title: Spark reading from local filesystem on all workers
body: <p>I have a spark cluster and am attempting to create an RDD from files located on each individual worker machine. In the scenario all the files contain different dat. For example </p>
  
  <pre><code>machine1: /tmp/data/1.csv
  machine2: /tmp/data/2.csv
  machine3: /tmp/data/3.csv
  machine4: /tmp/data/4.csv
  </code></pre>
  
  <p>I am attempting something like this.</p>
  
  <pre><code>Welcome to
     ____              __
    / __/__  ___ _____/ /__
   _\ \/ _ \/ _ `/ __/  '_/
  /__ / .__/\_,_/_/ /_/\_\   version 2.2.0
     /_/
  
  Using Python version 2.7.13 (default, May 10 2017 20:04:28)
  SparkSession available as 'spark'.
  &gt;&gt;&gt; file = sc.textFile("file:///tmp/data/*")
  &gt;&gt;&gt; file.count()
  5
  </code></pre>
  
  <p>Unfortunately the cluster only reads the file from the machine where pyspark is executed from. Is there a way to get the RDD to contain files from all of the machines? </p>
  
  <p>Note: I am not looking to stand up an HDFS cluster, etc. Just interested to see if this is possible.</p>