my machine?

Raghav Over a year ago

If your running in a clustered mode you need to copy the file across all the node of same shared file system. Then spark reads that file other wise you should use HDFS.

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