PySpark error: "Input path does not exist"

Question

I am new to Spark and I code in Python.

Following exactly my "Learning Spark" guidelines, I see "You don't need to have Hadoop installed to run Spark"

Yet when I simply try to count the lines in one file using Pyspark I get the following error. What am I missing?

>>> lines = sc.textFile("README.md")
15/02/01 13:27:12 INFO MemoryStore: ensureFreeSpace(32728) called with curMem=0,
 maxMem=278019440
15/02/01 13:27:12 INFO MemoryStore: Block broadcast_0 stored as values in memory
 (estimated size 32.0 KB, free 265.1 MB)
>>> lines.count()
15/02/01 13:27:18 WARN NativeCodeLoader: Unable to load native-hadoop library fo
r your platform... using builtin-java classes where applicable
15/02/01 13:27:18 WARN LoadSnappy: Snappy native library not loaded
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 847, in co
unt
    return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 838, in su
m
    return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 759, in re
duce
    vals = self.mapPartitions(func).collect()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 723, in co
llect
    bytesInJava = self._jrdd.collect().iterator()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\ja
va_gateway.py", line 538, in __call__
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\pr
otocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.collect.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: fil
e:/C:/Spark/spark-1.1.0-bin-hadoop1/bin/README.md
        at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.j
ava:197)
        at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.ja
va:208)
        at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:179)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:5
6)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1135)
        at org.apache.spark.rdd.RDD.collect(RDD.scala:774)
        at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala
:305)
        at org.apache.spark.api.java.JavaRDD.collect(JavaRDD.scala:32)
        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:231)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
        at py4j.Gateway.invoke(Gateway.java:259)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:207)
        at java.lang.Thread.run(Unknown Source)

>>> lines.first()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 1167, in f
irst
    return self.take(1)[0]
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\pyspark\rdd.py", line 1126, in t
ake
    totalParts = self._jrdd.partitions().size()
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\ja
va_gateway.py", line 538, in __call__
  File "C:\Spark\spark-1.1.0-bin-hadoop1\python\lib\py4j-0.8.2.1-src.zip\py4j\pr
otocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o20.partitions.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: fil
e:/C:/Spark/spark-1.1.0-bin-hadoop1/bin/README.md
        at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.j
ava:197)
        at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.ja
va:208)
        at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:179)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
        at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
        at scala.Option.getOrElse(Option.scala:120)
        at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
        at org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.sc
ala:50)
        at org.apache.spark.api.java.JavaRDD.partitions(JavaRDD.scala:32)
        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:231)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
        at py4j.Gateway.invoke(Gateway.java:259)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:207)
        at java.lang.Thread.run(Unknown Source)

>>>

Answer 1

I have not tried to run spark in a Windows system, but it seems to me that the problem is:

py4j.protocol.Py4JJavaError: An error occurred while calling o26.collect. : org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: fil e:/C:/Spark/spark-1.1.0-bin-hadoop1/bin/README.md

You have to refer correctly the file to load. If you run pyspark from spark folder (i.e.: C:\spark), then lines = sc.textFile("README.md") is correct. But if you run pyspark from bin (i.e.: C:\spark\bin) you have to refer it the: lines = sc.textFile("../README.md"), or use the absolute path to the file.

Answer 2

This is the solution for this error that i was getting on Spark cluster that is hosted in windows:

Load the raw HVAC.csv file, parse it using the function

data = sc.textFile("wasb:///HdiSamples/SensorSampleData/hvac/HVAC.csv")

We use (wasb:///) to allow Hadoop to access azure blog storage file and the three slashes is a relative reference to the running node container folder.

For example: If the path for your file in File Explorer in Spark cluster dashboard is:

sflcc1\sflccspark1\HdiSamples\SensorSampleData\hvac

So to describe the path is as follows: sflcc1: is the name of the storage account. sflccspark: is the cluster node name.

So we refer to the current cluster node name with the relative three slashes.

Hope this helps.

Answer 3

I am a little late to the party. I had a similar problem (ec2 spark cluster). In my case, hdfs dint have the file I was looking for. Thus, I had to manually add the files I wanted using the following command

~/ephemeral-hdfs/bin/hadoop fs -put /dir/filename.txt filename.txt

hopefully that was helpful.