title: Clarification needed regarding map/reduce jobs to computer average of dataset?
body: <p>I am learning map/reduce, I was working on the problem of computing mean of all the values in a particular column of the dataset.</p>
  <p>All of the solutions I have seen so far on the do something along the lines of this</p>
  <blockquote>
  <p>Dataset<br/>
  1<br/>
  2<br/>
  3<br/>
  4<br/>
  5<br/></p>
  <p>map job: generate key,value pairs with key  as 1<br/>
  (1,1)<br/>
  (1,2)<br/>
  (1,3)<br/>
  (1,3)<br/>
  (1,4)<br/></p>
  </blockquote>
  <p>Now since key is same for the map job's output, all of them will be sent to one reducer, over there we can do the calculation sum/count</p>
  <p>However, my question is that, is it intuitive to have same key for all entries? Isn't the purpose of map/reduce to split the jobs so that we can have them running concurrently? Doesn't having the same key for all the map's out defeat the purpose of the map/reduce?</p>
  <p>Is this the algorithm to compute the average the most efficient one? or Can it be improved further?</p>