title: Python MapReduce Hadoop Streaming Job that requires multiple input files?
body: <p>I have two files in my cluster <code>File A</code> and <code>File B</code> with the following data - </p>
  
  <p>File A</p>
  
  <pre><code>#Format: 
  #Food Item | Is_A_Fruit (BOOL)
  
  Orange | Yes
  Pineapple | Yes
  Cucumber | No
  Carrot | No
  Mango | Yes
  </code></pre>
  
  <p>File B</p>
  
  <pre><code>#Format:
  #Food Item | Vendor Name
  
  Orange | Vendor A
  Pineapple | Vendor B
  Cucumber | Vendor B
  Carrot | Vendor B
  Mango | Vendor A
  </code></pre>
  
  <p>Basically I want to find out 
  <strong>How many fruits are each vendor selling?</strong></p>
  
  <p>Expected output:</p>
  
  <pre><code>Vendor A | 2
  Vendor B | 1
  </code></pre>
  
  <p>I need to do this using hadoop streaming python map reduce.</p>
  
  <p>I have read how to do a basic word count, I read from <code>sys.stdin</code> and emit <code>k,v</code> pairs for the reducer to then reduce.</p>
  
  <p>How do I approach this problem?</p>
  
  <p>My main concern is how to read from multiple files and then compare them in Hadoop Streaming.</p>
  
  <p>I can do this in normal python (i.e without MapReduce &amp; Hadoop, it's straightforward.) but it's infeasible for the sheer size of data that I have with me.</p>