title: Amazon EC2 & S3 When using Python / SQLite?
body: <p>Suppose that I have a huge <code>SQLite</code> file (say, 500[MB]) stored in Amazon <code>S3</code>. 
  Can a <code>python</code> script that is run on a small <code>EC2</code> instance <strong>directly access and modify</strong> that <code>SQLite</code> file? or must I first copy the file to the <code>EC2</code> instance, change it there and then copy over to <code>S3</code>? </p>
  
  <p>Will the I/O be efficient?</p>
  
  <p>Here's what I am trying to do. As I wrote, I have a 500[MB] <code>SQLite</code> file in <code>S3</code>. I'd like to start say <code>10 different Amazon EC2 instances</code> that will each read a subset of the file and do some processing (every instance will handle a different subset of the 500[MB] <code>SQLite</code> file). Then, once processing is done, every instance will update only the subset of the data it dealt with (as explained, there will be <em>no overlap of data</em> among processes).</p>
  
  <p>For example, suppose that the <code>SQLite</code> file has say <strong>1M rows</strong>:</p>
  
  <p><code>instance 1</code> will deal with (and update) rows <code>0 - 100000</code></p>
  
  <p><code>instance 2</code> will will deal with (and update) rows <code>100001 - 200000</code></p>
  
  <p>.........................</p>
  
  <p><code>instance 10</code> will deal with (and update) rows <code>900001 - 1000000</code></p>
  
  <hr>
  
  <p>Is it at all possible? Does it sound OK? any suggestions / ideas are welcome.</p>