Spark - Iterating through all rows in dataframe comparing multiple columns for each row against another

| match_id | player_id | team | win |
|    0     |      1    |   A  |  A  |
|    0     |      2    |   A  |  A  |
|    0     |      3    |   B  |  A  |
|    0     |      4    |   B  |  A  |
|    1     |      1    |   A  |  B  |
|    1     |      4    |   A  |  B  |
|    1     |      8    |   B  |  B  |
|    1     |      9    |   B  |  B  |
|    2     |      8    |   A  |  A  |
|    2     |      4    |   A  |  A  |
|    2     |      3    |   B  |  A  |
|    2     |      2    |   B  |  A  |

I have a dataframe that looks like above.

I need to to create a map (key,value) pair such that for every

(k=>(player_id_1, player_id_2), v=> 1 ), if player_id_1 wins against player_id_2 in a match

and

(k=>(player_id_1, player_id_2), v=> 0 ), if player_id_1 loses against player_id_2 in a match

I will have to thus iterate through the entire data frame comparing each player id to another based upon the other 3 columns.

I am planning to achieve this as follows.

1. Group by match_id

2. In each group for a player_id check against other player_id's the following

   a. If match_id is same and team is different Then

        if team =  win
          (k=>(player_id_1, player_id_2), v=> 0 )
        else team != win
          (k=>(player_id_1, player_id_2), v=> 1 )
   

For example, after partitioning by matches consider match 1. player_id 1 needs to be compared to player_id 2,3 and 4. While iterating, record for player_id 2 will be skipped as the team is same for player_id 3 as team is different the team & win will be compared. As player_id 1 was in team A and player_id 3 was in team B and team A won the key-value formed would be

((1,3),1)

I have a fair idea of how to achieve this in imperative programming but I am really new to scala and functional programming and can't get a clue as to how while iterating through every row for a field create a (key,value) pair by having checks on other fields.

I tried my best to explain the problem. Please do let me know if any part of my question is unclear. I would be happy to explain the same. Thank you.

P.S: I am using Spark 1.6